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20bafcc58f8f787bb18c74366b6a43b514938a7f | ace889efe2683b178c4195f8209b955e78392f88 | /3.1- POPULARITY OF MUSIC RECORDS.R | aef6c78301f4b0feb98f9b51ffb14a9d1bdd56fa | [] | no_license | lucaslrolim/MITx-15.071x | 7517bc79fb6a51a6189e9d90e333afcca29f3dbe | 3cbbc08ce90d1744b90745390628f8b2be8c7862 | refs/heads/master | 2016-09-13T13:06:51.777416 | 2016-06-04T05:00:08 | 2016-06-04T05:00:08 | 57,808,487 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,044 | r | 3.1- POPULARITY OF MUSIC RECORDS.R | songs <- read.csv("./data/songs.csv")
# How many observations (songs) are from the year 2010?
length(songs$year[songs$year == 2010])
# How many songs does the dataset include for which the artist name is "Michael Jackson"?
length(songs$artistname[songs$artistname == "Michael Jackson"])
# Which of these songs by Michael Jackson made it to the Top 10? Select all that apply.
songs$songtitle[songs$artistname == "Michael Jackson" & songs$Top10 == 1]
# The variable corresponding to the estimated time signature (timesignature) is discrete, meaning that it only takes
# integer values (0, 1, 2, 3, . . . ). What are the values of this variable that occur in our dataset? Select all that apply.
unique(songs$timesignature)
# Which timesignature value is the most frequent among songs in our dataset?
table(songs$timesignature)
# Out of all of the songs in our dataset, the song with the highest tempo is one of the following songs. Which one is it?
songs$songtitle[which.max(songs$tempo)]
# Creating Our Prediction Model
SongsTrain = subset(songs, songs$year <= 2009)
SongsTest = subset(songs, songs$year > 2009)
nrow(SongsTrain)
nonvars = c("year", "songtitle", "artistname", "songID", "artistID")
SongsTrain = SongsTrain[ , !(names(SongsTrain) %in% nonvars) ]
SongsTest = SongsTest[ , !(names(SongsTest) %in% nonvars) ]
SongsLog1 = glm(Top10 ~ ., data=SongsTrain, family=binomial)
# What is the correlation between the variables "loudness" and "energy" in the training set?
cor(SongsTrain$energy,SongsTrain$loudness)
SongsLog2 = glm(Top10 ~ . - loudness, data=SongsTrain, family=binomial)
SongsLog3 = glm(Top10 ~ . - energy, data=SongsTrain, family=binomial)
# Make predictions on the test set using Model 3. What is the accuracy of
# Model 3 on the test set, using a threshold of 0.45?
predictTest = predict(SongsLog3,type = "response", newdata = SongsTest)
table(SongsTest$Top10,predictTest > 0.45)
# What would the accuracy of the baseline model be on the test set?
length(SongsTest$Top10[SongsTest$Top10 != 1]) / nrow(SongsTest)
|
ea0e39a852f2a8fa6c6360b25168ba305240078c | b59142edc6f40f965bdb9ff4bda1cdc54c2ddb60 | /man/zinb_regulated.Rd | 2b1701334276257624a2b2c633f18671ab6bccae | [] | no_license | SkadiEye/RZiMM | 8de9b45f116ebf1cda8c474897bfd7f6eebddba8 | c846b2e61c404a073e65b5121372d88fcaf6b5b1 | refs/heads/master | 2022-09-29T05:06:50.478481 | 2022-09-24T06:01:21 | 2022-09-24T06:01:21 | 248,108,047 | 3 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,840 | rd | zinb_regulated.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/2-1-c-RZiMM-Count.R
\name{zinb_regulated}
\alias{zinb_regulated}
\title{A Zero-Inflated Negative Binomial Model
- for RZiMM-Count Model}
\usage{
zinb_regulated(
y,
g_ind,
m_ind = NULL,
g_list = NULL,
m_list = NULL,
gm_list = NULL,
lambda1 = 1,
lambda2 = 0,
n_iter = 10,
epsilon = 10^-6,
output = "vec",
epsilon0 = 10^-6,
plot = FALSE,
max_l_alpha = 10,
min_l_alpha = -10,
max_est = 100,
min_est = -100,
max_logit_pi = 100,
min_logit_pi = -100
)
}
\arguments{
\item{y}{A vector of expression levels}
\item{g_ind}{A vector of cell-type clusters}
\item{m_ind}{Sample index, for adjusting within-sample correlation; if set to be NULL,
the within-sample correlation will not be adjusted.}
\item{g_list}{A list for cell-type cluster indices}
\item{m_list}{A list for batch indices}
\item{gm_list}{A two-way list for cell-type cluster and batch indices}
\item{lambda1}{Weight of regularization for group effects.}
\item{lambda2}{Weight of regularization for sample effects.}
\item{n_iter}{Maximum number of total iteration.}
\item{epsilon}{Iteration stops when the change from last iteration is smaller than this value.}
\item{output}{Whether export a vector or a list}
\item{epsilon0}{A small number for calculating the number of parameters.}
\item{plot}{Whether plotting the trajectories for the loss and log-likelihood}
\item{max_l_alpha}{Upper threshold for l}
\item{min_l_alpha}{Lower threshold for l}
\item{max_est}{Upper threshold for estimate}
\item{min_est}{Lower threshold for estimate}
\item{max_logit_pi}{Upper threshold for pi}
\item{min_logit_pi}{Lower threshold for pi}
}
\value{
Returns a vector of parameters.
}
\description{
A Zero-Inflated Negative Binomial Model
- for RZiMM-Count Model
}
|
849ae287dc98892aebfa196d42dfe8d817a66811 | 4dadd6709aef1a3da646f1b7f8fc23b3c017d88f | /sna_um_cleanup.R | 71b24e0d1c5d8928da783618ff9e2c6dfa769ba7 | [] | no_license | katerinabc/sna_at_um | eefc97aa9e9b158e53bfb2e5447861fc57c3faac | d09a13928f368f440aadb5a1216d7b0e8f52dfdb | refs/heads/master | 2021-01-10T09:39:54.059691 | 2016-04-11T00:55:26 | 2016-04-11T00:55:26 | 55,929,876 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,803 | r | sna_um_cleanup.R | #Script to analyze SNA at UM dataset.
#Created on XX November 2015
#Updated March 6th 2016
#next step: combine answers for project 2
# analysis ----------------------------------------------------------------
#do logistic regression
#calculate network characteristics for its members.
#indegree, outdegree, density for each group
#beweenness
#structural equivalence
#if part of triad....
#argument: inverted U curve RS between ties and performance.
#if collaboration judged good = center in network, many links
# Questions ------------------------------------------------------
# what is answer 0 in att3? missing values?
# preface ------------------------------------------------------
# start with a clean working space
rm(list=ls())
# load previous data
# set the working directory
setwd("~/Dropbox/Network Analysis Innovation at UM/paper")
# load packages
# library(statnet)
library(statnet)
# load data ------------------------------------------------------
# att1.raw<-read.csv("attribute.csv", sep=";", header=T, stringsAsFactors=F) #project info (name, position, performance)
# #check last 4 rows are empty
# tail(att1.raw)
# att1.raw<-att1.raw[1:129,]
# att2.raw<-read.csv("attribute_Final.csv", header=T, na.strings=c(999999, "#N/A")) #indi info (gener, faculty)
# att3.raw<-read.csv("attribute_kwsharingFINAL.csv", header=T, na.strings=("#N/A"), stringsAsFactors=F) #kw sharing info
att.raw<-read.csv("SnaUmAttributes.csv", header=T)
att.all.raw<-read.csv("SnaUmAttributesAll.csv", header=T)
net.raw<-read.table("edgearray_maastricht.csv", header=T, sep=";",
stringsAsFactors=F, na.strings=c("Not in list","MV"))
#take out empty rows. problem with excel
net.raw<-net.raw[!net.raw$Ego == "",1:29]
str(net.raw)
#something is weird here. column 7 imported as character vector
net.raw[,c(7)]<-as.numeric(net.raw[,c(7)])
# Data preparation: Attributes ------------------------------------------------
# this section is only needed when cleaning up and combining the different
# attributes file. This is not needed anymore. Thus commented out.
# #recode answers from text to numbers, only need to do once
# att3 <- att3.raw
# att3 <- apply(att3, 2, function(x){replace(x, x == "strong disagreement",1)})
# att3 <- as.data.frame(apply(att3, 2, function(x){replace(x, x == "disagreement",2)}))
# att3 <- as.data.frame(apply(att3, 2, function(x){replace(x, x == "neutral",3)}))
# att3 <- as.data.frame(apply(att3, 2, function(x){replace(x, x == "agreement",4)}))
# att3 <- as.data.frame(apply(att3, 2, function(x){replace(x, x == "strong agreement",5)}))
# att3<-apply(att3, 2, as.numeric) #the code above changes the character vector into factor because it is a mix of numbers and characters. factor is the smalled common type that allows numbers and characters. this line of code transforms it back to numbers
# rownames(att3)<-att3.raw[,1]
# #hist(att3)# plot answers
#
# #find people with no faculty number
#
# names(att1.raw)
# att1.raw[which(is.na(att1.raw[,2])),]
# att1.raw[68,2]<-6 #6 = sbe
# att1.raw[93,2]<-6
# att1.raw[122,2]<-2 #FHML = 2
#
# names(att2.raw)
# att2.raw[which(is.na(att2.raw[,6])),]
# att2.raw[c(61, 63, 81),6]<-2
# att2.raw[c(67, 72, 82),6]<-6
#
# #Fix the names
# att1.raw[1,1]<- "A. Duijvestijn"
# att1.raw[27,1]<-"Eleonore Kuhler"
# att1.raw[64,1]<- "Karen Konings"
# att1.raw[73,1]<-"M van Zandvoort"
# att1.raw[88,1]<-"Mirko Reithler"
# att1.raw[101,1]<-"Peter Wilms van Kersbergen"
#
# att2.raw[,1] <- as.character(att2.raw[,1])
# att2.raw[1,1] <- "A. Duijvestijn"
# att2.raw[27,1] <-"Eleonore Kuhler"
# att2.raw[64,1] <- "Karen Konings"
# att2.raw[73,1] <-"M van Zandvoort"
# att2.raw[87,1]<-"Mirko Reithler"
# att2.raw[99,1]<-"Peter Wilms van Kersbergen"
#
# rownames(att3)[24]<-att1.raw[64,1]
# rownames(att3)[35] <- att1.raw[112,1]
# rownames(att3)[51]<-att1.raw[64,1]
#
# #att 3 has some people filling out the questionnaire several times
# #take out NA's
# idx<-which(is.na(att3[,2]))
# att3<-att3[-idx,]
# #take out duplicates
# att3<-att3[!duplicated(rownames(att3)),]
# att3<-att3[order(rownames(att3)),]
# rownames(att3)[5] <- att1.raw[10,1]
# rownames(att3)[13] <- "Eleonore Kuhler"
# rownames(att3)[16] <- att1.raw[35,1]
# rownames(att3)[25] <- att1.raw[67,1]
# rownames(att3)[26] <- att1.raw[70,1]
# rownames(att3)[49] <- att1.raw[126,1]
#
# # #combine all the attribute datasets
# # #matching rows to att1
# att2<-att2.raw[match(att1.raw[,1], att2.raw[,1]),]
# att3.mod<-att3[match(att1.raw[,1], rownames(att3)),]
#
# #combine att1 and att2 with background variables
# att.12<-cbind(att1.raw, att2)
# write.csv(att.12, "SnaUmAttributes.csv")
#
# att.all<-cbind(att1.raw, att2, rownames(att3.mod), att3.mod) #combine att1, att2, att3
# att.all<-att.all[,c(1,4,13,14,2:3,5:12,15:44)]
# idx<-which(is.na(att.all[,3])) #att 3 had the smallest data, could take NA's out
# att.all<-att.all[-idx,]
# att.all<-att.all[,c(1,5:ncol(att.all))]
# write.csv(att, "SnaUmAttributesAll.csv")
# Data preparation: Networks ----------------------------------------------
#create get info network by averaging getinfo and give info
#give info needs to be transposed(columbs become rows and rows colums)
#project 1
getinfo.raw<-net.raw[,c(1:2, 9)]
getinfo.raw$dyad<-paste(getinfo.raw[,1], getinfo.raw[,2], sep="_")
length(unique(getinfo.raw[,4])) #671 unique dyads
giveinfo.raw<-net.raw[,c(2,1,12)] #transposed of giveinfo
giveinfo.raw$dyad<-paste(giveinfo.raw[,1], giveinfo.raw[,2], sep="_")
length(unique(giveinfo.raw[,4])) #671 unique dyads
#sort give info.raw so that col1 and col2 match the colum names in getinfo.raw
#if I sort both alpahebetically it should work
getinfo.raw<-getinfo.raw[order(getinfo.raw[,4]),]
giveinfo.raw<-giveinfo.raw[order(giveinfo.raw[,4]),]
lapply(list(getinfo.raw, giveinfo.raw), dim) #returns dimension of both objects
#check if both data frames have the same dyads
table(getinfo.raw[,4] == giveinfo.raw[,4]) #true only 37 times
#find the matches
idx.gt<-which(getinfo.raw[,4] %in% giveinfo.raw[,4]) #563 dyads are in both data frames
idx.gv<-which(giveinfo.raw[,4] %in% getinfo.raw[,4])
#subset for the dyads that are in both dataframes
getinfo.raw2<-getinfo.raw[idx,]
#do the same for giveinfo
giveinfo.raw2<-giveinfo.raw[which(giveinfo.raw[,4] %in% getinfo.raw[,4]),]
#dim: 563 - 4
#sort both alphabetically
getinfo.raw2<-getinfo.raw2[order(getinfo.raw2[,4]),]
giveinfo.raw2<-giveinfo.raw2[order(giveinfo.raw2[,4]),]
#inspet giveinfo.raw2 manually
match(giveinfo.raw2[,4], getinfo.raw2[,4])
#row 35 is twice (Anita van Gils_Jeannette Hommes)
#remove 1 instance
giveinfo.raw2<-giveinfo.raw2[-35,]
#again subset for same dyads
getinfo.raw3<-getinfo.raw2[which(getinfo.raw2[,4] %in% giveinfo.raw2[,4]),]
giveinfo.raw3<-getinfo.raw2[which(giveinfo.raw2[,4] %in% giveinfo.raw2[,4]),]
lapply(list(getinfo.raw3, giveinfo.raw3), dim)
getinfo.raw3<-getinfo.raw3[match(getinfo.raw3[,4], giveinfo.raw3[,4]),]
getinfo.raw3<-getinfo.raw3[-nrow(getinfo.raw3),]
#check if dyads in same order
table(getinfo.raw3[,4] == giveinfo.raw3[,4]) #finally ok
#create information seeking for project 1
#composed of the average of getinfo.raw2 and giveinfo.raw2
#added to it the dyads than don't have a match. these numbers could be biased.
ie1.raw<-cbind(getinfo.raw3, giveinfo.raw3)
table(ie1.raw[, 4] == ie1.raw[,8])#check again if same dyads
#calculate row mean and
ie1<-cbind(ie1.raw[,1:2], ie1 = rowMeans(ie1.raw[,c(3,7)], na.rm=T))
#add the dyads for which I only have one sided information
ie1<-rbind(ie1, setNames(getinfo.raw[-idx.gt,1:3], names(ie1)),
setNames(giveinfo.raw[-idx.gv,1:3], names(ie1)))
#take out those who have NA has value
ie1<-ie1[!is.na(ie1[,3]),]
#check for duplicates
ie1$dyad<-paste(ie1[,1], ie1[,2], sep="-")
table(duplicated(ie1$dyad)) #8 dubbles. inspect them
idx.du<-which(duplicated(ie1$dyad))
dyad.dub<-ie1[idx.du,4]
View(ie1[ie1$dyad %in% dyad.dub, ])
View(ie1[ie1[,4] == dyad.dub,])
#no difference in ie1 frequency
#exception: dyad: Paul Adriaas - Wim G: 4 and 2.5. -> Take the 2.5, that is based on the average.
ie1<-ie1[-idx.du,]
table(duplicated(ie1$dyad)) #no duplicates.
#Project 2
save.image("snaatum.RData")
net.raw<-cbind(net.raw, ie_pr1, ie_pr2, ie_pr3)
#RUN LOOP ON SERVER
# this loops creates a valued network for every column in the network file
for (i in 3:ncol(net.raw)){
# subset the netwwork file. i selects the column
#mat<-as.matrix(subset(net.raw, select=c(1:2,i)))
# create a network object
nv<-as.network(subset(net.raw, select=c(1:2,i)), directed=T,
matrix.type="edgelist",ignore.eval=F, names.eval="value")
# assign edge value to the network objects
nv%e%"value" <- net.raw[,i]
# assign an name (identifier) for the network object nv
assign(paste(colnames(net.raw)[i], "val", sep= "."),nv)
}
# this loops creates a binary network for every column in the network file.
# The standard ERGM procedure only takes binary data for the dependent netwokr.
for (i in 3:ncol(net.raw)){
# subset the netwwork file. i selects the column
df<-subset(net.raw, select=c(1:2,i))
df[which(df[,3] < 4),3 ]<-0
df[which(df[,3] > 3),3 ]<-1
# dichotomize the network
# in the object df, take the rows for which the 3rd column has a value above 3
# and then replace the value that is in column 3 with 0
# create a network object
#does something funny
n<-network(df, directed=T, matrix.type="edgelist")
# assign an name (identifier) for the network object
assign(colnames(net.raw)[i],n)
}
# # prepare mega matrix for ERGM --------------------------------------------
#
# # take information sharing network
# # turn ties that exists to 0 and ties that can NOT exists to 1
# net.mega_pr1<-as.sociomatrix(ie_pr1)
# # convert into
# net.mega_pr1[net.mega_pr1[,3] ,] <-0
|
7551217c4f04af5e1900964e92f33a60cae0cf71 | d2de2f3709fb5a8fb0a7cd468f28a960452f170c | /includes/bernoulli.R | 058b2e2bb395b4f1d854e582e61ace19870120d9 | [] | no_license | spolischook/probability-theory | 8c1eaef6874e76b1f5108527065ce9d83a010658 | d0537c92a323923feaefcf0701a347710196403a | refs/heads/master | 2020-03-29T01:04:11.710925 | 2018-11-12T08:08:38 | 2018-11-12T08:08:38 | 149,369,743 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 275 | r | bernoulli.R | # Title : Formula Bernoulli
# Objective : Authomate probability computitions
# Created by: spolischook
# Created on: 9/19/18
source("combinatorics.R")
fBernoulli <- function(p, m, n) {
q = 1 - p
omega <- combinations(m, n)
return (omega * p^m * q^(n-m))
}
|
7e8cdf2a9ef5d6dce980728dfd38bfd8949b6f2d | 5d598bd59b2b566fd49bc1c6bd8fe0c803df32d1 | /scripts/loadDataHomePower.R | 6ff0c4b201626ebf59037e62466b58510af432cb | [] | no_license | caritosteph/exdata-plotting1-proyect1 | 846c822eb0b55d8775b2b678dd2442367c9bed34 | 759f8d65d2e27ff8580fdbf67c9cfa5e3c6fc20f | refs/heads/master | 2021-05-29T16:25:34.742054 | 2015-07-12T22:12:52 | 2015-07-12T22:12:52 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,118 | r | loadDataHomePower.R | #Lubridate makes it easier to work with dates
#and times by providing functions to identify and parse
#date-time data
library(lubridate)
# Save it to an new file for easy access later.
if (!file.exists('houseHoldPower.rds')) {
download.file(paste0('https://d396qusza40orc.cloudfront.net/',
'exdata%2Fdata%2Fhousehold_power_consumption.zip'),
method='curl', destfile='raw-power-data.zip')
unzip('raw-power-data.zip')
# Read data into a table
homePower.df <- read.table('household_power_consumption.txt', header=TRUE,
sep=';', na.strings='?',
colClasses=c(rep('character', 2),
rep('numeric', 7)))
# Convert dates and times
homePower.df$Date <- dmy(homePower.df$Date)
homePower.df$Time <- hms(homePower.df$Time)
# Combine date and time
homePower.df$date.time <-homePower.df$Date + homePower.df$Time
# Save file
saveRDS(homePower.df, file='houseHoldPower.rds.rds')
} else {
homePower.df <- readRDS('houseHoldPower.rds')
} |
bcfe0543fbade0c4f5736b00aa9be79da3de7be0 | ad92f7dc6023fc05c51a175f93782229ef36e062 | /R/internal.R | 9073f874eab5282dab97f768ecf67f464b604f8f | [] | no_license | aoles/remedy | e0bf9668ac78e4d8cd6393ba192e2f41c21e2465 | efd85f33733aa9295500d4ed78367c1a8144a047 | refs/heads/master | 2020-03-20T11:31:07.959921 | 2018-06-23T12:25:51 | 2018-06-23T12:25:51 | 137,405,448 | 0 | 0 | null | 2018-06-14T20:22:57 | 2018-06-14T20:22:57 | null | UTF-8 | R | false | false | 675 | r | internal.R | #' @importFrom rstudioapi getSourceEditorContext insertText
add_prefix <- function(prefix) {
a <- rstudioapi::getSourceEditorContext()
s <- a$selection
n <- length(s)
# if no text is selected
if (n == 1L && nchar(s[[1L]]$text) == 0L) {
pos <- s[[1L]]$range$start
pos[2L] <- 1
rstudioapi::insertText(location = pos, text = prefix)
} else {
for (i in s)
rstudioapi::insertText(location = i$range$start, text = prefix)
}
}
enclose <- function(prefix, postfix = prefix) {
a <- rstudioapi::getSourceEditorContext()
for (s in a$selection)
rstudioapi::insertText(location = s$range, text = sprintf("%s%s%s", prefix, s$text, postfix))
}
|
06ba590afa3043c01f9142010eefe3fce943b6d6 | e0e0b867a2caef5899addb185f5641a1ff6c23b0 | /R/import.R | b7535c621552592db32e90fe9a96a4136942fec8 | [
"MIT"
] | permissive | mstaniak/giosDownloader | fba1d21f13ba1590fe2b748b9c913d4e3c480f83 | a8b3b220fa6174378e7c32e69c5664550965a042 | refs/heads/master | 2022-02-15T23:07:09.937778 | 2019-08-26T12:14:34 | 2019-08-26T12:14:34 | 103,324,881 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,859 | r | import.R | #' Check if there are any measurement on a given stations for chosen year and polutant.
#'
#' @param chosenYear year of measurements
#' @param chosenPolutant measured polutant
#' @param station station of interest. Please provide current names of stations (values, not names of stationCodes vector)
#'
#' @return logical value: TRUE if the data is available, FALSE if it's not
#'
#' @export
#'
isAvailable <- function(chosenYear, chosenPolutant, station) {
availability %>%
dplyr::filter(year == chosenYear,
polutant == chosenPolutant) %>%
dplyr::select_(.dots = station) %>%
unlist(use.names = FALSE)
}
#' Import from one GIOŚ .xlsx file.
#'
#' @param station chr, name of the chosen station.
#' @param polutant chr, name of chosen polutant.
#' @param year chr, chosen year (yyyy).
#' @param path chr, path to folder containing .xlsx file or path to .xlsx file.
#' @param noHours chr, 1 or 24, defaults to 1 (hourly meas.)
#' @param skip int, number of rows to skip, defaults to 3.
#' Do not change if unchanged GIOŚ files are used.
#' @param exact lgl, TRUE if path is a path to .xlsx file.
#'
#' @return tibble
#'
importOneXLSX <- function(station, polutant, year, path = getwd(),
noHours = "1", skip = 3, exact = FALSE) {
fileList <- character(0)
if(exact) {
fileList <- path
} else {
fileList <- list.files(path, pattern = "*.xlsx") %>%
grep(pattern = year, value = TRUE, fixed = TRUE) %>%
grep(pattern = polutant, value = TRUE, fixed = TRUE) %>%
grep(pattern = paste0(noHours, "g"), value = TRUE, fixed = TRUE)
}
emptyFrame <- tibble::tibble(measDate = character(0),
station = character(0),
polutant = character(0),
measurement = character(0))
if(length(fileList) == 0) return(emptyFrame)
srcFile <- paste(path, fileList, sep = "/")
colNames <- colnames(readxl::read_excel(srcFile))
if(!sum(grepl(colNames, pattern = station))) {
isOld <- sum(grepl(names(stationCodes), pattern = station))
isNew <- sum(grepl(stationCodes, pattern = station))
if(isOld) {
station <- stationCodes[station]
} else if(isNew) {
station <- names(stationCodes)[grep(stationCodes, pattern = station)]
}
if(!sum(grepl(colNames, pattern = station))) {
return(emptyFrame)
}
}
tmpFrame <- tibble::as_tibble(readxl::read_excel(srcFile, skip = skip,
col_names = FALSE))
colnames(tmpFrame) <- colNames
colnames(tmpFrame)[1] <- "measDate"
tmpFrame <- tmpFrame[, c("measDate", station)]
colnames(tmpFrame)[2] <- "measurement"
tmpFrame %>%
dplyr::mutate(measurement = stringr::str_replace_all(measurement, ",", ".")) %>%
dplyr::mutate(measurement = as.numeric(measurement),
station = station,
polutant = polutant)
}
#' Import data for one station, multiple years and polutants.
#'
#' @param station chr, name of the chosen station.
#' @param polutants chr, names of chosen polutants.
#' @param years chr, chosen years (yyyy).
#' @param path chr, path to folder containing .xlsx file or path to .xlsx file.
#' @param noHours chr, 1 or 24, defaults to 1 (hourly meas.)
#' @param skip int, number of rows to skip, defaults to 3.
#' Do not change if unchanged GIOŚ files are used.
#' @param exact lgl, TRUE if path is a path to .xlsx file.
#'
#' @return tibble
#'
#' @export
#'
#' @examples
#' \dontrun{
#' importGiosFromXLSX("DsWrocKorzA", c("NOx", "SO2"), c("2015", "2014"))
#' # Default settings, .xlsx files are in the working directory.
#' importGiosFromXLSX("DsWrocKorzA", c("NOx", "SO2"), c("2015", "2014"), "path-to-the-folder")
#' # Importing from a different (not working) directory.
#' }
#'
importGiosFromXLSX <- function(station, polutants = NULL, years = NULL, path = getwd(),
noHours = "1", skip = 3, exact = FALSE) {
if(!exact & (is.null(polutants) | is.null(years))) stop("Years and polutants must be given if exact = FALSE")
if(exact & path == getwd()) stop("Paths to files must be given if exact = TRUE")
tmpResult <- vector("list", length(polutants)*length(years))
for(i in polutants) {
for(j in years) {
tmpResult[[paste0(i, j)]] <- importOneXLSX(station, i, j, path, noHours, skip, exact)
}
}
tmpResult %>%
dplyr::bind_rows() %>%
dplyr::select(station, polutant, measDate, measurement) %>%
dplyr::mutate(measDate = lubridate::ymd_hms(measDate)) %>%
dplyr::mutate(measDate = lubridate::round_date(measDate, unit = "hour")) %>%
dplyr::filter(lubridate::year(measDate) %in% years)
}
#' Import from one .csv file.
#'
#' @inheritParams importOneXLSX
#'
#' @return tibble
#'
importOneCSV <- function(station, polutant, year, path = getwd(),
noHours = "1", skip = 3, exact = FALSE) {
fileList <- character(0)
if(exact) {
fileList <- path
} else {
fileList <- list.files(path, pattern = "*.csv") %>%
grep(pattern = year, value = TRUE, fixed = TRUE) %>%
grep(pattern = polutant, value = TRUE, fixed = TRUE) %>%
grep(pattern = paste0(noHours, "g"), value = TRUE, fixed = TRUE)
}
emptyFrame <- tibble::tibble(measDate = character(0),
station = character(0),
polutant = character(0),
measurement = character(0))
if(length(fileList) == 0) return(emptyFrame)
srcFile <- paste(path, fileList, sep = "/")
tmpFrame <- readr::read_csv(srcFile, col_names = TRUE)
colnames(tmpFrame)[1] <- "measDate"
tmpFrame <- tmpFrame[-(1:2), ]
colNames <- colnames(tmpFrame)
if(!sum(grepl(colNames, pattern = station))) {
isOld <- sum(grepl(names(stationCodes), pattern = station))
isNew <- sum(grepl(stationCodes, pattern = station))
if(isOld) {
station <- stationCodes[station]
} else if(isNew) {
station <- names(stationCodes)[grepl(x = stationCodes, pattern = station)]
}
if(!sum(grepl(colNames, pattern = station))) {
return(emptyFrame)
}
}
tmpFrame <- tmpFrame[, c("measDate", station)]
colnames(tmpFrame)[2] <- "measurement"
tmpFrame %>%
dplyr::mutate(measurement = stringr::str_replace_all(measurement,
",", ".")) %>%
dplyr::mutate(measurement = as.numeric(measurement),
station = station,
polutant = polutant)
}
#' Import data for one station, multiple years and polutants from .csv files.
#'
#' @inheritParams importGiosFromXLSX
#'
#' @return tibble
#'
#' @export
#'
#' @examples
#' \dontrun{
#' importGiosFromXLSX("DsWrocKorzA", c("NOx", "SO2"), c("2015", "2014"))
#' # Default settings, .xlsx files are in the working directory.
#' importGiosFromXLSX("DsWrocKorzA", c("NOx", "SO2"), c("2015", "2014"), "path-to-the-folder")
#' # Importing from a different (not working) directory.
#' }
#'
importGiosFromCSV <- function(station, polutants = NULL, years = NULL, path = getwd(),
noHours = "1", skip = 3, exact = FALSE) {
if(!exact & (is.null(polutants) | is.null(years))) stop("Years and polutants must be given if exact = FALSE")
if(exact & path == getwd()) stop("Paths to files must be given if exact = TRUE")
tmpResult <- vector("list", length(polutants)*length(years))
for(i in polutants) {
for(j in years) {
tmpResult[[paste0(i, j)]] <- importOneCSV(station, i, j, path, noHours, skip, exact)
}
}
tmpResult %>%
dplyr::bind_rows() %>%
dplyr::select(station, polutant, measDate, measurement) %>%
dplyr::mutate(measDate = lubridate::ymd_hms(measDate)) %>%
dplyr::mutate(measDate = lubridate::round_date(measDate, unit = "hour")) %>%
dplyr::filter(lubridate::year(measDate) %in% years)
}
|
47c45124b1e8efdd70468ee3683e7395f594d41e | 833fef7d1324f759c7627e46851f6d863f87d07a | /work_groups/occurrence/funcao-limpeza.R | 70ddd255c422db9d3bed1c0752aba9ad59caa9b2 | [
"MIT"
] | permissive | LEEClab/SDMGroup | a3796145d04706bf94c4f23ec0d7d6062f00ba27 | e511d46a2645523675816c345a91d596fd10965b | refs/heads/master | 2020-04-06T04:06:30.723505 | 2018-02-26T14:43:49 | 2018-02-26T14:43:49 | 83,036,807 | 6 | 1 | null | 2017-06-04T03:50:39 | 2017-02-24T11:55:14 | R | ISO-8859-1 | R | false | false | 7,136 | r | funcao-limpeza.R |
limpeza<-function(occ.tmp=data.frame(na),
background.pol,
remove_latlongNA0=TRUE,
remove_anoNA=TRUE,
remove_limiteespacial=TRUE,
check.country=TRUE,
check.state=TRUE,
check.municipality=TRUE,
check.citycoor=TRUE,
check.centroid=TRUE){
### atribuindo ID
ID=1:nrow(occ.tmp)
occ.tmp=cbind(ID,occ.tmp)
occ.tmp.out <- occ.tmp.out.temp <- {}
#remove_latlongNA0=TRUE # testar com lastInterpreted !!!
if(remove_latlongNA0==TRUE){
occ.tmp.out.temp <- occ.tmp[is.na(occ.tmp$latitude),]
occ.tmp <- occ.tmp[!is.na(occ.tmp$latitude),]
if (NROW(occ.tmp.out.temp)>0){
remove.type = 'lat. NA'
occ.tmp.out.temp <- cbind(occ.tmp.out.temp, reason=rep(remove.type,NROW(occ.tmp.out.temp)))
occ.tmp.out <- rbind(occ.tmp.out,occ.tmp.out.temp)}
occ.tmp.out.temp <- occ.tmp[is.na(occ.tmp$longitude),]
occ.tmp <- occ.tmp[!is.na(occ.tmp$longitude),]
if (NROW(occ.tmp.out.temp)>0){
remove.type = 'long. NA'
occ.tmp.out.temp <- cbind(occ.tmp.out.temp, reason=rep(remove.type,NROW(occ.tmp.out.temp)))
occ.tmp.out <- rbind(occ.tmp.out,occ.tmp.out.temp)}
occ.tmp.out.temp <- occ.tmp[occ.tmp$latitude %in% 0,]
occ.tmp <- occ.tmp[!occ.tmp$latitude %in% 0,]
if (NROW(occ.tmp.out.temp)>0){
remove.type = 'lat. 0'
occ.tmp.out.temp <- cbind(occ.tmp.out.temp, reason=rep(remove.type,NROW(occ.tmp.out.temp)))
occ.tmp.out <- rbind(occ.tmp.out,occ.tmp.out.temp)}
occ.tmp.out.temp <- occ.tmp[occ.tmp$longitude %in% 0,]
occ.tmp <- occ.tmp[!occ.tmp$longitude == 0,]
if (NROW(occ.tmp.out.temp)>0){
remove.type = 'long. 0'
occ.tmp.out.temp <- cbind(occ.tmp.out.temp,reason=rep(remove.type,NROW(occ.tmp.out.temp)))
occ.tmp.out <- rbind(occ.tmp.out,occ.tmp.out.temp)}
}
#remove_anoNA=TRUE
if(remove_anoNA==TRUE){
occ.tmp.out.temp <- occ.tmp[is.na(occ.tmp$yearcollected),]
occ.tmp <- occ.tmp[!is.na(occ.tmp$yearcollected),] # retira anos NA
if (NROW(occ.tmp.out.temp)>0)
{remove.type = 'Ano NA'
occ.tmp.out.temp <- cbind(occ.tmp.out.temp, reason=rep(remove.type,NROW(occ.tmp.out.temp)))
occ.tmp.out <- rbind(occ.tmp.out,occ.tmp.out.temp)}
}
#remove_limiteespacial=TRUE # testar com lastInterpreted !!!
if(remove_limiteespacial==TRUE & NROW(occ.tmp)>0)
{
occ.tmp.points <- SpatialPointsDataFrame(cbind(occ.tmp$longitude,occ.tmp$latitude),occ.tmp)
CRS.new=background.pol@proj4string
proj4string(occ.tmp.points) <- CRS.new
occ.tmp.points_in <- occ.tmp.points[!is.na(over(occ.tmp.points ,geometry(background.pol))),] # dentro do poligono
occ.tmp.points_out <- occ.tmp.points[is.na(over(occ.tmp.points ,geometry(background.pol))),] # fora do poligono
occ.tmp.out.temp <- occ.tmp.points_out@data
if (NROW(occ.tmp.out.temp)>0)
{
remove.type = 'Out of boundary polygon'
occ.tmp.out.temp <- cbind(occ.tmp.out.temp, reason=rep(remove.type,NROW(occ.tmp.out.temp)))
occ.tmp.out <- rbind(occ.tmp.out,occ.tmp.out.temp)}
}
set4=occ.tmp.points_in@data
set4$reason=NA
#set4$ID=1:nrow(set4) # subir
# ### 1. Check country
#check.country=TRUE
if(check.country==T){
PAIS=corroboracion_pais(set4,paises)
set4$ok_country=rep(NA,nrow(set4)) ;set4$ok_country[PAIS[[1]]]=1
set4$suggest_country=PAIS[[3]]
cat("Country concordance checked ")} # deixar msn como prints e não como janelas
#### 2. check state # just working for Brasil
#check.state=TRUE
if(check.state==T){
#set5<-set4[which(set4$suggest_country=="Brasil"),] #2055
DEP=corroboracion_dep(set4,estado)
ok_state=rep(NA,nrow(set4)) ;ok_state[DEP[[1]]]=1
set4=cbind(set4,ok_state)
set4$suggest_state=DEP[[3]]
cat("State concordance checked ")
}
### 3. check municipality
#check.municipality=T
if (check.municipality==T){
A=corroboracion(set4,mpios)
set4$ok_mun=NA ;set4$ok_mun[A[[1]]]=1
set4$suggest_mun=A[[3]]
cat("Municipality concordance checked ","!")}
##### 4. check that the coordinate is equal to the coordinate of the city municipality
#check.citycoor=T
if(check.citycoor==T){
sedes_lat=which(set4$latitude %in% sedes$lat)
set4$coord_mun=NA
for ( i in sedes_lat){
filas=which(sedes$lat %in% set4$latitude[i]) #elige latitudes iguales a las latitudes de municipalidades
respuesta=NULL
for(j in filas){# revisa si para esas latitides iguales tambien las longitudes son igules
resp=set4$longitude[i]==sedes$long[j]
respuesta=c(respuesta,resp)
}
if (TRUE %in%respuesta){# si lat y longitud son igual a la de la municipalidad pone 1 de lo contrario 0
set4$coord_mun[i]=1
}else{set4$coord_mun[i]=0}
}
mun.ref=set4$ID[which(set4$coord_mun==1)] # identifica cuales refID tienen la misma cordenada que la municipalidad
#
if (length(mun.ref)!=0){
cat(paste0("IDs ",mun.ref, " have the coordenate of the municipality, Be careful","!"))} else
{cat(paste0("All the coordenate are different from the municipality","!"))}
}
##### 7. check that the name coordinate is equal to the centroid of the municipality
#####
#check.centroid=T
if(check.centroid==T){
dat=set4
CRS.new=centroid@proj4string
coordinates(dat)=~longitude+latitude
proj4string(dat) <- CRS.new
ovm=over(dat,centroid)
mun_cent=ovm[,c("Longmuncen", "Latmuncent")]
set4=cbind(set4,mun_cent)
dif.lon=abs(set4$longitude-set4$Longmuncen)
dif.lat=abs(set4$latitude-set4$Latmuncent)
dif=cbind(dif.lon,dif.lat)
iqual=which(dif[,1]<0.001 & dif[,2]<0.01)
set4$coord_mun_cent=NA
if (length(iqual)!=0){
set4$coord_mun_cent[iqual]=1
cat("IDs", set4$ID[iqual], "have the coordinate of the centroid of the municipality")
}else{
cat("All the coordenate are different from the municipality")
}
}
# salva dados e limpeza
# geo.out=set.fin[which(is.na(set.fin$ok_country)|is.na(set.fin$ok_state)|is.na(set.fin$ok_mun)|set.fin$coord_mun==1|set.fin$coord_mun_cent==1),]
# geo.ok=set.fin[which(set.fin$ok_country==1 & set.fin$ok_state==1 & set.fin$ok_mun==1 & is.na(set.fin$coord_mun==1) & is.na(set.fin$coord_mun_cent)),]
project="clean.results"
if(NROW(occ.tmp.out)>0){
occ.tmp.out=cbind(occ.tmp.out,ok_country=NA,suggest_country=NA,ok_state=NA, suggest_state=NA,ok_mun=NA,suggest_mun=NA,coord_mun=NA,Longmuncen=NA,Latmuncent=NA,coord_mun_cent=NA)
occ.tmp.out.all=rbind(occ.tmp.out, set4)
save_occurrence_records(occ.tmp.out.all,'limpeza-out',project,'clean_results')
return(occ.tmp.out.all)
} else{
save_occurrence_records(set4,'limpeza-out',project,'clean_results')
return(set4)
}
#save_occurrence_records(occ.tmp.out,'limpeza-out',project,'clean_out',sep='\t')
#save_occurrence_records(geo.ok,'limpeza-ok',project,'clean_in',sep='\t')
}
|
8f94b826ce2e9ab7f8f88dd6bc9b0046dfc2cab1 | b50dbb928f0fec2a278f03d277af50d725ef0619 | /morex_v3/pericentromere/get_pericentromere_positions.R | 2fb48baa7c291f85f056eab6697da8014c631506 | [] | no_license | MorrellLAB/morex_reference | 2768861cf4324864cb0317e771be600cc59286e1 | 9bd91f1063e836604d036b8239f769c97a4e82f9 | refs/heads/master | 2023-06-11T13:49:38.423120 | 2023-06-08T16:55:12 | 2023-06-08T16:55:12 | 191,996,559 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,627 | r | get_pericentromere_positions.R | #!/usr/bin/env Rscript
# This script is based on Ana Poet's approach to finding the pericentromeric positions.
# See: https://github.com/lilei1/9k_BOPA_SNP/blob/master/script/PositionCentromeres.R
# Morex v3
# Dependencies
library(gtools)
library(data.table)
library(tidyverse)
### User provided arguments
# List of SNPs in centromeric region. Taken from supplemental table in Munoz et al 2011.
pericentromere_fp <- "~/GitHub/morex_reference/morex_v2/centromere/Table6_pericentromeres_cM.txt"
supp_table6_fp <- "~/GitHub/morex_reference/morex_v2/centromere/Supp_Table6-tpg2plantgenome2011080023-sup-0009.csv"
# Cross reference table between POPA (Munoz et al) and BOPA SNPs
crossref_fp <- "~/GitHub/morex_reference/morex_v2/centromere/Names_9K_BOPA_SNPs_CrossRef.csv"
# Vcf file with BOPA SNP position in new reference
vcf_fp <- "~/GitHub/morex_reference/morex_v3/50k_9k_BOPA_SNP/9k_idt95_noRescuedSNPs.vcf"
# Where do we want to output our files?
out_dir <- "~/GitHub/morex_reference/morex_v3/pericentromere"
#-----------
# Read in data
pericentromere <- read.table(pericentromere_fp, header = F)
consensus_map <- read.csv(supp_table6_fp, skip = 2)
crossref <- read.csv(crossref_fp)
vcf <- read.table(vcf_fp)
# Make column names more meaningful
colnames(vcf) <- c("CHROM", "POS", "ID", "REF", "ALT", "QUAL", "FILTER", "INFO")
# STEP 0: Prepare data frames
# Subset VCF so that we only use SNP positions that had unique BLAST hits.
# Some SNPs hit in multiple places non-uniquely and can't be used, we'll remove these.
# Duplicate SNP hits all contain the pattern "ALTCHR" in the info field
vcf.sub <- vcf[!vcf$INFO %like% "ALTCHR", ]
# Add "chr" to beginning of string to match vcf
consensus_map$Chromosome.1 <- gsub(pattern="^", replacement="chr", x=consensus_map$Chromosome.1)
# Pull out markers in the pericentromere region
pcentromere <- data.frame(Marker = character(),
Chr = character(),
cM = numeric())
for (i in pericentromere$V1) {
if (i == "chr1H") {
next # "NA" for chr1H, so we can't figure out pericentromere here
} else {
start_pos <- pericentromere[pericentromere$V1 == i, 2]
end_pos <- pericentromere[pericentromere$V1 == i, 3]
if (start_pos == end_pos) {
temp_chr <- consensus_map[consensus_map$Chromosome.1 == i, ]
temp_pcent <- temp_chr[grep(start_pos, temp_chr$cM), ]
} else {
temp_pcent <- consensus_map[consensus_map$Chromosome.1 == i & consensus_map$cM >= start_pos & consensus_map$cM <= end_pos, ]
}
# Pull out only relevant columns
current_chr <- data.frame(Marker = temp_pcent$Marker,
Chr = temp_pcent$Chromosome.1,
cM = temp_pcent$cM)
# Combine with main dataframe
pcentromere <- rbind(pcentromere, current_chr)
}
}
# STEP 1: change centromere SNP names to BOPA names
# Find which SNPs have BOPA names
# 129 markers in pcentromere df
# 7,864 markers in crossref df
Shared <- intersect(pcentromere$Marker, crossref$POPA) # 182 remaining in Shared
# Subset data frames
cen_sh <- pcentromere[pcentromere$Marker %in% Shared, ]
crossref_sh <- crossref[crossref$POPA %in% Shared, ]
# Order cross ref SNPs as in CENTROMERE
crossref_sh_or <- crossref_sh[match(cen_sh$Marker, crossref_sh$POPA), ]
# Merge to get all info together
pcent_bopa <- merge(x = cen_sh, y = crossref_sh_or,
by.x = "Marker",
by.y = "POPA")
# STEP 2: Find position of BOPA SNPs in the new reference genome
vcf_pcent <- vcf.sub[vcf.sub$ID %in% pcent_bopa$BOPA_C | vcf.sub$ID %in% pcent_bopa$BOPA, ] # 118 are present
# Sort VCF by chromosome then by position
vcf_pcent_ordered <- vcf_pcent[order(vcf_pcent$CHROM, vcf_pcent$POS), ]
# Find max and min of pericentromere positions
pcent_table <- vcf_pcent_ordered %>%
group_by(CHROM) %>%
summarise(min_pos = min(POS),
max_pos = max(POS))
# "NA" for chr1H positions
final_table <- rbind(tibble(CHROM = "chr1H", min_pos = "NA", max_pos = "NA"), pcent_table)
# STEP 3: Save to output files
# Save table to file
write.table(final_table,
paste(out_dir, "pericentromere_physPos.txt", sep = "/"),
quote=F,
row.names=F,
col.names=F,
sep="\t")
# Also save vcf containing SNPs in the pericentromeric region identified here
write.table(vcf_pcent_ordered,
paste(out_dir, "9k_snps_pericentromere.vcf", sep = "/"),
quote=F,
row.names=F,
col.names=F,
sep="\t")
|
144bdd3c27ffd0cb989b41445c01c3cff88224a4 | 1c7a127d6f51567bde7264d768537059a2563cec | /social_network_info_spreading_simulation/network_influence_spread_simulation.R | 287c485fc4e0071f5c16e7c0684b310f7010b6a5 | [
"Apache-2.0"
] | permissive | Haydart/RDataScienceIntroduction | 51aa1bc6599e783ced09fadc25714d0bab6af36d | 8bec4be5368f4f3fbb1cea052401361ccd499e7d | refs/heads/master | 2021-08-15T04:01:27.576793 | 2017-11-17T09:38:09 | 2017-11-17T09:38:09 | 109,005,831 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,241 | r | network_influence_spread_simulation.R | library(igraph)
# prepare network data
raw_graph_data = read.csv(file="social_network_graph_data.csv", header=FALSE, sep=" ")
graph_data <- raw_graph_data[3:nrow(raw_graph_data), 1:2]
str(graph_data)
raw_graph <- graph_from_data_frame(graph_data, directed=TRUE)
graph <- simplify(raw_graph)
# Erdos-Renyi graph graph <- erdos.renyi.game(p.or.m=.05, n=35, directed = TRUE)
# check if graph has 167 vertices & 5783 edges
print(vcount(graph))
print(ecount(graph))
# CONDUCT DISTRIBUTION SIMULATION
random_attempts_count <- 4
iterations_within_attempt <- 10
legend_descriptions <- lapply(c(1:iterations_within_attempt), paste0, " gen")
plot_file_names <- lapply(c(1:random_attempts_count), paste0, "attempt.jpg")
plot_colors <- c("black", "blanchedalmond", "blue", "blueviolet", "cyan", "darkgoldenrod1", "darkolivegreen1", "firebrick1", "deeppink", "lightskyblue")
for(attempt in 1:random_attempts_count) {
print(paste(as.character(attempt), "attempt"))
# mark all nodes as non-infected
for(vertex_index in V(graph)) {
V(graph)[vertex_index]$infected <- F
V(graph)[vertex_index]$color <- "green"
}
# set a random node to be infected
first_infected_index <- sample(1:vcount(graph), 1)
print(random_index)
# start infection from the node with highest betweenness value
#first_infected_index <- V(graph)[betweenness(graph) == max(betweenness(graph))]
#print(betweenness(graph))
#print(paste("The node with highest betweenness is ", first_infected_index))
V(graph)[first_infected_index]$infected <- T
V(graph)[first_infected_index]$color <- plot_colors[1]
# create 0-init vertex size matrix where the source-of-infection node is 1
size_matrix = matrix(0, nrow = vcount(graph), ncol = 1)
size_matrix[first_infected_index, 1] <- 1
infection_sources <- V(graph)[first_infected_index]
# simulate 10 iterations of infection propagation
for(index in 1:iterations_within_attempt) {
neighbors_to_be_infected <- list()
for(infection_source in infection_sources) {
neighbors_to_be_infected <- union(neighbors_to_be_infected, neighbors(graph, V(graph)[infection_source], mode = "out"))
}
# rule out the nodes that were infected earlier
neighbors_to_be_infected <- setdiff(neighbors_to_be_infected, V(graph)[infected == T])
infection_sources <- neighbors_to_be_infected
print(paste("Iteration", index, " infected ", length(infection_sources), " nodes."))
#infect neighbors of sampled nodes
for(neighbor in neighbors_to_be_infected) {
V(graph)[neighbor]$infected <- T
V(graph)[neighbor]$color <- plot_colors[index+1]
}
}
# plot the graph after 10 propagation iterations & save it to file
jpeg(as.character(plot_file_names[attempt]), width = 1280, height = 768)
plot(graph, rescale = TRUE, ylim=c(-1, 1), xlim=c(-1.1, 0.9), edge.arrow.size = .5, asp = 0,
vertex.label=NA, vertex.size=2.5 + 1.5 * as.matrix(size_matrix), layout=layout.drl(graph), main = paste("Random attempt no. ", attempt))
#plot(graph, vertex.size = 10)
legend(-1.15, .75, y.intersp = .75, xjust = .5, yjust = .5, legend=as.vector(legend_descriptions), bty = "n", fill = plot_colors, cex=1.5)
dev.off()
} |
6a0f8fe691727391c29ae0c884d09f12007753e2 | 251ddd5c2af5268d1d1624c1804c7c94da25ca5f | /Study/Dal.R | 48ecf10bad80aeb4b72c052d9c565abbb201e622 | [] | no_license | mandar2812/FrugalML | 5e9ca461b74df8a2c1d4e2694d6dfbbee1ab6909 | bbf60a36880abd4fdd72640f1b7ccfd667f5d516 | refs/heads/master | 2021-01-12T07:59:23.611050 | 2016-12-11T23:42:01 | 2016-12-11T23:42:01 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,074 | r | Dal.R | oldwd <- getwd()
setwd("/home/mikhail/Desktop/GitProjects/FrugalML")
files <- list.files()
# features of a data set
qualities <- read.csv("openml_data_qualities.csv")
separate_qualities <- split(qualities, qualities$dataset)
dataSize <- qualities[qualities$quality == "InstanceCount", ]
dataSize <- dataSize[order(dataSize$value, decreasing = TRUE), ]
# results of algorithms
evaluations <- read.csv("openml_evaluations_all.csv")
subEval <- evaluations[1:800, ]
separate_evaluations <- split(evaluations, evaluations$task_id)
# save names of data sets for further analysis
allMeasures <- data.frame()
# new map for storing values
map <- new.env(hash=T, parent=emptyenv())
# selects data sets by size
filterSize <- TRUE
if (filterSize) {
instanceCount <- 25
dataSize <- dataSize[dataSize$value >= instanceCount, ]
} else {
instanceCount <- "all"
}
processedSet <- 0
for (i in 1: length(separate_evaluations)) {
# make a copy of set
x <- as.data.frame(separate_evaluations[i])
x <- setNames(x, c("task_id", "dataset", "algo", "error_message", "accuracy", "auroc", "training_millis", "testing_millis", "confusion_matrix"))
# receive name
xName <- as.character(x$dataset[1])
allMeasures[i, 1] = xName
if (xName %in% dataSize$dataset) {
processedSet <- processedSet + 1
if (processedSet == 1) {
algorithms <- x$algo
algorithmsdf <- data.frame(algorithms)
algorithmsdf$colour = "black"
algorithmsdf$colour[1:15]="red"
algorithmsdf$colour[16:30]="orange"
algorithmsdf$colour[31:45]="yellow"
algorithmsdf$colour[46:60]="green"
algorithmsdf$colour[61:75]="blue"
algorithmsdf$colour[76:90]="grey"
algorithmsdf$colour[91:nrow(algorithmsdf$colour)]="violet"
shortNames <- strtrim(algorithms, 5)
}
# change type from factor to numeric
x$auroc <- as.numeric(as.character(x$auroc))
x <- x[!is.na(x$auroc), ]
x$training_millis <- as.numeric(as.character(x$training_millis))
x$testing_millis <- as.numeric(as.character(x$testing_millis))
# check if training time in milliseconds and change to seconds
x <- x[!is.na(x$training_millis), ]
if (max(x$training_millis > 1000000)) {
x$training_millis <- x$training_millis / 1000
}
x <- x[x$training_millis < 100000, ]
# check if testing time in milliseconds and change for seconds
x <- x[!is.na(x$testing_millis), ]
if (max(x$testing_millis > 1000000)) {
x$testing_millis <- x$testing_millis / 1000
}
x <- x[x$testing_millis < 100000, ]
# caclulate and log combined time for each algorithm
x$combineTime <- log(x$training_millis + x$testing_millis)
x$combineTime[x$combineTime < 0] = 0
# filter columns and save AUC, training time and name of algorithm
x <- data.frame(x$auroc, x$combineTime, x$algo)
smallX <- setNames(x, c("AUC", "TrainTime", "Algorithm"))
smallX <- smallX[order(smallX$AUC,smallX$TrainTime,decreasing=TRUE),]
smallX$AUC <- smallX$AUC * -1
# calculate Pareto front
paretoFront = smallX[which(!duplicated(cummin(smallX$TrainTime))),]
# insert new key or update existing one
for (alg in 1: nrow(paretoFront)) {
algName <- as.character(paretoFront[alg, 3])
if (is.null(map[[algName]])) {
map[[algName]] <- 1
} else {
frequency <- map[[algName]] + 1
map[[algName]] <- frequency
}
}
if (processedSet == 1) {
if (filterSize) {
png(filename = paste("plots/size", instanceCount, ".png", sep = ""), width = 6144, height = 6144)
} else {
png(filename= "plots/all.png", width = 6144, height = 6144)
}
# plot(paretoFront[,1:2], col = rainbow(103) , xlim=c(min(paretoFront$AUC), 0), ylim=c(0,max(paretoFront$TrainTime)), pch = 20, cex = 0.9)
plot(paretoFront[,1:2], col = "white", xlim=c(min(paretoFront$AUC), -0.9), ylim=c(0,5), pch = 20, cex = 0.9)
text(paretoFront[,1:2], labels = substr(paretoFront[, 3], 0, 4), cex = 0.7, pos = 3 )
legend("topright", legend = strtrim(algorithms, 70), col = algorithmsdf$colour, pch = 20, lty = 1, cex = 1.2, pt.cex = 1.2)
}
else {
# points(paretoFront[,1:2], col = rainbow(103) , xlim=c(min(paretoFront$AUC), 0), ylim=c(0,max(paretoFront$TrainTime)), pch = 20, cex = 0.9)
points(paretoFront[,1:2], col = "white", xlim=c(min(paretoFront$AUC), -0.9), ylim=c(0, 5), pch = 20, cex = 0.9)
text(paretoFront[,1:2], labels = substr(paretoFront[, 3], 0, 4), cex = 0.7, pos = 3 )
}
}
}
dev.off()
getValue <- function(x) {
map[[x]]
}
# find all keys in map
kys <- ls(map)
# create data frame
algValues <- data.frame();
for (i in 1:length(kys)) {
algValues[i, 1] <- kys[i]
algValues[i, 2] <- as.character(getValue(kys[i]))
}
algValues$V2 <- as.numeric(algValues$V2)
algValues <- algValues[order(algValues$V2, decreasing = TRUE), ]
algValues <- setNames(algValues, c("Algorithm", "Number of times on Pareto front"))
if (filterSize) {
write.csv(algValues, paste("selectedSets", instanceCount, ".csv", sep = ""))
} else {
write.csv(algValues, "allSets.csv")
}
# clean map
rm(map)
# store in arff format for external processing
writeArff <- FALSE
if (writeArff) {
library(foreign)
write.arff(tds, "tactivity.arff", "\n", deparse(substitute(tds)))
}
setwd(oldwd)
|
9bbd1fd68ecf111e174aa16d72d8fd669fe414b9 | c744f8c5401d2ca760d22d556fa2ecbebde39a56 | /rkvstore/man/rkv_iterator_size.Rd | 22d6663c9bffcc9a0fd82dc7b0944cbe548197eb | [
"Apache-2.0"
] | permissive | sshyran/oracle-nosql-drivers | 398816164de9a7507f7363ffbc868dfd73b85761 | 1f421d9ac561ee7ac4b37e084b1e97ae67f562b5 | refs/heads/master | 2022-03-13T23:52:03.170425 | 2016-04-07T14:42:14 | 2016-04-07T14:42:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 887 | rd | rkv_iterator_size.Rd | % File rnosql/man/rkv_iterator_size.Rd
\name{rkv_iterator_size}
\alias{rkv_iterator_size}
\title{Return the number of elements in the store iterator.}
\description{
Returns the number of items contained in the iterator. This function can only be used with iterators returned by the rkv_multiget_iterator(). The iterators returned by other functions, such as rkv_store_iterator(), are not usable by this function.
}
\usage{
rkv_iterator_size(iterator)
}
\arguments{
\item{iterator}{(kvIterator object) The iterator parameter is the handle to the iterator for which you want sizing information. }
}
\value{
(int)The number of items contained in the iterator.
}
\examples{
key <- rkv_create_key_from_uri(store, "/user/smith")
iterator <- rkv_multiget_iterator(store, key)
nRec <- rkv_iterator_size(iterator)
rkv_release_iterator(iterator)
}
\seealso{
\code{\link{rkv_multiget_iterator}}.
} |
0987ae2fe1d037d675bde1d3a39187e11f8ee11b | 9aafde089eb3d8bba05aec912e61fbd9fb84bd49 | /codeml_files/newick_trees_processed/10481_1/rinput.R | a199239e0a972d14312cac940cc9b52f0145caf9 | [] | no_license | DaniBoo/cyanobacteria_project | 6a816bb0ccf285842b61bfd3612c176f5877a1fb | be08ff723284b0c38f9c758d3e250c664bbfbf3b | refs/heads/master | 2021-01-25T05:28:00.686474 | 2013-03-23T15:09:39 | 2013-03-23T15:09:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 137 | r | rinput.R | library(ape)
testtree <- read.tree("10481_1.txt")
unrooted_tr <- unroot(testtree)
write.tree(unrooted_tr, file="10481_1_unrooted.txt") |
8572c92b7f66ef512c5689865b1e496764cbfc43 | 784f76e349f2654c6c40adc6a66b0f026c649911 | /man/plotBindingPotential.Rd | c0b6d4becdb3b78905dee02103f0a39d5abd9d07 | [] | no_license | bjw34032/oro.pet | a3116696b06e9b6b0e7c00eef8cd332b8665c3a8 | 6a5d06370c90bcdc63c87ee36b116cf6a94c5858 | refs/heads/master | 2022-09-07T10:46:02.908052 | 2022-08-20T08:32:46 | 2022-08-20T08:32:46 | 32,883,375 | 1 | 1 | null | 2016-10-01T13:26:13 | 2015-03-25T18:32:46 | R | UTF-8 | R | false | true | 2,065 | rd | plotBindingPotential.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plot.R
\name{plotBindingPotential}
\alias{plotBindingPotential}
\title{Plot Baseline Versus Post-Treatment Binding Potentials}
\usage{
plotBindingPotential(
base,
drug,
lty45 = 2,
lty = 1,
lwd45 = 2,
lwd = 3,
col45 = "darkgrey",
col = "orange",
pch = 1,
cex = 1,
xlim = range(0, base, 0.5),
ylim = range(0, drug, 0.5),
xlab = expression(BP[ND]^{
Base
}),
ylab = expression(BP[ND]^{
Drug
}),
...
)
}
\arguments{
\item{base}{is the vector of baseline binding potentials across brain
regions.}
\item{drug}{is the vector of post-treatment binding potentials across brain
regions.}
\item{lty45}{is the line type for the 45-degree line.}
\item{lty}{is the line type for the estimated regression line.}
\item{lwd45}{is the line width for the 45-degree line.}
\item{lwd}{is the line width for the estimated regression line.}
\item{col45}{is the color for the 45-degree line.}
\item{col}{is the color for the estimated regression line.}
\item{pch}{is the plotting character symbol.}
\item{cex}{is the size of the plotting symbol.}
\item{xlim}{is the range of values on the x-axis.}
\item{ylim}{is the range of values on the y-axis.}
\item{xlab}{is the label on the x-axis.}
\item{ylab}{is the label on the y-axis.}
\item{...}{additional arguments to be passed to the \code{plot}
function.}
}
\value{
A plot is shown, NULL is returned
}
\description{
Inspired by the Lassen plot (Cunningham et al., 2010) this is a
straightforward graphical summary of pre-treatment versus post-treatment
binding potentials for a single subject across multiple brain regions.
}
\details{
See the reference below.
}
\references{
Cunningham VJ, Rabiner EA, Slifstein M, Laruelle M (2010).
Measuring drug occupancy in the absence of a reference region: the Lassen
plot re-visited, \emph{Journal of Cerebral Blood Flow & Metababolism},
\bold{30}, 46-50.
}
\seealso{
\code{\link{par}}, \code{\link{plot}}
}
\author{
Brandon Whitcher \email{bwhitcher@gmail.com}
}
|
17976ac710417f9b630c6b0b28330defdacdbea7 | 793429873754b0f7b44ec4f04e7f877976cb87cf | /manipulation/map/2-encoder-map.R | 228d11f7946f0a45f7f2e95c267dd103fad802b7 | [] | no_license | IALSA/ialsa-2018-amsterdam | c11d16451a1fd4b6d7f703371a73aba5febd76e1 | 7af21117757da67337ab54a95a04a092a249a51c | refs/heads/master | 2021-09-04T22:10:53.310639 | 2018-01-22T15:41:29 | 2018-01-22T15:41:29 | 108,875,067 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 15,200 | r | 2-encoder-map.R | # The purpose of this script is to create multistate measure to be used in modelling
# knitr::stitch_rmd(script="./manipulation/rename-classify.R", output="./manipulation/rename-classify.md")
#These first few lines run only when the file is run in RStudio, !!NOT when an Rmd/Rnw file calls it!!
rm(list=ls(all=TRUE)) #Clear the variables from previous runs.
# ---- load-sources ------------------------------------------------------------
# Call `base::source()` on any repo file that defines functions needed below. Ideally, no real operations are performed.
source("./scripts/functions-common.R") # used in multiple reports
source("./scripts/graph-presets.R") # fonts, colors, themes
source("./scripts/graph-general.R") # simple, elemental displays
source("./scripts/graph-specific.R") # complex, composite displays
# ---- load-packages -----------------------------------------------------------
# Attach these packages so their functions don't need to be qualified: http://r-pkgs.had.co.nz/namespace.html#search-path
library(magrittr) #Pipes
# Verify these packages are available on the machine, but their functions need to be qualified: http://r-pkgs.had.co.nz/namespace.html#search-path
requireNamespace("ggplot2", quietly=TRUE)
requireNamespace("dplyr", quietly=TRUE) #Avoid attaching dplyr, b/c its function names conflict with a lot of packages (esp base, stats, and plyr).
requireNamespace("testit", quietly=TRUE)
requireNamespace("msm", quietly=TRUE)
# requireNamespace("plyr", quietly=TRUE)
# ---- declare-globals ---------------------------------------------------------
path_input <- "./data/unshared/derived/dto-1-tuned.rds"
path_output <- "./data/unshared/derived/dto-2-encoded.rds"
options(
origin="1970-01-01"
)
# ---- load-data ---------------------------------------------------------------
# load the product of 0-ellis-island.R, a list object containing data and metad
dto <- readRDS(path_input)
# ---- inspect-data -------------------------------------------------------------
names(dto)
# 1st element - raw data, as obtained from ialsa-study-curator for RUSH-MAP study
# dplyr::tbl_df(dto[["raw"]])
# 2nd element - meta data, info about variables
# dto[["meta"]]
# 3rd element - data produced by the greeter script
# dto[["greeted"]]
# 4th element - data produced by the tuner script
# dto[["tuned"]]
# ---- tweak-data --------------------------------------------------------------
ds <- dto[["tuned"]]
ds_long <- ds
# ---- describe-before-encoding --------------------------------------------------------------
# if died==1, all subsequent focal_outcome==DEAD.
# during debuggin/testing use only a few ids, for manipulation use all
set.seed(43)
ids <- sample(unique(ds$id),3) # randomly select a few ids
# custom select a few ids that give different pattern of data. To be used for testing
ids <- c(33027) #,33027, 50101073, 6804844, 83001827 , 56751351, 13485298, 30597867)
# ---- encode-missing-states ---------------------------
## ## ## - STEP 1 - Missing values on criterion
# create an object ds_miss from ds_long
# x <- c(NA, 5, NA, 7)
determine_censor <- function(x, is_right_censored){
ifelse(is_right_censored, -2,
ifelse(is.na(x), -1, x)
)
}
# new way
# # create file with missing information
# make_ds_miss <- function(
# d,
# variable
# ){
# d_long <- d %>%
# dplyr::mutate_(
# "target" = variable
# ) %>%
# as.data.frame()
#
# (N <- length(unique(d_long$id))) # sample size
# subjects <- as.numeric(unique(d_long$id)) # list the ids
#
# for(i in 1:N){
# # for(i in unique(ds$id)){ # use this line for testing
# # Get the individual data:
# (dta.i <- d_long[d_long$id==subjects[i],]) # select a single individual
# # (dta.i <- d_long[d_long$id==6804844,]) # select a single individual # use this line for testing
# (dta.i <- as.data.frame(dta.i %>% dplyr::arrange(-age_at_visit))) # enforce sorting
# (dta.i$missed_last_wave <- (cumsum(!is.na(dta.i$target))==0L)) # is the last obs missing?
# (dta.i$presumed_alive <- is.na(any(dta.i$age_at_death))) # can we presume subject alive?
# (dta.i$right_censored <- dta.i$missed_last_wave & dta.i$presumed_alive) # right-censored?
# # dta.i$target_recoded <- determine_censor(dta.i$target, dta.i$right_censored) # use when tracing
# (dta.i$target <- determine_censor(dta.i$target, dta.i$right_censored)) # replace in reality
# (dta.i <- as.data.frame(dta.i %>% dplyr::arrange(age_at_visit)))
# (dta.i <- dta.i %>% dplyr::select(-missed_last_wave, -right_censored ))
# # Rebuild the data:
# if(i==1){d_miss <- dta.i}else{d_miss <- rbind(d_miss,dta.i)}
# }
# # this part is not finished yet, need to make replacing the old variable
# d_miss <- d_miss %>%
# # drop original variable
# dplyr::mutate(
# mmse = target
# ) %>%
# dplyr::select(-target)
#
# return(d_miss)
# }
# # usage
# ds_miss <- ds_long %>% make_ds_miss(variable = "mmse")
# old way
(N <- length(unique(ds_long$id))) # sample size
subjects <- as.numeric(unique(ds_long$id)) # list the ids
# ds_long_temp <- ds_long
# i <- 5;
for(i in 1:N){
# for(i in unique(ds$id)){ # use this line for testing
# Get the individual data:
# ds_long <- ds_long_temp %>%
# dplyr::select(id, fu_year, age_at_visit,died, age_death, mmse) %>%
# as.data.frame()
(dta.i <- ds_long[ds_long$id==subjects[i],]) # select a single individual
# (dta.i <- ds_long[ds_long$id==6804844,]) # select a single individual # use this line for testing
(dta.i <- as.data.frame(dta.i %>% dplyr::arrange(-age_at_visit))) # enforce sorting
(dta.i$missed_last_wave <- (cumsum(!is.na(dta.i$mmse))==0L)) # is the last obs missing?
(dta.i$presumed_alive <- is.na(any(dta.i$age_at_death))) # can we presume subject alive?
# (dta.i$presumed_alive <- is.na(any(dta.i$age_death))) # can we presume subject alive?
(dta.i$right_censored <- dta.i$missed_last_wave & dta.i$presumed_alive) # right-censored?
# dta.i$mmse_recoded <- determine_censor(dta.i$mmse, dta.i$right_censored) # use when tracing
(dta.i$mmse <- determine_censor(dta.i$mmse, dta.i$right_censored)) # replace in reality
(dta.i <- as.data.frame(dta.i %>% dplyr::arrange(age_at_visit)))
(dta.i <- dta.i %>% dplyr::select(-missed_last_wave, -right_censored ))
# Rebuild the data:
if(i==1){ds_miss <- dta.i}else{ds_miss <- rbind(ds_miss,dta.i)}
}
# the created data set (ds_miss) replaces NA in mmse with cencored values (-1, -2) where appropriate
ds_long %>%
dplyr::filter(id %in% ids) %>%
as.data.frame() %>%
print()
# note the updated value in mmse and the new variabe presumed_alive
ds_miss %>%
dplyr::filter(id %in% ids) %>%
print()
# ds_miss contains the mmse values corrected for missing states (-1,-2)
# (e.g. if last mmse observation was NA, but we new respondent was alive)
# and presumed_alive indicator
# ---- encode-multi-states ------------------------------
## ## ## - STEP 2 - Criterion into a state variable
# in this step we focus on a variable that will identify the states to be modeled
# a few things will happen:
# 1) age_at_visit will become age (needed for passing to msm estimation routines)
# 2) variable state will replace mmse (the basis for it)
# 3) mmse will reappear at the end of the set
# 4) a NEW ROW will be created for each subject containing the measure at death
encode_multistates <- function(
d, # data frame in long format
outcome_name, # measure to compute live states
age_name, # age at each wave
age_death_name, # age of death
dead_state_value # value to represent dead state
){
# declare arguments for debugging
# d = ds_miss
# outcome_name = "mmse";age_name = "age_at_visit";age_death_name = "age_death";dead_state_value = 4
(subjects <- sort(unique(d$id))) # list subject ids
(N <- length(subjects)) # count subject ids
d[,"raw_outcome"] <- d[,outcome_name] # create a copy
# standardize names
colnames(d)[colnames(d)==outcome_name] <- "state" # ELECT requires this name
colnames(d)[colnames(d)==age_name] <- "age" # ELECT requires this name
# for(i in unique(ds$id)){ # use this line for testing
for(i in 1:N){
# Get the individual data: i = 1
(dta.i <- d[d$id==subjects[i],])
# (dta.i <- ds_long[ds_long$id==6804844,]) # select a single individual # use this line for testing
# Encode live states
dta.i$state <- ifelse(
dta.i$state > 26, 1, ifelse( # healthy
dta.i$state <= 26 & dta.i$state >= 23, 2, ifelse( # mild CI
dta.i$state < 23 & dta.i$state >= 0, 3, dta.i$state))) # mod-sever CI
# Is there a death? If so, add a record:
(death <- !is.na(dta.i[,age_death_name][1]))
if(death){
(record <- dta.i[1,])
(record$state <- dead_state_value)
(record$age <- dta.i[,age_death_name][1])
(ddta.i <- rbind(dta.i,record))
}else{ddta.i <- dta.i}
# Rebuild the data:
if(i==1){dta1 <- ddta.i}else{dta1 <- rbind(dta1,ddta.i)}
}
# dta1[,age_death_name] <- NULL
colnames(dta1)[colnames(dta1)=="raw_outcome"] <- outcome_name
dta1[dta1$state == dead_state_value,outcome_name] <- NA_real_
dta1[dta1$state == dead_state_value,"fu_year"] <- NA_real_
return(dta1)
}
ds_ms <-
encode_multistates(
d = ds_miss,
outcome_name = "mmse", # currently supports mmse only, todo: rework functins in tidyverse
age_name = "age_at_visit",
age_death_name = "age_at_death",
dead_state_value = 4
)
# review
ds_miss %>%
dplyr::filter(id %in% 50107169) %>%
print()
# notice how
ds_ms %>%
dplyr::filter(id %in% 50107169) %>%
print()
# 1) age_at_visit becomes age (needed for modeling)
# 2) variable state replaces mmse (which was the basis for it)
# 3) mmse reappears at the end of the set
# 4) most importantly, ds_ms creates an additional row for each subject
# this row identifies measurement at death of which we know for sure only age
# all other which refer to the time of death should be set to NA as unknown
# ---- correct-values-at-death -----------------------
## ## ## - STEP 3 - Longitudinal corrections
# previous step created an additional row for each subject, measurement at death
# the values in other variables carried over into the new row
# this is ok with time-invariant measures (to the left of wave/fu_year)
# but NOT ok with time-variant measures (to the right of wave/fu_year)
# time-invariant measures must be replaced with NA in measures at the moment of death
correct_values_at_death <- function(
ds, # data frame in long format with multistates encoded
outcome_name, # measure to correct value in
dead_state_value # value that represents dead state
){
ds[ds$state == dead_state_value, outcome_name] <- NA_real_
return(ds)
}
ds_corrected <- ds_ms # inherits the form
# manually correct values for data_at_visit
ds_corrected[ds_corrected$state == 4, "date_at_visit"] <- NA # because of date format
# automatically correct values for time-variant measures
ds_corrected <- ds_corrected %>% correct_values_at_death("wave",4)
# ds_corrected <- ds_corrected %>% correct_values_at_death("date_at_visit",4)
ds_corrected <- ds_corrected %>% correct_values_at_death("dementia",4)
ds_corrected <- ds_corrected %>% correct_values_at_death("cogn_global",4)
ds_corrected <- ds_corrected %>% correct_values_at_death("gait",4)
ds_corrected <- ds_corrected %>% correct_values_at_death("grip",4)
ds_corrected <- ds_corrected %>% correct_values_at_death("htm",4)
ds_corrected <- ds_corrected %>% correct_values_at_death("bmi",4)
ds_corrected <- ds_corrected %>% correct_values_at_death("physact",4)
ds_corrected <- ds_corrected %>% correct_values_at_death("firstobs",4)
# ds_corrected <- ds_corrected %>% correct_values_at_death("income_40",4)
# ds_corrected <- ds_corrected %>% correct_values_at_death("cogact_old",4)
# ds_corrected <- ds_corrected %>% correct_values_at_death("socact_old",4)
# ds_corrected <- ds_corrected %>% correct_values_at_death("soc_net",4)
# ds_corrected <- ds_corrected %>% correct_values_at_death("social_isolation",4)
# TODO: automate this step
# presumed_alive is logical for the state of data at the time of access
# TODO: place the presumed_alive variabe together with time invariant variables
# inspect the resultant data set
ds_corrected %>%
dplyr::filter(id %in% ids) %>%
print()
# ds_corrected contains data set of long structure with respect to time
# It has been augmented with the measurement at the time of death
# and corrected for time-invariant and time-variant values in other variabels
# ---- inspect-created-multistates ----------------------------------
# compare before and after ms encoding
view_id <- function(ds1,ds2,id){
cat("Data set A:","\n")
print(ds1[ds1$id==id,] %>% as.data.frame())
cat("\nData set B","\n")
print(ds2[ds2$id==id,])
}
# view a random person for sporadic inspections
set.seed(39)
ids <- sample(unique(ds_miss$id),1)
# ids <- 68914513
ids <- c(33027) # trailing NA; presumed alive
# ids <- c(1243685) # dead; only 4 obs; developed dementia on the last one
view_id(ds_long, ds_miss, 33027) # NA -> -1 OR -2
view_id(ds_miss, ds_ms, 33027) # age_at_visit -> age; mmse -> state; + mmse
view_id(ds_long, ds_ms, 33027) # all together
view_id(ds_long, ds_miss, 402800) # NA -> -1 OR -2 # nothing happens for this person
view_id(ds_miss, ds_ms, 402800) # age_at_visit -> age; mmse -> state; + mmse
view_id(ds_ms,ds_corrected, 402800) # longitudinal corrections
# to find people you might use for examples
target_cohort <-
ds_long %>%
dplyr::filter(died==1) %>%
dplyr::group_by(id) %>%
dplyr::summarize(n=n()) %>%
dplyr::filter(n==2) %>%
dplyr::arrange(id)
# ----- transitions-matrix -----------------------------
# simple frequencies of states
table(ds_corrected$state)
# examine transition matrix
# msm::statetable.msm(state,id,ds_ms)
knitr::kable(msm::statetable.msm(state,id,ds_ms))
# TODO: examine transition cases for missing states (-2 and -1)
# ---- save-to-disk ------------------------------------------------------------
# Save as a compress, binary R dataset. It's no longer readable with a text editor, but it saves metadata (eg, factor information).
# at this point there exist two relevant data sets:
# ds_long - subset of variables focal to the project
# ds_miss - missing states are encoded
# ds_ms - multi states are encoded
# it is useful to have access to all three while understanding/verifying encodings
names(dto)
dto[["encoded"]] <- list()
dto[["encoded"]][["missing"]] <- ds_miss # we preserve these forms to compare later
dto[["encoded"]][["multistate"]] <- ds_ms # we preserve these forms to compare later
dto[["encoded"]][["corrected"]] <- ds_corrected # we preserve these forms to compare later
names(dto)
names(dto$encoded)
saveRDS(dto, file=path_output, compress="xz")
|
33250697eac4fd69f2644821c7b0d9f5021d1133 | 4fa206129b99ef56086794c7f00a601572e2ae53 | /05练习题.R | 90bda574754e859c2ea0a0d9c3287c1e003986c6 | [] | no_license | yuxi-xi/R-homework | 8c4c3ad923876acd20c56f1fabd78a81d2a31094 | 49e30122591e71354e85b39c38956766e3029d00 | refs/heads/main | 2023-01-05T19:18:41.509915 | 2020-11-05T15:54:54 | 2020-11-05T15:54:54 | 308,061,434 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 446 | r | 05练习题.R | #5-1
a_list<-list(alpha=1,list(beta=2,gamma=3,delta=3),eta=NULL)
length(a_list)
b_list<-list(c(0,1,4,9),
16,
25,
36,
49,
NULL,
64,
NULL,
81,
NULL)
b_list
#5-2
(a_data_frame<-data.frame(
iris
))
colMeans(a_data_frame[1:4])
#5-3
a<-data.frame(
id=1
)
x<-cbind(beaver1,a)
b<-data.frame(
id=2
)
y<-cbind(beaver2,b)
rbind(x,y)
|
d8b1333eae9fd80b156abfc27c2ca1102a174878 | 6fd39305ca3170f97c995f027deb30b4213314b1 | /man/plot.SDD.Rd | 0df83bf33751fc1e15e94804a099b54350c8bdcc | [] | no_license | cran/SDD | c22c74d48c8bd428f360a83ff993e03c0e73fa13 | a5e4ec6e60365fd0dd0e943c91f6a0afdbbb07da | refs/heads/master | 2020-05-07T12:12:04.473973 | 2015-02-24T00:00:00 | 2015-02-24T00:00:00 | 17,693,505 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,295 | rd | plot.SDD.Rd | \name{plot.SDD}
\alias{plot.SDD}
\title{Plot Method for SDD objects}
\description{
Plot method for objects of class \code{"SDD"}.
}
\usage{
\method{plot}{SDD} (x, norm = FALSE, stability = FALSE, step = 5, \dots)
}
\arguments{
\item{x}{a \code{SDD} object}
\item{norm}{
an optional logical; if \code{TRUE}, when \code{dtype="ADF"} or when \code{dtype="ACF"}, the "normalized" p-values of the ADF are computed.
}
\item{stability}{an optional logical; if \code{TRUE}, when \code{dtype="RPADF"}, to evaluate the stability of the test-results a graphical representation of the confidence interval is displayed.
}
\item{step}{
an optional scalar; it sets the step between x-ticks in plot. Default value is 5.
}
\item{\dots}{graphics parameters to be passed to the plotting
routines.}
}
\value{
No values are returned from the plot function.
}
\author{
Luca Bagnato, Lucio De Capitani, Angelo Mazza and Antonio Punzo
}
\seealso{
\code{\link{SDD-package}}, \code{\link{ADF}}, \code{\link{SMI}}
}
\examples{
data("SMI")
res <- ADF(SMI, plot=FALSE)
plot(res)
}
% Add one or more standard keywords, see file 'KEYWORDS' in the
% R documentation directory.
\keyword{ ~kwd1 }
\keyword{ ~kwd2 }% __ONLY ONE__ keyword per line
|
d416c4e805d448d3dfae3df41e1218ffc4530c21 | 5150cf610a34c6c5be9b598277db1834d8fb16b4 | /tests/testthat/test-HOC.R | 84abb1f3224f0a6b0b91de1b38cf9438e2500066 | [] | no_license | SPI-Birds/pipelines | f3ab78668e526a47bd298b0f7f4127e274a4dfd0 | cb4bd41bc26d991fa54e520bb15b54333696b4cb | refs/heads/master | 2023-08-16T18:15:29.835023 | 2023-08-09T09:51:56 | 2023-08-09T09:51:56 | 153,275,927 | 0 | 3 | null | 2022-12-04T14:48:00 | 2018-10-16T11:42:17 | R | UTF-8 | R | false | false | 10,635 | r | test-HOC.R | testthat::skip_if(!exists("data_path"))
pipeline_output <- format_HOC(db = paste0(data_path, "/HOC_Hochstadt_Germany"))
test_that("HOC outputs all files...", {
expect_true(all("HOC" %in% pipeline_output$Brood_data$PopID))
expect_true(all("HOC" %in% pipeline_output$Capture_data$CapturePopID))
expect_true(all("HOC" %in% pipeline_output$Individual_data$PopID))
expect_true(all("HOC" %in% pipeline_output$Location_data$PopID))
})
test_that("Brood_data returns an expected outcome...", {
#We want to run tests for all possible outcomes of ClutchType_calculated
#Take a subset of only HOC data
HOC_data <- dplyr::filter(pipeline_output$Brood_data, PopID %in% "HOC")
#Test 1: Brood where clutch type = first
expect_equal(subset(HOC_data, BroodID == "2016-H101-1")$Species, "PARMAJ")
expect_equal(subset(HOC_data, BroodID == "2016-H101-1")$ClutchType_calculated, "first")
expect_equal(subset(HOC_data, BroodID == "2016-H101-1")$LayDate, as.Date("2016-04-22"))
expect_equal(subset(HOC_data, BroodID == "2016-H101-1")$ClutchSize, 7L)
expect_equal(subset(HOC_data, BroodID == "2016-H101-1")$BroodSize, NA_integer_)
expect_equal(subset(HOC_data, BroodID == "2016-H101-1")$NumberFledged, 0L)
expect_equal(subset(HOC_data, BroodID == "2016-H101-1")$AvgChickMass, NA_real_)
expect_equal(subset(HOC_data, BroodID == "2016-H101-1")$AvgTarsus, NA_real_)
#Test 2: Brood where clutch type = replacement (because first is known to have failed)
expect_equal(subset(HOC_data, BroodID == "2016-H111-2")$Species, "PARMAJ")
expect_equal(subset(HOC_data, BroodID == "2016-H111-2")$ClutchType_calculated, "replacement")
expect_equal(subset(HOC_data, BroodID == "2016-H111-2")$LayDate, as.Date("2016-05-30"))
expect_equal(subset(HOC_data, BroodID == "2016-H111-2")$ClutchSize, 6)
expect_equal(subset(HOC_data, BroodID == "2016-H111-2")$BroodSize, NA_integer_)
expect_equal(subset(HOC_data, BroodID == "2016-H111-2")$NumberFledged, 4)
expect_equal(round(subset(HOC_data, BroodID == "2016-H111-2")$AvgChickMass, 2), 13.65)
expect_equal(round(subset(HOC_data, BroodID == "2016-H111-2")$AvgTarsus, 2), 18.20)
#Test 3: Brood where clutch type = replacement (past the cutoff)
expect_equal(subset(HOC_data, BroodID == "2017-H101-2")$Species, "PARMAJ")
expect_equal(subset(HOC_data, BroodID == "2017-H101-2")$ClutchType_calculated, "replacement")
expect_equal(subset(HOC_data, BroodID == "2017-H101-2")$LayDate, as.Date("2017-06-03"))
expect_equal(subset(HOC_data, BroodID == "2017-H101-2")$ClutchSize, 6L)
expect_equal(subset(HOC_data, BroodID == "2017-H101-2")$BroodSize, NA_integer_)
expect_equal(subset(HOC_data, BroodID == "2017-H101-2")$NumberFledged, 1L)
expect_equal(round(subset(HOC_data, BroodID == "2017-H101-2")$AvgChickMass, 2), 12.00)
expect_equal(round(subset(HOC_data, BroodID == "2017-H101-2")$AvgTarsus, 2), 15.92)
#Test 4: Brood where clutch type = second
expect_equal(subset(HOC_data, BroodID == "2017-H71-2")$Species, "PARMAJ")
expect_equal(subset(HOC_data, BroodID == "2017-H71-2")$ClutchType_calculated, "second")
expect_equal(subset(HOC_data, BroodID == "2017-H71-2")$LayDate, as.Date("2017-05-31"))
expect_equal(subset(HOC_data, BroodID == "2017-H71-2")$ClutchSize, 8L)
expect_equal(subset(HOC_data, BroodID == "2017-H71-2")$BroodSize, NA_integer_)
expect_equal(subset(HOC_data, BroodID == "2017-H71-2")$NumberFledged, 4L)
expect_equal(round(subset(HOC_data, BroodID == "2017-H71-2")$AvgChickMass, 2), 13.75)
expect_equal(round(subset(HOC_data, BroodID == "2017-H71-2")$AvgTarsus, 2), 18.55)
})
test_that("Individual data returns an expected outcome...", {
#We want to run a test for each sex for individuals caught as adults and chicks
#Take a subset of only HOC data
HOC_data <- dplyr::filter(pipeline_output$Individual_data, PopID %in% "HOC")
#Test 1: First as adult
expect_equal(subset(HOC_data, IndvID == "C1X0940")$Sex, "F")
expect_equal(subset(HOC_data, IndvID == "C1X0940")$Species, "PARMAJ")
#They should have no BroodIDLaid or Fledged because she was never caught as a chick
expect_equal(subset(HOC_data, IndvID == "C1X0940")$BroodIDLaid, NA_character_)
expect_equal(subset(HOC_data, IndvID == "C1X0940")$BroodIDFledged, NA_character_)
#Ring age and season are as expected
expect_equal(subset(HOC_data, IndvID == "C1X0940")$RingSeason, 2014L)
expect_equal(subset(HOC_data, IndvID == "C1X0940")$RingAge, "adult")
#Test 2: Caught first as chick
expect_equal(subset(HOC_data, IndvID == "C1X0972")$Sex, NA_character_)
expect_equal(subset(HOC_data, IndvID == "C1X0972")$Species, "PARMAJ")
#Check that BroodIDLaid/Fledged are as expected
expect_equal(subset(HOC_data, IndvID == "C1X0972")$BroodIDLaid, "2014-H61-1")
expect_equal(subset(HOC_data, IndvID == "C1X0972")$BroodIDFledged, "2014-H61-1")
#Ring season is as expected
expect_equal(subset(HOC_data, IndvID == "C1X0972")$RingSeason, 2014)
expect_equal(subset(HOC_data, IndvID == "C1X0972")$RingAge, "chick")
})
test_that("Capture data returns an expected outcome...", {
#Take a subset of only HOC data
HOC_data <- dplyr::filter(pipeline_output$Capture_data, CapturePopID %in% "HOC")
#Test 1: Individual ringed as a chick
#Test there are the correct number of capture records
expect_equal(nrow(subset(HOC_data, IndvID == "C3F8488")), 5)
#Test that the first and 5th capture are as expected
expect_equal(subset(HOC_data, IndvID == "C3F8488")$CaptureDate[1], as.Date("2014-05-18"))
expect_equal(subset(HOC_data, IndvID == "C3F8488")$CaptureDate[5], as.Date("2016-06-27"))
#Test that age observed is as expected on first capture
#Test that age observed is as expected on 5th capture
expect_equal(subset(HOC_data, IndvID == "C3F8488")$Age_observed[1], 1L)
expect_equal(subset(HOC_data, IndvID == "C3F8488")$Age_observed[5], 4L)
#Test that age calculated is correct on first capture and last capture
expect_equal(subset(HOC_data, IndvID == "C3F8488")$Age_calculated[1], 1L)
expect_equal(subset(HOC_data, IndvID == "C3F8488")$Age_calculated[5], 7L)
#Test 2: Individual caught only as adult
#Test it has the correct number of capture records
expect_equal(nrow(subset(HOC_data, IndvID == "C3K7291")), 7)
#Test that the first capture is as expected
expect_equal(subset(HOC_data, IndvID == "C3K7291")$CaptureDate[1], as.Date("2016-05-09"))
#Test that the 7th capture is as expected
expect_equal(subset(HOC_data, IndvID == "C3K7291")$CaptureDate[7], as.Date("2019-05-08"))
#Test that first and last age observed is as expected
expect_equal(subset(HOC_data, IndvID == "C3K7291")$Age_observed[1], 4L)
expect_equal(subset(HOC_data, IndvID == "C3K7291")$Age_observed[7], 4L)
#Test that first and last age calculated is as expected
expect_equal(subset(HOC_data, IndvID == "C3K7291")$Age_calculated[1], 4L)
expect_equal(subset(HOC_data, IndvID == "C3K7291")$Age_calculated[7], 10L)
#Test 3: Individual caught as 1st year adult pre 2019
#This individual should be treated as EURING 4
#Test it has the correct number of capture records
expect_equal(nrow(subset(HOC_data, IndvID == "C3K7272")), 5)
#Test that the first capture is as expected
expect_equal(subset(HOC_data, IndvID == "C3K7272")$CaptureDate[1], as.Date("2016-03-08"))
#Test that the 7th capture is as expected
expect_equal(subset(HOC_data, IndvID == "C3K7272")$CaptureDate[5], as.Date("2019-05-09"))
#Test that first and last age observed is as expected
expect_equal(subset(HOC_data, IndvID == "C3K7272")$Age_observed[1], 4L)
expect_equal(subset(HOC_data, IndvID == "C3K7272")$Age_observed[5], 4L)
#Test that first and last age calculated is as expected
expect_equal(subset(HOC_data, IndvID == "C3K7272")$Age_calculated[1], 4L)
expect_equal(subset(HOC_data, IndvID == "C3K7272")$Age_calculated[5], 10L)
#Test 4: Individual caught as 1st year adult post 2019
#This individual should be treated as EURING 5
#Test it has the correct number of capture records
expect_equal(nrow(subset(HOC_data, IndvID == "C5E9102")), 2)
#Test that the first capture is as expected
expect_equal(subset(HOC_data, IndvID == "C5E9102")$CaptureDate[1], as.Date("2019-03-16"))
#Test that the 7th capture is as expected
expect_equal(subset(HOC_data, IndvID == "C5E9102")$CaptureDate[2], as.Date("2019-05-08"))
#Test that first and last age observed is as expected
expect_equal(subset(HOC_data, IndvID == "C5E9102")$Age_observed[1], 5L)
expect_equal(subset(HOC_data, IndvID == "C5E9102")$Age_observed[2], 4L)
#Test that first and last age calculated is as expected
expect_equal(subset(HOC_data, IndvID == "C5E9102")$Age_calculated[1], 4L)
expect_equal(subset(HOC_data, IndvID == "C5E9102")$Age_calculated[2], 4L)
#Test 5: Individual found dead in DeadCapture ID table
#Test it has the correct number of capture records
expect_equal(nrow(subset(HOC_data, IndvID == "C5E8940")), 3)
#Test that the first capture is as expected
expect_equal(subset(HOC_data, IndvID == "C5E8940")$CaptureDate[1], as.Date("2019-05-13"))
#Test that the 7th capture is as expected
expect_equal(subset(HOC_data, IndvID == "C5E8940")$CaptureDate[3], as.Date("2019-05-23"))
#Test that first and last age observed is as expected
expect_equal(subset(HOC_data, IndvID == "C5E8940")$Age_observed[1], 1L)
expect_equal(subset(HOC_data, IndvID == "C5E8940")$Age_observed[3], 1L)
#Test that first and last age calculated is as expected
expect_equal(subset(HOC_data, IndvID == "C5E8940")$Age_calculated[1], 1L)
expect_equal(subset(HOC_data, IndvID == "C5E8940")$Age_calculated[3], 1L)
})
test_that("Location_data returns an expected outcome...", {
#We want to run tests for nest boxes (there are no mistnets)
#Take a subset of only NIOO data
HOC_data <- dplyr::filter(pipeline_output$Location_data, PopID %in% "HOC")
#Test 1: Nestbox check
expect_true(subset(HOC_data, LocationID == "H1")$LocationType == "NB")
#Expect LocationID and NestboxID are the same
expect_true(subset(HOC_data, LocationID == "H1")$NestboxID == "H1")
#Expect Start and EndSeason is as expected
expect_equal(subset(HOC_data, LocationID == "H1")$StartSeason, 2014L)
expect_equal(subset(HOC_data, LocationID == "H1")$EndSeason, NA_integer_)
#Check that LocationID is in the expected PopID
expect_equal(subset(HOC_data, LocationID == "H1")$PopID, "HOC")
#Check that latitude and longitude are as expected
expect_equal(round(subset(HOC_data, LocationID == "H1")$Latitude, 2) %>% setNames(nm = NULL), 11.26)
expect_equal(round(subset(HOC_data, LocationID == "H1")$Longitude, 2) %>% setNames(nm = NULL), 48.06)
})
|
5f578119da67e7b4790dac1e221302ee03d74d15 | a646c5a1c7ddf5598b4fc4104a5456104e6b3dc3 | /plot1.R | 02b6b89e1f3d058778255af51149fce65dbdd8ce | [] | no_license | andrew57jm/ExData_Plotting1 | 491ad35431d4af0274e07eeb3017cac2a374def1 | e3ae61483f5b2860ef33d5077ea9024aa6e7132e | refs/heads/master | 2021-01-19T21:18:59.749355 | 2016-03-07T00:35:31 | 2016-03-07T00:35:31 | 53,272,803 | 0 | 0 | null | 2016-03-06T19:47:34 | 2016-03-06T19:47:33 | null | UTF-8 | R | false | false | 512 | r | plot1.R | plot1 <- function() {
library(data.table)
library(dplyr)
library(lubridate)
# read data and filter the dates we're interested in
pow <- fread("household_power_consumption.txt",na.strings=c("?"))
pow <- filter(pow,Date=="1/2/2007" | Date=="2/2/2007")
# open the png device
png(file="plot1.png")
# do the plot
hist(pow$Global_active_power,col="red",main="Global Active Power",
xlab = "Global Active Power (kilowatts)")
dev.off()
}
|
1529adf059d774efca80acc10dc53c362a0fb7cb | 207ffc920f0c738733643ee781ca6aa4ab801e80 | /metapop/metapop_r/MetaPop_Preprocessing_Summaries.R | 45007098137e175bd828b3b94c59456103549d7c | [
"MIT"
] | permissive | metaGmetapop/metapop | c1db90e08996971efe97ffc914ac0282b9dadd27 | 0fdc3e589202e79755fc2f09343e7c8196e8ab63 | refs/heads/master | 2023-04-10T14:53:32.198651 | 2023-04-04T12:47:27 | 2023-04-04T12:47:27 | 158,002,337 | 33 | 9 | null | null | null | null | UTF-8 | R | false | false | 10,758 | r | MetaPop_Preprocessing_Summaries.R | options <- commandArgs(trailingOnly = T)
#universal
directory_name <- options[1]
threads <- as.numeric(options[2])
library_location <- options[3]
if(library_location == ""){
library_location <- .libPaths()
}
ref_fasta <- options[4]
ref_genes <- options[5]
#Component specific
min_cov <- as.numeric(options[6])
min_dep <- as.numeric(options[7])
original_base_names <- options[9]
original_base_names <- strsplit(original_base_names, split=",")[[1]]
setwd(directory_name)
original_files <- options[8]
original_files <- list.files(original_files, full.names = T)
#For my own dev use
suppressMessages(suppressWarnings(library(doParallel, lib.loc = library_location)))
suppressMessages(suppressWarnings(library(data.table, lib.loc = library_location)))
suppressMessages(suppressWarnings(library(ggplot2, lib.loc = library_location)))
suppressMessages(suppressWarnings(library(cowplot, lib.loc = library_location)))
suppressMessages(suppressWarnings(library(bit64, lib.loc = library_location)))
cov_depth <- list.files(path="MetaPop/03.Breadth_and_Depth", full.names = T, pattern="breadth_and_depth")
c_d_names <- substring(cov_depth, 30, nchar(cov_depth)-22)
output_bams <- list.files(path = "MetaPop/02.Filtered_Samples", full.names = T)
output_bams <- output_bams[!grepl(".bai", output_bams)]
cl <- makeCluster(min(threads, detectCores()))
registerDoParallel(cl)
orig_read_counts <- foreach(i = original_files, .combine = c) %dopar% {
as.numeric(system(paste0("samtools view -c ", i), intern = T))
}
stopCluster(cl)
overall_summaries <- data.table(sample = original_base_names, read_counts = orig_read_counts, finish_counts = 0)
cl <- makeCluster(min(threads, detectCores()))
registerDoParallel(cl)
finish_read_counts <- foreach(i = output_bams, .combine = c) %dopar% {
as.numeric(system(paste0("samtools view -c ", i), intern = T))
}
stopCluster(cl)
overall_summaries$finish_counts[match(c_d_names, overall_summaries$sample)] <- finish_read_counts
overall_summaries[, removed := read_counts - finish_counts]
overall_summaries[, read_counts := NULL]
overall_summaries <- melt.data.table(overall_summaries, id.vars = c("sample"))
overall_summaries <- overall_summaries[order(overall_summaries$variable, decreasing = T),]
read_filtering <- ggplot(overall_summaries, aes(x = sample, y = value, fill = variable)) +
geom_bar(stat = "identity", position = "dodge") +
ylab("Read Count") +
xlab("Sample Origin") +
scale_fill_manual("Read\nFiltering", values = c("#2ca9e1", "grey65"), labels = c("Read Kept", "Read Removed")) +
coord_flip() +
theme_minimal()
read_filtering <- read_filtering + theme(axis.title = element_text(size = 14),
axis.text = element_text(size = 14))
title <- ggdraw() +
draw_label(
paste0("Preprocessing Overview\nRead Filtering"),
fontface = 'bold',
x = 0,
hjust = 0
) +
theme(
# add margin on the left of the drawing canvas,
# so title is aligned with left edge of first plot
plot.margin = margin(0, 0, 0, 7)
)
read_filtering <- plot_grid(title, read_filtering, nrow = 2, rel_heights = c(0.1, 0.9))
cl <- makeCluster(min(threads, detectCores()))
registerDoParallel(cl)
coverage_and_depth <- foreach(i = 1:length(cov_depth), .packages = c("data.table")) %dopar% {
tmp <- fread(cov_depth[i], sep = "\t")
tmp[, source := c_d_names[i]]
}
stopCluster(cl)
#Making these legends later is a pain, doing it this way is easy.
color_legend_plot <- ggplot(data = data.table(dots = c(1,2,3)), aes(x = dots, fill = factor(dots))) +
geom_bar()+
scale_fill_manual(name = "Contig\nAssessment", values = alpha(c("grey65","#2ca9e1", "#FF0000"), 1), labels = c("Low Coverage", "Low Depth", "Passing Contig"))+
theme(legend.text = element_text(size = 14),
legend.title = element_text(size = 14))
color_leg <- get_legend(color_legend_plot)
#We have the data to plot hists of coverage/depth passing, and can show both reads passing and contigs passing.
create_summary_viz <- function(depth_data, file_name, file_size, cov=min_cov, depth=min_dep, leg = color_leg){
group.colors <- c('Insufficient Coverage' = "grey65", 'Insufficient Depth' = "#2CA9E1", 'Passing Contig' = "#FF0000")
depth_data$pass_level <- ifelse(depth_data$V3 >= cov, ifelse(depth_data$V4 >= depth, "Passing Contig", "Insufficient Depth"), "Insufficient Coverage")
depth_data <- depth_data[order(depth_data$pass_level),]
donut_TF <- depth_data
donut_TF$coord <- seq(0, 1, length.out = nrow(donut_TF))
#Quirk of geom_rect; if the data is left like so, it will plot a single rectangle for each row of data around the donut.
#This will result in visual artifacts, slow plotting speed, and massively oversized PDFs.
donut_TF$ymax = cumsum(donut_TF$coord)
donut_TF$ymin = c(0, head(donut_TF$ymax, n=-1))
maxes <- donut_TF[, max(ymax, na.rm = T), by = pass_level]
donut_TF <- donut_TF[match(c("Insufficient Coverage", "Insufficient Depth", "Passing Contig")[c("Insufficient Coverage", "Insufficient Depth", "Passing Contig") %in% donut_TF$pass_level], donut_TF$pass_level),]
donut_TF$ymax[match(donut_TF$pass_level, maxes$pass_level)] <- maxes$V1
donut_TF[,ymax:=-ymax]
donut_TF[,ymin:=-ymin]
title <- ggdraw() +
draw_label(
paste0(file_name, "\nDepth and Coverage of Contigs"),
fontface = 'bold',
x = 0,
hjust = 0
) +
theme(
# add margin on the left of the drawing canvas,
# so title is aligned with left edge of first plot
plot.margin = margin(0, 0, 0, 7)
)
#Produces a contig-level summary of passage by
overview_donut <- ggplot(donut_TF, aes(fill=pass_level, xmin=4.5, xmax=6, ymin=ymin, ymax=ymax))+
geom_rect()+
coord_polar(theta="y") +
xlim(c(0, 6))+
theme(panel.grid=element_blank(),axis.text=element_blank(),axis.ticks=element_blank(),axis.title=element_blank(), panel.background = element_blank()) +
#annotate("text", x = 0, y = 0, label = file_name, size = 3) +
annotate("text", x = 0.70, y = 0, label = paste("Original Contigs:", nrow(depth_data)),size = 3) +
annotate("text", x = 0.20, y = 0, label = paste("Contigs with Passing Coverage and TAD:", sum(depth_data$pass_level == "Passing Contig")),size = 3) +
labs(title="")+
guides(fill=FALSE) +
scale_fill_manual(values = group.colors)
overview_donut <- plot_grid(NULL, overview_donut, leg, NULL, ncol = 4, rel_widths = c(.5, .85, .15, .3))
overview_donut <- plot_grid(title, overview_donut, nrow = 2, rel_heights = c(0.1, 0.9))
contig_breakdown <- ggplot(depth_data, aes(x = V3, y = V4, fill = pass_level)) +
geom_point(alpha = 0.6, shape = 21, color = "grey80", size = 4) +
geom_vline(xintercept = cov)+
geom_hline(yintercept = depth)+
theme_minimal() +
ylab("Truncated Average Sequencing Depth")+
xlab("Percent of Genome Covered")+
scale_fill_manual(values = group.colors)+
annotate("text", x = cov, y = Inf, label = paste("Minimum Coverage:", cov, " "), vjust=1, hjust = 1) +
annotate("text", x = Inf, y = depth*(19/20), label = paste("Minimum TAD:", depth), vjust = 1, hjust = 1) +
expand_limits(x = 0, y = 0)
contig_breakdown <- contig_breakdown + theme(legend.position = "none",
#axis.line = element_line("grey80"),
axis.title = element_text(size = 14),
axis.text = element_text(size = 14),
panel.grid = element_line(colour = "grey80"))
depth_boxplots <- ggplot(depth_data, aes(y = V4, fill = pass_level))+
geom_boxplot(outlier.color = "grey80", outlier.shape = 21, outlier.size = 4) +
scale_fill_manual(values = group.colors) +
ylab("Truncated Average Depth")+
xlab("")+
theme_minimal()+
expand_limits(y = 0)
depth_boxplots <- depth_boxplots + theme(legend.position = "none",
axis.text = element_blank(),
#axis.title = element_blank(),
axis.line.y = element_line("grey80"),
axis.ticks = element_blank(),
panel.grid = element_line(colour = "grey80"))
cov_boxplots <- ggplot(depth_data, aes(y = V3, fill = pass_level))+
geom_boxplot(outlier.color = "grey80", outlier.shape = 21, outlier.size = 4) +
scale_fill_manual(values = group.colors) +
xlab("")+
ylab("Percent of Genome Covered") +
expand_limits(y = 0) +
theme_minimal()+
coord_flip()
cov_boxplots <- cov_boxplots + theme(legend.position = "none",
axis.text = element_blank(),
#axis.title = element_blank(),
axis.line.x = element_line("grey80"),
axis.ticks = element_blank(),
panel.grid = element_line(colour = "grey80"))
c_d_plot <- plot_grid(cov_boxplots, NULL, contig_breakdown, depth_boxplots, ncol = 2, align = "hv",
rel_widths = c(3, 1), rel_heights = c(1, 3), axis = "xy")
legend_plot <- plot_grid(NULL, leg, NULL, NULL, ncol = 2,
rel_widths = c(3, 1), rel_heights = c(1, 4))
c_d_plot <- ggdraw(c_d_plot) + draw_plot(legend_plot)
c_d_plot <- plot_grid(
title, c_d_plot,
ncol = 1,
# rel_heights values control vertical title margins
rel_heights = c(0.1, 0.9)
)
return(list(overview_donut, c_d_plot))
}
pdf("MetaPop/12.Visualizations/preprocessing_summaries.pdf", width = 16, height = 9)
print(read_filtering)
for(i in 1:length(coverage_and_depth)){
tmp <- create_summary_viz(coverage_and_depth[[i]], c_d_names[i], file.size(cov_depth[i]))
print(tmp[[1]])
print(tmp[[2]])
}
dev.off()
|
fbc5d539c1f30239140306113e6ff9bc179b8247 | b28f74d681bb5dfbf34549c82a8c932f77c1b0a8 | /man/pep_to_prn.Rd | b8711090b3ba167d52d4b9a1b2de48eaf53d79ec | [
"MIT"
] | permissive | sailfish009/proteoQ | b07e179e9fe27a90fd76cde2ed7caa55e793e9d6 | e6a4fe79a21f9a9106a35d78c2ce42d59e9d82e2 | refs/heads/master | 2022-12-25T20:06:40.340740 | 2020-10-15T20:18:14 | 2020-10-15T20:18:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,060 | rd | pep_to_prn.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/peptable.R
\name{pep_to_prn}
\alias{pep_to_prn}
\title{Helper of Pep2Prn}
\usage{
pep_to_prn(id, method_pep_prn, use_unique_pep, gn_rollup, ...)
}
\arguments{
\item{id}{Character string; one of \code{pep_seq}, \code{pep_seq_mod},
\code{prot_acc} and \code{gene}.}
\item{method_pep_prn}{Character string; the method to summarize the
\code{log2FC} and the \code{intensity} of peptides by protein entries. The
descriptive statistics includes \code{c("mean", "median", "weighted_mean",
"top_3_mean", "lfq_max", "lfq_top_2_sum", "lfq_top_3_sum", "lfq_all")} with
\code{median} being the default for TMT and \code{lfq_top_3_sum} for LFQ.
The representative \code{log10-intensity} of reporter (or LFQ) ions at the
peptide levels will be the weight when summarizing \code{log2FC} with
various \code{"top_n"} statistics or \code{"weighted_mean"}.}
\item{use_unique_pep}{Logical. If TRUE, only entries that are \code{TRUE} or
equal to \code{1} under the column \code{pep_isunique} in
\code{Peptide.txt} will be used, for summarizing the \code{log2FC} and the
\code{intensity} of peptides into protein values. The default is to use
unique peptides only. For \code{MaxQuant} data, the levels of uniqueness
are according to the \code{pep_unique_by} in \code{\link{normPSM}}. The
argument currently do nothing to \code{Spectrum Mill} data where both
unique and shared peptides will be kept.}
\item{gn_rollup}{Logical; if TRUE, rolls up protein accessions to gene names.}
\item{...}{\code{filter_}: Variable argument statements for the filtration of
data rows. Each statement contains a list of logical expression(s). The
\code{lhs} needs to start with \code{filter_}. The logical condition(s) at
the \code{rhs} needs to be enclosed in \code{exprs} with round parenthesis.
For example, \code{pep_len} is a column key in \code{Peptide.txt}. The
statement of \code{filter_peps_at = exprs(pep_len <= 50)} will remove
peptide entries with \code{pep_len > 50}.}
}
\description{
Helper of Pep2Prn
}
|
f6bc00ad9e90bf4377361ea1bbda86ee7ed67dbd | 9c2402aaf87d352954fd132e09e75b023354ee76 | /10번.R | eb6706faf0fe3e3454d46b8e699c321d1a5e59d8 | [] | no_license | tlfzgjadyd/R-Hadoop | 70ff542119763e2129ddc9a0410baed3e2ddf23b | 06e29d22d1cd61311f8bcb412bf19cb491ec03f8 | refs/heads/dev | 2022-12-29T17:43:21.380912 | 2020-10-15T09:01:14 | 2020-10-15T09:01:14 | 302,491,125 | 0 | 0 | null | 2020-10-15T09:01:15 | 2020-10-09T00:17:36 | R | UTF-8 | R | false | false | 68 | r | 10번.R | x <- c(1:4)
A <- matrix(x, 2, 2)
B <- matrix(x, 2, 2, byrow = T)
A
B |
6974a7be37de6a01961448dcb2fbc7984a34b6ad | fe1d921f0e087f5c5d58afa07511d79b114facfa | /inst/extdata/transition-probs/F3.R | abcab0d9c885fd30a98562ef6d0892c44e263b53 | [
"Apache-2.0"
] | permissive | Dordt-Statistics-Research/LaByRInth | 2acf9651a5a14db832ea975a2a44cd48c625f693 | ff44da887171586fc5ac40cd4f6ba701bc6d598f | refs/heads/master | 2021-01-01T04:53:07.756202 | 2018-10-19T01:06:51 | 2018-10-19T01:06:51 | 97,264,302 | 0 | 1 | null | 2017-08-04T19:52:09 | 2017-07-14T18:56:28 | null | UTF-8 | R | false | false | 183,163 | r | F3.R | ## Copyright 2018 Jason Vander Woude
##
## Licensed under the Apache License, Version 2.0 (the "License");
## you may not use this file except in compliance with the License.
## You may obtain a copy of the License at
##
## http://www.apache.org/licenses/LICENSE-2.0
##
## Unless required by applicable law or agreed to in writing, software
## distributed under the License is distributed on an "AS IS" BASIS,
## WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
## See the License for the specific language governing permissions and
## limitations under the License.
## This file is auto-generated by multi-model-symbolics.sage for F3
## generation plants. To generate a similar file for any generation of
## recombinant inbred line (RIL) plants, load this SAGE file in a SAGE
## interpreter and run the function 'parse_probs_to_R' with the single
## parameter being the generation of the plants where a value of 2 corresponds
## to F2, 3 corresponds to F3, etc. Running the function will print the required
## text for the R file to the console where it must be copied and pasted into a
## file called 'F{generation}.R' and saved in the appropriate directory.
## To download SAGE, visit https://www.sagemath.org/download.html
## There is a seperate function for each file because at runtime, only one
## function will be needed, so there is no need to source to code for all other
## generations
site.pair.transition.probs <- list(
list(
function(r) {
matrix(c(
1, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
11/16, 1/16, 1/16, 3/16,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
11/16, 1/16, 1/16, 3/16,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
3/8, 1/8, 1/8, 3/8,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
11/16, 1/16, 1/16, 3/16,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
7/16, 1/16, 1/16, 7/16,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
7/16, 1/16, 1/16, 7/16,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
3/16, 1/16, 1/16, 11/16,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
11/16, 1/16, 1/16, 3/16,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
7/16, 1/16, 1/16, 7/16,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
7/16, 1/16, 1/16, 7/16,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
3/16, 1/16, 1/16, 11/16,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
3/8, 1/8, 1/8, 3/8,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
3/16, 1/16, 1/16, 11/16,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
3/16, 1/16, 1/16, 11/16,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
0, 0, 0, 1,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0
), nrow=4, ncol=4, byrow=T)
}
),
list(
function(r) {
matrix(c(
11/16, 0, 0, 0,
1/16, 0, 0, 0,
1/16, 0, 0, 0,
3/16, 0, 0, 0
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
-1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 3/4*r^2 - 11/16*r + 11/16, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r, -1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 5/16*r,
1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r, -1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 5/8*r^2 - 5/16*r + 1/16, -1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 1/8*r^2, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r,
1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r, -1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 1/8*r^2, -1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 5/8*r^2 - 5/16*r + 1/16, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r,
-1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 5/16*r, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r, -1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 3/4*r^2 - 9/16*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 3/8*r^2 + 5/16*r + 3/8, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16, 1/8*r^5 - 1/4*r^4 + 1/8*r^3 + 1/8*r^2 - 3/16*r + 3/16,
-1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16, 1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 1/4*r^2 + 1/16*r, 1/8*r^5 - 1/4*r^4 + 1/8*r^3, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2,
-1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16, 1/8*r^5 - 1/4*r^4 + 1/8*r^3, 1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 1/4*r^2 + 1/16*r, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2,
1/8*r^5 - 1/4*r^4 + 1/8*r^3 + 1/8*r^2 - 3/16*r + 3/16, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 3/8*r^2 + 3/16*r
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/8, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16, 1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r + 3/16,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r + 7/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32,
1/16*r^4 - 3/16*r^2 + 1/4*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r,
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 3/16*r^2 + 1/4*r
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r + 7/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r + 7/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32,
1/16*r^4 - 3/16*r^2 + 1/4*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r,
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 3/16*r^2 + 1/4*r
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r + 7/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/8,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^5 - 1/4*r^4 + 1/8*r^3 + 1/8*r^2 - 3/16*r + 3/16, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16, 1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 3/8*r^2 + 5/16*r + 3/8,
-1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^5 - 1/4*r^4 + 1/8*r^3, 1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 1/4*r^2 + 1/16*r, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16,
-1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 1/4*r^2 + 1/16*r, 1/8*r^5 - 1/4*r^4 + 1/8*r^3, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16,
1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 3/8*r^2 + 3/16*r, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^5 - 1/4*r^4 + 1/8*r^3 + 1/8*r^2 - 3/16*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
-1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 5/16*r, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r, -1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 3/4*r^2 - 11/16*r + 11/16,
1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r, -1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 1/8*r^2, -1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 5/8*r^2 - 5/16*r + 1/16, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r,
1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r, -1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 5/8*r^2 - 5/16*r + 1/16, -1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 1/8*r^2, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r,
-1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 3/4*r^2 - 9/16*r + 3/16, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r, -1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 5/16*r
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
0, 0, 0, 11/16,
0, 0, 0, 1/16,
0, 0, 0, 1/16,
0, 0, 0, 3/16
), nrow=4, ncol=4, byrow=T)
}
),
list(
function(r) {
matrix(c(
11/16, 0, 0, 0,
1/16, 0, 0, 0,
1/16, 0, 0, 0,
3/16, 0, 0, 0
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 3/8*r^2 + 5/16*r + 3/8, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16, 1/8*r^5 - 1/4*r^4 + 1/8*r^3 + 1/8*r^2 - 3/16*r + 3/16,
-1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16, 1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 1/4*r^2 + 1/16*r, 1/8*r^5 - 1/4*r^4 + 1/8*r^3, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2,
-1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16, 1/8*r^5 - 1/4*r^4 + 1/8*r^3, 1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 1/4*r^2 + 1/16*r, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2,
1/8*r^5 - 1/4*r^4 + 1/8*r^3 + 1/8*r^2 - 3/16*r + 3/16, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 3/8*r^2 + 3/16*r
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
-1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 3/4*r^2 - 11/16*r + 11/16, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r, -1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 5/16*r,
1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r, -1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 5/8*r^2 - 5/16*r + 1/16, -1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 1/8*r^2, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r,
1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r, -1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 1/8*r^2, -1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 5/8*r^2 - 5/16*r + 1/16, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r,
-1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 5/16*r, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r, -1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 3/4*r^2 - 9/16*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/8, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16, 1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r + 3/16,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r + 7/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r,
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 3/16*r^2 + 1/4*r
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32,
1/16*r^4 - 3/16*r^2 + 1/4*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r + 7/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r + 7/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r,
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 3/16*r^2 + 1/4*r
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32,
1/16*r^4 - 3/16*r^2 + 1/4*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r + 7/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/8,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
-1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 5/16*r, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r, -1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 3/4*r^2 - 11/16*r + 11/16,
1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r, -1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 1/8*r^2, -1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 5/8*r^2 - 5/16*r + 1/16, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r,
1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r, -1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 5/8*r^2 - 5/16*r + 1/16, -1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 1/8*r^2, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r,
-1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 3/4*r^2 - 9/16*r + 3/16, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r, -1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 5/16*r
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^5 - 1/4*r^4 + 1/8*r^3 + 1/8*r^2 - 3/16*r + 3/16, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16, 1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 3/8*r^2 + 5/16*r + 3/8,
-1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^5 - 1/4*r^4 + 1/8*r^3, 1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 1/4*r^2 + 1/16*r, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16,
-1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 1/4*r^2 + 1/16*r, 1/8*r^5 - 1/4*r^4 + 1/8*r^3, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16,
1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 3/8*r^2 + 3/16*r, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^5 - 1/4*r^4 + 1/8*r^3 + 1/8*r^2 - 3/16*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
0, 0, 0, 11/16,
0, 0, 0, 1/16,
0, 0, 0, 1/16,
0, 0, 0, 3/16
), nrow=4, ncol=4, byrow=T)
}
),
list(
function(r) {
matrix(c(
3/8, 0, 0, 0,
1/8, 0, 0, 0,
1/8, 0, 0, 0,
3/8, 0, 0, 0
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/8, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/8, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/4*r^4 - 1/2*r^3 + 3/4*r^2 - 3/4*r + 3/8, -1/4*r^4 + 1/2*r^3 - 1/2*r^2 + 1/4*r, -1/4*r^4 + 1/2*r^3 - 1/2*r^2 + 1/4*r, 1/4*r^4 - 1/2*r^3 + 1/4*r^2 + 1/4*r,
-1/4*r^4 + 1/2*r^3 - 1/2*r^2 + 1/4*r, 1/4*r^4 - 1/2*r^3 + 3/4*r^2 - 1/2*r + 1/8, 1/4*r^4 - 1/2*r^3 + 1/4*r^2, -1/4*r^4 + 1/2*r^3 - 1/2*r^2 + 1/4*r,
-1/4*r^4 + 1/2*r^3 - 1/2*r^2 + 1/4*r, 1/4*r^4 - 1/2*r^3 + 1/4*r^2, 1/4*r^4 - 1/2*r^3 + 3/4*r^2 - 1/2*r + 1/8, -1/4*r^4 + 1/2*r^3 - 1/2*r^2 + 1/4*r,
1/4*r^4 - 1/2*r^3 + 1/4*r^2 + 1/4*r, -1/4*r^4 + 1/2*r^3 - 1/2*r^2 + 1/4*r, -1/4*r^4 + 1/2*r^3 - 1/2*r^2 + 1/4*r, 1/4*r^4 - 1/2*r^3 + 3/4*r^2 - 3/4*r + 3/8
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/16,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/8, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32,
1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32,
1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r + 3/16,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16,
1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/8
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/16,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/8, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32,
1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32,
1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r + 3/16,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16,
1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/8
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/4*r^4 - 1/2*r^3 + 1/4*r^2 + 1/4*r, -1/4*r^4 + 1/2*r^3 - 1/2*r^2 + 1/4*r, -1/4*r^4 + 1/2*r^3 - 1/2*r^2 + 1/4*r, 1/4*r^4 - 1/2*r^3 + 3/4*r^2 - 3/4*r + 3/8,
-1/4*r^4 + 1/2*r^3 - 1/2*r^2 + 1/4*r, 1/4*r^4 - 1/2*r^3 + 1/4*r^2, 1/4*r^4 - 1/2*r^3 + 3/4*r^2 - 1/2*r + 1/8, -1/4*r^4 + 1/2*r^3 - 1/2*r^2 + 1/4*r,
-1/4*r^4 + 1/2*r^3 - 1/2*r^2 + 1/4*r, 1/4*r^4 - 1/2*r^3 + 3/4*r^2 - 1/2*r + 1/8, 1/4*r^4 - 1/2*r^3 + 1/4*r^2, -1/4*r^4 + 1/2*r^3 - 1/2*r^2 + 1/4*r,
1/4*r^4 - 1/2*r^3 + 3/4*r^2 - 3/4*r + 3/8, -1/4*r^4 + 1/2*r^3 - 1/2*r^2 + 1/4*r, -1/4*r^4 + 1/2*r^3 - 1/2*r^2 + 1/4*r, 1/4*r^4 - 1/2*r^3 + 1/4*r^2 + 1/4*r
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/8,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16,
1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/8,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16,
1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
0, 0, 0, 3/8,
0, 0, 0, 1/8,
0, 0, 0, 1/8,
0, 0, 0, 3/8
), nrow=4, ncol=4, byrow=T)
}
),
list(
function(r) {
matrix(c(
11/16, 0, 0, 0,
1/16, 0, 0, 0,
1/16, 0, 0, 0,
3/16, 0, 0, 0
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r + 7/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r + 7/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/8,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
-1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 3/4*r^2 - 11/16*r + 11/16, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r, -1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 5/16*r,
1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r, -1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 5/8*r^2 - 5/16*r + 1/16, -1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 1/8*r^2, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r,
1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r, -1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 1/8*r^2, -1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 5/8*r^2 - 5/16*r + 1/16, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r,
-1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 5/16*r, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r, -1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 3/4*r^2 - 9/16*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32,
1/16*r^4 - 3/16*r^2 + 1/4*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32,
1/16*r^4 - 3/16*r^2 + 1/4*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^5 - 1/4*r^4 + 1/8*r^3 + 1/8*r^2 - 3/16*r + 3/16, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16, 1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 3/8*r^2 + 5/16*r + 3/8,
-1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^5 - 1/4*r^4 + 1/8*r^3, 1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 1/4*r^2 + 1/16*r, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16,
-1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 1/4*r^2 + 1/16*r, 1/8*r^5 - 1/4*r^4 + 1/8*r^3, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16,
1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 3/8*r^2 + 3/16*r, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^5 - 1/4*r^4 + 1/8*r^3 + 1/8*r^2 - 3/16*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 3/8*r^2 + 5/16*r + 3/8, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16, 1/8*r^5 - 1/4*r^4 + 1/8*r^3 + 1/8*r^2 - 3/16*r + 3/16,
-1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16, 1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 1/4*r^2 + 1/16*r, 1/8*r^5 - 1/4*r^4 + 1/8*r^3, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2,
-1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16, 1/8*r^5 - 1/4*r^4 + 1/8*r^3, 1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 1/4*r^2 + 1/16*r, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2,
1/8*r^5 - 1/4*r^4 + 1/8*r^3 + 1/8*r^2 - 3/16*r + 3/16, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 3/8*r^2 + 3/16*r
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r,
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 3/16*r^2 + 1/4*r
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r,
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 3/16*r^2 + 1/4*r
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
-1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 5/16*r, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r, -1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 3/4*r^2 - 11/16*r + 11/16,
1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r, -1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 1/8*r^2, -1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 5/8*r^2 - 5/16*r + 1/16, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r,
1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r, -1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 5/8*r^2 - 5/16*r + 1/16, -1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 1/8*r^2, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r,
-1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 3/4*r^2 - 9/16*r + 3/16, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r, -1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 5/16*r
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/8, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16, 1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r + 3/16,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r + 7/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r + 7/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
0, 0, 0, 11/16,
0, 0, 0, 1/16,
0, 0, 0, 1/16,
0, 0, 0, 3/16
), nrow=4, ncol=4, byrow=T)
}
),
list(
function(r) {
matrix(c(
7/16, 0, 0, 0,
1/16, 0, 0, 0,
1/16, 0, 0, 0,
7/16, 0, 0, 0
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 3/16*r^2 + 1/4*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 3/16*r^2 + 1/4*r
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 3/16*r^2 + 1/4*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 5/4*r^3 + 19/16*r^2 - 7/8*r + 7/16, -1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r, -1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r, 1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 3/4*r^3 - 1/16*r^2 + 1/2*r,
-1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r, 1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 5/4*r^3 + 15/16*r^2 - 3/8*r + 1/16, 1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 3/4*r^3 + 3/16*r^2, -1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r,
-1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r, 1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 3/4*r^3 + 3/16*r^2, 1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 5/4*r^3 + 15/16*r^2 - 3/8*r + 1/16, -1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r,
1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 3/4*r^3 - 1/16*r^2 + 1/2*r, -1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r, -1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r, 1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 5/4*r^3 + 19/16*r^2 - 7/8*r + 7/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
-1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r + 3/16, 1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32, 1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32, -1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r + 3/16,
1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32, -1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r, -1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r, 1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32,
1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32, -1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r, -1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r, 1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32,
-1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r + 3/16, 1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32, 1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32, -1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
1/16*r^4 - 3/16*r^2 + 1/4*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 3/16*r^2 + 1/4*r
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
-1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r + 3/16, 1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32, 1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32, -1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r + 3/16,
1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32, -1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r, -1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r, 1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32,
1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32, -1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r, -1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r, 1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32,
-1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r + 3/16, 1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32, 1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32, -1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 3/4*r^3 - 1/16*r^2 + 1/2*r, -1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r, -1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r, 1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 5/4*r^3 + 19/16*r^2 - 7/8*r + 7/16,
-1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r, 1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 3/4*r^3 + 3/16*r^2, 1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 5/4*r^3 + 15/16*r^2 - 3/8*r + 1/16, -1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r,
-1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r, 1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 5/4*r^3 + 15/16*r^2 - 3/8*r + 1/16, 1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 3/4*r^3 + 3/16*r^2, -1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r,
1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 5/4*r^3 + 19/16*r^2 - 7/8*r + 7/16, -1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r, -1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r, 1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 3/4*r^3 - 1/16*r^2 + 1/2*r
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 3/16*r^2 + 1/4*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
1/16*r^4 - 3/16*r^2 + 1/4*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 3/16*r^2 + 1/4*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
0, 0, 0, 7/16,
0, 0, 0, 1/16,
0, 0, 0, 1/16,
0, 0, 0, 7/16
), nrow=4, ncol=4, byrow=T)
}
),
list(
function(r) {
matrix(c(
7/16, 0, 0, 0,
1/16, 0, 0, 0,
1/16, 0, 0, 0,
7/16, 0, 0, 0
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 3/16*r^2 + 1/4*r
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 3/16*r^2 + 1/4*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 3/16*r^2 + 1/4*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
-1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r + 3/16, 1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32, 1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32, -1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r + 3/16,
1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32, -1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r, -1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r, 1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32,
1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32, -1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r, -1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r, 1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32,
-1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r + 3/16, 1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32, 1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32, -1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 5/4*r^3 + 19/16*r^2 - 7/8*r + 7/16, -1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r, -1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r, 1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 3/4*r^3 - 1/16*r^2 + 1/2*r,
-1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r, 1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 5/4*r^3 + 15/16*r^2 - 3/8*r + 1/16, 1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 3/4*r^3 + 3/16*r^2, -1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r,
-1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r, 1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 3/4*r^3 + 3/16*r^2, 1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 5/4*r^3 + 15/16*r^2 - 3/8*r + 1/16, -1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r,
1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 3/4*r^3 - 1/16*r^2 + 1/2*r, -1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r, -1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r, 1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 5/4*r^3 + 19/16*r^2 - 7/8*r + 7/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
1/16*r^4 - 3/16*r^2 + 1/4*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 3/16*r^2 + 1/4*r
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 3/4*r^3 - 1/16*r^2 + 1/2*r, -1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r, -1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r, 1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 5/4*r^3 + 19/16*r^2 - 7/8*r + 7/16,
-1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r, 1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 3/4*r^3 + 3/16*r^2, 1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 5/4*r^3 + 15/16*r^2 - 3/8*r + 1/16, -1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r,
-1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r, 1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 5/4*r^3 + 15/16*r^2 - 3/8*r + 1/16, 1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 3/4*r^3 + 3/16*r^2, -1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r,
1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 5/4*r^3 + 19/16*r^2 - 7/8*r + 7/16, -1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r, -1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r, 1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 3/4*r^3 - 1/16*r^2 + 1/2*r
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
-1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r + 3/16, 1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32, 1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32, -1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r + 3/16,
1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32, -1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r, -1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r, 1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32,
1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32, -1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r, -1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r, 1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32,
-1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r + 3/16, 1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32, 1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32, -1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 3/16*r^2 + 1/4*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 3/16*r^2 + 1/4*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
1/16*r^4 - 3/16*r^2 + 1/4*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
0, 0, 0, 7/16,
0, 0, 0, 1/16,
0, 0, 0, 1/16,
0, 0, 0, 7/16
), nrow=4, ncol=4, byrow=T)
}
),
list(
function(r) {
matrix(c(
3/16, 0, 0, 0,
1/16, 0, 0, 0,
1/16, 0, 0, 0,
11/16, 0, 0, 0
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r + 7/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r + 7/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/8
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^5 - 1/4*r^4 + 1/8*r^3 + 1/8*r^2 - 3/16*r + 3/16, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 3/8*r^2 + 3/16*r,
-1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16, 1/8*r^5 - 1/4*r^4 + 1/8*r^3, 1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 1/4*r^2 + 1/16*r, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2,
-1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16, 1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 1/4*r^2 + 1/16*r, 1/8*r^5 - 1/4*r^4 + 1/8*r^3, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2,
1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 3/8*r^2 + 5/16*r + 3/8, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16, 1/8*r^5 - 1/4*r^4 + 1/8*r^3 + 1/8*r^2 - 3/16*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 3/16*r^2 + 1/4*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r,
1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 3/16*r^2 + 1/4*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r,
1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
-1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 3/4*r^2 - 9/16*r + 3/16, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r, -1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 5/16*r,
1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r, -1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 5/8*r^2 - 5/16*r + 1/16, -1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 1/8*r^2, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r,
1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r, -1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 1/8*r^2, -1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 5/8*r^2 - 5/16*r + 1/16, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r,
-1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 5/16*r, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r, -1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 3/4*r^2 - 11/16*r + 11/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
-1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 5/16*r, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r, -1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 3/4*r^2 - 9/16*r + 3/16,
1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r, -1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 1/8*r^2, -1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 5/8*r^2 - 5/16*r + 1/16, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r,
1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r, -1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 5/8*r^2 - 5/16*r + 1/16, -1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 1/8*r^2, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r,
-1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 3/4*r^2 - 11/16*r + 11/16, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r, -1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 5/16*r
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 3/16*r^2 + 1/4*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32,
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 3/16*r^2 + 1/4*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32,
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 3/8*r^2 + 3/16*r, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^5 - 1/4*r^4 + 1/8*r^3 + 1/8*r^2 - 3/16*r + 3/16,
-1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 1/4*r^2 + 1/16*r, 1/8*r^5 - 1/4*r^4 + 1/8*r^3, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16,
-1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^5 - 1/4*r^4 + 1/8*r^3, 1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 1/4*r^2 + 1/16*r, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16,
1/8*r^5 - 1/4*r^4 + 1/8*r^3 + 1/8*r^2 - 3/16*r + 3/16, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16, 1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 3/8*r^2 + 5/16*r + 3/8
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/16,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/8, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16, 1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r + 7/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r + 7/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
0, 0, 0, 3/16,
0, 0, 0, 1/16,
0, 0, 0, 1/16,
0, 0, 0, 11/16
), nrow=4, ncol=4, byrow=T)
}
),
list(
function(r) {
matrix(c(
11/16, 0, 0, 0,
1/16, 0, 0, 0,
1/16, 0, 0, 0,
3/16, 0, 0, 0
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r + 7/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r + 7/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/8,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 3/8*r^2 + 5/16*r + 3/8, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16, 1/8*r^5 - 1/4*r^4 + 1/8*r^3 + 1/8*r^2 - 3/16*r + 3/16,
-1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16, 1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 1/4*r^2 + 1/16*r, 1/8*r^5 - 1/4*r^4 + 1/8*r^3, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2,
-1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16, 1/8*r^5 - 1/4*r^4 + 1/8*r^3, 1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 1/4*r^2 + 1/16*r, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2,
1/8*r^5 - 1/4*r^4 + 1/8*r^3 + 1/8*r^2 - 3/16*r + 3/16, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 3/8*r^2 + 3/16*r
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r,
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 3/16*r^2 + 1/4*r
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r,
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 3/16*r^2 + 1/4*r
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
-1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 5/16*r, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r, -1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 3/4*r^2 - 11/16*r + 11/16,
1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r, -1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 1/8*r^2, -1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 5/8*r^2 - 5/16*r + 1/16, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r,
1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r, -1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 5/8*r^2 - 5/16*r + 1/16, -1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 1/8*r^2, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r,
-1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 3/4*r^2 - 9/16*r + 3/16, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r, -1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 5/16*r
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
-1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 3/4*r^2 - 11/16*r + 11/16, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r, -1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 5/16*r,
1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r, -1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 5/8*r^2 - 5/16*r + 1/16, -1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 1/8*r^2, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r,
1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r, -1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 1/8*r^2, -1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 5/8*r^2 - 5/16*r + 1/16, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r,
-1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 5/16*r, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r, -1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 3/4*r^2 - 9/16*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32,
1/16*r^4 - 3/16*r^2 + 1/4*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32,
1/16*r^4 - 3/16*r^2 + 1/4*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^5 - 1/4*r^4 + 1/8*r^3 + 1/8*r^2 - 3/16*r + 3/16, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16, 1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 3/8*r^2 + 5/16*r + 3/8,
-1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^5 - 1/4*r^4 + 1/8*r^3, 1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 1/4*r^2 + 1/16*r, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16,
-1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 1/4*r^2 + 1/16*r, 1/8*r^5 - 1/4*r^4 + 1/8*r^3, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16,
1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 3/8*r^2 + 3/16*r, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^5 - 1/4*r^4 + 1/8*r^3 + 1/8*r^2 - 3/16*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/8, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16, 1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r + 3/16,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r + 7/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r + 7/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
0, 0, 0, 11/16,
0, 0, 0, 1/16,
0, 0, 0, 1/16,
0, 0, 0, 3/16
), nrow=4, ncol=4, byrow=T)
}
),
list(
function(r) {
matrix(c(
7/16, 0, 0, 0,
1/16, 0, 0, 0,
1/16, 0, 0, 0,
7/16, 0, 0, 0
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 3/16*r^2 + 1/4*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 3/16*r^2 + 1/4*r
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 3/16*r^2 + 1/4*r
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
-1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r + 3/16, 1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32, 1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32, -1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r + 3/16,
1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32, -1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r, -1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r, 1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32,
1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32, -1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r, -1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r, 1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32,
-1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r + 3/16, 1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32, 1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32, -1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 3/4*r^3 - 1/16*r^2 + 1/2*r, -1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r, -1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r, 1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 5/4*r^3 + 19/16*r^2 - 7/8*r + 7/16,
-1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r, 1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 3/4*r^3 + 3/16*r^2, 1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 5/4*r^3 + 15/16*r^2 - 3/8*r + 1/16, -1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r,
-1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r, 1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 5/4*r^3 + 15/16*r^2 - 3/8*r + 1/16, 1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 3/4*r^3 + 3/16*r^2, -1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r,
1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 5/4*r^3 + 19/16*r^2 - 7/8*r + 7/16, -1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r, -1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r, 1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 3/4*r^3 - 1/16*r^2 + 1/2*r
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 3/16*r^2 + 1/4*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 3/16*r^2 + 1/4*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 5/4*r^3 + 19/16*r^2 - 7/8*r + 7/16, -1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r, -1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r, 1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 3/4*r^3 - 1/16*r^2 + 1/2*r,
-1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r, 1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 5/4*r^3 + 15/16*r^2 - 3/8*r + 1/16, 1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 3/4*r^3 + 3/16*r^2, -1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r,
-1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r, 1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 3/4*r^3 + 3/16*r^2, 1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 5/4*r^3 + 15/16*r^2 - 3/8*r + 1/16, -1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r,
1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 3/4*r^3 - 1/16*r^2 + 1/2*r, -1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r, -1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r, 1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 5/4*r^3 + 19/16*r^2 - 7/8*r + 7/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
-1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r + 3/16, 1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32, 1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32, -1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r + 3/16,
1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32, -1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r, -1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r, 1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32,
1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32, -1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r, -1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r, 1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32,
-1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r + 3/16, 1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32, 1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32, -1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
1/16*r^4 - 3/16*r^2 + 1/4*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
1/16*r^4 - 3/16*r^2 + 1/4*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 3/16*r^2 + 1/4*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
0, 0, 0, 7/16,
0, 0, 0, 1/16,
0, 0, 0, 1/16,
0, 0, 0, 7/16
), nrow=4, ncol=4, byrow=T)
}
),
list(
function(r) {
matrix(c(
7/16, 0, 0, 0,
1/16, 0, 0, 0,
1/16, 0, 0, 0,
7/16, 0, 0, 0
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 3/16*r^2 + 1/4*r
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 3/16*r^2 + 1/4*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 3/16*r^2 + 1/4*r
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 3/4*r^3 - 1/16*r^2 + 1/2*r, -1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r, -1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r, 1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 5/4*r^3 + 19/16*r^2 - 7/8*r + 7/16,
-1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r, 1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 3/4*r^3 + 3/16*r^2, 1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 5/4*r^3 + 15/16*r^2 - 3/8*r + 1/16, -1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r,
-1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r, 1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 5/4*r^3 + 15/16*r^2 - 3/8*r + 1/16, 1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 3/4*r^3 + 3/16*r^2, -1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r,
1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 5/4*r^3 + 19/16*r^2 - 7/8*r + 7/16, -1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r, -1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r, 1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 3/4*r^3 - 1/16*r^2 + 1/2*r
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
-1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r + 3/16, 1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32, 1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32, -1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r + 3/16,
1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32, -1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r, -1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r, 1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32,
1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32, -1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r, -1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r, 1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32,
-1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r + 3/16, 1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32, 1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32, -1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 3/16*r^2 + 1/4*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 3/16*r^2 + 1/4*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
-1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r + 3/16, 1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32, 1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32, -1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r + 3/16,
1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32, -1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r, -1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r, 1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32,
1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32, -1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r, -1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r, 1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32,
-1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r + 3/16, 1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32, 1/4*r^6 - 3/4*r^5 + r^4 - 3/4*r^3 + 5/16*r^2 - 1/16*r + 1/32, -1/4*r^6 + 3/4*r^5 - r^4 + 3/4*r^3 - 5/16*r^2 + 1/16*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 5/4*r^3 + 19/16*r^2 - 7/8*r + 7/16, -1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r, -1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r, 1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 3/4*r^3 - 1/16*r^2 + 1/2*r,
-1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r, 1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 5/4*r^3 + 15/16*r^2 - 3/8*r + 1/16, 1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 3/4*r^3 + 3/16*r^2, -1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r,
-1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r, 1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 3/4*r^3 + 3/16*r^2, 1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 5/4*r^3 + 15/16*r^2 - 3/8*r + 1/16, -1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r,
1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 3/4*r^3 - 1/16*r^2 + 1/2*r, -1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r, -1/4*r^6 + 3/4*r^5 - 9/8*r^4 + r^3 - 9/16*r^2 + 3/16*r, 1/4*r^6 - 3/4*r^5 + 9/8*r^4 - 5/4*r^3 + 19/16*r^2 - 7/8*r + 7/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
1/16*r^4 - 3/16*r^2 + 1/4*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 3/16*r^2 + 1/4*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
1/16*r^4 - 3/16*r^2 + 1/4*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
0, 0, 0, 7/16,
0, 0, 0, 1/16,
0, 0, 0, 1/16,
0, 0, 0, 7/16
), nrow=4, ncol=4, byrow=T)
}
),
list(
function(r) {
matrix(c(
3/16, 0, 0, 0,
1/16, 0, 0, 0,
1/16, 0, 0, 0,
11/16, 0, 0, 0
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r + 7/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r + 7/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/8
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
-1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 5/16*r, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r, -1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 3/4*r^2 - 9/16*r + 3/16,
1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r, -1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 1/8*r^2, -1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 5/8*r^2 - 5/16*r + 1/16, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r,
1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r, -1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 5/8*r^2 - 5/16*r + 1/16, -1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 1/8*r^2, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r,
-1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 3/4*r^2 - 11/16*r + 11/16, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r, -1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 5/16*r
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 3/16*r^2 + 1/4*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32,
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 3/16*r^2 + 1/4*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32,
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 3/8*r^2 + 3/16*r, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^5 - 1/4*r^4 + 1/8*r^3 + 1/8*r^2 - 3/16*r + 3/16,
-1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 1/4*r^2 + 1/16*r, 1/8*r^5 - 1/4*r^4 + 1/8*r^3, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16,
-1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^5 - 1/4*r^4 + 1/8*r^3, 1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 1/4*r^2 + 1/16*r, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16,
1/8*r^5 - 1/4*r^4 + 1/8*r^3 + 1/8*r^2 - 3/16*r + 3/16, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16, 1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 3/8*r^2 + 5/16*r + 3/8
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^5 - 1/4*r^4 + 1/8*r^3 + 1/8*r^2 - 3/16*r + 3/16, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 3/8*r^2 + 3/16*r,
-1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16, 1/8*r^5 - 1/4*r^4 + 1/8*r^3, 1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 1/4*r^2 + 1/16*r, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2,
-1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16, 1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 1/4*r^2 + 1/16*r, 1/8*r^5 - 1/4*r^4 + 1/8*r^3, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2,
1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 3/8*r^2 + 5/16*r + 3/8, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16, 1/8*r^5 - 1/4*r^4 + 1/8*r^3 + 1/8*r^2 - 3/16*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 3/16*r^2 + 1/4*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r,
1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 3/16*r^2 + 1/4*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r,
1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
-1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 3/4*r^2 - 9/16*r + 3/16, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r, -1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 5/16*r,
1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r, -1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 5/8*r^2 - 5/16*r + 1/16, -1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 1/8*r^2, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r,
1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r, -1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 1/8*r^2, -1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 5/8*r^2 - 5/16*r + 1/16, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r,
-1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 5/16*r, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r, -1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 3/4*r^2 - 11/16*r + 11/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/16,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/8, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16, 1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r + 7/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r + 7/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
0, 0, 0, 3/16,
0, 0, 0, 1/16,
0, 0, 0, 1/16,
0, 0, 0, 11/16
), nrow=4, ncol=4, byrow=T)
}
),
list(
function(r) {
matrix(c(
3/8, 0, 0, 0,
1/8, 0, 0, 0,
1/8, 0, 0, 0,
3/8, 0, 0, 0
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/16,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/8, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/16,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/8, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/4*r^4 - 1/2*r^3 + 1/4*r^2 + 1/4*r, -1/4*r^4 + 1/2*r^3 - 1/2*r^2 + 1/4*r, -1/4*r^4 + 1/2*r^3 - 1/2*r^2 + 1/4*r, 1/4*r^4 - 1/2*r^3 + 3/4*r^2 - 3/4*r + 3/8,
-1/4*r^4 + 1/2*r^3 - 1/2*r^2 + 1/4*r, 1/4*r^4 - 1/2*r^3 + 1/4*r^2, 1/4*r^4 - 1/2*r^3 + 3/4*r^2 - 1/2*r + 1/8, -1/4*r^4 + 1/2*r^3 - 1/2*r^2 + 1/4*r,
-1/4*r^4 + 1/2*r^3 - 1/2*r^2 + 1/4*r, 1/4*r^4 - 1/2*r^3 + 3/4*r^2 - 1/2*r + 1/8, 1/4*r^4 - 1/2*r^3 + 1/4*r^2, -1/4*r^4 + 1/2*r^3 - 1/2*r^2 + 1/4*r,
1/4*r^4 - 1/2*r^3 + 3/4*r^2 - 3/4*r + 3/8, -1/4*r^4 + 1/2*r^3 - 1/2*r^2 + 1/4*r, -1/4*r^4 + 1/2*r^3 - 1/2*r^2 + 1/4*r, 1/4*r^4 - 1/2*r^3 + 1/4*r^2 + 1/4*r
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/8, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32,
1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32,
1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/8,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16,
1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/8, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32,
1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 1/32, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/32,
1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/4*r^2 - 1/8*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/8,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16,
1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/4*r^4 - 1/2*r^3 + 3/4*r^2 - 3/4*r + 3/8, -1/4*r^4 + 1/2*r^3 - 1/2*r^2 + 1/4*r, -1/4*r^4 + 1/2*r^3 - 1/2*r^2 + 1/4*r, 1/4*r^4 - 1/2*r^3 + 1/4*r^2 + 1/4*r,
-1/4*r^4 + 1/2*r^3 - 1/2*r^2 + 1/4*r, 1/4*r^4 - 1/2*r^3 + 3/4*r^2 - 1/2*r + 1/8, 1/4*r^4 - 1/2*r^3 + 1/4*r^2, -1/4*r^4 + 1/2*r^3 - 1/2*r^2 + 1/4*r,
-1/4*r^4 + 1/2*r^3 - 1/2*r^2 + 1/4*r, 1/4*r^4 - 1/2*r^3 + 1/4*r^2, 1/4*r^4 - 1/2*r^3 + 3/4*r^2 - 1/2*r + 1/8, -1/4*r^4 + 1/2*r^3 - 1/2*r^2 + 1/4*r,
1/4*r^4 - 1/2*r^3 + 1/4*r^2 + 1/4*r, -1/4*r^4 + 1/2*r^3 - 1/2*r^2 + 1/4*r, -1/4*r^4 + 1/2*r^3 - 1/2*r^2 + 1/4*r, 1/4*r^4 - 1/2*r^3 + 3/4*r^2 - 3/4*r + 3/8
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r + 3/16,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16,
1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/8
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r + 3/16,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16,
1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/8
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
0, 0, 0, 3/8,
0, 0, 0, 1/8,
0, 0, 0, 1/8,
0, 0, 0, 3/8
), nrow=4, ncol=4, byrow=T)
}
),
list(
function(r) {
matrix(c(
3/16, 0, 0, 0,
1/16, 0, 0, 0,
1/16, 0, 0, 0,
11/16, 0, 0, 0
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^5 - 1/4*r^4 + 1/8*r^3 + 1/8*r^2 - 3/16*r + 3/16, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 3/8*r^2 + 3/16*r,
-1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16, 1/8*r^5 - 1/4*r^4 + 1/8*r^3, 1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 1/4*r^2 + 1/16*r, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2,
-1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16, 1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 1/4*r^2 + 1/16*r, 1/8*r^5 - 1/4*r^4 + 1/8*r^3, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2,
1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 3/8*r^2 + 5/16*r + 3/8, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16, 1/8*r^5 - 1/4*r^4 + 1/8*r^3 + 1/8*r^2 - 3/16*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
-1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 5/16*r, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r, -1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 3/4*r^2 - 9/16*r + 3/16,
1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r, -1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 1/8*r^2, -1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 5/8*r^2 - 5/16*r + 1/16, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r,
1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r, -1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 5/8*r^2 - 5/16*r + 1/16, -1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 1/8*r^2, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r,
-1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 3/4*r^2 - 11/16*r + 11/16, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r, -1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 5/16*r
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/16,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/8, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16, 1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r + 7/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 3/16*r^2 + 1/4*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r,
1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 3/16*r^2 + 1/4*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32,
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r + 7/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r + 7/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 3/16*r^2 + 1/4*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r,
1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 3/16*r^2 + 1/4*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32,
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r + 7/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/8
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
-1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 3/4*r^2 - 9/16*r + 3/16, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r, -1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 5/16*r,
1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r, -1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 5/8*r^2 - 5/16*r + 1/16, -1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 1/8*r^2, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r,
1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r, -1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 1/8*r^2, -1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 5/8*r^2 - 5/16*r + 1/16, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r,
-1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 5/16*r, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r, -1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 3/4*r^2 - 11/16*r + 11/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 3/8*r^2 + 3/16*r, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^5 - 1/4*r^4 + 1/8*r^3 + 1/8*r^2 - 3/16*r + 3/16,
-1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 1/4*r^2 + 1/16*r, 1/8*r^5 - 1/4*r^4 + 1/8*r^3, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16,
-1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^5 - 1/4*r^4 + 1/8*r^3, 1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 1/4*r^2 + 1/16*r, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16,
1/8*r^5 - 1/4*r^4 + 1/8*r^3 + 1/8*r^2 - 3/16*r + 3/16, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16, 1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 3/8*r^2 + 5/16*r + 3/8
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
0, 0, 0, 3/16,
0, 0, 0, 1/16,
0, 0, 0, 1/16,
0, 0, 0, 11/16
), nrow=4, ncol=4, byrow=T)
}
),
list(
function(r) {
matrix(c(
3/16, 0, 0, 0,
1/16, 0, 0, 0,
1/16, 0, 0, 0,
11/16, 0, 0, 0
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
-1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 5/16*r, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r, -1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 3/4*r^2 - 9/16*r + 3/16,
1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r, -1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 1/8*r^2, -1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 5/8*r^2 - 5/16*r + 1/16, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r,
1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r, -1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 5/8*r^2 - 5/16*r + 1/16, -1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 1/8*r^2, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r,
-1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 3/4*r^2 - 11/16*r + 11/16, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r, -1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 5/16*r
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^5 - 1/4*r^4 + 1/8*r^3 + 1/8*r^2 - 3/16*r + 3/16, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 3/8*r^2 + 3/16*r,
-1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16, 1/8*r^5 - 1/4*r^4 + 1/8*r^3, 1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 1/4*r^2 + 1/16*r, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2,
-1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16, 1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 1/4*r^2 + 1/16*r, 1/8*r^5 - 1/4*r^4 + 1/8*r^3, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2,
1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 3/8*r^2 + 5/16*r + 3/8, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16, 1/8*r^5 - 1/4*r^4 + 1/8*r^3 + 1/8*r^2 - 3/16*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/16,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/8, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16, 1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r + 7/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 3/16*r^2 + 1/4*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32,
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 3/16*r^2 + 1/4*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r,
1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r + 7/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r,
1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r + 7/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 3/16*r^2 + 1/4*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32,
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 3/8*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 3/16*r^2 + 1/4*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/32*r + 1/32, 1/16*r^4 - 1/16*r^2 + 1/32*r, 1/16*r^4 - 1/4*r^3 + 5/16*r^2 - 5/32*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 3/32*r,
1/16*r^4 - 3/16*r^2 + 5/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/4*r^3 + 7/16*r^2 - 7/16*r + 7/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
-1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r, 1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 1/8*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32,
1/16*r^4 - 1/8*r^3 + 3/16*r^2 - 3/16*r + 3/16, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, -1/16*r^4 + 1/8*r^3 - 1/8*r^2 + 1/16*r + 1/32, 1/16*r^4 - 1/8*r^3 + 1/16*r^2 + 1/16*r + 7/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
-1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r, 1/8*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 1/4*r + 1/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r,
1/8*r^4 - 1/4*r^3 + 1/8*r^2 + 1/8*r + 3/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16, -1/8*r^4 + 1/4*r^3 - 1/4*r^2 + 1/8*r + 1/16, 1/8*r^4 - 1/4*r^3 + 3/8*r^2 - 3/8*r + 3/8
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 3/8*r^2 + 3/16*r, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^5 - 1/4*r^4 + 1/8*r^3 + 1/8*r^2 - 3/16*r + 3/16,
-1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 1/4*r^2 + 1/16*r, 1/8*r^5 - 1/4*r^4 + 1/8*r^3, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16,
-1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2, 1/8*r^5 - 1/4*r^4 + 1/8*r^3, 1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 1/4*r^2 + 1/16*r, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16,
1/8*r^5 - 1/4*r^4 + 1/8*r^3 + 1/8*r^2 - 3/16*r + 3/16, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16, -1/8*r^5 + 1/4*r^4 - 1/4*r^3 + 1/8*r^2 - 1/16*r + 1/16, 1/8*r^5 - 1/4*r^4 + 3/8*r^3 - 3/8*r^2 + 5/16*r + 3/8
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
-1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 3/4*r^2 - 9/16*r + 3/16, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r, -1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 5/16*r,
1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r, -1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 5/8*r^2 - 5/16*r + 1/16, -1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 1/8*r^2, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r,
1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 1/8*r, -1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 1/8*r^2, -1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 5/8*r^2 - 5/16*r + 1/16, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r,
-1/8*r^5 + 3/8*r^4 - 3/8*r^3 + 5/16*r, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r, 1/8*r^5 - 3/8*r^4 + 1/2*r^3 - 3/8*r^2 + 3/16*r, -1/8*r^5 + 3/8*r^4 - 5/8*r^3 + 3/4*r^2 - 11/16*r + 11/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
0, 0, 0, 3/16,
0, 0, 0, 1/16,
0, 0, 0, 1/16,
0, 0, 0, 11/16
), nrow=4, ncol=4, byrow=T)
}
),
list(
function(r) {
matrix(c(
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
1, 0, 0, 0
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
11/16, 1/16, 1/16, 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
11/16, 1/16, 1/16, 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
3/8, 1/8, 1/8, 3/8
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
11/16, 1/16, 1/16, 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
7/16, 1/16, 1/16, 7/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
7/16, 1/16, 1/16, 7/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
3/16, 1/16, 1/16, 11/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
11/16, 1/16, 1/16, 3/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
7/16, 1/16, 1/16, 7/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
7/16, 1/16, 1/16, 7/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
3/16, 1/16, 1/16, 11/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
3/8, 1/8, 1/8, 3/8
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
3/16, 1/16, 1/16, 11/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
3/16, 1/16, 1/16, 11/16
), nrow=4, ncol=4, byrow=T)
},
function(r) {
matrix(c(
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 1
), nrow=4, ncol=4, byrow=T)
}
)
)
|
b4fd727fef99e41d0ff844861abf7e588af02142 | 446e8ed6dc4b6f01a62e0944bcafc1e7bdde1652 | /man/show_themes.Rd | 4c4db8d6b933c26c31b33ac427ebefb73a630dc8 | [
"MIT"
] | permissive | jlopezper/essurvey | 2f24d269334fde904a2ed4b76ce771e98c85b252 | 1bd88d41ca714ad5ad5d8ac61b71566142d8322e | refs/heads/master | 2020-04-27T13:19:53.757688 | 2019-03-08T13:24:39 | 2019-03-08T13:24:39 | 174,365,791 | 0 | 0 | null | 2019-03-07T14:59:43 | 2019-03-07T14:59:43 | null | UTF-8 | R | false | true | 625 | rd | show_themes.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/show_funs.R
\name{show_themes}
\alias{show_themes}
\title{Return available themes in the European Social Survey}
\usage{
show_themes()
}
\value{
character vector with available themes
}
\description{
This function returns the available themes in the European Social Survey.
However, contrary to \code{\link{show_countries}} and \code{\link{show_country_rounds}},
themes can not be downloaded as separate datasets. This and
\code{\link{show_theme_rounds}} serve purely for informative purposes.
}
\examples{
show_themes()
}
|
2c9be175fdaada0053952eecaaa0ac2426a37348 | 83f7cbd1728e1756ce26f249f23e562143de6b5c | /SimulationMain.Revisions.wPCs.vs1.dev.R | e7c877ef589a9f58057fc01fb7d741b274065744 | [] | no_license | mturchin20/InterPath | 765135535783c63122ba5d006ba671ac1cb4448f | 4b07e1377aa6bfd14cda7ed5e1ffb02ce52bd494 | refs/heads/master | 2021-09-08T23:55:41.264911 | 2021-09-06T23:28:53 | 2021-09-06T23:28:53 | 136,239,536 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,887 | r | SimulationMain.Revisions.wPCs.vs1.dev.R | #library("MAPITR")
library("devtools")
devtools::load_all("/users/mturchin/LabMisc/RamachandranLab/MAPITR")
args <- commandArgs()
#print(args)
X.File <- args[6]
Genes.File <- args[7]
Covars.File <- args[8]
Genes.Analysis.File <- args[9]
Output1.File <- args[10]
seed.value <- as.numeric(as.character(args[11]))
n.datasets <- as.numeric(as.character(args[12]))
pve <- as.numeric(as.character(args[13]))
rho <- as.numeric(as.character(args[14]))
pc.var <- as.numeric(as.character(args[15]))
#ngenes <- as.numeric(as.character(args[16]))
nsnps <- as.numeric(as.character(args[16]))
ncausal1 <- as.numeric(as.character(args[17]))
ncausal2 <- as.numeric(as.character(args[18]))
#ncausal3 <- args[19]
set.seed(seed.value)
X <- as.matrix(read.table(X.File, header=T));
Genes <- read.table(Genes.File, header=F);
Covars <- read.table(Covars.File, header=T);
Genes.Analysis <- read.table(Genes.Analysis.File, header=F);
PCs <- as.matrix(Covars[,(ncol(Covars)-9):ncol(Covars)]);
Xmean=apply(X, 2, mean); Xsd=apply(X, 2, sd); X=t((t(X)-Xmean)/Xsd)
ind = nrow(X); nsnp = ncol(X)
### Define the Simulation Parameters ###
#n.datasets = 1 #Total Number of Simulations
#pve = 0.6; #Heritability of the trait
#rho = 0.8; #Proportion of the heritability caused by additive effects {0.8, 0.5}
#pc.var = 0.1
### Set Up Causal Pathways in Three Groups
#ngenes = 20; #number of genes needed to get around 1000 SNPs pulled for G1, G2, and G3
nsnps = 1000;
ncausal1 = .02; ncausal2 = .02 #Percent of SNPs needed for G1 and G2
ncausal3 = 1-(ncausal1+ncausal2) #Remaining SNPs to be allocated to G3
### Create a list to save the final Results ###
#pval_mat = matrix(nrow = nrow(Genes),ncol = n.datasets); pval_mat <- cbind(Genes[,1], pval_mat);
#genes_chosen = matrix(nrow = ngenes, ncol = n.datasets);
#G1_snps = matrix(nrow = ncausal1,ncol = n.datasets)
#G2_snps = matrix(nrow = ncausal2,ncol = n.datasets)
### Run the Analysis ###
for(i in 1:n.datasets) {
#Select Causal Pathways
gene.ids = 1:nrow(Genes)
# genes.pulled.ids.temp <- sample(gene.ids, 1, replace=F);
gene.ids.sampled <- sample(gene.ids);
snp.total <- 0;
genes.pulled.ids <- c();
genes.pulled.SNPs <- c();
snp.total.running <- c(0);
numloop <- 1;
while ((snp.total <= 1000) || (numloop >= length(gene.ids))) {
gene.temp <- Genes[gene.ids.sampled[numloop],];
genes.pulled.ids <- c(genes.pulled.ids, gene.ids.sampled[numloop]);
genes.pulled.SNPs <- c(genes.pulled.SNPs, unlist(strsplit(as.character(gene.temp[1,2]), ",")));
snp.total <- length(genes.pulled.SNPs);
snp.total.running <- c(snp.total.running, snp.total);
numloop <- numloop + 1;
}
genes.pulled <- Genes[genes.pulled.ids,];
print(c(numloop, snp.total, length(genes.pulled.ids), snp.total.running[length(snp.total.running)-1]));
# for (j in 1:nrow(genes.pulled)) {
# genes.pulled.SNPs <- c(genes.pulled.SNPs, unlist(strsplit(as.character(genes.pulled[j,2]), ",")));
# }
genes.pulled.SNPs.uniq <- unique(genes.pulled.SNPs);
genes.pulled.SNPs.uniq.ids <- 1:length(genes.pulled.SNPs.uniq)
s1.ids=sample(genes.pulled.SNPs.uniq.ids, round(ncausal1*length(genes.pulled.SNPs.uniq)), replace=F)
s2.ids=sample(genes.pulled.SNPs.uniq.ids[-s1.ids], round(ncausal2*length(genes.pulled.SNPs.uniq)), replace=F)
s3.ids.size <- round(ncausal3*length(genes.pulled.SNPs.uniq)); if (s3.ids.size > length(genes.pulled.SNPs.uniq.ids[-c(s1.ids,s2.ids)])) { s3.ids.size <- s3.ids.size - 1; }
s3.ids=sample(genes.pulled.SNPs.uniq.ids[-c(s1.ids,s2.ids)], s3.ids.size, replace=F)
s1 <- genes.pulled.SNPs.uniq[s1.ids]
s2 <- genes.pulled.SNPs.uniq[s2.ids]
s3 <- genes.pulled.SNPs.uniq[s3.ids]
### Simulate the Additive Effects ###
SNPs.additive = c(s1,s2,s3);
# print(dim(X))
# print(SNPs.additive)
Xmarginal = X[,SNPs.additive]
beta=rnorm(dim(Xmarginal)[2])
y_marginal=c(Xmarginal%*%beta)
beta=beta*sqrt(pve*rho/var(y_marginal))
y_marginal=Xmarginal%*%beta
### Simulate Pairwise Interaction matrix ###
Xepi = c(); b = c()
for(j in 1:ncausal1){
Xepi = cbind(Xepi,X[,s1[j]]*X[,s2])
}
### Simulate the Pairwise Effects ###
beta=rnorm(dim(Xepi)[2])
y_epi=c(Xepi%*%beta)
beta=beta*sqrt(pve*(1-rho)/var(y_epi))
y_epi=Xepi%*%beta
### Define the effects of the PCs ###
beta=rnorm(dim(PCs)[2])
y_pcs=c(PCs%*%beta)
beta=beta*sqrt(pc.var/var(y_pcs))
y_pcs=PCs%*%beta
### Simulate the (Environmental) Error/Noise ###
y_err=rnorm(ind)
y_err=y_err*sqrt((1-pve-pc.var)/var(y_err))
### Simulate the Total Phenotypes ###
y=y_marginal+y_epi+y_pcs+y_err
y=(y-mean(y))/sd(y)
### Check dimensions ###
print(dim(X)); print(length(y));
######################################################################################
######################################################################################
######################################################################################
ptm <- proc.time() #Start clock
MAPITR_Output <- MAPITR(X,y,Genes.Analysis[1:10,],Covariates=PCs)
print(proc.time() - ptm) #Stop clock
print(head(MAPITR_Output$Results))
# write.table(y, paste(Output1.File, ".Simulation.Pheno.txt", sep=""), quote=FALSE, col.name=FALSE, row.name=FALSE);
# write.table(genes.pulled, paste(Output1.File, ".Simulation.nGenes.txt", sep=""), quote=FALSE, col.name=FALSE, row.name=FALSE);
# write.table(s1, paste(Output1.File, ".Simulation.nCausal1.txt", sep=""), quote=FALSE, col.name=FALSE, row.name=FALSE);
# write.table(s2, paste(Output1.File, ".Simulation.nCausal2.txt", sep=""), quote=FALSE, col.name=FALSE, row.name=FALSE);
# write.table(MAPITR_Output$Results, paste(Output1.File, ".Results.Output.txt", sep=""), quote=FALSE, col.name=TRUE, row.name=FALSE);
# ### Save Results ###
# pval_mat[,j] = pvals
# G1_snps[,j] = Pthwys_1
# G2_snps[,j] = Pthwys_2
}
#Save final Results
#Final = list(pval_mat,G1_snps,G2_snps)
|
884f1dbc46f1cb5e18e2999608ca0a8037220b57 | 09d8eb9f8703e8fd5c089eb58a559d4242b44a55 | /plot1.R | c20670d3fdb3ba7c91dc5b10bcc4a169a19de09f | [] | no_license | yachabach/ExData_Plotting1 | 098ef2360d3cf67112720faaf1cf913f5f43f75b | 1fb5a5af132888212366f6d7bdfc69f3ca4df393 | refs/heads/master | 2022-12-12T11:46:09.509680 | 2020-08-24T12:25:00 | 2020-08-24T12:25:00 | 289,589,814 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 601 | r | plot1.R | # Generate plot #1 from coursera assignment
#
Plot1 <- function() {
# Load data from working directory
#
fileNm <- "household_power_consumption.txt"
hd <- fread(fileNm, nrows = 1)
y <- fread(fileNm, skip = 64800, nrows = 5000, header = TRUE)
colnames(y) = colnames(hd)
#subset textual date data
z <- y[grep('^1/2/2007|^2/2/2007',y$Date),]
# Open png output device
#
png(file = "plot1.png")
#draw histogram as assigned
#
with(z,hist(as.numeric(Global_active_power), col = "red", xlab = "Global Active Power (kilowatts)", main = "Global Active Power"))
dev.off()
} |
4f1d9c90390c9c54814a141c35965f2febebc08c | e08f3b0790613a5c81f72db72e5a912a63052288 | /estimate r 9-21.R | ae30aa08b054f7e3da0955594de9ab200b7cbaef | [] | no_license | MSTRoth/PSC_Homeless | edb33718997ac2a1e4c9884529f5510369166aba | 943ce81b2657ea8bb54e2bd238c82feaed22f062 | refs/heads/master | 2020-06-07T06:47:29.544425 | 2019-08-16T20:23:46 | 2019-08-16T20:23:46 | 192,951,593 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,720 | r | estimate r 9-21.R | ####first and last#####
options(scipen=10000)
library(tidyverse)
####Estimates#####
setwd("X:/1 Marielle Folder/Spending Data/Estimates files")
###actual and estimated values by year####
#####quarter views####
quarters <- read_csv("quarters.csv")
data <- quarters %>%
gather("quarter_FY","outlay", 2:57) %>%
separate("quarter_FY", c("quarter", "FY"), sep = "_") %>%
filter(quarter =="Q3"|quarter == "Q4")
dataDoD <- data %>%
filter(X1 == "Department of Defense--Military Programs")
dataDHS <- data %>%
filter(X1 == "Department of Homeland Security")
dataVA <- data %>%
filter(X1 == "Department of Veterans Affairs")
dataTotal <- data %>%
filter(X1 == "Total Outlays")
ggplot(data, aes(x=`quarter`, y=outlay))+
geom_bar(stat = "identity", position =position_dodge())+
facet_grid(FY~X1) +
theme(axis.text.x = element_text(angle = -45))+
labs(title = "obligated amount by quarter by fiscal year")
#colnames(dataDoD)
ggplot(dataDoD, aes(x=`quarter`, y=outlay, group = FY))+
geom_line(stat = "identity")+
facet_wrap("FY") +
theme(axis.text.x = element_text(angle = -45))+
labs(title = "obligated amount by quarter by fiscal year - DoD")
ggplot(dataDHS, aes(x=`quarter`, y=outlay, group = FY))+
geom_line(stat = "identity")+
facet_wrap("FY") +
theme(axis.text.x = element_text(angle = -45))+
labs(title = "obligated amount by quarter by fiscal year - DHS")
ggplot(dataVA, aes(x=`quarter`, y=outlay, group = FY))+
geom_line(stat = "identity")+
facet_wrap("FY") +
theme(axis.text.x = element_text(angle = -45))+
labs(title = "obligated amount by quarter by fiscal year - VA")
ggplot(dataTotal, aes(x=`quarter`, y=outlay, group = FY))+
geom_line(stat = "identity")+
facet_wrap("FY") +
theme(axis.text.x = element_text(angle = -45))+
labs(title = "obligated amount by quarter by fiscal year - Total")
######quarter percent total year####
pertot <- read_csv("percent of total year quarter.csv")
data<-pertot %>%
gather("quarter_FY", "percent_total", 2:57) %>%
separate("quarter_FY",c("quarter","FY"), sep = "_") %>%
select(-X58)
#%>%
# filter(quarter =="Q1"|quarter == "Q4")
ggplot(data, aes(x=quarter, y=percent_total, group = FY))+
geom_line(stat = "identity")+
facet_grid(FY~X1) +
theme(axis.text.x = element_text(angle = -45))+
labs(title = "quarter - percent of total FY obligations")
dataDoD <- data %>%
filter(X1 == "Department of Defense--Military Programs")
dataDHS <- data %>%
filter(X1 == "Department of Homeland Security")
dataVA <- data %>%
filter(X1 == "Department of Veterans Affairs")
dataTotal <- data %>%
filter(X1 == "Total Outlays")
ggplot(dataDoD, aes(x=`quarter`, y=percent_total, group = FY))+
geom_line(stat = "identity")+
facet_wrap("FY") +
theme(axis.text.x = element_text(angle = -45))+
labs(title = "quarter - percent of total FY obligations - DoD")
ggplot(dataDHS, aes(x=`quarter`, y=percent_total, group = FY))+
geom_line(stat = "identity")+
facet_wrap("FY") +
theme(axis.text.x = element_text(angle = -45))+
labs(title = "quarter - percent of total FY obligations - DHS")
ggplot(dataVA, aes(x=`quarter`, y=percent_total, group = FY))+
geom_line(stat = "identity")+
facet_wrap("FY") +
theme(axis.text.x = element_text(angle = -45))+
labs(title = "quarter - percent of total FY obligations - VA")
ggplot(dataTotal, aes(x=`quarter`, y=percent_total, group = FY))+
geom_line(stat = "identity")+
facet_wrap("FY") +
theme(axis.text.x = element_text(angle = -45))+
labs(title = "quarter - percent of total FY obligations - Total")
|
e2b90c44129da244e37b3909ff0bb6ba459c2235 | c122e77a34976b987ad45f70b5802e79098702a4 | /inst/shinyApps/hisreg/R/modFordelinger.R | 4c7b97dafde56b4760be49fe425ac7f218ada15d | [] | no_license | Rapporteket/hisreg | b4d212fdca74dbd8fcc4dfbbc2bc1922f84b90dc | 7e06f4d5d39c357696d35146f5da5508eaa20586 | refs/heads/master | 2022-09-20T06:04:16.198184 | 2022-05-03T14:26:44 | 2022-05-03T14:26:44 | 77,216,862 | 0 | 0 | null | 2020-02-24T11:10:24 | 2016-12-23T10:03:24 | R | UTF-8 | R | false | false | 9,786 | r | modFordelinger.R | #hjelpefunksjon som lager topp og bunntekst til tabellen
cont <- function(enh, usRole) {
if (usRole == "SC") {
avd = "Valgt avdeling"
} else {
avd = "Din avdeling"
}
if (enh == 1) {
htmltools::withTags(table(
class = "display",
thead(
tr(
th(rowspan = 2, "Kategori"),
th(class = "dt-center", colspan = 3, avd),
th(class = "dt-center", colspan = 3, "Landet forøveig")
),
tr(
lapply(rep(c("N", "Antall", "Andel"), 2), th)
)
)
))
} else{
htmltools::withTags(table(
class = "display",
thead(
tr(
lapply(c("Kategori", "N", "Antall", "Andel"), th)
)
)
))
}
}
#modul UI
modFordelingerUI <- function(id, varValg = varValgFordeling) {
ns <- shiny::NS(id)
enhvalg <- c("Egen avd. mot landet forøvrig" = 1,
"Hele landet" = 0, "Egen avdeling" = 2)
shiny::sidebarLayout(
shiny::sidebarPanel(width = 3,
div(id = ns("sbPanel"),
shiny::selectInput(ns("varSel"), label = "Velg variabel",
choices = varValg, selected = varValg[[1]]),
shiny::selectInput(ns("typInt"), label = "Type intervensjon",
choices = typInt, selected = 99),
shiny::uiOutput(ns("SC")),
shiny::selectInput(ns("enhSel"), label = "Velg enhe(er)",
choices = enhvalg, selected = enhvalg[[1]]),
shiny::dateRangeInput(ns("dateRan"), label = "Tidsperiode",
language = "no", separator = "til",
min = "2011-01-01", max = Sys.Date(),
start = "2011-01-01", end = Sys.Date()),
shiny::selectInput(ns("kjoSle"), label = "Kjønn", choices = kjoenn,
selected = 99),
shiny::sliderInput(ns("aldSli"), label = "Alder", min = 0,
max = 130, value = c(0, 130)),
shiny::uiOutput(ns("figfil"))),
shiny::actionLink(inputId=ns("nullstill"),
style="color:black" ,
label = "Nullstill Valg")
),
shiny::mainPanel(width = 9,
shiny::fluidRow(
shiny::column(6,
tabsetPanel(id = ns("tabs"),
tabPanel("Figur", value = "fig",
shiny::plotOutput(ns("figur"),
width = "95%",
height = "600px"),
downloadButton(ns("lastNedBilde"),
"Last ned bilde")),
tabPanel("Tabell", value = "tab",
DT::DTOutput(ns("tabell")),
downloadButton(ns("lastNed"),
"Last ned tabell")
)))
)
)#mainpanel
)#sidebarLayout
}
#moduleserver
modFordelinger <- function(input, output, session, rID, role, ss, RegData) {
output$figfil <- shiny::renderUI({
ns <- session$ns
if (input$tabs == "fig") {
shiny::selectInput(ns("outfil"), label = "Velg bildeformat",
choices = c("pdf", "png", "jpg", "bmp", "tif", "svg"))
}
})
output$SC <- renderUI({
ns <- session$ns
if (role == "SC") {
shiny::selectInput(ns("shSelect"), label = "Velg Avdeling",
choices = avdValg, selected = rID )
}
})
resh <- reactive(
if (role == "SC"){
req(input$shSelect)
} else {
rID
}
)
observeEvent(req(input$nullstill), {shinyjs::reset("sbPanel")})
data <- reactive({
hisreg::hisregFigAndeler(RegData = RegData,
valgtVar = input$varSel,
datoFra = min(input$dateRan),
datoTil = max(input$dateRan),
minald = input$aldSli[1],
maxald = input$aldSli[2],
reshID = resh(),
enhetsUtvalg = input$enhSel,
forlop1 = input$typInt,
erMann = as.numeric(input$kjoSle))
})
#data til en dataramme
df <- shiny::reactive({
if (is.table(data()$Andeler$Hoved)) {
if (input$enhSel == 1) {
data.frame("Kategori" = as.data.frame(data()$Andeler$Hoved)[, 1],
"Antall." = rep(data()$Antall$Hoved,
length(data()$Andeler$Hoved)),
"N." = round((as.data.frame(data()$Andeler$Hoved)[, 2] /
100) * data()$Antall$Hoved),
"Andel." = round(as.data.frame(data()$Andeler$Hoved)[, 2] /
100, digits = 3),
"Antall" = rep(data()$Antall$Rest,
length(data()$Andeler$Hoved)),
"N" = round((as.data.frame(data()$Andeler$Rest)[, 2] / 100) *
data()$Antall$Rest),
"Andel" = round(as.data.frame(data()$Andeler$Rest)[, 2] /
100, digits = 3))
} else {
data.frame("Kategori" = as.data.frame(data()$Andeler$Hoved)[, 1],
"Antall." = rep(data()$Antall$Hoved,
length(data()$Andeler$Hoved)),
"N." = round((as.data.frame(data()$Andeler$Hoved)[, 2] / 100) *
data()$Antall$Hoved),
"Andel." = round(as.data.frame(data()$Andeler$Hoved)[, 2] / 100
, digits = 3))
}
} else{
if (input$enhSel == 1) {
data.frame("Kategori" = data()$Grtxt,
"Antall." = rep(data()$Antall$Hoved,
length(data()$Andeler$Hoved)),
"N." = round((data()$Andeler$Hoved / 100) *
data()$Antall$Hoved),
"Andel." = round(data()$Andeler$Hoved / 100,
digits = 3),
"Antal" = rep(data()$Antall$Rest,
length(data()$Andeler$Hoved)),
"N" = round((data()$Andeler$Rest / 100) *
data()$Antall$Rest),
"Andel" = round(data()$Andeler$Rest / 100,
digits = 3))
} else {
data.frame("Kategori" = data()$Grtxt,
"Antall." = rep(data()$Antall$Hoved,
length(data()$Andeler$Hoved)),
"N." = round((data()$Andeler$Hoved / 100) *
data()$Antall$Hoved),
"Andel." = round(data()$Andeler$Hoved /
100, digits = 3))
}
}
})
#andelsfigurer
output$figur <- renderPlot(
hisreg::hisregFigAndeler(RegData = RegData, valgtVar = input$varSel,
datoFra = min(input$dateRan),
datoTil = max(input$dateRan),
minald = input$aldSli[1],
maxald = input$aldSli[2],
reshID = resh(), enhetsUtvalg = input$enhSel,
forlop1 = input$typInt,
erMann = as.numeric(input$kjoSle))
)
#tabell
observe({
cont <- cont(input$enhSel, role)
output$tabell <- DT::renderDT(
if (input$enhSel == 1) {
df() %>% datatable(selection = "none",
container = cont, rownames = FALSE,
options = list(dom = "t")) %>%
DT::formatPercentage(c("Andel", "Andel."), digits = 1)
} else{
df() %>% datatable(selection = "none",
container = cont, rownames = FALSE,
options = list(dom = "t")) %>%
DT::formatPercentage("Andel.", digits = 1)
}
)
})
output$lastNed <- downloadHandler(
filename = function() {
paste0(input$varSel, Sys.time(), ".csv")
},
content = function(file) {
tab <- df()
write.csv2(tab, file, row.names = F)
}
)
output$lastNedBilde <- downloadHandler(
filename = function(){
paste0(input$varSel, Sys.time(), '.', input$outfil)
},
content = function(file){
hisreg::hisregFigAndeler(RegData = RegData, valgtVar = input$varSel,
datoFra = min(input$dateRan),
datoTil = max(input$dateRan),
minald = input$aldSli[1],
maxald = input$aldSli[2],
reshID = resh(), enhetsUtvalg = input$enhSel,
forlop1 = input$typInt,
outfile = file,
erMann = as.numeric(input$kjoSle))
}
)
#logging
shiny::observe({
if (rapbase::isRapContext()) {
if (input$tabs == "fig") {
mld <- paste(
"Hisreg: figur - fordeling. variabel -",
input$varSel
)
} else if (input$tabs == "tab") {
mld <- paste(
"Hisreg: tabell - fordeling. variabel -",
input$varSel
)
}
rapbase::repLogger(
session = ss,
msg = mld
)
mldNLF <- paste(
"Hisreg: nedlasting figur - fordeling. variabel",
input$varSel
)
mldNLT <- paste(
"Hisreg: nedlasting tabell - fordeling. variabel",
input$varSel
)
shinyjs::onclick(
"lastNedBilde",
rapbase::repLogger(
session = ss,
msg = mldNLF
)
)
shinyjs::onclick(
"lastNed",
rapbase::repLogger(
ss,
msg = mldNLT
)
)
}
})
}
|
4fd8b240fd4dd0f125607744e55d5450a73dbb19 | c90e263ccf61d5d6c0bc33c715c85beec9cf6ef1 | /man/cenreg.Rd | 0fb00ec6dee22bdadc3dcd9ce6e6afdc501b3fa2 | [] | no_license | cran/NADA | d68537895922ff0d882fbd1e4db0ea5bc8b83bb1 | b3584c690049bd498b427db75ebee4fadcb24bb7 | refs/heads/master | 2020-12-24T13:18:24.287687 | 2020-03-22T08:46:09 | 2020-03-22T08:46:09 | 17,681,060 | 2 | 4 | null | null | null | null | UTF-8 | R | false | false | 4,729 | rd | cenreg.Rd | \name{cenreg}
\alias{cenreg}
\title{
Compute regression equations and likelihood correlation coefficient
for censored data.
}
\description{
Computes regression equations for singly censored data using maximum
likelihood estimation. Estimates of slopes and intercept, tests
for significance of parameters,and predicted quantiles (Median =
points on the line) with confidence intervals can be computed.
}
\usage{
cenreg(obs, censored, groups, ...)
}
\arguments{
\item{obs}{
Either a numeric vector of observations or a formula.
See examples below.
}
\item{censored}{
If a formula is not specified, this should be
a logical vector indicating TRUE where an observation in obs is
censored (a less-than value) and FALSE otherwise.
}
\item{groups}{
If a formula is not specified, this should be a numeric
or factor vector that represents the explanatory variable.
}
\item{...}{
Additional items that are common to this function and the \code{survreg}
function from the `survival' package. The most important of which
is `dist' and `conf.int'. See Details below.
}
} %END arguments
\details{
This routine is a front end to the \code{survreg} routine in the
\code{survival} package.
There are many additional options that are supported and documented
in \code{survfit}. Only a few have relevance to the evironmental
sciences.
A very important option is `dist' which specifies the distributional
model to use in the regression. The default is `lognormal'.
Another important option is `conf.int'. This is NOT an option to
\code{survreg} but is an added feature (due to some arcane details of
R it can't be documented above). The `conf.int' option specifies
the level for a two-sided confidence interval on the regression.
The default is 0.95. This interval will be used in when the output
object is passed to other generic functions such as \code{mean}
and \code{quantile}. See Examples below.
Also supported is a `gaussian' or a normal distribution. The use of
a gaussian distribution requires an interval censoring context for
left-censored data. Luckily, this routine automatically does this
for you -- simply specify `gaussian' and the correct manipulations
are done.
If any other distribution is specified besides lognormal or gaussian,
the return object is a raw survreg object -- it is up to the user to
`do the right thing' with the output (and input for that matter).
If you are using the formula interface: The \code{censored} and
\code{groups} parameters are not specified -- all information is
provided via a formula as the \code{obs} parameter. The formula
must have a \code{Cen} object as the response on the left of the
\code{~} operator and, if desired, terms separated by + operators
on the right. See examples below.
The reported likelihood r correlation coefficient measures the linear
association between y (groups) and x (obs), based on the difference
in log likelihoods between the fitted model and the null model.
Slopes and intercepts are fit by maximum likelihood. A lognormal
distribution is fit by default, with a normal distribution being an
option. Estimates of predicted values on the line can be obtained
by specifying the values for all x variables at which y is to be
predicted. Requesting the median (p=0.5) will provide estimates on
the line for a lognormal distribution. Estimates of the mean are
also possible, as are estimates of other percentiles. Equations for
confidence intervals follow those of Meeker and Escobar (1098).
}
\value{
Returns a \code{\link{summary.cenreg}} object.
}
\references{
Helsel, Dennis R. (2005). Nondectects and Data Analysis; Statistics for
censored environmental data. John Wiley and Sons, USA, NJ.
Meeker, W.Q. and L. A. Escobar (1998). Statistical Methods for
Reliability Data. John Wiley and Sons, USA, NJ.
}
\author{
R. Lopaka Lee <rclee@usgs.gov>
Dennis Helsel <dhelsel@practicalstats.com>
}
\seealso{
\code{\link{Cen}},
\code{\link{cenmle}},
\code{\link{predict-methods}}
}
\examples{
% with(TCEReg, cenreg(log(TCEConc), TCECen, PopDensity))
# (examples in Chap 12 of the NADA book)
data(TCEReg)
# Using the formula interface
with(TCEReg, cenreg(Cen(TCEConc, TCECen)~PopDensity))
# Two or more explanatory variables requires the formula interface
tcemle2 = with(TCEReg, cenreg(Cen(TCEConc, TCECen)~PopDensity+Depth))
# Prediction of quantiles at PopDensity=5 and Depth=110
predict(tcemle2, c(5, 110))
}
\keyword{survival}
|
8750daf8416361c94af717e0ae279be42cfe9cf1 | 4aebc58b3b753e9d99f44a90ef4f83aae474f917 | /server.R | 614265ebe52b8f1c12bde640e2e05108bb7a73e3 | [] | no_license | civilstat/MixedPanel | 55413b13e26549b65c50f088fc1e791f241d2d6c | fc8b8d081b6035252fce6c640b3bdebffe209cd9 | refs/heads/master | 2021-01-01T18:23:09.604697 | 2015-10-05T22:36:30 | 2015-10-05T22:36:30 | 33,269,799 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,154 | r | server.R | library(shiny)
# Define server logic to plot a Hypergeometric distribution with explanatory text
shinyServer(function(input, output) {
# Slider input for number of women
output$Nwomen <- renderUI({
sliderInput("Nwomen",
paste("Number of", input$subgroup, "candidates:"),
min = 0,
max = input$Ntotal,
value = min(8, input$Ntotal),
step = 1)
})
# Slider input for panel size
output$Npanel <- renderUI({
sliderInput("Npanel",
"Size of panel:",
min = 1,
max = input$Ntotal,
value = min(5, input$Ntotal),
step = 1)
})
# Slider input for nr women whose prob to evaluate
output$Nchosen <- renderUI({
sliderInput("Nchosen",
paste("Number of", input$subgroup, "panelists whose probability we want to evaluate:"),
min = 0,
max = min(input$Npanel, input$Nwomen),
value = c(0,0),
step = 1)
})
# Specify the hypergeometric distribution from the inputs
thisDhyper <- function(x) {
dhyper(x, m = input$Nwomen,
n = (input$Ntotal - input$Nwomen),
k = input$Npanel)
}
# Function that generates a plot of the distribution. The function
# is wrapped in a call to reactivePlot to indicate that:
# 1) It is "reactive" and therefore should be automatically
# re-executed when inputs change
# 2) Its output type is a plot
output$distPlot <- reactivePlot(function() {
minMorK <- min(input$Nwomen, input$Npanel)
thisBorder <- rep("black", minMorK + 1)
thisBorder[(input$Nchosen[1]:input$Nchosen[2])+1] <- "red"
barplot(100*thisDhyper(0:minMorK), names = 0:minMorK,
xlab = paste("Number of", input$subgroup, "members on a panel of", input$Npanel),
ylab = "Probability (%)",
border = thisBorder)
})
output$probText <- renderUI({
strNchosen <- ifelse(input$Nchosen[1] == input$Nchosen[2],
input$Nchosen[1],
paste(input$Nchosen[1], "to", input$Nchosen[2]))
HTML(
paste0("Let's say you started with <b>",
input$Ntotal,
"</b> candidates, of whom <b>",
input$Nwomen,
"</b> were ",
input$subgroup,
", and you randomly chose a panel of <b>",
input$Npanel,
"</b> people from this candidate pool.<br/>The probability that your panel has <b>",
strNchosen, "</b> ",
input$subgroup,
" members would be about <b>",
round(sum(100*thisDhyper(input$Nchosen[1]:input$Nchosen[2])),
1),
"%</b>.")
)
})
output$footnoteText <- renderUI({
HTML("(Calculations are based on the hypergeometric distribution, as in <a href='http://stats.stackexchange.com/questions/11836/probability-of-panel-assignment'>this StackExchange example</a>.<br/>Code available <a href='https://github.com/civilstat/MixedPanel'>on Github</a>.)")
})
})
|
88e80b88e81101525d37d9f36dca365c7f988e3a | 688185e8e8df9b6e3c4a31fc2d43064f460665f1 | /man/rad.Rd | ea89dc00201c51f5ec03ef402a809cc6eda8d7e1 | [] | no_license | IPS-LMU/emuR | 4b084971c56e4fed9032e40999eeeacfeb4896e8 | eb703f23c8295c76952aa786d149c67a7b2df9b2 | refs/heads/master | 2023-06-09T03:51:37.328416 | 2023-05-26T11:17:13 | 2023-05-26T11:17:13 | 21,941,175 | 17 | 22 | null | 2023-05-29T12:35:55 | 2014-07-17T12:32:58 | R | UTF-8 | R | false | true | 786 | rd | rad.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/misc.R
\name{rad}
\alias{rad}
\title{Function to convert between Hertz and Radians}
\usage{
rad(vec, samfreq = 20000, hz = TRUE)
}
\arguments{
\item{vec}{A numerical vector of frequencies in Hz or radians}
\item{samfreq}{A single element numerical vector of the sampling frequency.
Defaults to 20000 Hz}
\item{hz}{Logical. If TRUE, convert from Hz to radians otherwise from radians
to hz}
}
\description{
convert between Hertz and Radians
}
\examples{
# 4000 Hz in radians at a sampling frequency of 8000 Hz
rad(4000, 8000)
# pi/2 and pi/4 radians in Hz at a sampling frequency of 10000 Hz
rad(c(pi/2, pi/4), 10000, FALSE)
}
\seealso{
\code{\link{help}}
}
\author{
Jonathan Harrington
}
\keyword{math}
|
2a400ff9074effff1bbda2e9817c751fcb393cd2 | db26d059b38d4c7e51aae2fb4b6a72efe8007c43 | /ceRNA_helper/get_correlation.R | f4ba27db08cd25b19d4f3483f9accf84d0b101e7 | [] | no_license | duc-do/DyCeR | 5f5f3f4fb099a3052770fedb36221bbb57638444 | 76a651e97a5bec8b7c748b7256c8342614e5b91d | refs/heads/master | 2021-08-30T16:39:36.233992 | 2017-12-18T17:51:25 | 2017-12-18T17:51:25 | 114,670,223 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,012 | r | get_correlation.R | ### ----- variables ------#####
cat(paste(" compute correlation and pvalue","\n"))
#mRNA.expression = lasso.result$residual.expression
mRNA.expression = regression.data$mRNA
cor.info = WGCNA::corAndPvalue(t(mRNA.expression),use = "pairwise.complete.obs")
pair.dt = pair.dt
### ----- analysis ------#####
cluster = parallel::makeCluster(parallel::detectCores()-1)
parallel::clusterExport(cl = cluster,varlist = c("pair.dt", "cor.info"),envir = environment())
cat(paste(" assign correlation","\n"))
pair.dt$cor = pbapply::pbsapply(cl = cluster, X = 1:nrow(pair.dt), FUN = function(index){
RNAi = pair.dt$RNAi[index]; RNAj = pair.dt$RNAj[index]
return(cor.info$cor[RNAi,RNAj])
})
cat(paste(" assign correlation pvalues","\n"))
pair.dt$cor.pvalue = pbapply::pbsapply(cl = cluster, X = 1:nrow(pair.dt), FUN = function(index){
RNAi = pair.dt$RNAi[index]; RNAj = pair.dt$RNAj[index]
return(cor.info$p[RNAi,RNAj])
})
stopCluster(cluster)
### ----- clean-up ------#####
rm(mRNA.expression, cor.info) |
ba58df4fcc37033a676765f80f9f7ee1bdf2bd54 | 2fb67b51e9b082a41cf31e748b6745b5cb7c743d | /modules/scfmLandcoverInit/scfmLandcoverInit.R | 732553418b237d9592147ee076bb29159e48dc80 | [] | no_license | tati-micheletti/SpaDESinAction | 4724314e1e28bba8f837f7a1a99fefedd52d2041 | 6a39918a0f1b18ccbb752276f007302a64cef508 | refs/heads/master | 2020-07-06T08:40:08.520939 | 2019-10-10T16:14:26 | 2019-10-10T16:14:26 | 202,958,164 | 0 | 4 | null | 2019-10-10T15:44:01 | 2019-08-18T04:06:27 | HTML | UTF-8 | R | false | false | 13,286 | r | scfmLandcoverInit.R | stopifnot(packageVersion("SpaDES") >= "0.99.0")
defineModule(sim,list(
name = "scfmLandcoverInit",
description = "Takes the LCC05 classification of 39 land cover classes, and reclassifies it to flammable and inflammable [1,0]",
keywords = c("fire", "LCC05", "land cover classification 2005", "BEACONs"),
childModules = character(),
authors = c(
person(c("Eliot", "J", "B"),"McIntire", email = "Eliot.McIntire@canada.ca", role = c("aut", "cre")),
person("Steve", "Cumming", email = "stevec@sbf.ulaval.ca", role = c("aut")),
person("Ian", "Eddy", email = "ian.eddy@canada.ca", role = c("aut"))),
version = numeric_version("0.1.0"),
spatialExtent = raster::extent(rep(NA_real_, 4)),
timeframe = as.POSIXlt(c("2005-01-01", NA)),
documentation = list("README.txt", "scfmLandcoverInit.Rmd"),
timeunit = "year",
citation = list(),
reqdPkgs = list("fasterize", "purrr", "raster", "sf", 'rgeos',
"PredictiveEcology/LandR@development",
"PredictiveEcology/reproducible@development"),
parameters = rbind(
defineParameter(".plotInitialTime", "numeric", start(sim), NA, NA, desc = "Initial time for plotting"),
defineParameter(".plotInterval", "numeric", NA_real_, NA, NA, desc = "Interval between plotting"),
defineParameter(".saveInitialTime", "numeric", NA_real_, NA, NA, desc = "Initial time for saving"),
defineParameter(".saveInterval", "numeric", NA_real_, NA, NA, desc = "Interval between save events"),
defineParameter("useCache", "logical", TRUE, NA, NA, desc = "Use cache"),
defineParameter("neighbours", "numeric", 8, NA, NA, desc = "Number of immediate cell neighbours"),
defineParameter("sliverThreshold", "numeric", 10000, NA, NA,
desc = "fire regime polygons with area less than this number will be merged")
),
inputObjects = bind_rows(
expectsInput(objectName = "ecodistrict", objectClass = "numeric", desc = "Ecodistrict number to use as studyArea"), # added
expectsInput(objectName="nNbrs", objectClass = "numeric", desc = "raster cell neighborhood defaults to 8"), # added
expectsInput(objectName = "studyArea", objectClass = "SpatialPolygonsDataFrame", desc = "",
sourceURL = "http://sis.agr.gc.ca/cansis/nsdb/ecostrat/district/ecodistrict_shp.zip"),
expectsInput(objectName = "vegMap", objectClass = "RasterLayer", desc = "Landcover to build flammability map",
sourceURL = "ftp://ftp.ccrs.nrcan.gc.ca/ad/NLCCLandCover/LandcoverCanada2005_250m/LandCoverOfCanada2005_V1_4.zip"),
expectsInput(objectName = "rasterToMatch", objectClass = "RasterLayer",
desc = "template raster for raster GIS operations.",
sourceURL = "https://drive.google.com/open?id=1WkTFn6RssKHgUBKSfAhDYcKS5Kb1Wsef"),
expectsInput(objectName = "fireRegimePolys", objectClass = "SpatialPolygonsDataFrame",
desc = "Areas to calibrate individual fire regime parameters. Defaults to ecoregions",
sourceURL = "http://sis.agr.gc.ca/cansis/nsdb/ecostrat/region/ecoregion_shp.zip"),
expectsInput(objectName = "ageMap", objectClass = "RasterLayer",
desc = "Default map to define flammable map",
sourceURL = NA),
expectsInput(objectName = "flammableMap", objectClass = "RasterLayer", desc = "map of landscape flammability")
),
outputObjects = bind_rows(
createsOutput(objectName = "cellsByZone", objectClass = "data.frame",
desc = "explains which raster cells are in which polygon"),
createsOutput(objectName = "flammableMap", objectClass = "RasterLayer", desc = "map of landscape flammability"),
createsOutput(objectName = "landscapeAttr", objectClass = "list", desc = "list of polygon attributes inc. area"),
createsOutput(objectName = "landscapeAttrHARVEST", objectClass = "list", desc = "list of polygon attributes inc. area especifically for harvest"),
createsOutput(objectName = "fireRegimeRas", objectClass = "RasterLayer",
desc = "Rasterized version of fireRegimePolys with values representing polygon ID")
)
)
)
doEvent.scfmLandcoverInit = function(sim, eventTime, eventType, debug = FALSE) {
switch(eventType,
init = {
sim <- Init(sim)
sim <- scheduleEvent(sim, P(sim)$.plotInitialTime, "scfmLandcoverInit", "plot")
},
plot = {
Plot(sim$vegMap, new = TRUE, title = "Vegetation Classes")
# schedule future event(s)
sim <- scheduleEvent(sim, time(sim) + P(sim)$.plotInterval, "scfmLandcoverInit", "plot")
},
warning(paste("Undefined event type: '", events(sim)[1, "eventType", with = FALSE],
"' in module '", events(sim)[1, "moduleName", with = FALSE], "'", sep = "")))
return(invisible(sim))
}
Init <- function(sim) {
message("checking sim$fireRegimePolys for sliver polygons...")
if (sf::st_is_longlat(sim$fireRegimePolys) & is.na(P(sim)$sliverThreshold)) {
sim$fireRegimePolys <- projectInputs(x = sim$fireRegimePolys, targetCRS = crs(sim$vegMap))
stop("You must supply P(sim)$sliverThreshold for fireRegimePolys with an unprojected CRS")
}
sim$fireRegimePolys$trueArea <- gArea(sim$fireRegimePolys, byid = TRUE)
if (is.na(P(sim)$sliverThreshold)) {
sim@params[[currentModule(sim)]]$sliverThreshold <- 1e4
}
if (any(sim$fireRegimePolys$trueArea < P(sim)$sliverThreshold)) {
message("sliver polygon(s) detected. Merging to their nearest valid neighbour")
sim$fireRegimePolys <- Cache(deSliver, sim$fireRegimePolys, threshold = P(sim)$sliverThreshold,
userTags = c("deSliver", currentModule(sim)))
}
if (is.null(sim$fireRegimePolys$PolyID)) {
if (is.null(sim$fireRegimePolys$REGION_)) {
sim$fireRegimePolys$PolyID <- row.names(sim$fireRegimePolys)
} else {
sim$fireRegimePolys$PolyID <- as.numeric(sim$fireRegimePolys$REGION_)
}
}
temp <- sf::st_as_sf(sim$fireRegimePolys)
temp$PolyID <- as.numeric(temp$PolyID) #fasterize needs numeric; row names must stay char
sim$fireRegimeRas <- fasterize::fasterize(sf = temp, raster = sim$vegMap, field = "PolyID")
sim$flammableMap <- LandR::defineFlammable(sim$vegMap, filename2 = NULL)
sim$flammableMap <- setValues(raster(sim$flammableMap), sim$flammableMap[])
sim$flammableMap <- mask(sim$flammableMap, mask = sim$fireRegimeRas)
# This makes sim$landscapeAttr & sim$cellsByZone
outs <- Cache(genFireMapAttr,
sim$flammableMap,
sim$fireRegimePolys,
P(sim)$neighbours)
sim$landscapeAttr <- outs$landscapeAttr
sim$landscapeAttrHARVEST <- genFireMapAttrHARVEST(flammableMap = sim$flammableMap,
nNbrs = sim$nNbrs) # TO COMPARE WITH THE OTHER OUT
sim$cellsByZone <- outs$cellsByZone
return(invisible(sim))
}
genFireMapAttr <- function(flammableMap, fireRegimePolys, neighbours) {
#calculate the cell size, total area, and number of flammable cells, etc.
#All areas in ha
cellSize <- prod(res(flammableMap)) / 1e4 # in ha
if (neighbours == 8)
w <- matrix(c(1, 1, 1, 1, 0, 1, 1, 1, 1), nrow = 3, ncol = 3)
else if (neighbours == 4)
w <- matrix(c(0, 1, 0, 1, 0, 1, 0, 1, 0), nrow = 3, ncol = 3)
else
stop("illegal neighbours specification")
makeLandscapeAttr <- function(flammableMap, weight, fireRegimePolys) {
neighMap <- focal(x = flammableMap, w = w, na.rm = TRUE) #default function is sum(...,na.rm)
# extract table for each polygon
valsByPoly <- raster::extract(neighMap, fireRegimePolys, cellnumbers = TRUE)
valsByPoly <- lapply(valsByPoly, na.omit)
names(valsByPoly) <- fireRegimePolys$PolyID
uniqueZoneNames <- fireRegimePolys$PolyID #get unique zones.
valsByZone <- lapply(uniqueZoneNames, function(ecoName) {
aa <- valsByPoly[names(valsByPoly) == ecoName]
if (is.list(aa))
aa <- do.call(rbind, aa)
return(aa)
})
names(valsByZone) <- uniqueZoneNames
# Derive frequency tables of number of flammable cells, per polygon type, currently ECOREGION
nNbrs <- lapply(valsByZone, function(x) {
nNbrs <- tabulate(x[, 2] + 1, 9)#depends on sfcmLandCoverInit
names(nNbrs) <- 0:8
return(nNbrs)
})
nFlammable <- lapply(valsByZone, function(x) {
sum(getValues(flammableMap)[x[, 1]], na.rm = TRUE) #sums flammable pixels in FRI polygons
})
landscapeAttr <- purrr::transpose(list(cellSize = rep(list(cellSize), length(nFlammable)),
nFlammable = nFlammable,
nNbrs = nNbrs,
cellsByZone = lapply(valsByZone, function(x) x[, 1])))
landscapeAttr <- lapply(landscapeAttr, function(x) {
append(x, list(burnyArea = x$cellSize * x$nFlammable))
})
names(landscapeAttr) <- names(valsByZone)
return(landscapeAttr)
}
landscapeAttr <- makeLandscapeAttr(flammableMap, w, fireRegimePolys)
cellsByZoneFn <- function(flammableMap, landscapeAttr) {
cellsByZone <- data.frame(cell = 1:ncell(flammableMap), zone = NA_character_, stringsAsFactors = FALSE)
for (x in names(landscapeAttr)) {
cellsByZone[landscapeAttr[[x]]$cellsByZone, "zone"] <- x
}
return(cellsByZone)
}
cellsByZone <- cellsByZoneFn(flammableMap, landscapeAttr)
return(invisible(list(landscapeAttr = landscapeAttr, cellsByZone = cellsByZone)))
}
### template initilization
.inputObjects <- function(sim) {
invisible(tryCatch(dev.off(), error = function(e){}))
dev.new()
dev.new()
quickPlot::clearPlot()
dPath <- dataPath(sim) #where files will be downloaded
cacheTags = c(currentModule(sim), "function:.inputObjects")
if (!suppliedElsewhere("ecodistrict", sim)) {
sim$ecodistrict <- 644
}
if (!suppliedElsewhere("studyArea", sim)) {
message("study area not supplied. Using Ecodistrict 644")
sim$studyArea <- Cache(prepInputs, url = extractURL(objectName = "studyArea", sim = sim),
destinationPath = getPaths()$inputPath, targetCRS = "+proj=aea +lat_1=20 +lat_2=60 +lat_0=40 +lon_0=-96 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs +ellps=WGS84", filename2 = "studyArea", overwrite = TRUE) %>%
.[.[["ECODISTRIC"]] %in% sim$ecodistrict, ]
}
if (!suppliedElsewhere("rasterToMatch", sim)) {
message("rasterToMatch not supplied. generating from Canada Forest's Age Map using cropped to studyArea")
sim$rasterToMatch <- Cache(prepInputs, url = extractURL(objectName = "rasterToMatch", sim = sim),
targetFile = "can_age04_1km.tif",
studyArea = sim$studyArea,
destinationPath = getPaths()$inputPath, userTags = "objectName:RTM",
filename2 = NULL)
}
if (!suppliedElsewhere("vegMap", sim)) {
message("vegMap not supplied. Using default LandCover of Canada 2005 V1_4a")
sim$vegMap <- LandR::prepInputsLCC(year = 2005,
destinationPath = dPath,
studyArea = sim$studyArea,
rasterToMatch = sim$rasterToMatch,
filename2 = TRUE,
overwrite = TRUE,
userTags = c("cacheTags", "vegMap"))
}
if (!suppliedElsewhere("fireRegimePolys", sim)) {
message("fireRegimePolys not supplied. Using default ecoregions of Canada cropped to study Area")
sim$fireRegimePolys <- Cache(prepInputs, url = extractURL("fireRegimePolys", sim),
destinationPath = dPath,
studyArea = sim$studyArea,
rasterToMatch = sim$rasterToMatch,
filename2 = TRUE,
overwrite = TRUE,
userTags = c("cacheTags", "fireRegimePolys"))
}
if (!suppliedElsewhere("ageMap",sim)){
message(crayon::yellow("Age map was not provided. Using Canada's age map from 2004 1km resolution"))
sim$ageMap <- Cache(prepInputs, url = "https://drive.google.com/open?id=1WkTFn6RssKHgUBKSfAhDYcKS5Kb1Wsef",
targetFile = "can_age04_1km.tif",
studyArea = sim$studyArea,
destinationPath = dataPath(sim), userTags = "objectName:ageMap",
filename2 = NULL)
}
if (!suppliedElsewhere("flammableMap", sim)){
sim$flammableMap <- sim$ageMap
names(sim$flammableMap) <- "flammableMap"
}
if (!suppliedElsewhere("sliverThreshold", sim)){
sim$sliverThreshold <- 10000
}
if (!suppliedElsewhere("numTypes", sim)){
sim$numTypes <- 8
}
if (!suppliedElsewhere("landscapeAttr", sim)){
sim$landscapeAttr = list(cellSize = 6.25)
}
if (!suppliedElsewhere("nNbrs", sim)){
sim$nNbrs <- 8
}
if (!suppliedElsewhere("areaInHa", sim)){
sim$areaInHa = pi
}
return(invisible(sim))
}
|
ada7091e334737d6e2f51b0b65c1941ace30eeb7 | e04fdb3830feea19fc2d7f0dc937b21039af996b | /Software/RBrownie/man/addHS.Rd | 4513f79fe2eb899d501c5afad0bdd0fbb1c44c90 | [] | no_license | bomeara/omearatenure | c53b2bb41f313e84cd551c59317918657a3fbef1 | dd0fac37c26712e71bfdf0ad39e36b9b29c8f616 | refs/heads/master | 2020-04-08T08:33:03.815826 | 2014-11-03T07:26:40 | 2014-11-03T07:26:40 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,176 | rd | addHS.Rd | \name{addHS}
\alias{addHS}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Add Heuristic search command to brownie object
}
\description{
This function adds a character string to the the \code{commands} slot of a brownie object (or list of brownie objects) which instructs the brownie program to run a heuristic search.
}
\usage{
addHS(obj,
nreps = NULL,
rearrlimit = NULL,
maxnumspp = NULL,
minnumspp = NULL,
minsamp = NULL,
structwt = NULL,
pthreshold = NULL,
subsample = NULL,
movefreq = NULL,
file = NULL,
showtries = NULL,
coal = NULL,
ms = NULL,
aic.mode = NULL,
gridwidth = NULL,
gridsize = NULL,
maxrecursions = NULL,
branch.export = NULL,
usetempfile = FALSE)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{obj}{
A brownie object (or list of brownie objects)
}
\item{nreps}{
Number of random starting species trees to use
}
\item{rearrlimit}{
Limit search to X rearrangements for each nrep
}
\item{maxnumspp}{
The maximum number of species to split the samples into.
}
\item{minnumspp}{
The minimum number of samples per species (NOT the number of species).
}
\item{minsamp}{
The minimum number of samples per species (NOT the number of species).
}
\item{structwt}{
StructWt
}
\item{pthreshold}{
Run search until a certain p-value threshold is reach.
}
\item{subsample}{
%% ~~Describe \code{subsample} here~~
}
\item{movefreq}{
Sets the relative proportion of times to try: 1.) Species tree branch swaps, 2.) Moving samples from one species to another, 3.) Increasing the number of species, 4.) Decreasing the number of species, 5.) Attempt to reroot the species tree. If these don't sum to one, the program will automatically scale them up or down.
}
\item{file}{
The brownie core write the output of the analysis to this file (optional)
}
\item{showtries}{
%% ~~Describe \code{showtries} here~~
}
\item{coal}{
Not currently used
}
\item{ms}{
Not currently used
}
\item{aic.mode}{
Not currently used
}
\item{gridwidth}{
affects grid search
}
\item{gridsize}{
affects grid search
}
\item{maxrecursions}{
affects grid search
}
\item{branch.export}{
If set to 0, returns a single estimate of the best branch lengths on the species tree. If set to 1, returns a table of equally-good branch lengths. If set to 2, returns a table of the best and neighboring branch lengths, suitable for doing a contour plot of score versus branch lengths
}
\item{usetempfile}{
Should a tempfile be written to (instead of file). Not currently used.
}
}
\details{
Instructs Brownie to perform a heuristic search on \code{obj} when \code{obj} is run.
%% From brownie command-line:
%% \begin{tabular}{l | c | r}
%% Keyword & Option type & Current setting \\
%% \hline
%% NReps & <integer-value> & 5 \\
%% RearrLimit & <integer-value>|None & None \\
%% MaxNumSpp & <integer-value> & 100 \\
%% MinNumSpp & <integer-value> & 1 \\
%% MinSamp & <integer-value> & 3 \\
%% StructWt & <double> & 0.5 \\
%% PThreshold & <double> & 1.0 \\
%% Subsample & <double> & 2.0 \\
%% MoveFreq & (number number number number number [number]) & (0.8 0.01 0.01 0.01 0.17 0.0 ) \\
%% File & <file-name> & besttrees.tre \\
%% ShowTries & No|Yes & No \\
%% AIC_mode & 0|1|2|3|4 & 4 \\
%% GridWidth & <double> & 10.0 \\
%% GridSize & <integer-value> & 15.0 \\
%% MaxRecursions & <integer-value> & 20 \\
%% Branch_export & 0|1|2 & 2 \\
%% \hline
%% \end{tabular}
}
\value{
Returns the original object (\code{obj}) with 'hs' command added.
}
\references{
%% ~put references to the literature/web site here ~
}
\author{
J. Conrad Stack
}
\note{
%% ~~further notes~~
}
%% ~Make other sections like Warning with \section{Warning }{....} ~
\seealso{
%% ~~objects to See Also as \code{\link{help}}, ~~~
}
\examples{
data(parrot)
ptmp = clearCommands(parrot)
ptmp = addHS(ptmp,file="besttrees.tre2")
commands(ptmp)
}
% Add one or more standard keywords, see file 'KEYWORDS' in the
% R documentation directory.
\keyword{ methods }
|
5b4a0c0e9b5498b16d27259a8eda75dfe43afed4 | 9327560c9c09fbf7670e6723d7a821352906d875 | /Matrices/NamingMatrix.R | 32f108baf3b2b988d733f7a3ef2232cb5cdfc988 | [
"Apache-2.0"
] | permissive | Siddharth1698/R-programming---A-TO-Z---For-Data-Science | 5037f90824ebdcc4be7facc5401915d7dd4de659 | 63b127ecaeae86d6d7cb8497e9aca698fe94b8ec | refs/heads/master | 2020-05-26T09:30:00.537644 | 2019-06-02T20:12:32 | 2019-06-02T20:12:32 | 188,186,917 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 198 | r | NamingMatrix.R | tempRec <- rep(c("a","B","zZ"),each = 3)
tempRec
Bravo <- matrix(tempRec,3,3)
Bravo
rownames(Bravo) <- c("HOW","ARE","YOU")
Bravo
colnames(Bravo) <- c("x","Y","Z")
Bravo
Bravo["HOW","Y"] <- 0
Bravo
|
07c994ddff9121a85e03dcdc9043ba277b027126 | 2448d4800d4336b53489bcce3c17a32e442a7716 | /man/install_version.Rd | 97dcc424d532dfdc930e4597dd3c25def712e33f | [] | no_license | vsbuffalo/devtools | 17d17fd1d2fb620fef8d9883dffed389f80e39fb | 782e6b071d058eea53aae596a3c120d61df2f0b4 | refs/heads/master | 2020-12-24T10:41:24.637105 | 2016-02-18T14:03:05 | 2016-02-18T14:03:05 | 52,121,375 | 2 | 0 | null | 2016-02-19T22:42:43 | 2016-02-19T22:42:43 | null | UTF-8 | R | false | true | 1,748 | rd | install_version.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/install-version.r
\name{install_version}
\alias{install_version}
\title{Install specified version of a CRAN package.}
\usage{
install_version(package, version = NULL, repos = getOption("repos"),
type = getOption("pkgType"), ...)
}
\arguments{
\item{package}{package name}
\item{version}{If the specified version is NULL or the same as the most
recent version of the package, this function simply calls
\code{\link{install}}. Otherwise, it looks at the list of
archived source tarballs and tries to install an older version instead.}
\item{repos}{
character vector, the base URL(s) of the repositories
to use, e.g., the URL of a CRAN mirror such as
\code{"http://cran.us.r-project.org"}. For more details on
supported URL schemes see \code{\link{url}}.
Can be \code{NULL} to install from local files, directories or URLs:
this will be inferred by extension from \code{pkgs} if of length one.
}
\item{type}{character, indicating the type of package to download and
install. Will be \code{"source"} except on Windows and some OS X
builds: see the section on \sQuote{Binary packages} for those.
}
\item{...}{Other arguments passed on to \code{\link{install}}.}
}
\description{
If you are installing an package that contains compiled code, you will
need to have an R development environment installed. You can check
if you do by running \code{\link{has_devel}}.
}
\author{
Jeremy Stephens
}
\seealso{
Other package installation: \code{\link{install_bitbucket}},
\code{\link{install_github}}, \code{\link{install_git}},
\code{\link{install_svn}}, \code{\link{install_url}},
\code{\link{install}}, \code{\link{uninstall}}
}
|
1c668fbb38be00e0ceab5fb3991bef60760fe99b | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/futile.any/examples/anylength.Rd.R | a121c380e8c7470fd355b57b59808ac455913758 | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 246 | r | anylength.Rd.R | library(futile.any)
### Name: anylength
### Title: Get the generic length of an object
### Aliases: anylength
### Keywords: attribute
### ** Examples
m <- matrix(c(1,2,3,4,5,6), ncol=2)
anylength(m)
v <- c(1,2,3,4,5)
anylength(v)
|
2c3457b170f1d2865d416929f6244bd55056d2c9 | dd8132404e8c7b028cb13cba904c50aace01c6a7 | /swt/src/spc/se.u/se_mod3.r | 5128171d39110feb4a6643f4db1d39792021dc0e | [] | no_license | arnoldrobbins/gt-swt | d0784d058fab9b8b587f850aeccede0305d5b2f8 | 2922b9d14b396ccd8947d0a9a535a368bec1d6ae | refs/heads/master | 2020-07-29T09:41:19.362530 | 2019-10-04T11:36:01 | 2019-10-04T11:36:01 | 209,741,739 | 15 | 0 | null | null | null | null | UTF-8 | R | false | false | 796 | r | se_mod3.r | include "source/copy.r"
include "source/cprow.r"
include "source/dohelp.r"
include "source/display_message.r"
include "source/domark.r"
include "source/doopt.r"
include "source/dosopt.r"
include "source/dotlit.r"
include "source/getrange.r"
include "source/xindex.r"
include "source/makset.r"
include "source/doundo.r"
include "source/draw_box.r"
include "source/getstr.r"
include "source/getwrd.r"
include "source/join.r"
include "source/litmesg.r"
include "source/move.r"
include "source/settab.r"
include "source/uniquely_name.r"
include "source/getkn.r"
include "source/pvem.r"
include "source/saynum.r"
include "source/knscan.r"
include "source/doglob.r"
include "source/doshell.r"
include "source/whereami.r"
|
b9be64d14d9511e0ef20b9e82098507188f8f49e | 4a5266dbc98a6b96ccd2204b17ebe4994480904a | /tests/testthat/test_middle_date.R | 74ea047cae437a64e78d25757ced1d6b11827035 | [
"MIT"
] | permissive | piotr-ole/gantt | 3a454b89ddae8fa5a33a0336b0e41bb1e70697ba | e59e27b8abf7ba7b9c8da0a299862ce08caf7af1 | refs/heads/master | 2020-09-13T12:56:50.945369 | 2020-01-22T14:23:38 | 2020-01-22T14:23:38 | 222,786,863 | 7 | 0 | null | null | null | null | UTF-8 | R | false | false | 238 | r | test_middle_date.R | test_that("calculate middle date", {
expect_equal(middle_date(as.Date('2020-01-01'), as.Date('2020-01-03')), as.Date('2020-01-02'))
expect_equal(middle_date(as.Date('2020-01-01'), as.Date('2020-01-04')), as.Date('2020-01-03'))
})
|
4761560270bebcfccf9f985d0a53a4146ca763cc | 18492dec7cab25005f797a8e36f207c7d228a107 | /plot4.R | 2cf10506562d8e15245dce1a966f193c7f29723a | [] | no_license | rhosek/homework | 19ad971137b593ee9cf2a31106a70c7395f4a99a | 5927e48ed8d9c57076e5cf658c720af1343e118f | refs/heads/master | 2016-09-02T02:38:41.755958 | 2015-10-18T23:23:44 | 2015-10-18T23:23:44 | 42,406,600 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,924 | r | plot4.R | ## First, download the zipped data file to 'downloads' directory using the given link and then extract the file
## Change the R working directory to 'downloads/exdata-data-household_power_consumption' to read in the portion of the file of interest
setwd("C:/Users/Ronald/Downloads/exdata-data-household_power_consumption")
## Read the first 20 lines of file to see header and file organization
## The file is sequential in time with one entry per minute. In other words, each minute is one row in file.
## The Date and time sampling starts on 12/16/2006 at approximately 17:24
## We are only interested in values sampled on 2/1 and 2/2/2007
## At one sample/minute, there will be 24 x 60 = 1440 samples/day
## We would like to skip X lines in the file to get us near 2/1
## We then read at least 2 x 1440 lines to make sure we include all of the samples for 2/1 and 2/2
## There are approximately 46 days and 7 hours from the time the file starts to the beginning of 2/1
## This translates to 66,600 minutes. So we will skip 66,600 lines and then read the next 3000
## This should give us a dataframe with the records we need which can be sub-setted to get only 2/1 and 2/2.
df1 <- read.table("household_power_consumption.txt", header = TRUE, sep = ";", na.strings = "?", skip=66600, nrow = 3000)
## We also need to get the right variable (column) names.
df2 <- read.table("household_power_consumption.txt", header = TRUE, sep = ";", nrow = 3)
n <- names(df2)
names(df1) <- n
## Change the working directory back to homework
setwd("~/R/Homework")
## Get lubridate for handling date-time variables
library(dplyr)
library(lubridate)
## create df3 to keep df1 intact
df3 <- df1
df3$Date <- dmy(df3$Date)
## subset for the two days of interest
df3 <- subset(df3, df3$Date > dmy("31/1/2007") & df3$Date < dmy("3/2/2007"))
par(mfcol = c(2,2))
par(mar=c(3, 3, 2, 2), ps = 12, pty = "s")
plot(df3$Global_active_power, type="l", pch=0, ylab="Global Active Power", xaxt='n')
axis(1, c(1, 1440,2880), c("Thu","Fri", "Sat"))
par(mar=c(3, 3, 2, 2), ps = 12, pty = "s")
plot(df3$Sub_metering_1,type="l", pch=0, ylab="Energy Sub Metering", xaxt='n', col="black")
points(df3$Sub_metering_2,type="l", pch=0, col="red")
points(df3$Sub_metering_3,type="l", col="blue")
axis(1, c(1, 1440,2880), c("Thu","Fri", "Sat"))
legend("topright", inset=0, cex = .5, c("Sub_metering_1", "Sub_metering_2","Sub_metering_3"), lty=c(1,1,1), col=c("black","red","blue"), horiz=FALSE, bty="n")
par(mar=c(4, 3, 2, 2), ps = 12, pty = "s")
plot(df3$Voltage,type="l", pch=0, ylab="Voltage", xlab = "datetime", xaxt='n')
axis(1, c(1, 1440,2880), c("Thu","Fri", "Sat"))
par(mar=c(4, 3, 2, 2), ps = 12, pty = "s")
plot(df3$Global_reactive_power, type="l", pch=0, ylab = "Global_reactive_power", xlab = "datetime", xaxt='n')
axis(1, c(1, 1440,2880), c("Thu","Fri", "Sat"))
dev.print(png, "plot4.png", width = 480, height = 480, units = "px")
|
472b843d5cbef8efa90bd55614f044a7d9939e67 | c1a4c35179dd8862fbc40b09eea92e3f491a81e3 | /tests/testthat.R | 50c2d1b80ba42a871ae9914f708988c89eb7dc00 | [
"MIT"
] | permissive | sdhutchins/documenteR | 3a5f16dbcbdb2b0369572d459e40fa28abb5e74d | 461d25295ec2bb58a4e7ad95311d4b53a07c670d | refs/heads/master | 2020-03-27T17:11:12.483900 | 2019-05-07T19:02:09 | 2019-05-07T19:02:09 | 146,833,831 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 64 | r | testthat.R | library(testthat)
library(documenteR)
test_check("documenteR")
|
e529415651e823d04dba2b7961b9cf7e0f5351ba | 867acb8f398b7d8b04670c5be619aec972fb7415 | /week-3/cachematrix.R | 0680586b9f257ffffade8f13b7a82c526fda5915 | [] | no_license | rlambert2/rprogramming | aeb3a44f23b339d2d65f769dcfb6adbec132f1c8 | daf67dd033ef0518d009654ece3cd5198311c639 | refs/heads/master | 2021-05-03T04:52:57.274529 | 2016-09-30T01:00:06 | 2016-09-30T01:00:06 | 69,115,368 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,158 | r | cachematrix.R | ## These functions create a matrix object that caches its inverse,
## then computes the inverse of the matrix, and checks to see if that value
## is in the cache. If it is, it returns the cached value. If it's not
## it returns the newly calculated value.
## makeCacheMatrix creates a matrix object that caches its inverse, and
## makes a list of functions that will be used in the cacheSolve function.
makeCacheMatrix <- function(x = matrix()) {
inv = NULL
set = function(y) {
x <<- y
inv <<- NULL
}
get = function() x
setinv = function(inverse) inv <<- inverse
getinv = function() inv
list(set=set, get=get, setinv=setinv, getinv=getinv)
}
## cacheSolve checks to see if the inverse of this matrix
## has been cached. If it has, it returns the cached matrix.
## If it hasn't, it calcs and returns the inverse of the matrix.
cacheSolve <- function(x, ...) {
## Return a matrix that is the inverse of 'x'
inv = x$getinv()
if (!is.null(inv)){
message("getting cached data")
return(inv)
}
mat.data = x$get()
inv = solve(mat.data, ...)
x$setinv(inv)
inv
} |
bc9267d4244437bb2613687a403cdd9ea25a2f30 | 020dd885e28b843c8eccc7fbcf0ea74c0f64d2bc | /Analyze.R | d8e14e8248c3adb30fb020aa81d4695aa5d76b96 | [] | no_license | MUST-learning/R-studio | c28a3a22de66ba0cc6a8feb6632fb6a39141118c | 0b7b6d4b822c941783d550aa25d69aa0cd9ad8e4 | refs/heads/master | 2021-07-21T18:22:51.209471 | 2017-11-01T02:23:15 | 2017-11-01T02:23:15 | 105,896,570 | 0 | 0 | null | null | null | null | ISO-8859-3 | R | false | false | 4,032 | r | Analyze.R |
#read the data
gcds <- read.csv("crdds.csv", header=TRUE,
sep=",")
# get columns' name and descriptions
source("readcolnames.R") # load the definition of functions defined in a file
colinf <- readcolnames()
colnames(gcds) <- append(colinf[[2]], 'Class')
coldes <- append(colinf[[1]], 'Class')
# Describe data
#descriptive statistics£Ĵone by one
#This one is not recommended, interval is more suitable
table(data1$credit_usage)
barplot(table(data1$credit_usage))
library(ggplot2)
# suppressMessages(library(ggplot2))
# The good and bad customers
freq = tabulate(gcds$Class)
# mydata$v1 <- factor(mydata$v1,
# levels = c(1,2,3),
# labels = c("red", "blue", "green"))
pie <- ggplot(gcds, aes(x="", fill = factor(Class))) + geom_bar(width=1) + coord_polar(theta = "y") +
# theme_void() +
labs(x = "") +
scale_fill_discrete(name="Class", breaks=c("1", "2"), labels=c("good", "bad")) +
theme(axis.title.x=element_blank(), axis.ticks.x=element_blank()) +
theme(legend.position = c(0.9, 0.9), plot.title = element_text(hjust = 0.5)) +
ggtitle("Distribution of Class") +
annotate("text", x=1, y=700, label="bad=243") +
annotate("text", x=1, y=150, label="good=557")
print(pie)
# Sampling
# tranform character column to factor
coltypes <- sapply(gcds, mode)
for (i in 1:ncol(gcds))
{
if (coltypes[[i]] == "character")
gcds[[i]] <- factor(gcds[[i]])
}
# gcds$Class[gcds$Class == 1] <- 'Good'
# gcds$Class[gcds$Class == 2] <- 'Bad'
gcds$Class <- factor(gcds$Class, levels=c(1,2), labels=c("Good", "Bad"))
set.seed(2)
ids <- 1:nrow(gcds)
trainratio <- 0.8
trainids <- sample(ids, trunc(length(ids)*trainratio))
trainset <- gcds[trainids, ]
testset <- gcds[-trainids, ]
# head(trainset, 2)
# Feature Selection
library(caret)
# library(class)
library(LiblineaR)
# gcds2 <- gcds[complete.cases(gcds), ]
control <- trainControl(method="repeatedcv", number=5, repeats=4)
model <- train(Class~., data=trainset, method="regLogistic", trControl=control)
importance <- varImp(model, scale=FALSE)
# print(importance)
plot(importance)
# Modeling
# Select top 10 important features
fbyimp <- row.names(importance[[1]])
top15 <- fbyimp[1:15]
selfeas <- c(top15, "Class")
# decision tree model
library(tree)
treemodel <- tree(Class ~ ., data=trainset[,selfeas]) #train the model
plot(treemodel, main="classification tree")
text(treemodel, pretty=0)
cprob <-predict(treemodel, testset[, top15])
chat <- rep("Good", nrow(cprob) )
chat[cprob[,1]>=0.5] <- "Bad"
chat0 <- factor(chat,labels = c("Good","Bad"))
#ROCR 0.734
library(ROCR)
tab <- table(chat0, testset$Class)
print(tab)
pred <- prediction(as.numeric(chat0), as.numeric(testset$Class))
pref <- performance(pred, "tpr", "fpr")
auc <- performance(pred, "auc")
print(auc@y.values)
plot(pref)
# support vector machine 0.694
control <- trainControl(method="cv", savePredictions = T, classProbs = T)
svmmodel <- train(Class~., data=trainset[, selfeas], method="svmRadialSigma",
trControl=control)
chat <- predict(svmmodel, newdata = testset[, top15])
tab <- table(chat, testset$Class)
print(tab)
pred <- prediction(as.numeric(chat), as.numeric(testset$Class))
pref <- performance(pred, "tpr", "fpr")
auc <- performance(pred, "auc")
print(auc@y.values)
plot(pref)
# neural networks model 0.744
library(caret)
parameters<- expand.grid(.decay = c(0.5, 0.1), .size = c(5, 6, 7))
nnmodel <- train(trainset[, top15], trainset[,"Class"],
method="nnet", tuneLength = 10, trace = F, maxit = 1000,
tuneGrid= parameters)
chat <- predict(nnmodel, newdata = testset[, top15])
tab <- table(chat, testset$Class)
print(tab)
pred <- prediction(as.numeric(chat), as.numeric(testset$Class))
pref <- performance(pred, "tpr", "fpr")
auc <- performance(pred, "auc")
print(auc@y.values)
plot(pref)
|
a8576e0bdee8ee22e913adf0f7e82fb238f97de0 | e573dd102635ce15f9270cc4ab91a6a3d1878979 | /R/read-any-bitmap.R | e8f41fa2d19db6a9c98cb80e1da4e87faa2b2ad4 | [] | no_license | jefferis/readbitmap | c0dd88f813f44480217744b31f8531dd0b8d4c16 | 9e4080ef73f4d689c616fde7a5530996580aea16 | refs/heads/master | 2021-01-23T03:28:43.142674 | 2018-06-27T11:48:41 | 2018-06-27T11:48:41 | 2,964,648 | 2 | 3 | null | 2018-05-21T11:52:24 | 2011-12-12T14:27:24 | R | UTF-8 | R | false | false | 3,862 | r | read-any-bitmap.R | # Functions to identify and read a variety of bitmap image formats.
###############################################################################
#' Identify the type of an image using the magic value at the start of the file
#'
#' Currently works for png, jpeg, BMP, and tiff images. Will seek to start of
#' file if passed a connection. For details of magic values for files, see e.g.
#' \url{http://en.wikipedia.org/wiki/Magic_number_(programming)#Magic_numbers_in_files}
#' @param source Path to file or connection
#' @param Verbose Whether to write a message to console on failure (Default
#' \code{FALSE})
#' @return character value corresponding to standard file extension of image
#' format (i.e. jpg, png, bmp, tif) or \code{NA_character_} on failure.
#' @export
#' @examples
#' jpegfile=system.file("img", "Rlogo.jpg", package="jpeg")
#' image_type(jpegfile)
#' jpeg_pretending_to_be_png=tempfile(fileext = '.png')
#' file.copy(jpegfile, jpeg_pretending_to_be_png)
#' image_type(jpeg_pretending_to_be_png)
#' unlink(jpeg_pretending_to_be_png)
image_type<-function(source,Verbose=FALSE){
if (inherits(source, "connection"))
seek(source, 0)
magic = readBin(source, what = 0L, n = 8, size = 1L, signed = FALSE)
if(isTRUE(all.equal(magic[1:2], c(66, 77))))
return('bmp')
else if(isTRUE(all.equal(magic[1:8],
c(0x89,0x50,0x4E,0x47,0x0D, 0x0A, 0x1A, 0x0A))) )
return('png')
else if(isTRUE(all.equal(magic[1:2], c(0xFF, 0xD8))))
return('jpg')
else if(isTRUE(all.equal(magic[1:4], c(0x49, 0x49, 0x2A, 0x00))))
return('tif')
else if(isTRUE(all.equal(magic[1:4], c(0x4D, 0x4D, 0x00, 0x2A))))
return('tif') # otherwise we failed to identify the file
if(Verbose) warning("Failed to identify image type of: ",source,
' with magic: ',format.hexmode(as.raw(magic)))
return(NA_character_)
}
#' Read in a bitmap image in JPEG, PNG, BMP or TIFF format
#'
#' By default uses magic bytes at the start of the file to identify the image
#' type (rather than the file extension). Currently uses readers in bmp, jpeg,
#' png, and tiff packages.
#' @param f Path to image file
#' @param channel Integer identifying channel to return for an RGB image
#' @param IdentifyByExtension Identify by file extension only (Default FALSE)
#' @param ... Additional parameters passed to underlying image readers
#' @return Objects returned by \code{\link[jpeg]{readJPEG}},
#' \code{\link[png]{readPNG}}, \code{\link[bmp]{read.bmp}}, or
#' \code{\link[tiff]{readTIFF}}. See their documentation for details.
#' @importFrom png readPNG
#' @importFrom jpeg readJPEG
#' @importFrom bmp read.bmp
#' @importFrom tiff readTIFF
#' @export
#' @seealso \code{\link{image_type}}, \code{\link[jpeg]{readJPEG}},
#' \code{\link[png]{readPNG}}, \code{\link[bmp]{read.bmp}},
#' \code{\link[tiff]{readTIFF}}
#' @examples
#' img1=read.bitmap(system.file("img", "Rlogo.jpg", package="jpeg"))
#' str(img1)
#' img2 <- read.bitmap(system.file("img", "Rlogo.png", package="png"))
#' # nb the PNG image has an alpha channel
#' str(img2)
read.bitmap<-function(f,channel,IdentifyByExtension=FALSE,...){
if(!file.exists(f)) stop("File: ",f," does not exist.")
if(IdentifyByExtension)
ext=tolower(sub(".*\\.([^.]+)$","\\1",f))
else
ext=image_type(f)
readfun=switch(ext,png=readPNG,jpeg=readJPEG,jpg=readJPEG,bmp=read.bmp,
tif=readTIFF,tiff=readTIFF,
stop("File f: ",f," does not appear to be a PNG, BMP, JPEG, or TIFF"))
im=readfun(f,...)
if(!missing(channel) && length(dim(im))==3) im=im[,,channel]
#BMP stores the alpha channel first (ARGB order), convert to RGBA for consistency with PNG files
if (ext=="bmp" && (length(dim(im))==3) && dim(im)[3] == 4)
{
att <- attributes(im)
im <- array(c(im[,,2:4],im[,,1]),dim(im))
attributes(im) <- att
}
im
}
|
b75c90d7810e252b5062719b59c23e3c259f7bd5 | 502ab31e267cf5096b01ce1ccb97fd36ace11ee9 | /4. extract_CUSIP.R | ee19dcf89f343fec3fd14b2afd8a5a851f57f408 | [] | no_license | SpyridonGkikopoulos/Block_Codes | 3cf2fee5c638d8b293008cd44e528e925cbc37fc | e1e5142bd6647b4004c2c76c326032f16415e926 | refs/heads/master | 2023-06-11T21:51:44.523883 | 2021-07-06T04:46:40 | 2021-07-06T04:46:40 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,932 | r | 4. extract_CUSIP.R | start_QTR <- "2018Q4"
end_QTR <- "2018Q4"
### use this dir to download files
out_dir <- "/Users/evolkova/Documents/Blocks/"
### generate all dates
get_dates <- function(start_QTR, end_QTR) {
require(data.table)
require(zoo)
require(lubridate)
require(dplyr)
end_QTR <- end_QTR %>%
as.yearqtr("%YQ%q") %>%
as.Date()
all_dates <- start_QTR %>%
as.yearqtr("%YQ%q") %>%
as.Date()
while (all_dates[length(all_dates)] < ymd(end_QTR)) {
all_dates <- c(all_dates, all_dates[length(all_dates)] %m+% months(3))
}
return(all_dates)
}
### regex to extract CUSIP from filing
extract_CUSIP <- function(EFiling) {
require(stringr)
## get 6 lines before and 4 lines after line CUSIP
pat_1 <- "((\\n.*){6})CUSIP.*((\\n.*){4})"
get_block <- str_extract(EFiling, pat_1)
# set pattern to extract CUSIP
pat_2 <- "(?=\\d.*\\d)[a-zA-Z0-9]{9}|\\d\\w{6} \\w\\w \\w|\\d\\w{5} \\w\\w \\w|
[a-zA-Z0-9]{7}\\r|\\d\\w{5} \\w\\w\\w|(?=#.*\\d)[a-zA-Z0-9]{9}|(?=\\w\\d.*)[a-zA-Z0-9]{9}|
\\d\\w{5}-\\w\\w-\\w|\\d\\w{5}-\\w\\w\\w|\\d\\w{6}|\\d\\w{5}-\\w{2}-\\w|\\d\\w{5}\\n.*\\n.*|
\\d\\w{2} \\d\\w{2} \\d\\w{2}|\\d\\w{2} \\w{3} \\d{2} \\d|\\d{6} \\d{2} \\d|
\\d\\w{4} \\w{1} \\w{2} \\w|\\w{6} \\d{2} \\d{1}|\\d{3} \\d{3} \\d{3}|\\d{6} \\d{2} \\d{1}|
\\w{3} \\w{3} \\d{2} \\d{1}|\\w{5} \\w{1} \\d{2} \\d{1}|\\d{6} \\d{1} \\d{2}|
\\d{3} \\d{3} \\d{1} \\d{2}|\\d\\w{2}\\n.*\\d\\w{2}|\\d{6} \\d{2}\\n.*|\\d{5} \\d{2} \\d{1}|
\\d{5} \\w{1} \\w{2} \\w{1}|\\d\\w{5}|\\d\\w{2}-\\w{3}-\\w{3}"
# Extract CUSIP from within the blocks extracted
CUSIP <- str_extract(get_block, pat_2)
return(CUSIP)
}
### remove duplicates from CIK-CUSIP map
CUSIP_table <- function(CUSIP) {
require(data.table)
CUSIP_df <- data.table(CUSIP)
CUSIP_df[, quarter := paste0(year(dates[i]), quarter(dates[i]))]
CUSIP_df[, CUSIP := gsub("\\s", "", CUSIP)]
CUSIP_df[, CUSIP := gsub("-", "", CUSIP)]
CUSIP_df[, CUSIP := toupper(CUSIP)]
CUSIP_df[, CUSIP6 := substr(CUSIP, 1, 6)]
CUSIP_df[, CUSIP := substr(CUSIP, 1, 8)]
return(CUSIP_df)
}
CUSIP_all <- NULL
dates <- get_dates(start_QTR, end_QTR)
for (i in 1:length(dates)) {
print(Sys.time())
print(paste0("year: ", year(dates[i]), ", quarter: ", quarter(dates[i])))
dbname <- paste0(out_dir, "Clean Forms/sc13_", year(dates[i]), quarter(dates[i]), ".sqlite")
## connect to db
con <- dbConnect(drv = RSQLite::SQLite(), dbname = dbname)
## Fetch data into data frame
res <- dbSendQuery(con, "SELECT * FROM filings")
res1 <- dbFetch(res, n = -1)
sec_name <- paste0(out_dir, "Parsed Forms/", year(dates[i]), quarter(dates[i]), ".rds")
sec_header <- readRDS(sec_name)
match <- match(sec_header$FILENAME, res1$FILENAME)
CUSIP <- extract_CUSIP(res1$FILING)
CUSIP_df <- CUSIP_table(CUSIP)
sec_header$CUSIP <- CUSIP_df$CUSIP[match]
sec_header$CUSIP6 <- CUSIP_df$CUSIP6[match]
saveRDS(sec_header, sec_name)
dbDisconnect(con)
}
|
9597695dca4539f79193bdbe5aeb0d820776d69a | 42250f07c9cd59b962e23b5f94801a195c04f6cc | /tests/testthat/test_mlr_learners_surv_glmnet.R | 5374207fc3f65b4c32548ee99d1644c605c21d81 | [
"LGPL-3.0-only"
] | permissive | mlr-org/mlr3survival | fbb262e6a60f02cc775f1d4e06da6ee51c9a71f1 | a1cbd60a9ac9e8078daa837f6b99bf5ba2bbc45c | refs/heads/master | 2020-04-06T22:55:19.048008 | 2019-10-21T09:09:58 | 2019-10-21T09:09:58 | 157,852,127 | 8 | 0 | MIT | 2019-07-01T15:33:58 | 2018-11-16T10:31:22 | R | UTF-8 | R | false | false | 222 | r | test_mlr_learners_surv_glmnet.R | context("surv.glmnet")
test_that("autotest", {
learner = mlr_learners$get("surv.glmnet")
expect_learner(learner)
result = run_autotest(learner, exclude = "feat_single")
expect_true(result, info = result$error)
})
|
54979b3b51e571d41c3041ed455813a725c20ad9 | 745295046a9499a459f379c2931db70e21cd39e3 | /components-rwrapper/src/main/resources/plot_line_chart.R | 43839bbfeb30dfc9d47fc7b4e52f8f5c46447113 | [] | no_license | AnalyticsWorkbench/Components | bc109595c5707388ffe2c319cdbbfdadedabfde6 | cd7ab7eeaed7480712a3270cc9f81af0a7041634 | refs/heads/master | 2022-12-09T15:05:38.538578 | 2019-05-23T15:23:48 | 2019-05-23T15:23:48 | 136,925,042 | 2 | 1 | null | 2022-12-05T23:55:36 | 2018-06-11T12:44:40 | JavaScript | UTF-8 | R | false | false | 231 | r | plot_line_chart.R | plot_line_chart <- function(data) {
values <- c(data)
plot(values[[1]],type="o")
}
resultData <- list(dataUrl='line.data',plotUrl='line.png')
write.table(data, file="line.data")
png('line.png')
plot_line_chart(data)
dev.off()
|
8725aab7d1c5ec803133edb4b8738d28e0e6c7ca | a76f598b25592f0b49bf0865b2b92dfaf92357e9 | /tests/testthat/test_slise.R | c27f007aac83e4ba8e4dd87b9772c6b4ab1777e6 | [
"MIT"
] | permissive | edahelsinki/slise | a22b90e0ce06223a93bcf35bde96207254a602d5 | a7bc95384282168aa1642008b31d9159ab02eb72 | refs/heads/master | 2023-04-08T13:01:14.106004 | 2023-04-03T12:24:29 | 2023-04-03T12:24:29 | 203,325,030 | 8 | 1 | MIT | 2023-01-17T10:36:53 | 2019-08-20T07:39:52 | R | UTF-8 | R | false | false | 4,408 | r | test_slise.R | context("Tests for the slise algorithm")
source("setup.R")
test_that("Check SLISE", {
for (i in c(rep(c(4, 8, 16), 2))) {
data <- data_create(i * 30, i, floor(i * 0.5), 0.03, 0.3, 0.3)
slise1 <- slise.fit(data$X, data$Y, epsilon = 0.1, lambda1 = 0)
slise2 <- slise.fit(data$X, data$Y, epsilon = 0.1, lambda1 = 0.1)
ols <- .lm.fit(data$X, data$Y)$coefficients
slise1_loss <- slise1$value
slise2_loss <- slise2$value
ols1_loss <- loss_sharp(ols, data$X, data$Y, epsilon = 0.1, lambda1 = 0)
ols2_loss <- loss_sharp(ols, data$X, data$Y, epsilon = 0.1, lambda1 = 0.1)
expect_lt(slise1_loss, ols1_loss)
expect_lt(slise2_loss, ols2_loss)
x <- rnorm(length(data$alpha) - 1)
y <- sum(x * data$alpha[-1]) + data$alpha[[1]]
expl1_alpha <- slise.explain(data$X, data$Y, 0.03, x, y, 0)$coefficients
expl2_alpha <- slise.explain(data$X, data$Y, 0.03, x, y, 0.1)$coefficients
expect_equal(sum(expl1_alpha[-1] * x) + expl1_alpha[[1]], y)
expect_equal(sum(expl2_alpha[-1] * x) + expl2_alpha[[1]], y)
}
})
test_that("Check SLISE formula", {
data <- data.frame(y = rnorm(8), a = rnorm(8), b = rnorm(8))
model <- slise.formula(y ~ a * b + abs(a), data, 0.1, normalise = TRUE)
expect_equal(length(model$coefficients), 5)
expect_equal(length(model$normalised_coefficients), 5)
expect_true(model$intercept)
model <- slise.formula(y ~ a * b + abs(a) + 0, data, 0.1, normalise = TRUE)
expect_equal(length(model$coefficients), 5)
expect_equal(length(model$normalised_coefficients), 4)
expect_true(model$intercept)
model <- slise.formula(y ~ a * b + abs(a) + 0, data, 0.1, normalise = FALSE, lambda1 = 0.1)
expect_equal(length(model$coefficients), 4)
expect_false(model$intercept)
model <- slise.formula(y ~ poly(a, b, degree = 2), data, 0.1, normalise = FALSE, lambda1 = 0.1)
expect_equal(length(model$coefficients), 6)
expect_true(model$intercept)
})
test_that("Check SLISE comb", {
for (i in c(rep(5, 3))) {
data <- data_create(i * 30, i, floor(i * 0.5), 0.03, 0.3, 0.3)
x <- data$X[1, ]
y <- data$clean[1]
expl1 <- slise.explain_comb(data$X, data$Y, 0.03, x, y, variables = 3)
expl2 <- slise.explain_comb(data$X, data$Y, 0.10, x, y, variables = 3)
expect_equal(predict(expl1, x)[[1]], y)
expect_equal(predict(expl2, x)[[1]], y)
expect_gte(3, sparsity(expl1$alpha[-1]))
expect_gte(3, sparsity(expl2$alpha[-1]))
}
})
test_that("Check SLISE predict", {
for (i in c(rep(c(4, 8), 2))) {
data <- data_create(i * 30, i, floor(i * 0.5), 0.03, 0.3, 0.3)
Y2 <- data$Y - min(data$Y)
Y2 <- Y2 / max(Y2)
expl1 <- slise.explain(data$X, data$Y, 0.2, data$X[1, ], data$clean[1])
expl2 <- slise.explain(data$X, Y2, 0.3, data$X[1, ], Y2[1], logit = TRUE)
expl3 <- slise.explain(data$X, Y2, 0.3, 2, logit = TRUE, normalise = TRUE)
fit1 <- slise.fit(data$X, data$Y, 0.1, 0.1)
Y1 <- predict(expl1, data$X)
Y2b <- predict(expl2, data$X)
Y2c <- predict(expl3, data$X[2, ])
Y3 <- predict(fit1, data$X)
expect_equal(Y1[1], data$clean[1])
expect_equal(Y2[2], c(Y2c))
expect_equal(expl1$subset, c(abs(predict(expl1, data$X) - data$Y) < expl1$epsilon))
expect_equal(expl2$subset, c(abs(predict(expl2, data$X, logit = TRUE) - expl2$Y) < expl2$epsilon))
expect_equal(expl3$subset, c(abs(predict(expl3, data$X, logit = TRUE) - expl3$Y) < expl3$epsilon))
expect_equal(fit1$subset, c(abs(predict(fit1, data$X) - data$Y) < fit1$epsilon))
}
})
test_that("Check SLISE unscale", {
for (i in c(rep(c(4, 8, 16), 2))) {
data <- data_create(i * 30, i, floor(i * 0.3), 0.03, 0.3, 0.3)
slise1 <- slise.fit(data$X, data$Y, epsilon = 0.1, normalise = TRUE)
data2 <- slise.preprocess(data$X, data$Y, 0.1, intercept = TRUE, normalise = TRUE)
subset1 <- mean(abs(predict(slise1, data$X) - data$Y) <= slise1$epsilon)
subset2 <- mean(abs(data2$X %*% slise1$normalised_alpha - data2$Y) < slise1$normalised_epsilon)
expect_equal(subset1, mean(slise1$subset))
expect_equal(subset2, mean(slise1$subset))
}
}) |
e1ea1a9475676c1e0868310a8e48ed797be460b5 | ba892b7dda59a3024307699aaee1d42d4af30e03 | /manipulation/7-subsets-for-analysis.R | 6715f4540e2779aa1f9a0cd4e31ec28fbe72b14e | [] | no_license | IALSA/longitudinal-response-pattern | 5b9d5b3ce292b27e1eaed7b8ed9d6ee8c616fa0d | d293812cb7f0cea8d8e9a4a447bb87843f2ff24f | refs/heads/master | 2020-04-09T16:52:07.699990 | 2016-07-15T20:06:43 | 2016-07-15T20:06:43 | 61,064,550 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,673 | r | 7-subsets-for-analysis.R | ## Project title: MAP
## Created on: June 03 2016
## Author: Jamie Knight
## Data: Creating final subsets for analysis. MAP for use in Thesis
## Summary:
## ---------------------------------------------------------------------- ##
options(width=160)
rm(list=ls())
cat("\f")
# --- load-packages ------
search() # see which pachages are currently attached
library(dplyr)
library(magrittr)
# requireNamespace("dplyr")
# ----- load-data ------
getwd()
#full version long
ds <-readRDS("./data/unshared/derived/ds4.rds")
#full version wide
# dsw <-readRDS("./data/derived/ds4w.rds")
#complete cases for BSIT and niareagansc
# ds1 <- readRDS("./data/derived/ds4ls.rds")
#complete cases for BSIT
# ds2 <- readRDS("./data/derived/ds4ls2.rds")
str(ds)
names(ds)
# ---- tweak-data -------------------
ds <- ds %>%
dplyr::mutate(
id = as.integer(projid)
)
# ---- subset-variables-cdx-1 ---------------------------------
t <- table(ds$fu_year, ds$cdx, useNA = "always"); t[t==0] <- "."; t
names(ds)
keep_these_ids <- ds %>%
dplyr::select(id, fu_year, cdx) %>%
dplyr::filter(fu_year==0, cdx == 1 ) %>%
dplyr::select(id)
keep_these_ids <- as.integer(keep_these_ids$id)
length(unique(keep_these_ids)) # 1236
length(unique(ds$id)) # 1803
ds_subset_1 <- ds %>%
dplyr::filter(id %in% keep_these_ids)
length(unique(ds_subset_1$id))
ds_subset_1 %>%
# dplyr::slice(1:10)
dplyr::group_by(cdx) %>%
dplyr::summarize(count = n())
t <- table(ds_subset_1$fu_year, ds_subset_1$cdx, useNA = "always"); t[t==0] <- "."; t
names(ds)
# ---- subset-variables-smell-test-2 ---------------------------------
t <- table(ds$fu_year, ds$total_smell_test, useNA = "always"); t[t==0] <- "."; t
t <- table(ds_subset_1$fu_year, ds_subset_1$total_smell_test, useNA = "always"); t[t==0] <- "."; t
names(ds)
keep_these_ids <- ds_subset_1 %>%
dplyr::select(id, fu_year, total_smell_test) %>%
dplyr::filter(fu_year == 0, !is.na(total_smell_test)) %>%
dplyr::select(id)
keep_these_ids <- as.integer(keep_these_ids$id)
length(unique(keep_these_ids)) # 1054
length(unique(ds$id)) # 1803
ds_subset_2 <- ds_subset_1 %>%
dplyr::filter(id %in% keep_these_ids)
length(unique(ds_subset_2$id))
ds_subset_2 %>%
# dplyr::slice(1:10)
dplyr::group_by(total_smell_test) %>%
dplyr::summarize(count = n())
t <- table(ds_subset_2$fu_year, ds_subset_2$total_smell_test, useNA = "always"); t[t==0] <- "."; t
names(ds)
# ---- subset-variables-smell-test-3 ---------------------------------
t <- table(ds_subset_2$fu_year, ds_subset_2$total_smell_test, useNA = "always"); t[t==0] <- "."; t
keep_these_ids <- ds_subset_2 %>%
dplyr::select(id, fu_year, total_smell_test) %>%
dplyr::group_by(id) %>%
dplyr::summarize(tst_count = n()) %>%
dplyr::filter(!tst_count %in% c(1,2)) %>%
dplyr::select(id)
keep_these_ids <- as.integer(keep_these_ids$id)
length(unique(keep_these_ids)) # 890
length(unique(ds$id)) # 1803
ds_subset_3 <- ds_subset_2 %>%
dplyr::filter(id %in% keep_these_ids) %>%
dplyr::group_by(id) %>%
dplyr::mutate(tst_count = n()) %>%
dplyr::ungroup()
ds_subset_3 %>%
# dplyr::slice(1:10)
dplyr::group_by(tst_count) %>%
dplyr::summarize(count = n())
t <- table(ds_subset_3$fu_year, ds_subset_3$tst_count, useNA = "always"); t[t==0] <- "."; t
names(ds)
# ---- subset-base -----------------
#
# # base subset should only include people who do not have dementia at baseline
# #change original file ie. ds to be each of the subsets above.
#
# sub1<-ds
#
# #subset to remove any participants with dementia at baseline
# #cdx is assesment while alive, 1:normal, 2-3:mild MCI, 4-5:AD
# varsREM <- sub1[ which(sub1$fu_year==0 & sub1$cdx != 1),]
#
#
# sub2 <- sub1
# for (rows in varsREM["projid"]) {
# for (id in rows) {
# sub2 <- sub2[sub2$projid != id, ]
# }
# }
#
# dplyr::n_distinct(sub2$projid)#ds: 1239 (564 less)|ds1:392 | ds2:1239
# dplyr::n_distinct(sub1$projid) #ds:1803 subjects | ds1:650 | ds2:1660
# dplyr::n_distinct(varsREM$projid) #ds: #564 | ds1:258 | ds2: 421
# #correct numer was removed
#
# #sub2 = all people who were cognitively normal at baseline
#
# # rename the subsets
# dsbase <- sub2 # 1239
# dplyr::n_distinct(dsbase$projid)
# max(dsbase$fu_year)# NA, why not working?
# dsbase$projid %>% unique() %>% length()
#
# ds1base <- sub2 # 392, people who have BSIT and niareagansc
# max(ds1base$fu_year) #17
# dplyr::n_distinct(ds1base$projid)
#
# ds2base<- sub2 #1239, people who have BSIT
# max(ds2base$fu_year) #18
# dplyr::n_distinct(ds2base$projid)
#ds and ds2 are the same number of people?
#use ds1 for autopsy pathology investigation and ds2 as other
# -------reshape data from long to wide-------
names(ds1base)
max(ds1base$fu_year) #17
measures <- names(ds1base)[c(-1,-2, -4, -6, -7, -9:-16, -28, -32, -33, -36)]
measures #time varying measures
ds1w <- reshape(ds1base, v.names=measures, timevar='fu_year', idvar='projid', direction="wide")
names(ds1w)
####help here####
# sub out people who do not have a BSIT scores at baseline
keep_ids <- ds1w[ds1w$total_smell_test.0 != is.na(), "projid"]
sub1 <- ds1w %>% dplyr::filter(projid %in% keep_ids)
#sub3: people who have 2 or more waves of BSIT or overall?
#3sub4: people who have 3 or more waves of BSIT or overall?
# ---- save ---------
#save subset data
saveRDS(dsbase, "./data/derived/COX/dsbase.rds")
write.csv(dsbase, "./data/derived/CSV/dsbase.csv")
saveRDS(ds1base, "./data/derived/COX/ds1base.rds")
write.csv(ds1base, "./data/derived/CSV/ds1base.csv")
saveRDS(ds2base, "./data/derived/COX/ds2base.rds")
write.csv(ds2base, "./data/derived/CSV/ds2base.csv")
|
ebbe49ad52ab80e44a748c427cc80ad343d91f42 | cdae97e4ecf760987cf8c534f24cb553e5fdf00b | /Liniar vs Multiple Liniar Regression.r | b1b64c1dab82993d22931d60607ba9975491b0e9 | [] | no_license | AndraRaco/Statistics-project | 4b42de91c5f78ff3085b367c2e6d019f3ad37175 | 333a88443275100cc27cc231fefb68cad19c1bae | refs/heads/master | 2022-06-21T05:59:02.959745 | 2020-05-09T14:23:51 | 2020-05-09T14:23:51 | 255,165,343 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,427 | r | Liniar vs Multiple Liniar Regression.r | # Import dataset
dataset <- as.data.frame(Seatbelts)
View(dataset)
#----------------------1----------------------------
# Media penstru fiecare coloana
Mean <- apply(dataset, 2, mean)
View(Mean)
# Dispersia
Var <- var(dataset)
View(Var)
# Quantile
Quantile <- apply(dataset, 2, quantile)
View(Quantile)
# Boxplot
#boxplot(DriversKilled~ kms, data = dataset, xlab = "Number of drivers killed",
# ylab = "Number of kms", main = "Seatbelts Data")
#boxplot(DriversKilled~ drivers, data = dataset, xlab = "Number of drivers killed",
# ylab = "Number of kms", main = "Seatbelts Data")
#install.packages("tidyverse")
boxplot(dataset$DriversKilled, main = "DriversKilled")
boxplot(dataset$drivers, main = "drivers")
boxplot(dataset$rear, main = "rear")
boxplot(dataset$front, main = "front")
boxplot(dataset$kms, main = "kms")
boxplot(dataset$PetrolPrice, main = "PetrolPrice")
boxplot(dataset$VanKilled, main = "VanKilled")
#install.packages('ggplot2')
library(ggplot2)
ggplot(dataset, aes(x = factor(law), y = DriversKilled)) + geom_boxplot(fill = "pink") +
ylab ("Monthly Driver Mortality") + xlab("Before and after law introduced")
boxplot(dataset$DriversKilled, main = "DriversKilled")
boxplot(dataset$drivers, main = "drivers")
boxplot(dataset$rear, main = "rear")
boxplot(dataset$front, main = "front")
boxplot(dataset$kms, main = "kms")
boxplot(dataset$PetrolPrice, main = "PetrolPrice")
boxplot(dataset$VanKilled, main = "VanKilled")
boxplot(
dataset$DriversKilled ~ dataset$law,
xlab = "law",
ylab = "Drivers killed",
main = "Drivers killed vs law",
las = 1
)
#----------------------2----------------------------
#Simple Linear Regression
library(caTools)
split = sample.split(dataset$DriversKilled, SplitRatio = 2/3)
training_set = subset(dataset, split == TRUE)
test_set = subset(dataset, split == FALSE)
#Fitting Multiple Linear regression to the Training Set
regressor2 = lm(formula = DriversKilled ~ drivers , data = training_set)
#Predicting the Test set results
y_pred2 = predict(regressor2, newdata = test_set)
# Visualising the Training set results
library(ggplot2)
ggplot() +
geom_point(aes(x = training_set$drivers, y = training_set$DriversKilled),
colour = 'red') +
geom_line(aes(x = training_set$drivers, y = predict(regressor2, newdata = training_set)),
colour = 'blue') +
ggtitle('DriversKilled vs Drivers (Training set)') +
xlab('Drivers') +
ylab('DriversKilled')
# Visualising the Test set results
library(ggplot2)
ggplot() +
geom_point(aes(x = test_set$drivers, y = test_set$DriversKilled),
colour = 'red') +
geom_line(aes(x = training_set$drivers, y = predict(regressor2, newdata = training_set)),
colour = 'blue') +
ggtitle('DriversKilled vs Drivers (Test set)') +
xlab('Drivers') +
ylab('DriversKilled')
#Multiple Linear Regression
#Splitting the dataset into the Training set and Test set
library(caTools)
split = sample.split(dataset$DriversKilled, SplitRatio = 2/3)
training_set = subset(dataset, split == TRUE)
test_set = subset(dataset, split == FALSE)
#Fitting Multiple Linear regression to the Training Set
regressor = lm(formula =DriversKilled ~ ., data = training_set)
summary(regressor)
#Predicting the Test set results
y_pred = predict(regressor, newdata = test_set)
#Building the optimal model using Backward Elimination
regressor=lm(formula=DriversKilled ~ drivers + front + kms + PetrolPrice + VanKilled + law
,data=dataset)
summary(regressor)
regressor=lm(formula=DriversKilled ~ drivers + kms + PetrolPrice + VanKilled + law
,data=dataset)
summary(regressor)
regressor=lm(formula=DriversKilled ~ drivers + kms + VanKilled + law
,data=dataset)
summary(regressor)
regressor=lm(formula=DriversKilled ~ drivers + kms + law
,data=dataset)
summary(regressor)
regressor=lm(formula=DriversKilled ~ drivers + kms
,data=dataset)
summary(regressor)
modelSummary <- summary(regressor) # capture model summary as an object for multiple liniar regression
modelSummary2 <- summary(regressor2) # capture model summary as an object for simple liniar regression
#R squared test
modelSummary2$r.squared
modelSummary$r.squared
#Adjusted R squared test
modelSummary2$adj.r.squared
modelSummary$adj.r.squared
#AIC test
AIC(regressor2)
AIC(regressor)
#BIC test
BIC(regressor2)
BIC(regressor)
#F statistic test
modelSummary2$fstatistic[1]
modelSummary$fstatistic[1]
#Standard error
modelCoeffs2 <- modelSummary2$coefficients # model coefficients SLR(simple liniar regression)
beta.estimate2 <- modelCoeffs2["drivers", "Estimate"] # get beta estimate for DriversKilled
std.error2 <- modelCoeffs2["drivers", "Std. Error"] # get std.error for DriversKilled
std.error2
modelCoeffs <- modelSummary$coefficients # model coefficients MLR(multiple liniar regression)
beta.estimate <- modelCoeffs["drivers", "Estimate"] # get beta estimate for DriversKilled
std.error <- modelCoeffs["drivers", "Std. Error"] # get std.error for DriversKilled
std.error
#----------------------3----------------------------
library(DiscreteLaplace)
# The Probability mass function
p<-0.7
q<-0.45
x<--15:15
prob<-ddlaplace(x, p, q)
plot(x, prob, type="h", main="The Probability mass function")
# The cumulative distribution function
cmd<-pdlaplace(x, p, q)
plot(x,cmd,main="The cumulative distribution function")
|
4311de60d85fc663f3111b3a00dd6d6619c8f73b | 56e22dd051b4ecc6bf96a7fa93d6dcecc74eebcb | /man/install_goldman_sans.Rd | 78c9e3ed88db649154cf8e47588b988fd25f5fa5 | [
"MIT"
] | permissive | yjunechoe/hrbragg | 44337b14d1a23b3d3757ac5dae3c2899913ff01b | 8f8b1f098d02329632fff26a89083237fa8cc75f | refs/heads/master | 2023-06-14T13:32:21.889462 | 2021-07-10T12:36:19 | 2021-07-10T12:36:19 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 829 | rd | install_goldman_sans.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/install-fonts.R
\name{install_goldman_sans}
\alias{install_goldman_sans}
\alias{install_goldman_sans_condensed}
\title{Install Goldman Sans}
\usage{
install_goldman_sans()
install_goldman_sans_condensed()
}
\description{
Goldman Sans is a clean, modern typeface designed for dense data-rich
environments. From open letter shapes, enlarged x-height, and optical
balancing of individual letterforms, we designed Goldman Sans with
clarity in mind. Both normal and condensed versions are provided.
}
\note{
\href{https://design.gs.com/d/legal/goldman-sans-license}{Goldman Sans Restricted Font License}
}
\examples{
install_goldman_sans()
install_goldman_sans_condensed()
}
\references{
\href{https://design.gs.com/d/story/goldman-sans/}{Goldman Sans}
}
|
ed4a92b285a9ca4c091a56e6a0773dc27d301fa9 | b53beb0e3daa03b1d5aca5d8c0737737ebd919b5 | /aggregateSummary.R | 7be31dc6735e1944e9fabc76ca501538842241b9 | [
"MIT"
] | permissive | manning-lab/aggregateAssociation | 0fa3d102dd9621bd4dbd95d54af0cbe2aba2c54c | 6897d7f4b42cc8898b3f2a06e93d081562f0bfe2 | refs/heads/master | 2021-07-10T23:45:20.904315 | 2018-09-21T16:34:03 | 2018-09-21T16:34:03 | 125,053,850 | 1 | 1 | MIT | 2018-03-22T15:47:14 | 2018-03-13T13:13:06 | R | UTF-8 | R | false | false | 5,279 | r | aggregateSummary.R | # summary.R
# Description: Generate a summary of association results including quantile-quantile and manhattan plots for all groups. Also generates CSV files of all associations.
# Inputs:
# label : prefix for output filename (string)
# assoc.files : comma separated list of association results, output of aggAssocTest (string)
# Check if required packages are installed
packages <- c("qqman","data.table","stringr")
to_install <- packages[!(packages %in% installed.packages()[,"Package"])]
if(length(to_install)) install.packages(to_install,repos='http://cran.us.r-project.org')
# Load packages
lapply(packages, library, character.only = TRUE)
# Parse inputs
input_args <- commandArgs(trailingOnly=T)
label <- input_args[1]
assoc.files <- unlist(strsplit(input_args[2],","))
minmac <- as.numeric(input_args[3])
var.info <- list()
# remove any empty files because R apparently can't handle loading an empty file.
info = file.info(assoc.files)
assoc.files = rownames(info[!(info$size == 0), ])
if (length(assoc.files) == 0){
fwrite(list(),paste(label, ".assoc.csv", sep=""),sep=",",row.names=F)
fwrite(list(), paste(label, ".topassoc.csv", sep=""),row.names=F)
png(filename = paste(label,".association.plots.png",sep=""),width = 11, height = 11, units = "in", res=800, type = "cairo")
dev.off()
} else {
# Prep for association files
assoc.compilation <- c()
# Loop through association files
for (i in seq(1,length(assoc.files))) {
load(assoc.files[i])
# get group results
res <- assoc$results
# get variant info for each group
var.list <- list()
for (g in names(assoc$variantInfo)){
df <- assoc$variantInfo[[g]]
if (NROW(df) == 0){
next
}
df$group <- g
df$pval <- res[rownames(res) == g,"pval_0"]
var.list[[g]] <- df
}
var.df <- do.call(rbind,var.list)
var.info[[i]] <- var.df
if (!is.na(res)[1]){
print(dim(res))
res <- res[!is.na(res[,"pval_0"]),]
# add chromosome and minimum position to results
chrs <- c()
pos <- c()
for (g in rownames(res)){
chrs <- c(chrs,var.df[var.df$group == g,]$chr[1])
pos <- c(pos,min(var.df[var.df$group == g,]$pos))
}
res$chr <- chrs
res$pos <- pos
assoc.compilation <- rbind(assoc.compilation, res)
if (i == 1) {
write.table(res,paste(label, ".assoc.csv", sep=""),sep=",",row.names=F)
} else {
write.table(res,paste(label, ".assoc.csv", sep=""),col.names=FALSE,sep=",",row.names=F, append=TRUE)
}
}
}
}
assoc.compilation <- assoc.compilation[!is.na(assoc.compilation$pval_0),]
assoc.compilation$chr <- ifelse(assoc.compilation$chr == "X", 23, as.numeric(assoc.compilation$chr))
# Calculate genomic control
lam = function(x,p=.5){
x = x[!is.na(x)]
chisq <- qchisq(1-x,1)
round((quantile(chisq,p)/qchisq(p,1)),2)
}
png(filename = paste(label,".association.plots.png",sep=""),width = 11, height = 22, units = "in", res=800, type = "cairo")
par(mfrow=c(2,1))
# qq plot
qq(as.numeric(assoc.compilation[,"pval_0"]),main=label)
legend('topleft',c(paste0("GC = ", lam(assoc.compilation[,"pval_0"]))))
manhattan(assoc.compilation,chr="chr",bp="pos",p="pval_0", main=label)
dev.off()
assoc.compilation$MarkerName <- rownames(assoc.compilation)
write.csv(assoc.compilation, paste(label, ".groupAssoc.csv", sep=""),row.names=FALSE,quote=FALSE)
var.df = do.call(rbind,var.info)
fwrite(var.df, file = paste(label, ".all.variants.groupAssoc.csv", sep=""), row.names = F)
# now make the mac thresholded plots
var.df$mac <- 2*var.df$n.obs*var.df$freq
cummac <- data.frame(group = row.names(assoc.compilation), cummac = NA, stringsAsFactors = F)
# get cum mac per group
for (gind in seq(1,nrow(assoc.compilation))){
g <- row.names(assoc.compilation)[gind]
cummac[cummac$group == g, ]$cummac <- sum(var.df[var.df$group == g,]$mac)
}
assoc.compilation$group <- row.names(assoc.compilation)
assoc.compilation <- merge(assoc.compilation, cummac, by.x = "group", by.y = "group", all.x = T)
assoc.compilation <- assoc.compilation[assoc.compilation$cummac > minmac,]
if (nrow(assoc.compilation) == 0 ){
png(filename = paste(label,".cummac.",as.character(minmac),".association.plots.png",sep=""),width = 11, height = 22, units = "in", res=800, type = "cairo")
dev.off()
write.csv(list(), paste(label,".cummac.",as.character(minmac), ".groupAssoc.csv", sep=""))
fwrite(list(), file = paste(label,".cummac.",as.character(minmac), ".all.variants.groupAssoc.csv", sep=""), row.names = F)
} else {
png(filename = paste(label,".cummac.",as.character(minmac),".association.plots.png",sep=""),width = 11, height = 22, units = "in", res=800, type = "cairo")
par(mfrow=c(2,1))
# qq plot
qq(as.numeric(assoc.compilation[,"pval_0"]),main=label)
legend('topleft',c(paste0("GC = ", lam(assoc.compilation[,"pval_0"]))))
manhattan(assoc.compilation,chr="chr",bp="pos",p="pval_0", main=label)
dev.off()
write.csv(assoc.compilation, paste(label,".cummac.",as.character(minmac), ".groupAssoc.csv", sep=""))
# var.df = do.call(rbind,var.info)
fwrite(var.df, file = paste(label,".cummac.",as.character(minmac), ".all.variants.groupAssoc.csv", sep=""), row.names = F)
}
|
c63e75be15b96511ac5cb0bd4d3299035b63cd3b | 918e582aa9c88bfd261af310d56b79ace2bd328a | /R/random_walk.R | 568e1b1c71c7ac134c6e28c11bee6c86f83d714e | [] | no_license | cran/SteinerNet | bba40d0d6a96aae8f5c6e623df07903f04fbbdc0 | 7133431c232fc5ed73ec7fa697043dd3cf704d1c | refs/heads/master | 2021-01-20T22:28:58.947559 | 2020-09-07T08:50:08 | 2020-09-07T08:50:08 | 17,718,267 | 3 | 10 | null | 2018-06-25T17:57:04 | 2014-03-13T17:01:45 | R | UTF-8 | R | false | false | 2,940 | r | random_walk.R | gRandWalk <- function (glist, numofterminals, terminalpr) {
nodlist <- c()
g <- glist[[1]]
seed <- sample(1:length(V(g)), 1)
i <- 1
while (i < numofterminals) {
seed <- sample(unlist(neighborhood(g, 1, nodes = seed, mode = "all")), 1)
prob <- runif(1, min = 0, max = 1)
if (prob < terminalpr) {
nodlist <- c( nodlist, as.numeric(V(g)[seed]) )
nodlist <- unique(nodlist)
i <- length(nodlist)
}
}
return(nodlist)
}
####--------------------------------------- Documentation ---------------------------------------####
#' Select terminals
#'
#' @description Provides random walk procedure. Starting from the randomly selected node, choose a neighbour node uniformly
#' randomly, until given number of terminals won't be found.
#'
#' @usage generate_st_samples(graph, ter_number, prob)
#'
#' @param graph an igraph graph; should be undirected, otherwise it is converted to undirected.
#' @param ter_number a numeric vector; each element indicates the number of terminals to be selected and length of vector
#' indicates the number of terminal sets to be picked.
#' @param prob a numeric vector of the same length as ter_number; prob[i] defines a probability with which each
#' next node accepted or rejected while selecting ter_number[i] terminals.
#'
#' @return A list of the same length as ter_number. Each element of list contains a vector of ids of selected vertices.
#'
#' @examples
#' generate_st_samples(graph = graph("Zachary"),
#' ter_number = c(3, 4),
#' prob = c(0.1, 0.2))
#'
#' @references 1. Afshin Sadeghi and Holger Froehlich, "Steiner tree methods for optimal sub-network
#' identification: an empirical study", BMC Bioinformatics 2013 14:144
#'
#' @export
####------------------------------------- End Documentation -------------------------------------####
generate_st_samples <- function (graph, ter_number, prob) {
ter_list <- c()
glist <- c()
glist[[1]] <- as.undirected(graph)
if (length(ter_number) != length(prob))
stop("The length of ter_number is not equal to the length of prob")
for (i in 1:length(ter_number)) {
if (ter_number[i] >= length(V(glist[[1]])))
stop("Number of terminals to be selected is greater than number of vertices in graph")
if ( !((prob[i] > 0) & (prob[i] <= 1)) )
stop("prob doesn't belong to (0, 1]")
ter_list[[i]] <- gRandWalk(glist = glist, numofterminals = ter_number[i],
terminalpr = prob[i])
}
return(ter_list)
}
|
cf35e41d2c67c3dffa696a302592de818cd02d6f | 97a6fb83ad026cbedd48b0f1493b3837876ffebe | /RE_level_simulation/Translocation/scripts/translocation.r | 70786d84fdfe850b04550f84f1d5c7b74b1e7471 | [] | no_license | ay-lab/AveSim | 039d22791bda9ab8208af4d3f06c52cd1d9a1d4b | e4e47e90d643f04f9212fb2a9d64d964c8bfdf5f | refs/heads/master | 2021-01-20T12:21:38.997584 | 2019-11-28T04:38:10 | 2019-11-28T04:38:10 | 101,714,230 | 3 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,559 | r | translocation.r | library(BSgenome.Hsapiens.UCSC.hg19)
args <- commandArgs(trailingOnly = TRUE)
#Rscript insertion.r chr1 chr2 150000000 240000000 3000000 3000001
chr = read.table("../scripts/chr.list",header=F)
if (as.character(args[6]) == "yes"){
j = 1
while (j <= length(chr$V1)){
if (as.character(args[1]) != as.character(chr$V1[j]) & as.character(args[2]) != as.character(chr$V1[j])){
x.a = getSeq(Hsapiens, as.character(chr$V1[j]))
sequence = as.character(x.a)
name = paste(">",chr$V1[j],sep="")
fileConn <- file(as.character(paste("Normal.",chr$V1[j],".genome.fasta",sep="")))
header = as.character(paste(name,sequence,sep="\n"))
writeLines(header,fileConn)
close(fileConn)
}
j = j+1
}
}
if (as.character(args[1]) != as.character(args[2])){
full.A = getSeq(Hsapiens, as.character(args[1]))
a = getSeq(Hsapiens, as.character(args[1]),start=start(full.A)[[1]],end=as.numeric(args[3])-1)
b = getSeq(Hsapiens, as.character(args[1]),start=as.numeric(args[3]), end=as.numeric(args[4]))
c = getSeq(Hsapiens, as.character(args[1]),start=as.numeric(args[4])+1, end=end(full.A)[[1]])
a = as.character(a)
b = as.character(b)
c = as.character(c)
fileConn <- file(as.character(paste("Normal.",args[1],".genome.fasta",sep="")))
sequence = as.character(full.A)
name = paste(">",args[1],sep="")
header = as.character(paste(name,sequence,sep="\n"))
writeLines(header,fileConn)
close(fileConn)
fileConn <- file(as.character(paste("Translocation_Donor.",args[1],".genome.fasta",sep="")))
sequence = as.character(paste(a,c,sep=""))
name = paste(">",args[1],sep="")
header = as.character(paste(name,sequence,sep="\n"))
writeLines(header,fileConn)
close(fileConn)
full.B = getSeq(Hsapiens, as.character(args[2]))
fileConn <- file(as.character(paste("Normal.",args[2],".genome.fasta",sep="")))
sequence = as.character(full.B)
name = paste(">",args[2],sep="")
header = as.character(paste(name,sequence,sep="\n"))
writeLines(header,fileConn)
close(fileConn)
d = getSeq(Hsapiens, as.character(args[2]),start=start(full.B)[[1]],end=as.numeric(args[5]))
e = getSeq(Hsapiens, as.character(args[2]),start=as.numeric(args[5])+1, end=end(full.B)[[1]])
fileConn <- file(as.character(paste("Translocation_Receiver.",args[2],".genome.fasta",sep="")))
sequence = as.character(paste(b,e,sep=""))
name = paste(">",args[2],sep="")
header = as.character(paste(name,sequence,sep="\n"))
writeLines(header,fileConn)
close(fileConn)
}
|
5964f844139768af509715439fb89e333e2bb803 | f7c205369b307ef300fabbeb59bfd9043c73bad8 | /pl1 d.R | 97376d024cc7ae8273c531a095654845c98e4679 | [] | no_license | b62ropaa/R | 26ba444f38cd00ef9ab7ee33692df00d662e92ca | 218fa8327a8bb234fc4b47e2b3951b7a39da8a6b | refs/heads/master | 2020-04-13T03:20:36.251013 | 2019-09-09T10:54:14 | 2019-09-09T10:54:14 | 162,928,557 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 200 | r | pl1 d.R | print("Introduzca el cateto1")
cat1<-scan(n=1)
print("Introduzca el cateto 2")
cat2<-scan(n=1)
hip<-sqrt(cat1^2+cat2^2)
cat("La hipotenusa del triangulo de cateto 1=",cat1,"y cateto 2=",cat2,"es",hip) |
f1527b90d7d23e156ce17101c6581f2643516d64 | 19040283cd6c4468a7537a912c0c2d0fcca76a92 | /parse-corpus-coma.R | 4479e2389e43c6317cbd7af1c7d95385818c5035 | [] | no_license | daniel-jettka/corpus-analytics-with-R | 9a2d8b757879495f0a954b4bac5cf15c4d8a1515 | 13ba506c7af51985ef61a139766f2d374f2b396d | refs/heads/main | 2023-03-08T08:56:52.591582 | 2021-03-02T18:41:33 | 2021-03-02T18:41:33 | 311,933,042 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,624 | r | parse-corpus-coma.R | library(xml2)
############ Samples of Parameters ############
# Base directory of EXMARaLDA corpus (local)
# base_directory <- c("C:/Users/Daniel/Desktop/demo-corpus/")
#
# Name of coma file (path from base directory)
# coma_file <- "EXMARaLDA_DemoKorpus.coma"
#
# XPath to find paths to content files (here: EXS files)
# c_xpath <- ".//*[local-name()='Transcription' and *[local-name()='Description']/*[local-name()='Key' and @Name='segmented']='true']/*[local-name()='NSLink']"
################################################
### Sample call of function (read into list) ###
#
# 1. read EXS from Coma file (for EXMARaLDA corpus downloaded to local directory)
# content <- parse_coma("C:/Users/Daniel/Desktop/demo-corpus/", "EXMARaLDA_DemoKorpus.coma", ".//*[local-name()='Transcription' and *[local-name()='Description']/*[local-name()='Key' and @Name='segmented']='true']/*[local-name()='NSLink']")
#
# 2. read EXB from Coma file (for EXMARaLDA corpus downloaded to local directory)
# content <- parse_coma("C:/Users/Daniel/Desktop/demo-corpus/", "EXMARaLDA_DemoKorpus.coma", ".//*[local-name()='Transcription' and *[local-name()='Description']/*[local-name()='Key' and @Name='segmented']='false']/*[local-name()='NSLink']")
#
################################################
parse_coma <- function(base_directory, coma_file, c_xpath){
xml <- read_xml(paste(base_directory, coma_file, sep=""))
# read content into list
content <- list()
content_files <- xml_text(xml_find_all(xml, c_xpath))
for (c in content_files) {
content[[c]] <- read_xml(paste(base_directory, c, sep=""))
}
return(content)
}
|
4d053393fed49b3ff7f4587b9bbf3f38b5f0e85b | aeadfadb984277409faf5bde4f9544f47b82a2cb | /man/createConsoleAppender.Rd | 6ad3bca8a8ea072e3cf49678d3a42ef3df099f5d | [
"Apache-2.0"
] | permissive | cran/ParallelLogger | 500d886d83b5853d127660ad05336ac5593ad42c | 358c0c66e91e258dc1c3a86c99bd15a073185e54 | refs/heads/master | 2023-09-01T00:42:14.391609 | 2023-08-22T14:50:02 | 2023-08-22T16:34:27 | 145,897,044 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 836 | rd | createConsoleAppender.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Appenders.R
\name{createConsoleAppender}
\alias{createConsoleAppender}
\title{Create console appender}
\usage{
createConsoleAppender(layout = layoutSimple)
}
\arguments{
\item{layout}{The layout to be used by the appender.}
}
\description{
Create console appender
}
\details{
Creates an appender that will write to the console.
}
\examples{
appender <- createConsoleAppender(layout = layoutTimestamp)
logger <- createLogger(name = "SIMPLE",
threshold = "INFO",
appenders = list(appender))
registerLogger(logger)
logTrace("This event is below the threshold (INFO)")
logInfo("Hello world")
unregisterLogger("SIMPLE")
}
|
b3775b02a158b24774c401ffeeae549d71585f41 | 160fa6790946d4a704b580de3030ec9f6c14949d | /cachematrix.R | a9f699608cf0ac1ad3a995267989ea41dde6d3f3 | [] | no_license | Sarac96/ProgrammingAssignment2 | 6f972ac4f934a2700ce98c2ee9d3fb24f4093b94 | 7944c8ba6cd2905c0a802af0de776fd5df0c78c3 | refs/heads/master | 2022-12-24T21:24:30.129217 | 2020-09-22T08:10:10 | 2020-09-22T08:10:10 | 296,848,717 | 0 | 0 | null | 2020-09-19T11:01:05 | 2020-09-19T11:01:03 | null | UTF-8 | R | false | false | 1,213 | r | cachematrix.R | ## Computing the cache of a matrix can be a costly operation.
## This function creates a matrix whose inverse can be retrieved from a cache.
## makeCacheMatrix creates "special" matrix, which is a list containing:
## set the value of the matrix
## get the value of the matrix
## set the value of the inverse
## get the value of the inverse
makeCacheMatrix <- function(x = matrix()) {
inv_matrix <- NULL
set <- function(y) {
x <<- y
inv_matrix <<- NULL
}
get <- function() x
setinverse <- function(inverse) inv_matrix <<- inverse
getinverse <- function() inv_matrix
list(
set = set,
get = get,
setinverse = setinverse,
getinverse = getinverse
)
}
## cacheSolve computes the inverse of the matrix created through makeCacheMatrix.
## If the inverse was already computed, it only retrieves the cached value
cacheSolve <- function(x, ...) {
## Return a matrix that is the inverse of 'x'
inv_matrix <- x$getinverse()
if (!is.null(inv_matrix)) {
message("getting cached data")
return(inv_matrix)
}
data <- x$get()
inv_matrix <- solve(data, ...)
x$setinverse(inv_matrix)
inv_matrix
} |
0a0df98e945e49698211c2bf9bae4ff5da527a47 | f5502bea857396ec669339f268f37c77f0ceb805 | /R/get_geo.R | e57879ded26197113f48aed717663cbca06ad58c | [
"MIT"
] | permissive | roboton/covid-covariates | 0646191e20596db2a4f5b9f7fa9fde7ab281c159 | b7e962017e5e71a78d1eb8ed9eaa379d9db401b3 | refs/heads/master | 2022-11-22T06:43:04.250917 | 2020-07-27T11:35:56 | 2020-07-27T11:35:56 | 262,303,342 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,262 | r | get_geo.R | ##' .. content for \description{} (no empty lines) ..
##'
##' .. content for \details{} ..
##'
##' @title
##' @param level
##' @param country
##' @param subregion1
##' @param subregion2
##' @return
##' @author roboton
##' @export
##'
get_geo <- function(level, country, subregion1, subregion2,
sr_lookup) {
if (all(is.na(level))) {
# guess level from missing values
level <- case_when(
is.na(subregion1) ~ "country",
is.na(subregion2) ~ "state",
TRUE ~ "county")
}
country <- norm_geo_name(country, "country")
country_code <- countrycode(country, "country.name", "iso2c")
country_code <- coalesce(country_code, country)
subregion1 <- norm_geo_name(subregion1, "subregion1")
subregion1_code <- sr_lookup %>%
filter(level == "state") %>%
right_join(tibble(country, subregion1),
by = c("country", "subregion1")) %>%
mutate(subregion1_code = coalesce(subregion1_code, subregion1)) %>%
pull(subregion1_code)
subregion2 <- norm_geo_name(subregion2, "subregion2")
subregion2_code <- sr_lookup %>%
right_join(tibble(country, subregion1, subregion2),
by = c("country", "subregion1", "subregion2")) %>%
mutate(subregion2_code = coalesce(subregion2_code, subregion2)) %>%
pull(subregion2_code)
geocode <- tibble(country_code, subregion1_code, subregion2_code) %>%
rowwise() %>%
mutate(geocode = paste(na.omit(c(country_code, subregion1_code,
subregion2_code)), collapse = "-")) %>%
pull(geocode)
return(geocode)
}
norm_geo_name <- function(geo, type) {
# only handle US right now which is
# states: abbreviation
# country: US
# counties: fips? names?
case_when(
type == "subregion2" ~ geo %>%
str_remove(" city$") %>%
str_remove(" Municipality$") %>%
str_remove(" Census Area$") %>%
str_remove(" City and Borough$") %>%
str_remove(" Borough$") %>%
str_replace("LaSalle Parish", "La Salle Parish") %>%
str_replace("Kenai Peninsula", "Kenai Peninsula Borough") %>%
str_replace("Oglala Lakota County", "Shannon County"),
type == "subregion1" ~ geo,
type == "country" ~ geo,
TRUE ~ geo
) %>% return()
}
|
9cbf07387b6b40e5a4e71b59b87a038f72710693 | c06f1619dbd2007837f73cc184fdef6d821ad981 | /R/useless_functions.R | 6e974444a20c4125ffbeb767a982c132afdf2007 | [] | no_license | BenGriff42/useless | 34cba60dada4e01324cd418e38e5c8fa0cad8f2d | ef3686ae6cbe4095ad0bdd3ac7ed97f4785c2a35 | refs/heads/main | 2023-08-14T11:45:09.814151 | 2021-10-07T12:28:32 | 2021-10-07T12:28:32 | 414,521,717 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 988 | r | useless_functions.R |
#' Return input
#'
#' Function to return whatever you put in
#'
#' @param x Anything
#'
#' @return What you input
#' @export
#'
#' @examples
#' same(1)
#' same("same")
same = function(x){
return(x)
}
#' Nothing
#'
#' A function that always returns 0
#'
#' @param x Anything
#'
#' @return zero
#' @export
#'
#' @examples
#' nothing(1000)
#' nothing("nothing")
nothing = function(x){
return(0)
}
#' No
#'
#' A function that rejects everything
#'
#' @param x Anything
#'
#' @return Wrong input
#' @export
#'
#' @examples
#' no(-20)
#' no("no")
no = function(x){
print("Wrong input")
}
#' Bad linear model
#'
#' Finds LS solution of parameters for linear model. Much less efficient than lm command, would not recommend using.
#'
#' @param y response variable
#' @param X0 explanatory variable
#'
#' @return fitted parameters
#' @export
#'
#' @examples
bad.lm = function(y,X0){
d = length(X0[1,])
X = as.matrix(cbind(rep(1,length(y)),X0))
solve(t(X) %*% X) %*% t(X) %*% y
}
|
e527b1e0c5f75214c4849b99dbcaf66ed54185ca | 99cba6b0ed163c7b82b846e148d92d9ec16a0fba | /density_plot.r | 21dac392011ca53d551cbf048afa5f7ce0d30ca6 | [] | no_license | Xudeh/Predixcan-fw-brca | f777f5876317e5aaad564c40ff569d741704aec5 | d57f5bff5ec976c52549689d5f31a4d952cb4432 | refs/heads/master | 2022-02-12T10:21:24.310269 | 2017-10-24T22:09:36 | 2017-10-24T22:09:36 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 500 | r | density_plot.r |
cor.table=read.csv("R and p value in Tumor(420) and Normal(36).csv")
###density plot
plot (density(cor.table$R.in.tumor), col="red", lwd=2)
lines(density(cor.table$R.in.normal), col="green", lwd=2)
plot (density(cor.table$R.square.in.tumor), col="red", lwd=2)
lines(density(cor.table$R.square.in.normal), col="green", lwd=2)
###scatter plot
plot(cor.table$R.square.in.tumor, cor.table$R.square.in.normal, pch=19,cex=.5)
abline(lm(cor.table$R.square.in.tumor~cor.table$R.square.in.normal))
|
c935d92df6774c09fdaafaa4f4d9d22eefb75a99 | db12b990924703cd74748d8585cd9c11fafa6746 | /h2o-r/tests/testdir_munging/unop/runit_cor.R | db76e446370bb3428751160764d6fcafeff8c725 | [
"Apache-2.0"
] | permissive | h2oai/h2o-3 | 919019a8f297eec676011a9cfd2cc2d97891ce14 | d817ab90c8c47f6787604a0b9639b66234158228 | refs/heads/master | 2023-08-17T18:50:17.732191 | 2023-08-17T16:44:42 | 2023-08-17T16:44:42 | 17,371,412 | 6,872 | 2,345 | Apache-2.0 | 2023-09-14T18:05:40 | 2014-03-03T16:08:07 | Jupyter Notebook | UTF-8 | R | false | false | 1,231 | r | runit_cor.R | setwd(normalizePath(dirname(R.utils::commandArgs(asValues=TRUE)$"f")))
source("../../../scripts/h2o-r-test-setup.R")
##
# Test out the cor() functionality
# If H2O dataset x, get back cor data frame with dimension ncol(x)
# If NAs in the frame, they are skipped in calculation unless na.rm = F
# If any categorical columns, throw an error
##
test.cor <- function() {
hex <- as.h2o(iris)
Log.info("Slice out iris[,1] and get the correlation: ")
Log.info(paste("R:", cor(as.matrix(iris[,1])), "\tH2O:", cor(hex[,1])))
cor_R = cor(as.matrix(iris[,1]))
cor_h2o = cor(hex[,1])
expect_equal(cor_h2o, cor_R[1,1])
Log.info("Slice iris[,1:4] and get the correlation: ")
Log.info("The correlation of iris[,1:4] when read into R is: ")
iris_Rcor <- cor(iris[,1:4])
print(iris_Rcor)
Log.info("The correlation of iris[,1:4] when asking H2O is: ")
iris_H2Ocor <- as.data.frame(cor(hex[,1:4]))
print(iris_H2Ocor)
h2o_vec <- as.vector(unlist(iris_H2Ocor))
r_vec <- as.vector(unlist(iris_Rcor))
expect_equal(h2o_vec, r_vec, tol=1e-6)
for (i in c(1e1,1e2,1e3,1e4,1e5,1e6,1e7,1e8,1e9)) {
expect_equal(h2o.cor(as.h2o(c(i,1+i,10+i))), 1);
}
}
doTest("Test out the cor() functionality", test.cor) |
ed74c4e5fd7c269f7606409909c16f3e7c8eb6dd | 2d34708b03cdf802018f17d0ba150df6772b6897 | /googlecomputealpha.auto/man/TargetPoolsScopedList.warning.data.Rd | 129a6bbec68da9254c57a747a353ab79a675b04b | [
"MIT"
] | permissive | GVersteeg/autoGoogleAPI | 8b3dda19fae2f012e11b3a18a330a4d0da474921 | f4850822230ef2f5552c9a5f42e397d9ae027a18 | refs/heads/master | 2020-09-28T20:20:58.023495 | 2017-03-05T19:50:39 | 2017-03-05T19:50:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 617 | rd | TargetPoolsScopedList.warning.data.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/compute_objects.R
\name{TargetPoolsScopedList.warning.data}
\alias{TargetPoolsScopedList.warning.data}
\title{TargetPoolsScopedList.warning.data Object}
\usage{
TargetPoolsScopedList.warning.data()
}
\value{
TargetPoolsScopedList.warning.data object
}
\description{
TargetPoolsScopedList.warning.data Object
}
\details{
Autogenerated via \code{\link[googleAuthR]{gar_create_api_objects}}
No description
}
\seealso{
Other TargetPoolsScopedList functions: \code{\link{TargetPoolsScopedList.warning}},
\code{\link{TargetPoolsScopedList}}
}
|
2df43d8fda9ebcfe93321802542ddc934d3b2b92 | 406eb4f67e4f0ffe607dcaf39763f44418a78bff | /man/dcmEvidence.Rd | d6e2307ae62912db72ae1af8bfeaf403ccc221e2 | [] | no_license | cran/FIAR | 6af5007096986cef711b287691bd000d90183a3c | 25e7b6c1fa023da58578bdd3c7e6d9c52e388899 | refs/heads/master | 2021-01-19T06:43:30.247772 | 2018-06-05T10:48:21 | 2018-06-05T10:48:21 | 17,717,505 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 540 | rd | dcmEvidence.Rd | \name{dcmEvidence}
\alias{dcmEvidence}
\title{modelfit}
\description{Compute AIC and BIC of a DCM
}
\usage{
dcmEvidence(DCM, ts)
}
\arguments{
\item{DCM}{DCM object.}
\item{ts}{Timeseries to fit the model to.}
}
\value{Creates 2 extra fields DCM$AIC and DCM$BIC}
\seealso{spm.dcm.estimate}
\author{Bjorn Roelstraete}
\examples{
# Compute how well the model DCMex fits the timeseries DCMex$sim
ts <- dcmGenerate(DCMex, SNR=1, ar=.2, names=c('V1','V2','V3'))
DCMex <- dcmEvidence(DCMex, ts)
DCMex$AIC
DCMex$BIC
}
\keyword{ ~DCM }
|
d8bfb493a228b49a35ebab6bdb964a4713af4e2f | 0e269aab5f27deb9da70f53a839facdd99e0f692 | /scripts/plot_summ_2.R | cfbf997e15862c45781d48339aafc96a20cf4e78 | [] | no_license | kichacko/MSH-Resistance-Map | b5ec21657bcf468753e330e6b0ed9183da228c26 | 512a5937026f29e128b49908a0aa2bcc78e7ab59 | refs/heads/master | 2021-10-26T14:52:50.320905 | 2019-04-13T05:15:55 | 2019-04-13T05:15:55 | 112,489,522 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,849 | r | plot_summ_2.R | function_plot_summ_2 = function(input_data, subset_pathogen, subset_procedure, subset_year){
################# LOAD DATA ######################
df_01 = input_data
subset_year = as.numeric(as.character(subset_year))
################# SUBSET ######################
# Subset
df_01 = subset(df_01, df_01$Pathogen %in% subset_pathogen)
df_01 = subset(df_01, df_01$Procedure %in% subset_procedure)
df_01 = df_01[df_01$Year == subset_year, ]
df_01 = df_01[row.names(unique(df_01[ , c("Isolate.ID", "Pathogen", "Procedure", "Year")])),]
################# CALCULATIONS ######################
matrix_01 = do.call(
data.frame,
aggregate(
formula = Isolate.ID ~ Pathogen + Procedure + Unit,
data = df_01,
FUN = length
)
)
names(matrix_01) = c("Pathogen", "Procedure", "Unit", "Count")
matrix_01$Count = as.numeric(as.character(matrix_01$Count))
################# PLOT ######################
# plot
plot_01 = ggplot(
data = matrix_01,
aes(
x = Unit,
y = Count,
fill = Unit
)
) +
geom_bar(
position = "dodge",
stat = "identity",
color = "white"
) +
theme(
axis.title.x = element_blank(),
axis.text.x = element_text(size = 6, angle = 90),
axis.line.x = element_line(color="black"),
axis.line.y = element_line(color="black"),
panel.background = element_blank(),
panel.grid = element_line(color = "gray"),
strip.text.x = element_text(size = 8),
legend.position = "none"
) +
ylab("Number of isolates")
plot_01 = ggplotly(plot_01, tooltip = c("x", "fill","y"), pdf(file = NULL))
return(plot_01)
} |
31fc0412598d31fa0e6daa008a537fc2f6656946 | 8e52cfdf2db46054104efc466320b4ad0217ea7f | /man/do_feature_selection_by_random_forest.Rd | 53c90e70676bad9b89ebffe66cbef2e197dbf432 | [] | no_license | jfontestad/hospital-readmission | 82469e9e60203e11e7fe8d40452a3087ec4adad6 | 67f50933b54cfa22a69e1b2cb7a5a00168f2625e | refs/heads/master | 2023-03-17T00:33:29.619647 | 2019-08-30T03:17:49 | 2019-08-30T03:17:49 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 360 | rd | do_feature_selection_by_random_forest.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utilities.R
\name{do_feature_selection_by_random_forest}
\alias{do_feature_selection_by_random_forest}
\title{Title}
\usage{
do_feature_selection_by_random_forest(data_df, data_vars, outcome, data_dir,
output_pattern)
}
\arguments{
\item{output_pattern}{}
}
\description{
Title
}
|
b410129f59a4c0b43674eda954ae781ce23c77f5 | 69cb42388719a7137ff3afbef869e6d45fcef4d5 | /Classification/mmm.R | f5e31b56a1146df0f51da31dc29b61968192f9fb | [
"MIT"
] | permissive | Deekerubo/AccidentPrediction | 660ad1e28585a2d09f253712d73f20be0e7b0c0a | e2e9739a7c1be3a48947107c0e42468b8331e92e | refs/heads/master | 2020-03-26T02:10:48.346321 | 2019-06-04T11:10:35 | 2019-06-04T11:10:35 | 144,400,039 | 0 | 1 | MIT | 2023-05-10T21:18:31 | 2018-08-11T16:14:31 | R | UTF-8 | R | false | false | 703 | r | mmm.R | #perfome linear regression on training data
reg<- lm(Accident_Severity~.,data=train_data)
#predicttestdata using liner modelling
predict<- predict(reg,newdata=test_data)
predict
plot(predict)
#testing the posibility of accident severity with values less than
Tab<- table(test_data$Accident_Severity, predict>2.8 )
Tab
#calculate the miscalculation error
mcrate<- 1-sum(diag(Tab))/sum(Tab)
mcrate
View(mcrate)
plot(mcrate)
#............rpart decison tree.......
library(rpart)
#library(plyr)
#library(dplyr)
#training model1
model1<- rpart(Accident_Severity~., train_data, method ="class")
model1<-predict(model1, test_data,type ="class")
View(model1)
summary(model1)
plot(model1) |
f04310f8b39659390601affc263a74b5431a18c7 | 1d821994bcabecfbb2b5d10feca107ede2f95bc3 | /R/inverse.logit.r | 57ce2e452733b3204ac26d3704553953b41d7f9a | [
"MIT"
] | permissive | jae0/carstm | 1ca3c10790edf39abc938cadc4599839f92fd206 | 3fdfe0b6d8bf601acaf8b1732274d2ff217731ed | refs/heads/master | 2023-08-16T23:48:09.986292 | 2023-08-16T23:02:38 | 2023-08-16T23:02:38 | 117,303,833 | 0 | 0 | MIT | 2018-11-09T15:04:41 | 2018-01-13T01:27:33 | R | UTF-8 | R | false | false | 146 | r | inverse.logit.r |
inverse.logit = function( x ) {
# x should be the log odds ratio
oddsratio = exp(x)
prob = oddsratio / (1 + oddsratio )
return (prob)
}
|
94a3279678a5f03602e225e80005745bd3097ad2 | 2080044625e70fbe2407d923d1b3727da9ced1b2 | /glassKNNnw.R | 8bece9f789ceac4e8e017b6085e9d69255bb6670 | [] | no_license | kurla-sreevalli/DS_Rcode | 01a26929ed49f239fdf1b7d7eff50633ec857758 | 7d527816715954c993112ce1dcabd1a30fc26a1e | refs/heads/master | 2023-01-29T07:40:32.493397 | 2020-12-08T11:04:02 | 2020-12-08T11:04:02 | 294,662,354 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,916 | r | glassKNNnw.R | library(readr)
glass <- read_csv("C:/Users/sreevalli kurla ksv/Downloads/Excelr Data-20190131T150000Z-001/Excelr Data/Data Sets/KNN/glass.csv")
View(glass)
str(glass)
#table of type
table(glass$Type)
glassdf <- as.data.frame(glass)
# Replace types column names for 1,2,3,4,5,6,7 as type1,type2,type3,type4,type5,type6,type7. type is factor with 7 levels that is 1,2,3,4,5,6 and 7. We also replacing these seven enteries.
glassdf$Type <- factor(glassdf$Type, levels = c("1","2","3","4","5","6","7"), labels = c("type1","type2","type3","type4","type5","type6","type7"))
# table or proportation of enteries in the datasets. What % of entry is type1,type2,type3,type4,type5,type6 and type7
round(prop.table(table(glassdf$Type))*100,10)
summary(glassdf[c("RI","Na","Mg")])
#Create a function to normalize the data
norm <- function(x){
return((x-min(x))/(max(x)-min(x)))
}
#test normalization
norm(c(1,2,3,4,5))
norm(c(10,20,30,40,50))
#Apply the normalization function to wbcd dataset
glass_n <- as.data.frame(lapply(glassdf[1:9], norm))
View(glass_n)
#create training and test datasets
glass_train <- glass_n[1:149,]
glass_test <- glass_n[150:214,]
#Get labels for training and test datasets
glass_train_labels <- glassdf[1:149,10]
glass_test_labels <- glassdf[150:214,10]
# Build a KNN model on taining dataset
library("class")
library("caret")
# Building the KNN model on training dataset and also need labels which we are including c1. Once we build the preduction model
# we have to test on test dataset
glass_pred <- knn(train = glass_train, test = glass_test, cl = glass_train_labels, k=21)
class(glass_train)
class(glass_test)
## Now evualuation the model performance
# install package gmodels
install.packages("gmodels")
library("gmodels")
# Create cross table of predicted and actual
CrossTable( x = glass_test_labels, y = glass_pred)
|
0a9002a3d7ea62a8c74eacee499c53b50169e2c2 | 67565703b258987307a8c51a12c4c9b24ee3f7ca | /R/geom-colh.R | 238d36f309496e045ea81548d11b441ae9a12596 | [] | no_license | lionel-/ggstance | 2c049629963e4aac01a3a49f115f56e3936e560c | 7e303d755624d3764d4543a18c9abdbd410e2767 | refs/heads/master | 2023-04-03T17:08:36.786110 | 2023-03-14T16:59:59 | 2023-03-14T16:59:59 | 50,201,421 | 202 | 24 | null | 2023-03-14T17:00:02 | 2016-01-22T19:04:00 | R | UTF-8 | R | false | false | 1,258 | r | geom-colh.R | #' @rdname geom_barh
#' @eval rd_aesthetics("geom", "colh")
#' @export
geom_colh <- function(mapping = NULL, data = NULL,
position = "stackv",
...,
width = NULL,
na.rm = FALSE,
show.legend = NA,
inherit.aes = TRUE) {
layer(
data = data,
mapping = mapping,
stat = "identity",
geom = GeomColh,
position = position,
show.legend = show.legend,
inherit.aes = inherit.aes,
params = list(
width = width,
na.rm = na.rm,
...
)
)
}
#' @rdname ggstance-ggproto
#' @format NULL
#' @usage NULL
#' @export
GeomColh <- ggproto("GeomColh", ggplot2::GeomRect,
required_aes = c("y", "x"),
setup_data = function(data, params) {
data$width <- data$width %||%
params$width %||% (resolution(data$y, FALSE) * 0.9)
transform(data,
xmin = pmin(x, 0), xmax = pmax(x, 0),
ymin = y - width / 2, ymax = y + width / 2, width = NULL
)
},
draw_panel = function(self, data, panel_params, coord, width = NULL) {
# Hack to ensure that height is detected as a parameter
ggproto_parent(ggplot2::GeomRect, self)$draw_panel(data, panel_params, coord)
}
)
|
c8ba20fa86e63a9bc6ec99bae21a85c1c02a2f4d | 1e800858d0d3130be58458b284f31cc196f48f10 | /int.R | 380590988da804f95cdeb8912b0857cd9b878bf3 | [] | no_license | ShanYu3393/fusionFLM | 6f1ac495e680b10ac193abfdbff59b28c83cb12f | e216f63d2ff9adc86b64a1c78568966af22a92df | refs/heads/master | 2022-11-09T12:46:23.769244 | 2020-06-23T15:17:42 | 2020-06-23T15:17:42 | 273,521,201 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 101 | r | int.R | int <- function(func, time, X, t.range) {
sum(X * func(time) * (c(time[-1], t.range[2]) - time))
}
|
adc89d7c8a405bf536b349e52bde97a52c7bb6a2 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/KMsurv/examples/bmt.Rd.R | 7280a17ad67b3e25d67a17d1b9fc7183a2d0f3e2 | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 136 | r | bmt.Rd.R | library(KMsurv)
### Name: bmt
### Title: data from Section 1.3
### Aliases: bmt
### Keywords: datasets
### ** Examples
data(bmt)
|
de458da2deac405246e67efdfcba7d51c1d325ef | 135bfc3f68e44689cb31b9c321f5c6cfe860d614 | /BS_compiled_OutputsAnalysis.R | 1b79175bf0af64fdaf8a5a5ef09bfac1bac9b2a4 | [] | no_license | siminl/Project-Coupon | 16601669c54c681bbcbc21527b127d0561d03b2b | c49eceeabe52916bd0d89a5e724662aeff7f77f5 | refs/heads/master | 2021-07-20T13:54:49.284769 | 2021-02-21T16:40:58 | 2021-02-21T16:40:58 | 87,201,129 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 105,744 | r | BS_compiled_OutputsAnalysis.R | industries_to_check <- c(2,5,11,10,1)
# bs_outputs_df <- vector()
# bs_outputs_df <- sapply(bs_outputs_stored, function(x) sapply(x,function(xx) cbind(bs_outputs_df,c(xx$par,xx$convergence,xx$value))))
#
#
# bs_outputs_compile <- (t(rep(0,dim(bs_outputs_df[[1]])[1]+2)))
# colnames(bs_outputs_compile) <- c("ch","thetaalpha","thetaT","thetad","betad","theta0","beta0","conv","funvalue","mase_dfremain","type")
#
# for(i in industries_to_check){
# tmp <- data.frame(t(bs_outputs_df[[i]]),
# mase_dfremain = t(t(mase_remain[[i]])),type=i)
# colnames(tmp) <- c("ch","thetaalpha","thetaT","thetad","betad","theta0","beta0","conv","funvalue","mase_dfremain","type")
# bs_outputs_compile <- rbind(bs_outputs_compile,tmp)
# }
# bs_outputs_compile <- bs_outputs_compile[-1,]
# bs_outputs_compile <- bs_outputs_compile
#
# save(bs_outputs_compile,file= "bs_out_compile.Rdata")
# # the sales estimates during holiday time
# Scoef <- data.frame(type = c(2,5,11,10,1),
# Salpha = c(-4.47,-1.40,-3.01,-1.40,-3.20),
# Sd = c(3.36,6.33,1.0,0.01,0.04),
# ST = c(2.60,0.88,2.8,0.69,1.03))
# save(compiled_results,file = "compiled_results.Rdata")
# save(compiled_results_ls,file = "compiled_results_ls.Rdata")
compiled_results <- vector()
compiled_results_ls <- vector(mode = "list",length=11)
for(i in industries_to_check){
if(i == 2){
nonhol_data <- dealsfull%>%filter(is.na(holiday)==1,type%in%c(2,3))%>%{.->>dealsfull_type}%>%
filter(total_volume>quantile(dealsfull_type$total_volume,c(0.05)), total_volume<quantile(dealsfull_type$total_volume,c(0.95)))%>%
filter(original_price>quantile(dealsfull_type$original_price,c(0.4)), original_price<quantile(dealsfull_type$original_price,c(0.6)))%>%
{.->>sdopph}%>%
mutate(original_price = original_price/sd(original_price))
hol_data <- dealsfull%>%filter(is.na(holiday)==0,type%in%c(2,3),prep.period>0,offering_duration<=40)%>%{.->>dealsfull_type}%>%
filter(total_volume>quantile(dealsfull_type$total_volume,c(0.05)), total_volume<quantile(dealsfull_type$total_volume,c(0.95)))%>%
filter(original_price>quantile(dealsfull_type$original_price,c(0.4)), original_price<quantile(dealsfull_type$original_price,c(0.6)))%>%
{.->>sdop}%>%
mutate(original_price = original_price/sd(original_price))
# set.seed(100)
# rs <-list()
# for(b in 1:6){
# rs[[b]] <- sample(dim(hol_data)[1],dim(hol_data)[1],replace = T)
# }
# hol_data <- hol_data[rs[[1]],]
}else if(i %in% c(10)){
nonhol_data <- dealsfull%>%filter(is.na(holiday)==1,type==i)%>%{.->>dealsfull_type}%>%
filter(total_volume>quantile(dealsfull_type$total_volume,c(0.05)), total_volume<quantile(dealsfull_type$total_volume,c(0.95)))%>%{.->>sdopph}%>%
mutate(original_price = original_price/sd(original_price))
hol_data <- dealsfull%>%filter(is.na(holiday)==0,type==i,prep.period>0,offering_duration<=25)%>%{.->>dealsfull_type}%>%
filter(total_volume>quantile(dealsfull_type$total_volume,c(0.05)), total_volume<quantile(dealsfull_type$total_volume,c(0.95)))%>%
{.->>sdop}%>%
mutate(original_price = original_price/sd(original_price))
# set.seed(200)
# rs <-list()
# for(b in 1:1){
# rs[[b]] <- sample(dim(hol_data)[1],dim(hol_data)[1],replace = T)
# }
# hol_data <- hol_data[rs[[1]],]
#
}else if(i %in% c(11,1)){
nonhol_data <- dealsfull%>%filter(is.na(holiday)==1,type==i)%>%{.->>dealsfull_type}%>%
filter(total_volume>quantile(dealsfull_type$total_volume,c(0.05)), total_volume<quantile(dealsfull_type$total_volume,c(0.95)))%>%{.->>sdopph}%>%
mutate(original_price = original_price/sd(original_price))
hol_data <- dealsfull%>%filter(is.na(holiday)==0,type==i,prep.period>0,offering_duration<40)%>%{.->>dealsfull_type}%>%
filter(total_volume>quantile(dealsfull_type$total_volume,c(0.1)), total_volume<quantile(dealsfull_type$total_volume,c(0.9)))%>%
{.->>sdop}%>%
mutate(original_price = original_price/sd(original_price))
# set.seed(200)
# rs <-list()
# for(b in 1:1){
# rs[[b]] <- sample(dim(hol_data)[1],dim(hol_data)[1],replace = T)
# }
# hol_data <- hol_data[rs[[1]],]
}else{
nonhol_data <- dealsfull%>%filter(is.na(holiday)==1,type==i)%>%{.->>dealsfull_type}%>%
filter(total_volume>quantile(dealsfull_type$total_volume,c(0.05)), total_volume<quantile(dealsfull_type$total_volume,c(0.95)))%>%{.->>sdopph}%>%
mutate(original_price = original_price/sd(original_price))
hol_data <- dealsfull%>%filter(is.na(holiday)==0,type==i,prep.period>0,offering_duration<=40)%>%{.->>dealsfull_type}%>%
filter(total_volume>quantile(dealsfull_type$total_volume,c(0.05)), total_volume<quantile(dealsfull_type$total_volume,c(0.95)))%>%
{.->>sdop}%>%
mutate(original_price = original_price/sd(original_price))
}
if(i == 2){
hol_data <- hol_data%>%filter(city!="XA")
}else if(i %in% c(10)){
hol_data <- hol_data%>%filter(platform!="ww")
}else if(i %in% c(5)){
#hol_data <- hol_data%>%filter(platform!="ww",city!="GZ")
}
rt <- getPerformance(hol_data,sdop, nonhol_data, sdopph, output_ls=bs_outputs[[i]], type=i,mleout_preh,mleout_h )
compiled_results_ls[[i]] <- rt
compiled_results <- rbind(compiled_results,
data.frame(type.x=i,dsidx2 = rt$dsidx2vec, dsidx = rt$dsidxvec,
alterD = rt$alterDvec,alterDsigvec = rt$alterDsigvec,
deltaD = rt$deltaDvec,alterP = rt$alterP,deltaDvec = rt$deltaDvec,
mch = rt$mchs, mcnh = rt$mcnhs,mucp = rt$mucpvec,mucpnh=rt$mucpnhvec,
D1h = rt$D1hvec, D2h = rt$D2hvec,
D2nh = rt$D2nhvec, ratio = rt$ratiovec,
thetaalphahat = rt$thetaalpha, thetaThat = rt$thetaT,
thetadhat = rt$thetad, betadhat = rt$betad)
)
}
# ------- Start of displaying the estimators -------------
out_ls_name <- vector(mode="list",length=11)
bs_sample <- c(0,0)
for(i in industries_to_check){
root_path_base <- "/Users/siminli/Documents/GitHub/Project-Coupon/Deals_bootstrap_outputs/"
root_path_folder <- paste("output_type",i,"/",sep="")
root_path <- paste(root_path_base,root_path_folder,sep="")
if(i %in% c(10,11)){
out_ls_name[[i]] <- paste(root_path,list.files(path=root_path, pattern="out\\_[[:digit:]][[:digit:]]\\_[[:digit:]].*\\.rda"),sep="")
}else{
out_ls_name[[i]] <- paste(root_path,list.files(path=root_path,pattern="out\\_[[:digit:]]\\_[[:digit:]].*\\.rda"),sep="")
}
if(i %in% c(2) ){
bs_sample <- rbind(bs_sample,data.frame(type = i,
sample_number = word(str_extract(pattern="out\\_[[:digit:]]\\_[[:digit:]].*\\.rda",
out_ls_name[[i]]), 3, sep = "_")))
}else if(i %in% c(1)){
bs_sample <- rbind(bs_sample,data.frame(type = i,
sample_number = word(str_extract(pattern="out\\_[[:digit:]]\\_[[:digit:]].*\\.rda",
out_ls_name[[i]]), 2, sep = "_")))
}else if (i %in% c(5)){
bs_sample <- rbind(bs_sample,data.frame(type = i,
sample_number = word(word(str_extract(pattern="out\\_[[:digit:]]\\_[[:digit:]].*\\.rda",
out_ls_name[[i]]), 4, sep = "_"),1, sep="\\.")))
}else{
bs_sample <- rbind(bs_sample,data.frame(type = i,
sample_number = word(word(str_extract(pattern="out\\_[[:digit:]][[:digit:]]\\_[[:digit:]].*\\.rda",
out_ls_name[[i]]), 4, sep = "_"),1, sep="\\.")))
}
}
bs_sample <- bs_sample[-1,]
compiled_results <- cbind(compiled_results,bs_sample)
bs_outputs_compile <- cbind(bs_outputs_compile,compiled_results)%>%
filter(conv==0,mase_dfremain>0.55)
picked <- rep(0, dim(bs_outputs_compile)[1])
picked[c(1,13,23,38,66)] <- 1
estimates_sd <- (merge(unique(compiled_results),
bs_outputs_compile %>% sl(funvalue,mase_dfremain,mch,D1h) %>%
mt(picked = picked), by=c("mch","D1h"), all.y=TRUE) %>%
fl(is.na(funvalue)==0)) %>% arrange(type)
estimates_sd[-c(24,25,26,27,28,seq(15,20),40,seq(59,64),32,33,34,36,37,39,45,44,47,48,49,50,4,5,10,3,7,9,56,70),] %>%
mutate(mcratio = mucp/mucpnh) %>%
# gb(type, sample_number) %>%
# mt(minfuncval = (funvalue==min(funvalue))) %>%
# fl(minfuncval == 1) %>%
gb(type) %>% mt(mumch = mean(mch),
mumcnh = mean(mcnh),
mumcratio = mean(mcratio),
mudeltaDvec = mean(deltaDvec),
mualterD = mean(alterD),
mudsidx2 = mean(dsidx2),
muthetaalpha= mean(thetaalphahat),
muthetaT = mean(thetaThat),
muthetad = mean(thetadhat),
mubetad = mean(betadhat),
muratio = mean(ratio),
mualterP = mean(alterP),
muD1h = mean(D1h),
muD2h = mean(D2h),
muD2nh = mean(D2nh)) %>%
gb(type) %>%
summarise(ssize = n(),semch = sqrt(sum((mch-mumch)^2)/ssize),
semcnh = sqrt(sum((mcnh-mumcnh)^2)/ssize),
semcratio = sqrt(sum((mcratio-mumcratio)^2)/ssize),
sedeltaDvec = sqrt(sum((deltaDvec-mudeltaDvec)^2)/ssize),
sealterD = sqrt(sum((alterD-mualterD)^2)/ssize),
sedsidx2 = sqrt(sum((dsidx2-mudsidx2)^2)/ssize),
sethetaalpha = sqrt(sum((thetaalphahat-muthetaalpha)^2)/ssize),
sethetaT = sqrt(sum((thetaThat-muthetaT)^2)/ssize),
sethetad = sqrt(sum((thetadhat-muthetad)^2)/ssize),
sebetad = sqrt(sum((betadhat-mubetad)^2)/ssize),
seratio = sqrt(sum((ratio-muratio)^2)/ssize),
sealterP = sqrt(sum((alterP-mualterP)^2)/ssize),
seD1h = sqrt(sum((D1h-muD1h)^2)/ssize),
seD2h = sqrt(sum((D2h-muD2h)^2)/ssize),
seD2nh = sqrt(sum((D2nh-muD2nh)^2)/ssize)) -> se_df
se_df <- se_df[c(2,3,5,4,1),]
save("se_df", file = "se_df.Rdata")
#36,37
estimates_sd[-c(24,25,26,27,28,seq(15,20),seq(59,64),32,33,34,39,45,40,44,47,46,48,49,51,3,4,5,7,10,9,56,70),] %>%
mutate(mcratio = mucp/mucpnh) %>%
# gb(type, sample_number) %>%
# mt(minfuncval = (funvalue==min(funvalue))) %>%
# fl(minfuncval == 1) %>%
gb(type) %>% mt(mumch = mean(mch),
mumcnh = mean(mcnh),
mumcratio = mean(mcratio),
mudeltaDvec = mean(deltaDvec),
mualterD = mean(alterD),
mudsidx2 = mean(dsidx2),
muthetaalpha= mean(thetaalphahat),
muthetaT = mean(thetaThat),
muthetad = mean(thetadhat),
mubetad = mean(betadhat),
muratio = mean(ratio),
mualterP = mean(alterP),
muD1h = mean(D1h),
muD2h = mean(D2h),
muD2nh = mean(D2nh)) %>%
gb(type) %>%
summarise(ssize = n(),semch = sqrt(sum((mch-mumch)^2)/ssize),
semcnh = sqrt(sum((mcnh-mumcnh)^2)/ssize),
semcratio = sqrt(sum((mcratio-mumcratio)^2)/ssize),
sedeltaDvec = sqrt(sum((deltaDvec-mudeltaDvec)^2)/ssize),
sealterD = sqrt(sum((alterD-mualterD)^2)/ssize),
sedsidx2 = sqrt(sum((dsidx2-mudsidx2)^2)/ssize),
sethetaalpha = sqrt(sum((thetaalphahat-muthetaalpha)^2)/ssize),
sethetaT = sqrt(sum((thetaThat-muthetaT)^2)/ssize),
sethetad = sqrt(sum((thetadhat-muthetad)^2)/ssize),
sebetad = sqrt(sum((betadhat-mubetad)^2)/ssize),
seratio = sqrt(sum((ratio-muratio)^2)/ssize),
sealterP = sqrt(sum((alterP-mualterP)^2)/ssize),
seD1h = sqrt(sum((D1h-muD1h)^2)/ssize),
seD2h = sqrt(sum((D2h-muD2h)^2)/ssize),
seD2nh = sqrt(sum((D2nh-muD2nh)^2)/ssize)) -> tt
# 69/66, 38/37, 13/16
(bs_outputs_compile[c(1,16,23,38,66),]%>%mutate(mcratio = mucp/mucpnh)%>%
select(type,mch,mcnh,thetaalphahat,thetaThat,thetadhat,betadhat,dsidx,dsidx2,mcratio,mucp,alterD,alterP,deltaDvec,D1h,D2h,D2nh,ratio)) -> pest_df
#thetaThat = -0.87 for 5,
# S_alpha, S_T, S_d --------
Scoef_se <- c(0,0,0,0)
for(i in industries_to_check){
hol_data <- dealsfull%>%filter(is.na(holiday)==0,type==i,prep.period>0,offering_duration<=40)%>%{.->>dealsfull_type}%>%
filter(total_volume>quantile(dealsfull_type$total_volume,c(0.05)), total_volume<quantile(dealsfull_type$total_volume,c(0.95)))%>%
mutate(original_price = original_price/sd(original_price))
if(i == 2){
hol_data <- hol_data%>%filter(city!="XA")
}else if(i %in% c(10)){
hol_data <- hol_data%>%filter(platform!="ww")
}else if(i %in% c(5)){
#hol_data <- hol_data%>%filter(platform!="ww",city!="GZ")
}
summary(lm(data=hol_data%>%mutate(discprice = (1-discount)*original_price, # = price/partysize
logvol = log(total_volume),
perperson = original_price/partysize,
competitors_ct = (competitors/sd(competitors)),
logcomp = ifelse(competitors!=0,log(competitors),0),
fixlength = ifelse(offering_duration==15,1,
ifelse(offering_duration==30,2,
ifelse(offering_duration==45,3,0)))),
logvol~log(offering_duration)+I(log(offering_duration)^2)+
discprice+I(discprice^2)+
log(prep.period) + I((log(prep.period))^2) +
#log(discprice)+I(log(discprice)^2)+
I((original_price))+I(1/log(original_price))+I(1/(log(original_price)^2))+
logcomp+weekends+platform:weekends+
platform+city
)
) -> lm_S
Scoef_se <- rbind(Scoef_se,data.frame(type = i, t(coef(lm_S)[c(2,4,6),2])))
}
Scoef_se <- Scoef_se[-1,]
# the MASE and MAPE table --------
digs <- 4
rbind(Industry = c("Casual Dine","Fine Dine","Photography","Outdoor","Body Care"),
cbind(round(mase_ls[[2]][-1,][1,],digs),round(mase_ls[[5]][-1,][5,],digs),round(mase_ls[[11]][-1,][8,],digs),
round(mase_ls[[10]][-1,][6,],digs),round(mase_ls[[1]][-1,][26,],digs)),
cbind(round(mape_ls[[2]][-1,][1,],digs),round(mape_ls[[5]][-1,][5,],digs),round(mape_ls[[11]][-1,][8,],digs),
round(mape_ls[[10]][-1,][6,],digs),round(mape_ls[[1]][-1,][26,],digs))) -> err_table
rownames(err_table) <- c("Industry",
"MASE (discount, $\\alpha$)","MASE (launch day to holiday, $T$)","MASE (duration, $d$)",
"MAPE (discount, $\\alpha$)","MAPE (launch day to holiday, $T$)","MAPE (duration, $d$)")
print(xtable(err_table[,c(4,1,2,5,3)]), only.contents=TRUE, include.rownames=T,
include.colnames=T, floating=T, sanitize.rownames.function = identity,
file = 'err_table.tex')
# estimated structural parameters table -----
rbind(Industry = c("Casual Dine","Fine Dine","Photography","Outdoor","Body Care"),
# type = industries_to_check,
# c = signif(cvec,2),
# cnh = signif(cnhvec,2),
thetaalpha = format(round(pest_df$thetaalphahat,2)),
thetaalphasig = paste("(",format(round(se_df$sethetaalpha,2)),")",sep=""),
thetaT = format(round(pest_df$thetaThat,2)),
thetaTsig = paste("(",format(round(se_df$sethetaT,2)),")",sep=""),
thetad = format(round(pest_df$thetadhat,2)),
thetadsig = paste("(",format(round(se_df$sethetad,2)),")",sep=""),
betad = format(round(pest_df$betadhat,2)),
betadsig = paste("(",format(round(se_df$sebetad,2)),")",sep=""),
Salpha = format(round(Scoef$Salpha,2)),
Salphasig = paste("(",format(round(Scoef_se$discprice,2)),")",sep=""),
ST = format(round(Scoef$ST,2)),
STsig = paste("(",format(round(Scoef_se$log.prep.period.,2)),")",sep=""),
Sd = format(round(Scoef$Sd,2)),
Sdsig = paste("(",format(round(Scoef_se$log.offering_duration.,2)),")",sep=""),
# betad_nh=round(betad_nh,2),
# betaalpha_nh=round(betaalpha_nh,2),
sigma = format(round(gamma_h[industries_to_check,1],2)),
sigmasig = paste("(",format(round(gamma_h_sig[industries_to_check,1],2)),")",sep=""),
gamma = format(round(gamma_h[industries_to_check,2],2)),
gammasig = paste("(",format(round(gamma_h_sig[industries_to_check,2],2)),")",sep=""),
mch = format(round(pest_df$mch,2)),
mchsig = paste("(",format(round(se_df$semch,2)),")",sep=""),
mcnh = format(round(pest_df$mcnh,2)),
mcnhsig = paste("(",format(round(se_df$semcnh,2)),")",sep=""),
mcratiovec = format(round(pest_df$mcratio,2)),
mcratiovecsig = paste("(",format(round(se_df$semcratio,2)),")",sep=""),
ratiovec = format(round(pest_df$ratio,2)),
ratiosig = paste("(",format(round(se_df$seratio,2)),")",sep=""),
D1hvec = format(round(pest_df$D1h,0)),
D1hsigvec = paste("(",format(round(se_df$seD1h,0)),")",sep=""),
D2hvec = format(round(pest_df$D2h,2)),
D2hsigvec = paste("(",format(round(se_df$seD2h,2)),")",sep=""),
D2nhvec = format(round(pest_df$D2nh,2)),
D2nhsigvec = paste("(",format(round(se_df$seD2nh,2)),")",sep=""),
# mucpvec = signif(mucpvec,2),
# sigcpvec = signif(sigcpvec,2),
#dsidxvec = round(dsidxvec,2),
#dsidxsigvec = round(dsidxsigvec,2),
deltaDvec = format(round(pest_df$deltaDvec,2)),
deltaDsigvec = paste("(",format(round(se_df$sedeltaDvec,2)),")",sep=""),
# Varidxvec = round(Varidxvec,2),
dsidx2vec = format(round(pest_df$dsidx2,2)) ,
dsidx2sigvec = paste("(",format(round(se_df$sedsidx2,2)),")",sep=""),
alterDvec = format(round(pest_df$alterD,2)),
alterDsigvec = paste("(",format(round(se_df$sealterD,2)),")",sep=""),
alterPvec = format(round(pest_df$alterP,2)),
alterPsigvec = paste("(",format(round(se_df$sealterP,2)),")",sep="")
) -> StructEstTable
# StructEstTable <- StructEstTable[,c(4,1,2,5,3)]
# rownames(StructEstTable) <- c("Industry",
# # "Holiday cost parameter, $c_{h}$","Non-holiday cost parameter, $c_{nh}$",
# "Discount sensitivity in deal demand allocation, $\\widehat{\\theta}_{\\alpha}$",
# "s.e., discount sensitivity in demand allocation, $\\sigma_{\\theta_{\\alpha}}$",
# "Launch time sensitivity in deal demand allocation, $\\widehat{\\theta}_T$",
# "s.e., launch time sensitivity in demand allocation, $\\sigma_{\\theta_T}$",
# "Duration sensitivity in deal demand allocation, $\\widehat{\\theta}_d$",
# "s.e., duration sensitivity in demand allocation, $\\sigma_{\\theta_d}$",
# "Duration sensitivity, holiday full price customers, $\\widehat{\\beta}_d$",
# "s.e., duration sensitivity, holiday full price customers, $\\sigma_{\\beta_d}$",
# "Discount sensitivity in deal sales volume, $\\widehat{\\theta}_{S,\\alpha}$",
# "s.e., discount sensitivity in deal sales volume, $\\sigma_{\\theta_{S,\\alpha}}$",
# "Launch time sensitivity in deal sales volume, $\\widehat{\\theta}_{S,T}$",
# "s.e., launch time sensitivity in deal sales volume, $\\sigma_{\\theta_{S,T}}$",
# "Duration sensitivity in deal sales volume, $\\widehat{\\theta}_{S,d}$",
# "s.e., duration sensitivity in deal sales volume, $\\sigma_{\\theta_{S,d}}$",
# # "Duration sensitivity, non-holiday full price customers, $\\beta_{d,nh}$",
# # "Discount sensitivity, non-holiday full price customers,$\\beta_{\\alpha,nh}$",
# "Baseline holiday demand variance, $\\widehat{\\sigma}_h$",
# "Launch time sensitivity in demand variance, $\\widehat{\\gamma}_h$",
# "Holiday marginal service costs, $\\widehat{mc}_h$",
# "s.e., holiday marginal service costs, $\\sigma_{mc_{h}}$",
# "Non-holiday marginal service costs, $\\widehat{mc}_{nh}$",
# "s.e., non-holiday marginal service costs, $\\sigma_{mc_{nh}}$",
# "Ratio of marginal cost price ratio, $\\widehat{R}_c \\triangleq \\frac{\\widehat{mc}_{h}}{p_h}/\\frac{\\widehat{mc}_{nh}}{p_{nh}}$",
# "s.e., Ratio of marginal cost price ratio, $\\sigma_{R_c}$",
# "Holidays redemption ratio, $\\widehat{r}$",
# "s.e., holidays redemption ratio, $\\sigma_{r}$",
# "Holiday discount demand, $\\widehat{D}_{r,h}$",
# "s.e., holiday discount demand, $\\sigma_{D_{r,h}}$",
# "Holiday full price demand, $\\widehat{D}_{nr,h}$",
# "s.e., holiday full price demand, $\\sigma_{D_{nr,h}}$",
# "Non-holiday full price demand, $\\widehat{D}_{nr,nh}$",
# "s.e., non-holiday full price demand, $\\sigma_{D_{nr,nh}}$",
# # "Holiday margianl cost price ratio, $\\widehat{\\frac{mc_{h}}{p}}$",
# # "$\\sigma_{\\frac{mc_{h}}{p}}$",
# "Holiday demand swings, $\\widehat{\\delta}(\\omega')$",
# "s.e., holiday demand swings, $\\sigma_{\\delta(\\omega')}$",
# # "Variance reduction index, $\\mathcal{R}$",
# "Demand smoothing index, $\\mathcal{B}(\\omega',\\omega)$",
# "s.e., demand smoothing index, $\\sigma_{\\mathcal{B}(\\omega',\\omega)}$",
# "Percentage total demand change, $\\widehat{\\mathcal{M}}(\\omega,\\omega')$",
# "s.e., percentage total demand change, $\\sigma_{\\mathcal{M}(\\omega,\\omega')}$",
# "Total profit change, $\\widehat{\\mathcal{P}}(\\omega,\\omega')$",
# "s.e., total profit change, $\\sigma_{\\mathcal{P}(\\omega,\\omega')}$")
rownames(StructEstTable) <- c("Industry",
# "Holiday cost parameter, $c_{h}$","Non-holiday cost parameter, $c_{nh}$",
"Discount sensitivity in deal demand allocation, $\\widehat{\\theta}_{\\alpha}$",
"$\\sigma_{\\theta_{\\alpha}}$",
"Launch time sensitivity in deal demand allocation, $\\widehat{\\theta}_T$",
"$\\sigma_{\\theta_T}$",
"Duration sensitivity in deal demand allocation, $\\widehat{\\theta}_d$",
"$\\sigma_{\\theta_d}$",
"Duration sensitivity, holiday full price customers, $\\widehat{\\beta}_d$",
"$\\sigma_{\\beta_d}$",
"Discount sensitivity in deal sales volume, $\\widehat{\\theta}_{S,\\alpha}$",
"$\\sigma_{\\theta_{S,\\alpha}}$",
"Launch time sensitivity in deal sales volume, $\\widehat{\\theta}_{S,T}$",
"$\\sigma_{\\theta_{S,T}}$",
"Duration sensitivity in deal sales volume, $\\widehat{\\theta}_{S,d}$",
"$\\sigma_{\\theta_{S,d}}$",
# "Duration sensitivity, non-holiday full price customers, $\\beta_{d,nh}$",
# "Discount sensitivity, non-holiday full price customers,$\\beta_{\\alpha,nh}$",
"Baseline holiday demand variance, $\\widehat{\\sigma}_h$",
"$\\sigma_{\\sigma_h}$",
"Launch time sensitivity in demand variance, $\\widehat{\\gamma}_h$",
"$\\sigma_{\\gamma_h}$",
"Holiday marginal service costs, $\\widehat{mc}_h$",
"$\\sigma_{mc_{h}}$",
"Non-holiday marginal service costs, $\\widehat{mc}_{nh}$",
"$\\sigma_{mc_{nh}}$",
"Ratio of marginal cost price ratio, $\\widehat{R}_c \\triangleq \\frac{\\widehat{mc}_{h}}{p_h}/\\frac{\\widehat{mc}_{nh}}{p_{nh}}$",
"$\\sigma_{R_c}$",
"Holidays redemption ratio, $\\widehat{r}$",
"$\\sigma_{r}$",
"Holiday discount demand, $\\widehat{D}_{r,h}$",
"$\\sigma_{D_{r,h}}$",
"Holiday full price demand, $\\widehat{D}_{nr,h}$",
"$\\sigma_{D_{nr,h}}$",
"Non-holiday full price demand, $\\widehat{D}_{nr,nh}$",
"$\\sigma_{D_{nr,nh}}$",
# "Holiday margianl cost price ratio, $\\widehat{\\frac{mc_{h}}{p}}$",
# "$\\sigma_{\\frac{mc_{h}}{p}}$",
"Holiday demand swings, $\\widehat{\\delta}(\\omega')$",
"$\\sigma_{\\delta(\\omega')}$",
# "Variance reduction index, $\\mathcal{R}$",
"Demand smoothing index, $\\widehat{\\mathcal{B}}(\\omega^*,\\omega')$",
"$\\sigma_{\\mathcal{B}(\\omega^*,\\omega')}$",
"Percentage total demand change, $\\widehat{\\mathcal{M}}(\\omega^*,\\omega')$",
"$\\sigma_{\\mathcal{M}(\\omega^*,\\omega')}$",
"Total profit change, $\\widehat{\\mathcal{P}}(\\omega^*,\\omega')$",
"$\\sigma_{\\mathcal{P}(\\omega^*,\\omega')}$")
print(xtable(StructEstTable[,c(4,1,2,5,3)]),#tabular.environment = "longtable",
only.contents=TRUE, include.rownames=T,
include.colnames=T, floating=F, sanitize.rownames.function = identity,
file = 'table.tex')
# end of S_alpha, S_T, S_d --------
# ------- End of displaying the estimators -------------
#
View(bs_outputs_compile[c(1,14,23,25,32,40,64,66),]%>%mutate(mcratio = mucp/mucpnh)%>%
select(type, dsidx,dsidx2,mcratio,mucp,alterD,alterP,deltaDvec))
#69/66, 38/37
View(bs_outputs_compile[c(1,13,23,38,66),]%>%mutate(mcratio = mucp/mucpnh)%>%
select(type,mch,thetaalphahat,thetaThat,thetadhat,betadhat,dsidx,dsidx2,mcratio,mucp,alterD,alterP,deltaDvec))
# pt_est <- (bs_outputs_compile[c(1,13,23,38,66),]%>%mutate(mcratio = mucp/mucpnh)%>%
# select(type,thetaalphahat,thetaThat,thetadhat,betadhat,dsidx,dsidx2,mcratio,mucp,alterD,alterP,deltaDvec))
# save(pt_est, file = "point_estimator.Rdata")
# View(bs_outputs_compile%>%mutate(mcratio = mucp/mucpnh)%>%
# select(type, dsidx,dsidx2,mcratio,mucp,alterD,alterDsigvec,alterP,deltaDvec,mase_dfremain,funvalue,D1h,D2h,mch))
# mean estimators and the standard deviation -----
# demand-boosting ----
flattendemand_df <- rbind(data.frame(type = 1,dsidx2 = compiled_results_ls[[1]]$dsidx2[[29]]),
data.frame(type = 2,dsidx2 = compiled_results_ls[[2]]$dsidx2[[1]]),
data.frame(type = 5,dsidx2 = compiled_results_ls[[5]]$dsidx2[[5]]),
data.frame(type = 10,dsidx2 = compiled_results_ls[[10]]$dsidx2[[6]]),
data.frame(type = 11,dsidx2 = compiled_results_ls[[11]]$dsidx2[[8]]))
boostdemand_df <- rbind(data.frame(type = 1,changeind = compiled_results_ls[[1]]$dalter[[29]]),
data.frame(type = 2,changeind = compiled_results_ls[[2]]$dalter[[1]]),
data.frame(type = 5,changeind = compiled_results_ls[[5]]$dalter[[5]]),
data.frame(type = 10,changeind = compiled_results_ls[[10]]$dalter[[6]]),
data.frame(type = 11,changeind = compiled_results_ls[[11]]$dalter[[8]]))
plotdata <- rbind(data.frame(type = 1,totald = pmax(compiled_results_ls[[1]]$Dtotal[[26]],0),
totaldpri = compiled_results_ls[[1]]$Dtotalpri[[26]],
dalter = compiled_results_ls[[1]]$dalter[[26]]),
# data.frame(type = 2,totald = compiled_results_ls[[2]]$Dtotal[[1]][compiled_results_ls[[2]]$D2nhvalpri[[1]]<quantile(compiled_results_ls[[2]]$D2nhvalpri[[1]],0.6)],
# totaldpri = compiled_results_ls[[2]]$Dtotalpri[[1]][compiled_results_ls[[2]]$D2nhvalpri[[1]]<quantile(compiled_results_ls[[2]]$D2nhvalpri[[1]],0.6)],
# dalter = compiled_results_ls[[2]]$dalter[[1]][compiled_results_ls[[2]]$D2nhvalpri[[1]]<quantile(compiled_results_ls[[2]]$D2nhvalpri[[1]],0.6)]),
data.frame(type = 2,totald = compiled_results_ls[[2]]$Dtotal[[1]][compiled_results_ls[[2]]$Dtotalpri[[1]]<quantile(compiled_results_ls[[2]]$Dtotalpri[[1]],0.6)&
compiled_results_ls[[2]]$Dtotalpri[[1]]>quantile(compiled_results_ls[[2]]$Dtotalpri[[1]],0.1) ],
totaldpri = compiled_results_ls[[2]]$Dtotalpri[[1]][compiled_results_ls[[2]]$Dtotalpri[[1]]<quantile(compiled_results_ls[[2]]$Dtotalpri[[1]],0.6)&
compiled_results_ls[[2]]$Dtotalpri[[1]]>quantile(compiled_results_ls[[2]]$Dtotalpri[[1]],0.1)],
dalter = compiled_results_ls[[2]]$dalter[[1]][compiled_results_ls[[2]]$Dtotalpri[[1]]<quantile(compiled_results_ls[[2]]$Dtotalpri[[1]],0.6)&
compiled_results_ls[[2]]$Dtotalpri[[1]]>quantile(compiled_results_ls[[2]]$Dtotalpri[[1]],0.1)]),
data.frame(type = 5,totald =pmax(compiled_results_ls[[5]]$Dtotal[[5]],0),
totaldpri = compiled_results_ls[[5]]$Dtotalpri[[5]],
dalter = compiled_results_ls[[5]]$dalter[[5]]),
data.frame(type = 10,totald = pmax(compiled_results_ls[[10]]$Dtotal[[6]],0),
totaldpri = compiled_results_ls[[10]]$Dtotalpri[[6]],
dalter = compiled_results_ls[[10]]$dalter[[6]]),
data.frame(type = 11,totald = pmax(compiled_results_ls[[11]]$Dtotal[[8]],0),
totaldpri = compiled_results_ls[[11]]$Dtotalpri[[8]],
dalter = compiled_results_ls[[11]]$dalter[[8]]))%>%
group_by(type)%>%
mutate(deltad = totald - totaldpri)%>%
mutate(q90 = quantile(dalter,0.80),q10 = quantile(dalter,0.2))%>%
filter(dalter<q90,dalter>q10)%>%
merge(data.frame(type = c(2,5,11,10,1),
Industry = c("Casual Dining","Fine Dining","Photography","Outdoor","Body Care")),by="type",all.x = TRUE)
colpats <- c("Total Demand (Deals)"= "#66c2a5",
"Total Demand (No Deals)" = "#fc8d62")
ggplot(data=plotdata%>%filter())+
geom_histogram(aes(x=totaldpri,y = ..density..,fill="Total Demand (No Deals)"),color="grey30",bins = 50)+
#geom_line(aes(x=totaldpri,y = ..density..), stat = 'density', colour = '#fc8d62') +
geom_histogram(aes(x=totald,y = ..density..,fill = "Total Demand (Deals)"),color="grey30",bins = 50)+ #blueish
# geom_line(aes(x=totaldpri,y = ..density..), stat = 'density', colour = '#fc8d62')
#geom_line(aes(x=totald,y = ..density..), stat = 'density', colour = '#66c2a5') +
facet_grid(.~Industry,scales = "free")+
scale_fill_manual(values = colpats)+
labs(x="Total Demand (Units)", y = "", fill="")+
theme(legend.position = "bottom")+
coord_flip() -> p1
ggplot(data=plotdata)+
geom_histogram(aes(x=deltad,y = ..density..),fill="grey60",color="grey30",bins = 100)+
facet_grid(.~Industry,scales = "free")+
geom_vline(xintercept=0,linetype=2)+
labs(x="Change in Demand (Units)", y = "")+
coord_flip() -> p2
grid.arrange(p1,p2,nrow=2)
mylegend<-g_legend(p1)
grid.arrange(arrangeGrob(p1 + theme(legend.position="none"),
mylegend,
nrow=2,heights=c(16, 2)),
p2 + theme(legend.position="none"),nrow=2) -> p_demandboosting
# box plots for change in demand ------
ggplot(data=plotdata %>% filter(deltad>-1000))+
geom_boxplot(aes(x=Industry,y=deltad))+
# facet_grid(.~Industry,scales = "free")+
theme_bw(base_size = 18)+
labs(y="Change in Demand (Units)", x = "Industry") -> demanboosting_boxplot
library(snpar)
cdf_data <- c(0,0,0)
for(i in industries_to_check){
plotdata_i <- plotdata %>% fl(type ==i)
kde(plotdata_i$dalter, h = bw.nrd0(plotdata_i$dalter)/2,kernel = c("gaus")) -> cdf_plot
cdf_data <- rbind(cdf_data,data.frame(Fhat = cdf_plot$Fhat, x = cdf_plot$x, type = i) )
}
cdf_data <- cdf_data[-1,]
colpats <- c("Demand Increase"= "#66c2a5",
"Demand Valley" = "#fc8d62")
ggplot(cdf_data %>% #fl(type!=2) %>%
mt(DistType=ifelse(type%in%c(2,5),"Demand Increase","Demand Valley")),
aes( x = x, y = Fhat,
color=DistType, group=as.factor(type)))+
geom_line(aes(colour = DistType,group=as.factor(type)))+
# geom_hline(yintercept = 0, color="#993333")+
# geom_errorbar(aes(ymin=perc_cust_left_mean-1.96*perc_cust_left_sd, ymax=perc_cust_left_mean+1.96*perc_cust_left_sd), width=0.1, colour = 'blue')+
scale_color_manual(values = colpats)+
# xlim(c(-500,500))+
# theme(
# axis.line.x = element_line(colour = 'black', size=0.5, linetype='solid'),
# axis.line.y = element_line(colour = 'black', size=0.5, linetype='solid'),
# panel.grid.minor.y= element_line(colour = "grey", linetype = "dotted"))+
# # scale_y_continuous(breaks = seq(0.3, 0.6, by = 0.05))+
labs(title = "", x = "Percentage of Demand Boosting", y = "Cumulated Percentage of Firms",color ="")+
theme(plot.title = element_text(hjust = 0.8))+
scale_y_continuous(labels = scales::percent)+
# theme(panel.grid.minor.y= element_line(colour = "grey", linetype = "dotted"))+
theme(legend.position = "bottom",legend.title = element_blank()) -> cdf_dalter
colpats <- c("Demand Increase"= "grey10",
"Demand Valley" = "grey70")
ggplot(cdf_data %>% #fl(type!=2) %>%
mt(DistType=ifelse(type%in%c(2,5),"Demand Increase","Demand Valley")),
aes( x = x, y = Fhat,
color=DistType, group=as.factor(type)))+
scale_color_manual(values = colpats)+
geom_line(aes(colour = DistType,group=as.factor(type)))+
labs(title = "", x = "Percentage of Demand Boosting", y = "Cumulated Percentage of Firms",color ="")+
theme_bw(base_size = 18) +
theme(legend.position = "top",
legend.background = element_rect(fill="grey80", size=0.5, linetype="solid", color="black"))+
scale_y_continuous(labels = scales::percent) -> cdf_dalter_bw
mylegend<-g_legend(cdf_dalter_bw)
grid.arrange(demanboosting_boxplot,
arrangeGrob(mylegend, cdf_dalter_bw + theme(legend.position="none"),
nrow=2, heights = c(3, 18)),
ncol=2, widths = c(3,2)) -> demandboosting_bpcdf
grid.arrange(demanboosting_boxplot,cdf_dalter_bw ,
ncol=2, widths = c(3,2)) -> demandboosting_bpcdf
ggsave("~/Documents/GitHub/Project-Coupon/demandboosting_bpcdf.pdf",demandboosting_bpcdf,width = 18.17 ,height = 7.16,units = "in")
# demand-smoothing -------
plotdata <- rbind(data.frame(type = 1,holgap = compiled_results_ls[[1]]$holgap[[26]],
nonholgap = compiled_results_ls[[1]]$nonholgap[[26]],
dsidx2 = compiled_results_ls[[1]]$dsidx2[[26]]),
data.frame(type = 2,holgap = compiled_results_ls[[2]]$holgap[[1]],
nonholgap = compiled_results_ls[[2]]$nonholgap[[1]],
dsidx2 = compiled_results_ls[[2]]$dsidx2[[1]]),
data.frame(type = 5,holgap =(compiled_results_ls[[5]]$holgap[[5]]),
nonholgap = compiled_results_ls[[5]]$nonholgap[[5]],
dsidx2 = compiled_results_ls[[5]]$dsidx2[[5]]),
data.frame(type = 10,holgap = (compiled_results_ls[[10]]$holgap[[6]]),
nonholgap = compiled_results_ls[[10]]$nonholgap[[6]],
dsidx2 = compiled_results_ls[[10]]$dsidx2[[6]]),
data.frame(type = 11,holgap = (compiled_results_ls[[11]]$holgap[[8]][compiled_results_ls[[11]]$dsidx2[[8]]<quantile(compiled_results_ls[[11]]$dsidx2[[8]],0.8)&
compiled_results_ls[[11]]$dsidx2[[8]]>quantile(compiled_results_ls[[11]]$dsidx2[[8]],0.2)]),
nonholgap = compiled_results_ls[[11]]$nonholgap[[8]][compiled_results_ls[[11]]$dsidx2[[8]]<quantile(compiled_results_ls[[11]]$dsidx2[[8]],0.8)&
compiled_results_ls[[11]]$dsidx2[[8]]>quantile(compiled_results_ls[[11]]$dsidx2[[8]],0.2)],
dsidx2 = compiled_results_ls[[11]]$dsidx2[[8]][compiled_results_ls[[11]]$dsidx2[[8]]<quantile(compiled_results_ls[[11]]$dsidx2[[8]],0.8)&
compiled_results_ls[[11]]$dsidx2[[8]]>quantile(compiled_results_ls[[11]]$dsidx2[[8]],0.2)]))%>%
group_by(type)%>%
# mutate(smoothed = ifelse(type!=2,holval - nonholval, (nonholval - holval)))%>%
mutate(smoothed = (holgap - nonholgap))%>%
mutate(q90 = quantile(dsidx2,0.8),q10 = quantile(dsidx2,0.2))%>%
filter(dsidx2<q90,dsidx2>q10)%>%
merge(data.frame(type = c(2,5,11,10,1),
Industry = c("Casual Dining","Fine Dining","Photography","Outdoor","Body Care")),by="type",all.x = TRUE)
# tmp <- plotdata%>%filter(type==2)
# dsidx2_adj[[2]] <- (tmp$nonholval - tmp$holval)/abs(tmp$holval)
#
# plotdata <- data.frame(holval =holval, nonholval = nonholval, type = typei)%>%
# group_by(type)%>%
# mutate(smoothed = ifelse(type!=2,holval - nonholval, ((nonholval) - holval)/holval))%>%
# filter(type==2,nonholval>0,holval>0)%>%summarise(mean(smoothed))
colpats <- c("Demand Gap (Deals)"= "#66c2a5",
"Demand Gap (No Deals)" = "#fc8d62")
ggplot(data=plotdata%>%filter())+
geom_histogram(aes(x=nonholgap,y = ..density..,fill="Demand Gap (No Deals)"),color="grey30",bins = 50)+
#geom_line(aes(x=totaldpri,y = ..density..), stat = 'density', colour = '#fc8d62') +
geom_histogram(aes(x=holgap,y = ..density..,fill = "Demand Gap (Deals)"),color="grey30",bins = 50)+ #blueish
# geom_line(aes(x=totaldpri,y = ..density..), stat = 'density', colour = '#fc8d62')
#geom_line(aes(x=totald,y = ..density..), stat = 'density', colour = '#66c2a5') +
facet_grid(.~Industry,scales = "free")+
scale_fill_manual(values = colpats)+
labs(x="Demand Gap: Holiday-Non-holiday (Units Per Day)", y = "", fill="")+
theme(legend.position = "bottom")+
coord_flip() -> p1
ggplot(data=plotdata)+
geom_histogram(aes(x=smoothed,y = ..density..),fill="grey60",color="grey30",bins = 100)+
facet_grid(.~Industry,scales = "free")+
labs(x="Change in Demand Gap (Units Per Day)", y = "")+
geom_vline(xintercept=0,linetype =2)+
coord_flip() -> p2
# box plots for smoothed ------
ggplot(data=plotdata )+
geom_boxplot(aes(x=Industry,y=smoothed))+
# facet_grid(.~Industry,scales = "free")+
theme_bw(base_size = 18)+
labs(y="Change in Demand Gap (Units Per Day)", x = "Industry") -> demandsmoothing_boxplot
grid.arrange(p1,p2,nrow=2)
mylegend<-g_legend(p1)
grid.arrange(arrangeGrob(p1 + theme(legend.position="none"),
mylegend,
nrow=2,heights=c(16, 2)),
p2 + theme(legend.position="none"),nrow=2) -> p_demandsmoothing
cdf_data <- c(0,0,0)
for(i in c(2,5,11,10,1)){
plotdata_i <- plotdata %>% fl(type ==i)
kde(plotdata_i$dsidx2, h = bw.nrd0(plotdata_i$dsidx2)/2,kernel = c("gaus")) -> cdf_plot
cdf_data <- rbind(cdf_data,data.frame(Fhat = cdf_plot$Fhat, x = cdf_plot$x, type = i) )
}
cdf_data <- cdf_data[-1,]
colpats <- c("Large Marginal Cost Surge"= "#66c2a5",
"Low Marginal Cost Surge" = "#fc8d62")
ggplot(cdf_data %>% #fl(type!=2) %>%
mt(DistType=ifelse(type%in%c(1,5,10),"Large Marginal Cost Surge","Low Marginal Cost Surge")),
aes( x = x, y = Fhat,
color=DistType, group=as.factor(type)))+
geom_line(aes(colour =DistType,group=as.factor(type)))+
# geom_hline(yintercept = 0, color="#993333")+
# geom_errorbar(aes(ymin=perc_cust_left_mean-1.96*perc_cust_left_sd, ymax=perc_cust_left_mean+1.96*perc_cust_left_sd), width=0.1, colour = 'blue')+
scale_color_manual(values = colpats)+
scale_y_continuous(labels = scales::percent)+
# xlim(c(-500,500))+
# theme(
# axis.line.x = element_line(colour = 'black', size=0.5, linetype='solid'),
# axis.line.y = element_line(colour = 'black', size=0.5, linetype='solid'),
# panel.grid.minor.y= element_line(colour = "grey", linetype = "dotted"))+
# # scale_y_continuous(breaks = seq(0.3, 0.6, by = 0.05))+
labs(title = "", x = "Percentage of Demand Flattening", y = "Cumulated Percentage of Firms",color="")+
theme(legend.position = "bottom",legend.title = element_blank())-> cdf_smoothed
colpats <- c("Large Marginal Cost Surge"= "grey10",
"Low Marginal Cost Surge" = "grey70")
ggplot(cdf_data %>% #fl(type!=2) %>%
mt(DistType=ifelse(type%in%c(1,5,10),"Large Marginal Cost Surge","Low Marginal Cost Surge")),
aes( x = x, y = Fhat,
color=DistType, group=as.factor(type)))+
geom_line(aes(colour =DistType,group=as.factor(type)))+
scale_color_manual(values = colpats)+
scale_y_continuous(labels = scales::percent)+
labs(title = "", x = "Percentage of Demand Flattening", y = "Cumulated Percentage of Firms",color="")+
theme_bw(base_size = 18)+
theme(legend.position = "top",
legend.background = element_rect(fill="grey80", size=0.5, linetype="solid", color="black")) -> cdf_smoothed_bw
grid.arrange(demandsmoothing_boxplot,cdf_smoothed_bw ,
ncol=2, widths = c(3,2)) -> demandsmoothing_bpcdf
ggsave("~/Documents/GitHub/Project-Coupon/demandsmoothing_bpcdf.pdf",demandsmoothing_bpcdf,width = 18.17 ,height = 7.16,units = "in")
grid.arrange(cdf_dalter,cdf_smoothed,ncol=2)
grid.arrange(p_demandboosting,cdf_dalter,ncol=2,widths = c(2.5,1))
grid.arrange(p_demandsmoothing,cdf_smoothed,ncol=2,widths = c(2.5,1))
mylegend<-g_legend(p1)
grid.arrange(arrangeGrob(p1 + theme(legend.position="none"),
mylegend,
nrow=2,heights=c(16, 2)),
p2 + theme(legend.position="none"),nrow=2)
# the four quadrants ---------
figures_df <- bs_outputs_compile[c(1,13,23,38,66),]%>%mutate(mcratio = mucp/mucpnh)
figures_df <- bs_outputs_compile[c(1,16,23,38,66),]%>%mutate(mcratio = mucp/mucpnh)
industrynames <- dealsfull%>%filter(type %in% industries_to_check)%>%select(type,detailed_cat_price)%>%unique()
catnames <- data.frame(type = industries_to_check,
Industry = c("Casual Dine", "Fine Dine", "Photography", "Outdoor", "Body Care"))
plotdata <- merge(merge(industrynames,
data.frame(type = industries_to_check, figures_df),
by="type"),catnames,by="type")%>%
mutate(alterlabel = (Industry == "Fine Dine"))%>%{.->>plottmp}%>%
mutate(order_alterD = 6-rank(plottmp$alterD), order_dsidx = rank(abs(plottmp$dsidx2)))%>%
select(alterlabel,type, Industry,detailed_cat_price, mucp, mcratio, deltaD, alterD, dsidx2, order_alterD, order_dsidx)
ggplot(data=plotdata,aes(y=mcratio,x=deltaD))+geom_point(aes(size=order_dsidx,color=order_alterD))+
#geom_errorbarh(aes(xmax=deltad+1*deltadsig,xmin=deltad-1*deltadsig,y=(mcpratio)),size=0.7,height=0.0015) +
#geom_errorbar(aes(ymax=mcpratio+1*mcpratiosd,ymin=mcpratio-1*mcpratiosd,x=(deltad)),size=0.7,width=0.15) +
# geom_rect(mapping=aes(xmin=deltaD-1*deltaDsig, xmax=deltaD+1*deltaDsig, ymin=mcratio-1*mcratiosd, ymax=mcratio+1*mcaratiosd),
# fill="grey70", color="grey30",alpha=0.3)+
geom_label(data=plotdata%>%filter(alterlabel==FALSE),
aes(label=Industry),vjust = 1.0, hjust = 0.0 , nudge_x = 0.1)+
geom_label(data=plotdata%>%filter(alterlabel==TRUE),
aes(label=Industry),vjust = -0.2, hjust = 1.4, nudge_x = 0.1)+
theme(panel.background = element_rect(fill="grey90",color="grey90"),
#axis.title.x = element_blank(),axis.title.y=element_blank(),
axis.text.y = element_text(size=10,face="bold"),
axis.text.x = element_text(size=12,face="bold"),
legend.justification = c("center", "bottom"),
legend.text = element_text(size=12),
legend.title = element_text(size=12),
legend.background = element_rect(size=0.4, linetype="solid",
colour ="black"),
plot.margin=unit(c(0.5,1.5,0.5,1.0), "cm"))+
ylim(1, 8)+
geom_hline(yintercept=1,linetype=2)+
geom_vline(xintercept=0,linetype=2)+
labs(y=TeX("Ratio of Marginal Cost Price Ratio, $R_c$"),
x = TeX("Holiday Demand Swing, $\\widehat{\\delta}(\\omega')$"),
size=TeX("Mean-flattening (Order), $\\textit{B}(\\omega^*,\\omega')$"),
color = TeX("Mean-boosting (Order), $\\textit{M}(\\omega^*,\\omega')$"))+
theme(legend.text = element_text(size=10),legend.title = element_text(size=10),
legend.background = element_rect(color = NA),
legend.box = "horizontal",
legend.position = "bottom")+
scale_color_continuous(low = "black", high = "grey70")
#
# the deal parameteres ---------
obs_deal_parameters <- c(0,0,0,0,0)
obs_deal_parameters_ls <- vector(mode="list",length=11)
for(i in industries_to_check){
if(i == 2){
hol_data <- dealsfull%>%filter(is.na(holiday)==0,type%in%c(2,3),prep.period>0,offering_duration<40)%>%{.->>dealsfull_type}%>%
filter(total_volume>quantile(dealsfull_type$total_volume,c(0.05)), total_volume<quantile(dealsfull_type$total_volume,c(0.95)))%>%
filter(total_volume>quantile(dealsfull_type$total_volume,c(0.4)), total_volume<quantile(dealsfull_type$total_volume,c(0.6)))%>%
{.->>sdop}%>%
mutate(original_price = original_price/sd(original_price))
# set.seed(300)
# rs <-list()
# for(b in 1:6){
# rs[[b]] <- sample(dim(hol_data)[1],dim(hol_data)[1],replace = T)
# }
# hol_data <- hol_data[rs[[6]],]
#
}else if(i %in% c(10)){
hol_data <- dealsfull%>%filter(is.na(holiday)==0,type==i,prep.period>0)%>%{.->>dealsfull_type}%>%
filter(total_volume>quantile(dealsfull_type$total_volume,c(0.05)), total_volume<quantile(dealsfull_type$total_volume,c(0.95)))%>%
{.->>sdop}%>%
mutate(original_price = original_price/sd(original_price))
# set.seed(200)
# rs <-list()
# for(b in 1:1){
# rs[[b]] <- sample(dim(hol_data)[1],dim(hol_data)[1],replace = T)
# }
# hol_data <- hol_data[rs[[1]],]
#
}else if(i %in% c(11,1)){
hol_data <- dealsfull%>%filter(is.na(holiday)==0,type==i,prep.period>0,offering_duration<40)%>%{.->>dealsfull_type}%>%
filter(total_volume>quantile(dealsfull_type$total_volume,c(0.1)), total_volume<quantile(dealsfull_type$total_volume,c(0.9)))%>%
{.->>sdop}%>%
mutate(original_price = original_price/sd(original_price))
# set.seed(200)
# rs <-list()
# for(b in 1:1){
# rs[[b]] <- sample(dim(hol_data)[1],dim(hol_data)[1],replace = T)
# }
# hol_data <- hol_data[rs[[1]],]
}else{
hol_data <- dealsfull%>%filter(is.na(holiday)==0,type==i,prep.period>0,offering_duration<=40)%>%{.->>dealsfull_type}%>%
filter(total_volume>quantile(dealsfull_type$total_volume,c(0.05)), total_volume<quantile(dealsfull_type$total_volume,c(0.95)))%>%
{.->>sdop}%>%
mutate(original_price = original_price/sd(original_price))
}
obs_deal_parameters <- rbind(obs_deal_parameters,data.frame(type = i, trimeddf[[i]][[1]][,4:6],
total_volume = hol_data$total_volume[-jidx_ls[[i]][[1]]],
original_price = sdop$original_price[-jidx_ls[[i]][[1]]]))
obs_deal_parameters_ls[[i]] <- data.frame(trimeddf[[i]][[1]],
total_volume = hol_data$total_volume[-jidx_ls[[i]][[1]]],
original_price = sdop$original_price[-jidx_ls[[i]][[1]]])
colnames(obs_deal_parameters_ls[[i]]) <- c("discount","prep.period","offering_duration",
"discount","prep.period","offering_duration",
"total_volume", "original_price")
}
obs_deal_parameters <- obs_deal_parameters[-1,]
obs_deal_parameters <- obs_deal_parameters %>%
mutate(volumebins = total_volume - (total_volume%%20))
ggplot(obs_deal_parameters%>%filter(total_volume<200)) +
geom_point(aes(x=as.factor(type),y=prep.period))+
facet_wrap(.~volumebins)
# alpha --------
merge(merge(merge(merge(obs_deal_parameters_ls[[10]][,c(4,5,6,7,8)]%>%
mutate(volumebins = total_volume - (total_volume%%40))%>%
mutate(durationbins = offering_duration - (offering_duration%%3))%>%
mutate(preptbins = prep.period - (prep.period%%3))%>%
mutate(pricebins = original_price - (original_price%%50))%>%
mutate(alphabins = discount - (discount%%0.2))%>%
rename(disc10 = discount, T10 = prep.period),
obs_deal_parameters_ls[[2]][,c(4,5,6,7,8)]%>%
mutate(volumebins = total_volume - (total_volume%%40))%>%
mutate(durationbins = offering_duration - (offering_duration%%3))%>%
mutate(preptbins = prep.period - (prep.period%%3))%>%
mutate(pricebins = original_price - (original_price%%50))%>%
mutate(alphabins = discount - (discount%%0.2))%>%
rename(disc2 = discount, T2 = prep.period),
by=c("preptbins","volumebins"),all=TRUE)%>%
# group_by(volumebins,preptbins)%>%
# summarise(disc10mu=mean(disc10,na.rm = TRUE),disc2mu=mean(disc2,na.rm = TRUE),
# disc10sd=sd(disc10,na.rm = TRUE),disc2sd=sd(disc2,na.rm = TRUE))%>%
select(volumebins,preptbins,disc2,disc10),
obs_deal_parameters_ls[[11]][,c(4,5,6,7,8)]%>%
mutate(volumebins = total_volume - (total_volume%%40))%>%
mutate(durationbins = offering_duration - (offering_duration%%3))%>%
mutate(preptbins = prep.period - (prep.period%%3))%>%
mutate(pricebins = original_price - (original_price%%50))%>%
mutate(alphabins = discount - (discount%%0.2))%>%
rename(disc11 = discount, T11 = prep.period),
by=c("preptbins","volumebins"),all=TRUE)%>%
select(volumebins,preptbins, disc11,disc2,disc10),
obs_deal_parameters_ls[[1]][,c(4,5,6,7,8)]%>%
mutate(volumebins = total_volume - (total_volume%%40))%>%
mutate(durationbins = offering_duration - (offering_duration%%3))%>%
mutate(preptbins = prep.period - (prep.period%%3))%>%
mutate(pricebins = original_price - (original_price%%50))%>%
mutate(alphabins = discount - (discount%%0.2))%>%
rename(disc1 = discount, T1 = prep.period),
by=c("preptbins","volumebins"),all=TRUE)%>%
select(volumebins,preptbins, disc11,disc2,disc10,disc1),
obs_deal_parameters_ls[[5]][,c(4,5,6,7,8)]%>%
mutate(volumebins = total_volume - (total_volume%%40))%>%
mutate(durationbins = offering_duration - (offering_duration%%3))%>%
mutate(preptbins = prep.period - (prep.period%%3))%>%
mutate(pricebins = original_price - (original_price%%50))%>%
mutate(alphabins = discount - (discount%%0.2))%>%
rename(disc5 = discount, T5 = prep.period),
by=c("preptbins","volumebins"),all=TRUE)%>%
select(volumebins,preptbins, disc11,disc2,disc10,disc1,disc5)%>%
group_by(preptbins,volumebins)%>%
summarise(disc10mu=mean(disc10,na.rm = TRUE),disc2mu=mean(disc2,na.rm = TRUE),disc11mu=mean(disc11,na.rm = TRUE),
disc1mu=mean(disc1,na.rm = TRUE),disc5mu=mean(disc5,na.rm = TRUE),
disc10sd = sd(disc10,na.rm = TRUE),disc2sd = sd(disc2,na.rm = TRUE),disc11sd=sd(disc11,na.rm = TRUE),
disc1sd=sd(disc1,na.rm = TRUE),disc5sd=sd(disc5,na.rm = TRUE))%>%
filter(is.nan(disc10mu)+is.nan(disc2mu)+is.nan(disc11mu)+is.nan(disc1mu)+is.nan(disc5mu)<4) -> disc_cap_prept
colpats <- c("#66c2a5",
"#fc8d62",
"#8da0cb",
"#e78ac3",
"#a6d854")
colpats <- c("Outdoor"= "#66c2a5",
"Casual Dine" = "#66c2a5",
"Photography"= "#fc8d62",
"Fine Dine" ="#66c2a5",
"Body Care"= "#fc8d62")
ggplot(data=disc_cap_prept)+
geom_point(aes(x=as.factor(volumebins),y=disc10mu,color = "Outdoor"))+
geom_point(aes(x=as.factor(volumebins),y=disc2mu,color = "Casual Dine"))+
geom_point(aes(x=as.factor(volumebins),y=disc11mu,color = "Photography"))+
geom_point(aes(x=as.factor(volumebins),y=disc5mu,color = "Fine Dine"))+
geom_point(aes(x=as.factor(volumebins),y=disc1mu,color = "Body Care"))+
geom_errorbar(aes(x=as.factor(volumebins), ymin = disc10mu-1*disc10sd, ymax = disc10mu+1*disc10sd,color = "Outdoor",collbs = colpats[1]),width=0.1,alpha=0.8)+
geom_errorbar(aes(x=as.factor(volumebins), ymin = disc2mu-1*disc2sd, ymax = disc2mu+1*disc2sd,color="Casual Dine",collbs = colpats[1]),width=0.1,alpha=0.8)+
geom_errorbar(aes(x=as.factor(volumebins), ymin = disc11mu-1*disc11sd, ymax = disc11mu+1*disc11sd,color="Photography",collbs = colpats[2]),width=0.1,alpha=0.8)+
geom_errorbar(aes(x=as.factor(volumebins), ymin = disc5mu-1*disc5sd, ymax = disc5mu+1*disc5sd,color="Fine Dine",collbs = colpats[2]),width=0.1,alpha=0.8)+
geom_errorbar(aes(x=as.factor(volumebins), ymin = disc1mu-1*disc1sd, ymax = disc1mu+1*disc1sd,color = "Body Care",collbs = colpats[2]),width=0.1,alpha=0.8)+
facet_grid(preptbins~.)+
scale_color_manual(values = colpats)+
labs(x="Capacity",y="Discount", color="Industry", facet="Release Date to Holiday")+
theme(legend.position = "bottom",legend.title = element_blank()) -> palpha
# Launch day to holiday -------
merge(merge(merge(merge(obs_deal_parameters_ls[[10]][,c(4,5,6,7,8)]%>%
filter(offering_duration<40)%>%
mutate(volumebins = total_volume - (total_volume%%40))%>%
mutate(durationbins = offering_duration - (offering_duration%%3))%>%
mutate(preptbins = prep.period - (prep.period%%3))%>%
mutate(pricebins = original_price - (original_price%%50))%>%
mutate(alphabins = discount - (discount%%0.2))%>%
rename(disc10 = discount, T10 = prep.period),
obs_deal_parameters_ls[[2]][,c(1,2,3,7,8)]%>%
filter(offering_duration<40)%>%
mutate(volumebins = total_volume - (total_volume%%40))%>%
mutate(durationbins = offering_duration - (offering_duration%%3))%>%
mutate(preptbins = prep.period - (prep.period%%3))%>%
mutate(pricebins = original_price - (original_price%%50))%>%
mutate(alphabins = discount - (discount%%0.2))%>%
rename(disc2 = discount, T2 = prep.period),
by=c("alphabins","volumebins"),all=TRUE)%>%
# group_by(volumebins,preptbins)%>%
# summarise(disc10mu=mean(disc10,na.rm = TRUE),disc2mu=mean(disc2,na.rm = TRUE),
# disc10sd=sd(disc10,na.rm = TRUE),disc2sd=sd(disc2,na.rm = TRUE))%>%
select(volumebins,alphabins,T2,T10),
obs_deal_parameters_ls[[11]][,c(4,5,6,7,8)]%>%
filter(offering_duration<40)%>%
mutate(volumebins = total_volume - (total_volume%%40))%>%
mutate(durationbins = offering_duration - (offering_duration%%3))%>%
mutate(preptbins = prep.period - (prep.period%%3))%>%
mutate(pricebins = original_price - (original_price%%50))%>%
mutate(alphabins = discount - (discount%%0.2))%>%
rename(disc11 = discount, T11 = prep.period),
by=c("alphabins","volumebins"),all=TRUE)%>%
select(volumebins,alphabins, T11,T2,T10),
obs_deal_parameters_ls[[1]][,c(4,5,6,7,8)]%>%
filter(offering_duration<40)%>%
mutate(volumebins = total_volume - (total_volume%%40))%>%
mutate(durationbins = offering_duration - (offering_duration%%3))%>%
mutate(preptbins = prep.period - (prep.period%%3))%>%
mutate(pricebins = original_price - (original_price%%50))%>%
mutate(alphabins = discount - (discount%%0.2))%>%
rename(disc1 = discount, T1 = prep.period),
by=c("alphabins","volumebins"),all=TRUE)%>%
select(volumebins,alphabins, T11,T2,T10,T1),
obs_deal_parameters_ls[[5]][,c(1,2,3,7,8)]%>%
filter(offering_duration<40)%>%
mutate(volumebins = total_volume - (total_volume%%40))%>%
mutate(durationbins = offering_duration - (offering_duration%%3))%>%
mutate(preptbins = prep.period - (prep.period%%3))%>%
mutate(pricebins = original_price - (original_price%%50))%>%
mutate(alphabins = discount - (discount%%0.2))%>%
rename(disc5 = discount, T5 = prep.period),
by=c("alphabins","volumebins"),all=TRUE)%>%
select(volumebins,alphabins, T11,T2,T10,T1,T5)%>%
group_by(alphabins,volumebins)%>%
summarise(T10mu=mean(T10,na.rm = TRUE),T2mu=mean(T2,na.rm = TRUE),T11mu=mean(T11,na.rm = TRUE),
T1mu=mean(T1,na.rm = TRUE),T5mu=mean(T5,na.rm = TRUE),
T10sd = sd(T10,na.rm = TRUE),T2sd = sd(T2,na.rm = TRUE),T11sd=sd(T11,na.rm = TRUE),
T1sd=sd(T1,na.rm = TRUE),T5sd=sd(T5,na.rm = TRUE))%>%
filter(is.nan(T10mu)+is.nan(T2mu)+is.nan(T11mu)+is.nan(T1mu)+is.nan(T5mu)<3) -> T_cap_prept
colpats <- c("#66c2a5",
"#fc8d62",
"#8da0cb",
"#e78ac3",
"#a6d854")
colpats <- c("Outdoor"= "#66c2a5",
"Casual Dine" = "#66c2a5",
"Photography"= "#66c2a5",
"Fine Dine" = "#fc8d62",
"Body Care"= "#fc8d62")
collabs <- c("Late Release"= "#66c2a5",
"Early Release" = "#fc8d62")
ggplot(data=T_cap_prept)+
geom_point(aes(x=as.factor(volumebins),y=T10mu,color = "Outdoor"))+
geom_point(aes(x=as.factor(volumebins),y=T2mu,color = "Casual Dine"))+
geom_point(aes(x=as.factor(volumebins),y=T11mu,color = "Photography"))+
geom_point(aes(x=as.factor(volumebins),y=T5mu,color = "Fine Dine"))+
geom_point(aes(x=as.factor(volumebins),y=T1mu,color = "Body Care"))+
geom_errorbar(aes(x=as.factor(volumebins), ymin = T10mu-0.5*T10sd, ymax = T10mu+0.5*T10sd,color = "Outdoor",collabs="Late Release"),width=0.1,alpha=0.8)+
geom_errorbar(aes(x=as.factor(volumebins), ymin = T2mu-0.5*T2sd, ymax = T2mu+0.5*T2sd,color = "Casual Dine",collabs="Early Release"),width=0.1,alpha=0.8)+
geom_errorbar(aes(x=as.factor(volumebins), ymin = T11mu-0.5*T11sd, ymax = T11mu+0.5*T11sd,color = "Photography",collabs="Early Release"),width=0.1,alpha=0.8)+
geom_errorbar(aes(x=as.factor(volumebins), ymin = T5mu-0.5*T5sd, ymax = T5mu+0.5*T5sd,color = "Fine Dine",collabs="Late Release"),width=0.1,alpha=0.8)+
geom_errorbar(aes(x=as.factor(volumebins), ymin = T1mu-0.5*T1sd, ymax = T1mu+0.5*T1sd,color = "Body Care",collabs="Early Release"),width=0.1,alpha=0.8)+
facet_grid(alphabins~.)+
scale_color_manual(values = colpats)+
labs(x="Capacity",y="Launch Date to Holiday", color="Industry", facet="Discount")+
theme(legend.position = "bottom",legend.title = element_blank()) -> pT
# can add in another metric, the % of cover or higher than the other
x <- seq(4.1,50,0.1)
K <- 0.8
y1 <- (1+0.88*K)*(x/(1+x)) - (x/(1+x))
y2 <- (1+6.33*K)*((x-3.74)/(1+x-3.74)) - (x/(1+x))
ggplot()+
geom_line(aes(x=x,y=y1),color="red")+
geom_line(aes(x=x,y=y2),color="blue")
# duration ----------
merge(merge(merge(merge(obs_deal_parameters_ls[[10]][,c(4,5,6,7,8)]%>%
#filter(offering_duration<40)%>%
mutate(offering_duration = offering_duration-prep.period)%>%
mutate(volumebins = total_volume - (total_volume%%40))%>%
mutate(durationbins = offering_duration - (offering_duration%%3))%>%
mutate(preptbins = prep.period - (prep.period%%3))%>%
mutate(pricebins = original_price - (original_price%%50))%>%
mutate(alphabins = discount - (discount%%0.2))%>%
rename(disc10 = discount, T10 = prep.period, d10 = offering_duration),
obs_deal_parameters_ls[[2]][,c(1,2,3,7,8)]%>%
#filter(offering_duration<40)%>%
mutate(offering_duration = offering_duration-prep.period)%>%
mutate(volumebins = total_volume - (total_volume%%40))%>%
mutate(durationbins = offering_duration - (offering_duration%%3))%>%
mutate(preptbins = prep.period - (prep.period%%3))%>%
mutate(pricebins = original_price - (original_price%%50))%>%
mutate(alphabins = discount - (discount%%0.2))%>%
rename(disc2 = discount, T2 = prep.period, d2 = offering_duration),
by=c("alphabins","volumebins"),all=TRUE)%>%
# group_by(volumebins,preptbins)%>%
# summarise(disc10mu=mean(disc10,na.rm = TRUE),disc2mu=mean(disc2,na.rm = TRUE),
# disc10sd=sd(disc10,na.rm = TRUE),disc2sd=sd(disc2,na.rm = TRUE))%>%
select(volumebins,alphabins,d2,d10),
obs_deal_parameters_ls[[11]][,c(4,5,6,7,8)]%>%
# filter(offering_duration<40)%>%
mutate(offering_duration = offering_duration-prep.period)%>%
mutate(volumebins = total_volume - (total_volume%%40))%>%
mutate(durationbins = offering_duration - (offering_duration%%3))%>%
mutate(preptbins = prep.period - (prep.period%%3))%>%
mutate(pricebins = original_price - (original_price%%50))%>%
mutate(alphabins = discount - (discount%%0.2))%>%
rename(disc11 = discount, T11 = prep.period, d11 = offering_duration),
by=c("alphabins","volumebins"),all=TRUE)%>%
select(volumebins,alphabins, d11,d2,d10),
obs_deal_parameters_ls[[1]][,c(4,5,6,7,8)]%>%
# filter(offering_duration<40)%>%
mutate(offering_duration = offering_duration-prep.period)%>%
mutate(volumebins = total_volume - (total_volume%%40))%>%
mutate(durationbins = offering_duration - (offering_duration%%3))%>%
mutate(preptbins = prep.period - (prep.period%%3))%>%
mutate(pricebins = original_price - (original_price%%50))%>%
mutate(alphabins = discount - (discount%%0.2))%>%
rename(disc1 = discount, T1 = prep.period, d1 = offering_duration),
by=c("alphabins","volumebins"),all=TRUE)%>%
select(volumebins,alphabins, d11,d2,d10,d1),
obs_deal_parameters_ls[[5]][,c(1,2,3,7,8)]%>%
#filter(offering_duration<40)%>%
mutate(offering_duration = offering_duration-prep.period)%>%
mutate(volumebins = total_volume - (total_volume%%40))%>%
mutate(durationbins = offering_duration - (offering_duration%%3))%>%
mutate(preptbins = prep.period - (prep.period%%3))%>%
mutate(pricebins = original_price - (original_price%%50))%>%
mutate(alphabins = discount - (discount%%0.2))%>%
rename(disc5 = discount, T5 = prep.period, d5 = offering_duration),
by=c("alphabins","volumebins"),all=TRUE)%>%
select(volumebins,alphabins, d11,d2,d10,d1,d5)%>%
group_by(alphabins,volumebins)%>%
summarise(d10mu=mean(d10,na.rm = TRUE),d2mu=mean(d2,na.rm = TRUE),d11mu=mean(d11,na.rm = TRUE),
d1mu=mean(d1,na.rm = TRUE),d5mu=mean(d5,na.rm = TRUE),
d10sd = sd(d10,na.rm = TRUE),d2sd = sd(d2,na.rm = TRUE),d11sd=sd(d11,na.rm = TRUE),
d1sd=sd(d1,na.rm = TRUE),d5sd=sd(d5,na.rm = TRUE))%>%
filter(is.nan(d10mu)+is.nan(d2mu)+is.nan(d11mu)+is.nan(d1mu)+is.nan(d5mu)<3) -> d_cap_prept
colpats <- c("#66c2a5",
"#fc8d62",
"#8da0cb",
"#e78ac3",
"#a6d854")
colpats <- c("Outdoor"= "#fc8d62",
"Casual Dine" = "#fc8d62",
"Photography"= "#66c2a5",
"Fine Dine" ="#66c2a5",
"Body Care"= "#66c2a5")
ggplot(data=d_cap_prept)+
geom_point(aes(x=as.factor(volumebins),y=d10mu,color = "Outdoor"))+
geom_point(aes(x=as.factor(volumebins),y=d2mu,color = "Casual Dine"))+
geom_point(aes(x=as.factor(volumebins),y=d11mu,color = "Photography"))+
geom_point(aes(x=as.factor(volumebins),y=d5mu,color = "Fine Dine"))+
geom_point(aes(x=as.factor(volumebins),y=d1mu,color = "Body Care"))+
geom_errorbar(aes(x=as.factor(volumebins), ymin = d10mu-0.5*d10sd, ymax = d10mu+0.5*d10sd,color = "Outdoor",collbs = colpats[1]),width=0.1,alpha=0.8)+
geom_errorbar(aes(x=as.factor(volumebins), ymin = d2mu-0.5*d2sd, ymax = d2mu+0.5*d2sd,color="Casual Dine",collbs = colpats[1]),width=0.1,alpha=0.8)+
geom_errorbar(aes(x=as.factor(volumebins), ymin = d11mu-0.5*d11sd, ymax = d11mu+0.5*d11sd,color="Photography",collbs = colpats[2]),width=0.1,alpha=0.8)+
geom_errorbar(aes(x=as.factor(volumebins), ymin = d5mu-0.5*d5sd, ymax = d5mu+0.5*d5sd,color="Fine Dine",collbs = colpats[2]),width=0.1,alpha=0.8)+
geom_errorbar(aes(x=as.factor(volumebins), ymin = d1mu-0.5*d1sd, ymax = d1mu+0.5*d1sd,color = "Body Care",collbs = colpats[2]),width=0.1,alpha=0.8)+
facet_grid(alphabins~.)+
scale_color_manual(values = colpats)+
labs(x="Capacity",y="Duration", color="Industry", facet="Discount")+
theme(legend.position = "bottom",legend.title = element_blank()) -> pd
grid.arrange(palpha,pT,pd,ncol=3)
### data description --------
# t test -------
datadim_h <- vector()
datadim_nh <- vector()
data_h_ls <- vector(mode = "list", length = 11)
data_nh_ls <- vector(mode = "list", length = 11)
predays_t <- 20
pre.windows_t <- c(seq(21-predays_t,21),seq(44-predays_t,44),seq(92-predays_t,92))
for (i in industries_to_check){
print(i)
if(i == 2){
data_h <- dealsfull%>%filter(is.na(holiday)==0,type%in%c(2,3)) %>%
fl(prep.period>0,prep.period<40) %>%{.->>dealsfull_type}%>%
filter(original_price>quantile(dealsfull_type$original_price,c(0.4)), original_price<quantile(dealsfull_type$original_price,c(0.6))) %>%
{.->>sdop} %>%
filter(total_volume>quantile(dealsfull_type$total_volume,c(0.1)), total_volume<quantile(dealsfull_type$total_volume,c(0.9))) %>%
mutate(original_price_sd = original_price/sd(original_price))
data_h_ls[[i]] <- data_h
print(dim(sdop))
datadim_h <- c(datadim_h,dim(sdop)[1])
data_nh <- dealsfull%>% filter(type%in%c(2,3)) %>%
mutate(end_dow_dm = launch_dow_dm+offering_duration)%>%
filter(is.na(holiday)==1,type==i,end_dow_dm%in%pre.windows_t)%>%{.->>dealsfull_type}%>%{.->>sdop} %>%
filter(total_volume>quantile(dealsfull_type$total_volume,c(0.1)), total_volume<quantile(dealsfull_type$total_volume,c(0.9))) %>%
mutate(original_price_sd = original_price/sd(original_price))
# data_nh <- dealsfull%>%filter(is.na(holiday)==1,type%in%c(2)) %>%
# {.->>dealsfull_type}%>%
# # filter(total_volume>quantile(dealsfull_type$total_volume,c(0.4)), total_volume<quantile(dealsfull_type$total_volume,c(0.6))) %>%
# {.->>sdop} %>%
# filter(total_volume>quantile(dealsfull_type$total_volume,c(0.05)), total_volume<quantile(dealsfull_type$total_volume,c(0.95)))
data_nh_ls[[i]] <- data_nh
datadim_nh <- c(datadim_nh,dim(sdop)[1])
}else{
data_h <- dealsfull%>%filter(is.na(holiday)==0,type==i)%>%
{.->>sdop}%>%{.->>dealsfull_type} %>%
fl(total_volume>quantile(dealsfull_type$total_volume,c(0.1)), total_volume<quantile(dealsfull_type$total_volume,c(0.9))) %>%
mutate(original_price_sd = original_price/sd(original_price))
data_h_ls[[i]] <- data_h
datadim_h <- c(datadim_h,dim(sdop)[1])
data_nh <- dealsfull%>%
mutate(end_dow_dm = launch_dow_dm+offering_duration)%>%
filter(is.na(holiday)==1,type==i,end_dow_dm%in%pre.windows_t)%>%{.->>dealsfull_type}%>%{.->>sdop} %>%
filter(total_volume>quantile(dealsfull_type$total_volume,c(0.1)), total_volume<quantile(dealsfull_type$total_volume,c(0.9))) %>%
mutate(original_price_sd = original_price/sd(original_price))
# data_nh <- dealsfull%>%filter(is.na(holiday)==1,type==i)%>%
# {.->>sdop}%>%{.->>dealsfull_type} %>%
# fl(total_volume>quantile(dealsfull_type$total_volume,c(0.05)), total_volume<quantile(dealsfull_type$total_volume,c(0.95)))
data_nh_ls[[i]] <- data_nh
datadim_nh <- c(datadim_nh,dim(sdop)[1])
}
print(dim(sdop))
datasanalysis <- c(datasanalysis,dim(sdop)[1])
}
alphat <- vector()
Tt <- vector()
dt <- vector()
salest <- vector()
pt <- vector()
alphaks <- vector()
Tks <- vector()
dks <- vector()
salesks <- vector()
pks <- vector()
for(i in industries_to_check){
alphat <- c(alphat,t.test((1-data_nh_ls[[i]]$discount)*data_nh_ls[[i]]$original_price_sd,
(1-data_h_ls[[i]]$discount)*data_h_ls[[i]]$original_price_sd)$p.value)
# Tt <- c(Tt,t.test(data_nh_ls[[i]]$prep.period, data_h_ls[[i]]$prep.period)$p.value)
dt <- c(dt,t.test(data_nh_ls[[i]]$offering_duration, data_h_ls[[i]]$offering_duration)$p.value)
salest <- c(salest,t.test(data_nh_ls[[i]]$total_volume, data_h_ls[[i]]$total_volume)$p.value)
pt <- c(pt,t.test(data_nh_ls[[i]]$original_price, data_h_ls[[i]]$original_price)$p.value)
alphaks <- c(alphaks,ks.test((1-data_nh_ls[[i]]$discount)*data_nh_ls[[i]]$original_price_sd,
(1-data_h_ls[[i]]$discount)*data_h_ls[[i]]$original_price_sd)$p.value)
# Tks <- c(Tks,ks.test(data_nh_ls[[i]]$prep.period, data_h_ls[[i]]$prep.period)$p.value)
dks <- c(dks,ks.test(data_nh_ls[[i]]$offering_duration, data_h_ls[[i]]$offering_duration)$p.value)
salesks <- c(salesks,ks.test(data_nh_ls[[i]]$total_volume, data_h_ls[[i]]$total_volume)$p.value)
pks <- c(pks,ks.test(data_nh_ls[[i]]$original_price, data_h_ls[[i]]$original_price)$p.value)
}
## ks test --------
datadim_h <- vector()
datadim_nh <- vector()
data_h_ls <- vector(mode = "list", length = 11)
data_nh_ls <- vector(mode = "list", length = 11)
predays_t <- 15
pre.windows_t <- c(seq(21-predays_t,21),seq(44-predays_t,44),seq(92-predays_t,92))
for (i in industries_to_check){
print(i)
if(i == 2){
data_h <- dealsfull%>%filter(is.na(holiday)==0,type%in%c(2,3)) %>%
fl(offering_duration<40) %>%
{.->>dealsfull_type}%>%
filter(original_price>quantile(dealsfull_type$original_price,c(0.4)), original_price<quantile(dealsfull_type$original_price,c(0.6))) %>%
{.->>sdop} %>%
filter(total_volume>quantile(dealsfull_type$total_volume,c(0.1)), total_volume<quantile(dealsfull_type$total_volume,c(0.9))) %>%
mutate(original_price_sd = original_price/sd(original_price))
data_h_ls[[i]] <- data_h
print(dim(sdop))
datadim_h <- c(datadim_h,dim(sdop)[1])
data_nh <- dealsfull%>% filter(type%in%c(2,3)) %>%
mutate(end_dow_dm = launch_dow_dm+offering_duration)%>%
filter(is.na(holiday)==1,type==i,end_dow_dm%in%pre.windows_t)%>%{.->>dealsfull_type}%>%{.->>sdop} %>%
filter(total_volume>quantile(dealsfull_type$total_volume,c(0.1)), total_volume<quantile(dealsfull_type$total_volume,c(0.9))) %>%
mutate(original_price_sd = original_price/sd(original_price))
# data_nh <- dealsfull%>%filter(is.na(holiday)==1,type%in%c(2)) %>%
# {.->>dealsfull_type}%>%
# # filter(total_volume>quantile(dealsfull_type$total_volume,c(0.4)), total_volume<quantile(dealsfull_type$total_volume,c(0.6))) %>%
# {.->>sdop} %>%
# filter(total_volume>quantile(dealsfull_type$total_volume,c(0.05)), total_volume<quantile(dealsfull_type$total_volume,c(0.95)))
data_nh_ls[[i]] <- data_nh
datadim_nh <- c(datadim_nh,dim(sdop)[1])
}else{
data_h <- dealsfull%>%filter(is.na(holiday)==0,type==i)%>%
fl(offering_duration<40) %>%
{.->>sdop}%>%{.->>dealsfull_type} %>%
fl(total_volume>quantile(dealsfull_type$total_volume,c(0.1)), total_volume<quantile(dealsfull_type$total_volume,c(0.9))) %>%
mutate(original_price_sd = original_price/sd(original_price))
data_h_ls[[i]] <- data_h
datadim_h <- c(datadim_h,dim(sdop)[1])
data_nh <- dealsfull%>%
mutate(end_dow_dm = launch_dow_dm+offering_duration)%>%
filter(is.na(holiday)==1,type==i,end_dow_dm%in%pre.windows_t)%>%{.->>dealsfull_type}%>%{.->>sdop} %>%
filter(total_volume>quantile(dealsfull_type$total_volume,c(0.1)), total_volume<quantile(dealsfull_type$total_volume,c(0.9))) %>%
mutate(original_price_sd = original_price/sd(original_price))
# data_nh <- dealsfull%>%filter(is.na(holiday)==1,type==i)%>%
# {.->>sdop}%>%{.->>dealsfull_type} %>%
# fl(total_volume>quantile(dealsfull_type$total_volume,c(0.05)), total_volume<quantile(dealsfull_type$total_volume,c(0.95)))
data_nh_ls[[i]] <- data_nh
datadim_nh <- c(datadim_nh,dim(sdop)[1])
}
print(dim(sdop))
datasanalysis <- c(datasanalysis,dim(sdop)[1])
}
alphat_i <- matrix(rep(0,5^2), ncol= 5,byrow=T)
Tt_i <- matrix(rep(0,5^2), ncol= 5,byrow=T)
dt_i <- matrix(rep(0,5^2), ncol= 5,byrow=T)
alphaks_i <- matrix(rep(0,5^2), ncol= 5,byrow=T)
Tks_i <- matrix(rep(0,5^2), ncol= 5,byrow=T)
dks_i <- matrix(rep(0,5^2), ncol= 5,byrow=T)
iidx <- 1
jidx <- 1
for(i in industries_to_check){
jidx <- 1
for(j in industries_to_check){
alphat_i[iidx,jidx] <- t.test((1-data_h_ls[[i]]$discount)*data_h_ls[[i]]$original_price_sd,
(1-data_h_ls[[j]]$discount)*data_h_ls[[j]]$original_price_sd)$p.value
Tt_i[iidx,jidx]<- t.test(data_h_ls[[i]]$prep.period, data_h_ls[[j]]$prep.period)$p.value
dt_i[iidx,jidx]<- t.test(data_h_ls[[i]]$offering_duration, data_h_ls[[j]]$offering_duration)$p.value
alphaks_i[iidx,jidx] <- ks.test((1-data_h_ls[[i]]$discount)*data_h_ls[[i]]$original_price_sd,
(1-data_h_ls[[j]]$discount)*data_h_ls[[j]]$original_price_sd)$p.value
Tks_i[iidx,jidx] <- ks.test(data_h_ls[[i]]$prep.period, data_h_ls[[j]]$prep.period)$p.value
dks_i[iidx,jidx] <- ks.test(data_h_ls[[i]]$offering_duration, data_h_ls[[j]]$offering_duration)$p.value
jidx <- jidx + 1
}
iidx <- iidx +1
}
rbind(Industry = c("Casual Dine","Fine Dine","Photography","Outdoor","Body Care"),
# type = industries_to_check,
# c = signif(cvec,2),
# cnh = signif(cnhvec,2),
alphaks = format(signif(alphaks,2)),
alphat = format(signif(alphat,2)),
dks = format(signif(dks,2)),
dt = format(signif(dt,2)),
pks = format(signif(pks,2)),
pt = format(signif(pt,2))) -> testrestuls
alphaks_i <- format(signif(alphaks_i,2))
alphat_i <- format(signif(alphat_i,2))
Tks_i <- format(signif(Tks_i,2))
Tt_i <- format(signif(Tt_i,2))
dks_i <- format(signif(dks_i,2))
dt_i <- format(signif(dt_i,2))
alphaks_i[lower.tri(alphaks_i,diag=TRUE)] <- NA
alphat_i[lower.tri(alphat_i,diag=TRUE)] <- NA
Tks_i[lower.tri(Tks_i,diag=TRUE)] <- NA
Tt_i[lower.tri(Tt_i,diag=TRUE)] <- NA
dks_i[lower.tri(dks_i,diag=TRUE)] <- NA
dt_i[lower.tri(dt_i,diag=TRUE)] <- NA
# rbind(c(c("Casual Dine","Fine Dine","Photography","Outdoor","Body Care"),
# c("Casual Dine","Fine Dine","Photography","Outdoor","Body Care")),
# cbind((alphaks_i),alphat_i),
# cbind((Tks_i),Tt_i),
# cbind((dks_i),dt_i)) -> indtestresults
# rownames(indtestresults) <- c("Industry",
# "Casual Dine, $(1-\\alpha)p$","Fine Dine, $(1-\\alpha)p$",
# "Photography, $(1-\\alpha)p$","Outdoor, $(1-\\alpha)p$","Body Care, $(1-\\alpha)p$",
# "Casual Dine, $T$","Fine Dine, $T$","Photography, $T$",
# "Outdoor, $T$","Body Care, $T$",
# "Casual Dine, $d$","Fine Dine, $d$","Photography, $d$",
# "Outdoor, $d$","Body Care, $d$")
rbind(c("Casual Dine","Fine Dine","Photography","Outdoor","Body Care"),
(alphaks_i),alphat_i,
(Tks_i),Tt_i,
(dks_i),dt_i) -> indtestresults
rownames(indtestresults) <- c("Industry",
"Casual Dine, ks test, $(1-\\alpha)p$","Fine Dine, ks test, $(1-\\alpha)p$",
"Photography, ks test, $(1-\\alpha)p$","Outdoor, ks test, $(1-\\alpha)p$","Body Care, ks test, $(1-\\alpha)p$",
"Casual Dine, t test, $(1-\\alpha)p$","Fine Dine, t test, $(1-\\alpha)p$",
"Photography, t test, $(1-\\alpha)p$","Outdoor, t test, $(1-\\alpha)p$","Body Care, t test, $(1-\\alpha)p$",
"Casual Dine, ks test, $T$","Fine Dine, ks test, $T$","Photography, ks test, $T$",
"Outdoor, ks test, $T$","Body Care, ks test, $T$",
"Casual Dine, t test, $T$","Fine Dine, t test, $T$","Photography, t test, $T$",
"Outdoor, t test, $T$","Body Care, t test, $T$",
"Casual Dine, ks test, $d$","Fine Dine, ks test, $d$","Photography, ks test, $d$",
"Outdoor, ks test, $d$","Body Care, ks test, $d$",
"Casual Dine, t test, $d$","Fine Dine, t test, $d$","Photography, t test, $d$",
"Outdoor, t test, $d$","Body Care, t test, $d$")
print(xtable(indtestresults),#tabular.environment = "longtable",
only.contents=TRUE, include.rownames=T,
include.colnames=T, floating=F, sanitize.rownames.function = identity,
file = 'indtestresultstable1.tex')
# multivariate test, cramer test -------
datadim_h <- vector()
datadim_nh <- vector()
data_h_ls <- vector(mode = "list", length = 11)
data_nh_ls <- vector(mode = "list", length = 11)
predays_t <- 15
pre.windows_t <- c(seq(21-predays_t,21),seq(44-predays_t,44),seq(92-predays_t,92))
for (i in industries_to_check){
print(i)
if(i == 2){
data_h <- dealsfull%>%filter(is.na(holiday)==0,type%in%c(2,3)) %>%
fl(offering_duration<40) %>%
{.->>dealsfull_type}%>%
# filter(original_price>quantile(dealsfull_type$original_price,c(0.4)), original_price<quantile(dealsfull_type$original_price,c(0.6))) %>%
{.->>sdop} %>%
filter(total_volume>quantile(dealsfull_type$total_volume,c(0.1)), total_volume<quantile(dealsfull_type$total_volume,c(0.9))) %>%
mutate(original_price_sd = original_price/sd(original_price))
data_h_ls[[i]] <- data_h
print(dim(sdop))
datadim_h <- c(datadim_h,dim(sdop)[1])
data_nh <- dealsfull%>% filter(type%in%c(2,3)) %>%
mutate(end_dow_dm = launch_dow_dm+offering_duration)%>%
filter(is.na(holiday)==1,type==i,end_dow_dm%in%pre.windows_t)%>%{.->>dealsfull_type}%>%{.->>sdop} %>%
filter(total_volume>quantile(dealsfull_type$total_volume,c(0.1)), total_volume<quantile(dealsfull_type$total_volume,c(0.9))) %>%
mutate(original_price_sd = original_price/sd(original_price))
# data_nh <- dealsfull%>%filter(is.na(holiday)==1,type%in%c(2)) %>%
# {.->>dealsfull_type}%>%
# # filter(total_volume>quantile(dealsfull_type$total_volume,c(0.4)), total_volume<quantile(dealsfull_type$total_volume,c(0.6))) %>%
# {.->>sdop} %>%
# filter(total_volume>quantile(dealsfull_type$total_volume,c(0.05)), total_volume<quantile(dealsfull_type$total_volume,c(0.95)))
data_nh_ls[[i]] <- data_nh
datadim_nh <- c(datadim_nh,dim(sdop)[1])
}else{
data_h <- dealsfull%>%filter(is.na(holiday)==0,type==i)%>%
fl(offering_duration<40) %>%
{.->>sdop}%>%{.->>dealsfull_type} %>%
fl(total_volume>quantile(dealsfull_type$total_volume,c(0.1)), total_volume<quantile(dealsfull_type$total_volume,c(0.9))) %>%
mutate(original_price_sd = original_price/sd(original_price))
data_h_ls[[i]] <- data_h
datadim_h <- c(datadim_h,dim(sdop)[1])
data_nh <- dealsfull%>%
mutate(end_dow_dm = launch_dow_dm+offering_duration)%>%
filter(is.na(holiday)==1,type==i,end_dow_dm%in%pre.windows_t)%>%{.->>dealsfull_type}%>%{.->>sdop} %>%
filter(total_volume>quantile(dealsfull_type$total_volume,c(0.1)), total_volume<quantile(dealsfull_type$total_volume,c(0.9))) %>%
mutate(original_price_sd = original_price/sd(original_price))
# data_nh <- dealsfull%>%filter(is.na(holiday)==1,type==i)%>%
# {.->>sdop}%>%{.->>dealsfull_type} %>%
# fl(total_volume>quantile(dealsfull_type$total_volume,c(0.05)), total_volume<quantile(dealsfull_type$total_volume,c(0.95)))
data_nh_ls[[i]] <- data_nh
datadim_nh <- c(datadim_nh,dim(sdop)[1])
}
print(dim(sdop))
datasanalysis <- c(datasanalysis,dim(sdop)[1])
}
set.seed(123)
data_h_ls[[11]] <- data_h_ls[[11]][sample(dim(data_h_ls[[11]])[1],200,replace = T),]
library(cramer)
cramertestresult1 <- matrix(rep(0,5^2), ncol= 5,byrow=T)
cramertestresult_ls1 <- vector(mode="list",length=25)
iidx <- 1
jidx <- 1
ct <- 1
for(i in industries_to_check){
jidx <- iidx+1
for(j in industries_to_check[(iidx+1):5]){
cramer_holi <- cbind((1-data_h_ls[[i]]$discount)*data_h_ls[[i]]$original_price_sd, data_h_ls[[i]]$prep.period, data_h_ls[[i]]$offering_duration)
cramer_holj <- cbind((1-data_h_ls[[j]]$discount)*data_h_ls[[j]]$original_price_sd, data_h_ls[[j]]$prep.period, data_h_ls[[j]]$offering_duration)
cramertestresult_ls1[[ct]] <- cramer.test(cramer_holi,cramer_holj,conf.level=0.95,replicates=500,sim="permutation")
# print(cramertestresult_ls1[[ct]]$p.value)
cramertestresult1[iidx,jidx] <- cramertestresult_ls1[[ct]]$p.value
jidx <- jidx + 1
ct <- ct + 1
print(ct)
}
iidx <- iidx +1
}
cramertestresult1[4,5] <- cramer.test(cbind((1-data_h_ls[[10]]$discount)*data_h_ls[[10]]$original_price_sd, data_h_ls[[10]]$prep.period, data_h_ls[[10]]$offering_duration),
cbind((1-data_h_ls[[1]]$discount)*data_h_ls[[1]]$original_price_sd, data_h_ls[[1]]$prep.period, data_h_ls[[1]]$offering_duration),
conf.level=0.95,replicates=500,sim="permutation")$p.value
cramertestresult1 <- format(signif(cramertestresult1,2))
cramertestresult1[lower.tri(cramertestresult1,diag=TRUE)] <- NA
rbind(c("Casual Dining","Fine Dining","Photography","Outdoor","Body Care"),
cramertestresult1) -> cramertable
rownames(cramertable) <- c("Industry",
"Casual Dining","Fine Dining",
"Photography","Outdoor","Body Care")
print(xtable(cramertable),#tabular.environment = "longtable",
only.contents=TRUE, include.rownames=T,
include.colnames=T, floating=F, sanitize.rownames.function = identity,
file = 'cramertable.tex')
rbind(c("Casual Dine","Fine Dine","Photography","Outdoor","Body Care"),
(alphaks_i),
(Tks_i),
(dks_i),
cramertestresult1) -> cramer_kstable
rownames(cramer_kstable) <- c("Industry",
"Casual Dining, $(1-\\alpha)p$","Fine Dining, $(1-\\alpha)p$",
"Photography, $(1-\\alpha)p$","Outdoor, $(1-\\alpha)p$","Body Care, $(1-\\alpha)p$",
"Casual Dining, $T$","Fine Dining, $T$","Photography, $T$",
"Outdoor, $T$","Body Care, $T$",
"Casual Dining, $d$","Fine Dining, $d$","Photography, $d$",
"Outdoor, $d$","Body Care, $d$",
"Casual Dining, Cramer","Fine Dining, Cramer",
"Photography, Cramer","Outdoor, Cramer","Body Care, Cramer")
print(xtable(cramer_kstable),#tabular.environment = "longtable",
only.contents=TRUE, include.rownames=T,
include.colnames=T, floating=F, sanitize.rownames.function = identity,
file = 'cramer_kstable.tex')
## multivariate ks test -------
#
# library(GSAR)
# multikstestresult <- matrix(rep(0,5^2), ncol= 5,byrow=T)
# multikstestresult_ls <- vector(mode="list",length=25)
# iidx <- 1
# jidx <- 1
# ct <- 1
# for(i in industries_to_check){
# jidx <- iidx+1
#
# for(j in industries_to_check[(iidx+1):5]){
#
# multiks_holi <- cbind((1-data_h_ls[[i]]$discount)*data_h_ls[[i]]$original_price_sd, data_h_ls[[i]]$prep.period, data_h_ls[[i]]$offering_duration)
# multiks_holj <- cbind((1-data_h_ls[[j]]$discount)*data_h_ls[[j]]$original_price_sd, data_h_ls[[j]]$prep.period, data_h_ls[[j]]$offering_duration)
#
# multikstestresult_ls[[ct]] <- GSAR::KStest(object = cbind(t(multiks_holi),t(multiks_holj)),
# group = c(rep(1,dim(multiks_holi)[1]),rep(2,dim(multiks_holj)[1])), nperm = 1000)
#
# multikstestresult[iidx,jidx] <- multikstestresult_ls[[ct]]$p.value
#
# jidx <- jidx + 1
# ct <- ct + 1
# print(ct)
# }
# iidx <- iidx +1
# }
# cramertestresult1[4,5] <- cramer.test(cbind((1-data_h_ls[[10]]$discount)*data_h_ls[[10]]$original_price_sd, data_h_ls[[10]]$prep.period, data_h_ls[[10]]$offering_duration),
# cbind((1-data_h_ls[[1]]$discount)*data_h_ls[[1]]$original_price_sd, data_h_ls[[1]]$prep.period, data_h_ls[[1]]$offering_duration),
# conf.level=0.95,replicates=500,sim="permutation")$p.value
#
#
#
# plot alpha, t, d in different industries ----
datadim_h <- vector()
datadim_nh <- vector()
data_h_ls <- vector(mode = "list", length = 11)
data_nh_ls <- vector(mode = "list", length = 11)
predays_t <- 15
pre.windows_t <- c(seq(21-predays_t,21),seq(44-predays_t,44),seq(92-predays_t,92))
for (i in industries_to_check){
print(i)
if(i == 2){
data_h <- dealsfull%>%filter(is.na(holiday)==0,type%in%c(2,3)) %>%
# fl(offering_duration<40) %>%
{.->>dealsfull_type}%>%
# filter(original_price>quantile(dealsfull_type$original_price,c(0.4)), original_price<quantile(dealsfull_type$original_price,c(0.6))) %>%
{.->>sdop} %>%
filter(total_volume>quantile(dealsfull_type$total_volume,c(0.1)), total_volume<quantile(dealsfull_type$total_volume,c(0.9))) %>%
mutate(original_price_sd = original_price/sd(original_price))
data_h_ls[[i]] <- data_h
print(dim(sdop))
datadim_h <- c(datadim_h,dim(sdop)[1])
data_nh <- dealsfull%>% filter(type%in%c(2,3)) %>%
mutate(end_dow_dm = launch_dow_dm+offering_duration)%>%
filter(is.na(holiday)==1,type==i,end_dow_dm%in%pre.windows_t)%>%{.->>dealsfull_type}%>%{.->>sdop} %>%
filter(total_volume>quantile(dealsfull_type$total_volume,c(0.1)), total_volume<quantile(dealsfull_type$total_volume,c(0.9))) %>%
mutate(original_price_sd = original_price/sd(original_price))
# data_nh <- dealsfull%>%filter(is.na(holiday)==1,type%in%c(2)) %>%
# {.->>dealsfull_type}%>%
# # filter(total_volume>quantile(dealsfull_type$total_volume,c(0.4)), total_volume<quantile(dealsfull_type$total_volume,c(0.6))) %>%
# {.->>sdop} %>%
# filter(total_volume>quantile(dealsfull_type$total_volume,c(0.05)), total_volume<quantile(dealsfull_type$total_volume,c(0.95)))
data_nh_ls[[i]] <- data_nh
datadim_nh <- c(datadim_nh,dim(sdop)[1])
}else{
data_h <- dealsfull%>%filter(is.na(holiday)==0,type==i)%>%
# fl(offering_duration<40) %>%
{.->>sdop}%>%{.->>dealsfull_type} %>%
fl(total_volume>quantile(dealsfull_type$total_volume,c(0.1)), total_volume<quantile(dealsfull_type$total_volume,c(0.9))) %>%
mutate(original_price_sd = original_price/sd(original_price))
data_h_ls[[i]] <- data_h
datadim_h <- c(datadim_h,dim(sdop)[1])
data_nh <- dealsfull%>%
mutate(end_dow_dm = launch_dow_dm+offering_duration)%>%
filter(is.na(holiday)==1,type==i,end_dow_dm%in%pre.windows_t)%>%{.->>dealsfull_type}%>%{.->>sdop} %>%
filter(total_volume>quantile(dealsfull_type$total_volume,c(0.1)), total_volume<quantile(dealsfull_type$total_volume,c(0.9))) %>%
mutate(original_price_sd = original_price/sd(original_price))
# data_nh <- dealsfull%>%filter(is.na(holiday)==1,type==i)%>%
# {.->>sdop}%>%{.->>dealsfull_type} %>%
# fl(total_volume>quantile(dealsfull_type$total_volume,c(0.05)), total_volume<quantile(dealsfull_type$total_volume,c(0.95)))
data_nh_ls[[i]] <- data_nh
datadim_nh <- c(datadim_nh,dim(sdop)[1])
}
print(dim(sdop))
datasanalysis <- c(datasanalysis,dim(sdop)[1])
}
meanalpha <- vector()
sdalpha <- vector()
meanT <- vector()
sdT <- vector()
meand <- vector()
sdd <- vector()
for(i in industries_to_check){
tmp <- ((1-data_h_ls[[i]]$discount)*data_h_ls[[i]]$original_price_sd)
tmp <- tmp[tmp<quantile(tmp, 0.8)&tmp>quantile(tmp, 0.2)]
meanalpha <- c(meanalpha,mean(((1-data_h_ls[[i]]$discount)*data_h_ls[[i]]$original_price_sd)))
sdalpha <- c(sdalpha,sd(tmp))
tmp <- (data_h_ls[[i]]$prep.period)
tmp <- tmp[tmp<quantile(tmp, 0.65)&tmp>quantile(tmp, 0.35)]
meanT <- c(meanT,mean( (data_h_ls[[i]]$prep.period)))
sdT <- c(sdT,sd(tmp))
tmp <- (data_h_ls[[i]]$offering_duration)
tmp <- tmp[tmp<quantile(tmp, 0.65)&tmp>quantile(tmp, 0.35)]
meand <- c(meand,mean((data_h_ls[[i]]$offering_duration)))
sdd <- c(sdd,sd(tmp))
}
plotdata <- data.frame(Industry = c("Casual Dine","Fine Dine","Photography","Outdoor","Body Care"),
meanalpha=meanalpha,sdalpha=sdalpha,
meanT=meanT,sdT=sdT,
meand=meand,sdd=sdd)
ggplot(data = plotdata)+
geom_point(aes(x=Industry,y=meanalpha),color="grey70")+
geom_errorbar(aes(x=Industry,ymin=meanalpha-sdalpha,ymax=meanalpha+sdalpha),color="grey70",width=0.2)+
geom_point(aes(x=Industry,y=meanT),color="grey50")+
geom_errorbar(aes(x=Industry,ymin=meanT-sdT,ymax=meanT+sdT),color="grey50",width=0.2)+
geom_point(aes(x=Industry,y=meand),color="grey30")+
geom_errorbar(aes(x=Industry,ymin=meand-sdd,ymax=meand+sdd),color="grey30",width=0.2)
plotdata <- data.frame(Industry = rep(c("Casual Dine","Fine Dine","Photography","Outdoor","Body Care"),3),
meanValues = c(meanalpha, meanT, meand),
sdValues = c(sdalpha, sdT, sdd),
Parameter = c(rep("Discount price",5),rep("Launch day to holiday",5),rep("Offering_duration",5)))
ggplot(data = plotdata)+
geom_point(aes(x=Industry, y = meanValues),width=0.2)+
geom_errorbar(aes(x=Industry, ymin = meanValues-sdValues, ymax = meanValues+sdValues),width=0.2)+
facet_grid(Parameter~.,scales = "free")
# placebo test --------
# randomly select 10 days
set.seed(123)
pseudoholidays <- sample(seq(7,95)[-holidays.doy],10,replace = FALSE)
pseudoholidays_idx_table <- data.frame(holiday_idx = seq(1,length(pseudoholidays),1),
h_first_day = pseudoholidays[order(pseudoholidays)],
h_last_day = pseudoholidays[order(pseudoholidays)])
merge(merge(dealsfull%>% mt(end_dow_dm = launch_dow_dm + offering_duration), pseudoholidays_idx_table,all = TRUE) %>%
mt(pseudoholidayind = (launch_dow_dm<=h_last_day)*(end_dow_dm>=h_first_day)) %>%
mt(prep.periodtmp = h_first_day - launch_dow_dm) %>% mt(prep.periodtmp1 = pmax(prep.periodtmp,0)) %>%
gb(event_id) %>% sm(pseudoholiday=sum(pseudoholidayind,na.rm=TRUE)>0,
pre.period = (min(prep.periodtmp1))),dealsfull,by="event_id") -> dealsfull_pseudo
datadim_h <- vector()
datadim_nh <- vector()
data_h_ls <- vector(mode = "list", length = 11)
data_nh_ls <- vector(mode = "list", length = 11)
for (i in industries_to_check){
print(i)
if(i == 2){
data_h <- dealsfull%>%filter(is.na(holiday)==0,type%in%c(2,3)) %>%
#fl(offering_duration<40) %>%
{.->>dealsfull_type}%>%
filter(original_price>quantile(dealsfull_type$original_price,c(0.4)), original_price<quantile(dealsfull_type$original_price,c(0.6))) %>%
{.->>sdop} %>%
filter(total_volume>quantile(dealsfull_type$total_volume,c(0.1)), total_volume<quantile(dealsfull_type$total_volume,c(0.9))) %>%
mutate(original_price_sd = original_price/sd(original_price))
data_h_ls[[i]] <- data_h
print(dim(sdop))
datadim_h <- c(datadim_h,dim(sdop)[1])
data_nh <- dealsfull_pseudo%>%filter((pseudoholiday)==1,type%in%c(2,3)) %>%
# fl(prep.period>0,prep.period<40) %>%
{.->>dealsfull_type}%>%
filter(original_price>quantile(dealsfull_type$original_price,c(0.4)), original_price<quantile(dealsfull_type$original_price,c(0.6))) %>%
{.->>sdop} %>%
filter(total_volume>quantile(dealsfull_type$total_volume,c(0.1)), total_volume<quantile(dealsfull_type$total_volume,c(0.9))) %>%
mutate(original_price_sd = original_price/sd(original_price))
# data_nh <- dealsfull%>%filter(is.na(holiday)==1,type%in%c(2)) %>%
# {.->>dealsfull_type}%>%
# # filter(total_volume>quantile(dealsfull_type$total_volume,c(0.4)), total_volume<quantile(dealsfull_type$total_volume,c(0.6))) %>%
# {.->>sdop} %>%
# filter(total_volume>quantile(dealsfull_type$total_volume,c(0.05)), total_volume<quantile(dealsfull_type$total_volume,c(0.95)))
data_nh_ls[[i]] <- data_nh
datadim_nh <- c(datadim_nh,dim(sdop)[1])
}else{
data_h <- dealsfull%>%filter(is.na(holiday)==0,type==i)%>%
{.->>sdop}%>%{.->>dealsfull_type} %>% #fl(offering_duration<40) %>%
fl(total_volume>quantile(dealsfull_type$total_volume,c(0.1)), total_volume<quantile(dealsfull_type$total_volume,c(0.9))) %>%
mutate(original_price_sd = original_price/sd(original_price))
data_h_ls[[i]] <- data_h
datadim_h <- c(datadim_h,dim(sdop)[1])
data_nh <- dealsfull_pseudo%>%filter((pseudoholiday)==1,type==i)%>%
{.->>sdop}%>%{.->>dealsfull_type} %>%
fl(total_volume>quantile(dealsfull_type$total_volume,c(0.1)), total_volume<quantile(dealsfull_type$total_volume,c(0.9))) %>%
mutate(original_price_sd = original_price/sd(original_price))
# data_nh <- dealsfull%>%filter(is.na(holiday)==1,type==i)%>%
# {.->>sdop}%>%{.->>dealsfull_type} %>%
# fl(total_volume>quantile(dealsfull_type$total_volume,c(0.05)), total_volume<quantile(dealsfull_type$total_volume,c(0.95)))
data_nh_ls[[i]] <- data_nh
datadim_nh <- c(datadim_nh,dim(sdop)[1])
}
print(dim(sdop))
datasanalysis <- c(datasanalysis,dim(sdop)[1])
}
alphat <- vector()
Tt <- vector()
dt <- vector()
salest <- vector()
pt <- vector()
alphaks <- vector()
Tks <- vector()
dks <- vector()
salesks <- vector()
pks <- vector()
for(i in industries_to_check){
alphat <- c(alphat,t.test((1-data_nh_ls[[i]]$discount)*data_nh_ls[[i]]$original_price_sd,
(1-data_h_ls[[i]]$discount)*data_h_ls[[i]]$original_price_sd)$p.value)
Tt <- c(Tt,t.test(data_nh_ls[[i]]$prep.period, data_h_ls[[i]]$prep.period)$p.value)
print(t.test(data_nh_ls[[i]]$prep.period, data_h_ls[[i]]$prep.period)$estimate)
dt <- c(dt,t.test(data_nh_ls[[i]]$offering_duration, data_h_ls[[i]]$offering_duration)$p.value)
print(t.test(data_nh_ls[[i]]$offering_duration, data_h_ls[[i]]$offering_duration)$estimate)
salest <- c(salest,t.test(data_nh_ls[[i]]$total_volume, data_h_ls[[i]]$total_volume)$p.value)
pt <- c(pt,t.test(data_nh_ls[[i]]$original_price, data_h_ls[[i]]$original_price)$p.value)
alphaks <- c(alphaks,ks.test((1-data_nh_ls[[i]]$discount)*data_nh_ls[[i]]$original_price_sd,
(1-data_h_ls[[i]]$discount)*data_h_ls[[i]]$original_price_sd)$p.value)
Tks <- c(Tks,ks.test(data_nh_ls[[i]]$prep.period, data_h_ls[[i]]$prep.period)$p.value)
dks <- c(dks,ks.test(data_nh_ls[[i]]$offering_duration, data_h_ls[[i]]$offering_duration)$p.value)
salesks <- c(salesks,ks.test(data_nh_ls[[i]]$total_volume, data_h_ls[[i]]$total_volume)$p.value)
pks <- c(pks,ks.test(data_nh_ls[[i]]$original_price, data_h_ls[[i]]$original_price)$p.value)
}
rbind(Industry = rep(c("Casual Dine","Fine Dine","Photography","Outdoor","Body Care"),2),
# type = industries_to_check,
# c = signif(cvec,2),
# cnh = signif(cnhvec,2),
c(format(signif(alphaks,2)),
format(signif(alphat,2))),
c(format(signif(Tks,2)),
format(signif(Tt,2))),
c(format(signif(dks,2)),
format(signif(dt,2))),
c(format(signif(pks,2)),
format(signif(pt,2)))) -> testrestuls
rownames(testrestuls) <- c("Industry", "Discount price, $(1-\\alpha)p$",
"Launch day to holiday, $T$",
"Duration, $d$",
"Original price, $p$")
# rbind(Industry = rep(c("Casual Dine","Fine Dine","Photography","Outdoor","Body Care"),2),
# # type = industries_to_check,
# # c = signif(cvec,2),
# # cnh = signif(cnhvec,2),
# cbind(alphaks = format(signif(alphaks,2)),
# alphat = format(signif(alphat,2))),
# cbind(Tks = format(signif(Tks,2)),
# Tt = format(signif(Tt,2))),
# cbind(dks = format(signif(dks,2)),
# dt = format(signif(dt,2))),
# cbind(pks = format(signif(pks,2)),
# pt = format(signif(pt,2)))) -> testrestuls
# rownames(testrestuls) <- c("Industry", "ks test: discount price, $(1-\\alpha)p$",
# "t test: discount price, $(1-\\alpha)p$",
# "ks test: launch day to holiday, $T$",
# "t test: launch day to holiday, $T$",
# "ks test: duration, $d$", "t test: duration, $d$",
# "ks test: original price, $p$", "t test: original price, $p$")
print(xtable(testrestuls),#tabular.environment = "longtable",
only.contents=TRUE, include.rownames=T,
include.colnames=T, floating=F, sanitize.rownames.function = identity,
file = 'testresultstable.tex')
# multivariate, placebo ------
set.seed(123)
pseudoholidays <- sample(seq(7,95)[-holidays.doy],10,replace = FALSE)
pseudoholidays_idx_table <- data.frame(holiday_idx = seq(1,length(pseudoholidays),1),
h_first_day = pseudoholidays[order(pseudoholidays)],
h_last_day = pseudoholidays[order(pseudoholidays)])
merge(merge(dealsfull%>% mt(end_dow_dm = launch_dow_dm + offering_duration), pseudoholidays_idx_table,all = TRUE) %>%
mt(pseudoholidayind = (launch_dow_dm<=h_last_day)*(end_dow_dm>=h_first_day)) %>%
mt(prep.periodtmp = h_first_day - launch_dow_dm) %>% mt(prep.periodtmp1 = pmax(prep.periodtmp,0)) %>%
gb(event_id) %>% sm(pseudoholiday=sum(pseudoholidayind,na.rm=TRUE)>0,
pre.period = (min(prep.periodtmp1))),dealsfull,by="event_id") -> dealsfull_pseudo
datadim_h <- vector()
datadim_nh <- vector()
data_h_ls <- vector(mode = "list", length = 11)
data_nh_ls <- vector(mode = "list", length = 11)
for (i in industries_to_check){
print(i)
if(i == 2){
data_h <- dealsfull%>%filter(is.na(holiday)==0,type%in%c(2,3)) %>%
fl(offering_duration<40) %>%
{.->>dealsfull_type}%>%
# filter(original_price>quantile(dealsfull_type$original_price,c(0.4)), original_price<quantile(dealsfull_type$original_price,c(0.6))) %>%
{.->>sdop} %>%
filter(total_volume>quantile(dealsfull_type$total_volume,c(0.1)), total_volume<quantile(dealsfull_type$total_volume,c(0.9))) %>%
mutate(original_price_sd = original_price/sd(original_price))
data_h_ls[[i]] <- data_h
print(dim(sdop))
datadim_h <- c(datadim_h,dim(sdop)[1])
data_nh <- dealsfull_pseudo%>%filter((pseudoholiday)==1,type%in%c(2,3)) %>%
fl(prep.period>0,prep.period<40) %>%
{.->>dealsfull_type}%>%
# filter(original_price>quantile(dealsfull_type$original_price,c(0.4)), original_price<quantile(dealsfull_type$original_price,c(0.6))) %>%
{.->>sdop} %>%
filter(total_volume>quantile(dealsfull_type$total_volume,c(0.1)), total_volume<quantile(dealsfull_type$total_volume,c(0.9))) %>%
mutate(original_price_sd = original_price/sd(original_price))
# data_nh <- dealsfull%>%filter(is.na(holiday)==1,type%in%c(2)) %>%
# {.->>dealsfull_type}%>%
# # filter(total_volume>quantile(dealsfull_type$total_volume,c(0.4)), total_volume<quantile(dealsfull_type$total_volume,c(0.6))) %>%
# {.->>sdop} %>%
# filter(total_volume>quantile(dealsfull_type$total_volume,c(0.05)), total_volume<quantile(dealsfull_type$total_volume,c(0.95)))
data_nh_ls[[i]] <- data_nh
datadim_nh <- c(datadim_nh,dim(sdop)[1])
}else{
data_h <- dealsfull%>%filter(is.na(holiday)==0,type==i)%>%
{.->>sdop}%>%{.->>dealsfull_type} %>%
fl(offering_duration<40) %>%
fl(total_volume>quantile(dealsfull_type$total_volume,c(0.1)), total_volume<quantile(dealsfull_type$total_volume,c(0.9))) %>%
mutate(original_price_sd = original_price/sd(original_price))
data_h_ls[[i]] <- data_h
datadim_h <- c(datadim_h,dim(sdop)[1])
data_nh <- dealsfull_pseudo%>%filter((pseudoholiday)==1,type==i)%>%
{.->>sdop}%>%{.->>dealsfull_type} %>%
fl(total_volume>quantile(dealsfull_type$total_volume,c(0.1)), total_volume<quantile(dealsfull_type$total_volume,c(0.9))) %>%
mutate(original_price_sd = original_price/sd(original_price))
# data_nh <- dealsfull%>%filter(is.na(holiday)==1,type==i)%>%
# {.->>sdop}%>%{.->>dealsfull_type} %>%
# fl(total_volume>quantile(dealsfull_type$total_volume,c(0.05)), total_volume<quantile(dealsfull_type$total_volume,c(0.95)))
data_nh_ls[[i]] <- data_nh
datadim_nh <- c(datadim_nh,dim(sdop)[1])
}
print(dim(sdop))
datasanalysis <- c(datasanalysis,dim(sdop)[1])
}
set.seed(123)
# data_h_ls[[11]] <- data_h_ls[[11]][sample(dim(data_h_ls[[11]])[1],200,replace = T),]
ct <- 1
cramernholvshol_ls <- vector(mode="list",length = 5)
cramernholvsholtest <- vector()
for(i in industries_to_check){
cramer_nhol <- cbind((1-data_nh_ls[[i]]$discount)*data_nh_ls[[i]]$original_price_sd, data_nh_ls[[i]]$prep.period, data_nh_ls[[i]]$offering_duration)
cramer_hol <- cbind((1-data_h_ls[[i]]$discount)*data_h_ls[[i]]$original_price_sd, data_h_ls[[i]]$prep.period, data_h_ls[[i]]$offering_duration)
cramernholvshol_ls[[ct]] <- cramer.test(cramer_nhol,cramer_hol,conf.level=0.95,replicates=500,sim="permutation")
cramernholvsholtest <- c(cramernholvsholtest,cramernholvshol_ls[[ct]]$p.value)
ct <- ct + 1
print(ct)
}
|
3613e44be16f0fcdaa9f28bf3a60ae4a53dc6680 | 2defb970de80008d3a5f77728bf3f896832fe2e1 | /R-proj/kc_read_data.R | 2a6f9700a5a45111091b8982d54c0aa9d2e57d57 | [] | no_license | FredHutch/COVID_modeling_schools | bd211844ebd00c5977ac6ad0ef8b298aa7d6a8f2 | 98c1a879e1685b78a21427780f0f34941c309034 | refs/heads/master | 2023-08-31T14:37:46.365370 | 2021-10-06T16:36:47 | 2021-10-06T16:36:47 | 413,988,503 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,713 | r | kc_read_data.R | # this file reads in the data used to fit the model and processes it into the required format
require(lubridate)
require(dplyr)
# Original sources ####
# data file downloaded from the DOH site uses data from King County downloaded on Oct 9th (cut down to 9-30)
# this is the data used to calibrate the model
#
# tests, cases, deaths and hospitalizations are daily and by age gropu (0-19,20-49,50-69,70+)
# dates must be saved in the format year-mo-day (ex. 2020-08-31)
# To get more recent: refilter IDM raw data from sft.wa.gov (PRIVATE!) into IDM_king_county.csv (checked in) run the
# script select_data.sh in the data directory (change input file if you have a new one) then...
#
# get tests, hosp & deaths separately using the following scripts:
# tally_tests.R, tally_hosp.R and tally_deaths.R
# sort and tally for each date using the following scripts:
# sort_test_data.sh, sort_hosp_data.sh and sort_death_data.sh
# then merge into one file using "merge_files.R"
#
# The file is "king_county_by_age.csv". Its header is shown here:
#date,tests1,tests2,tests3,tests4,pos1,pos2,pos3,pos4,daily pos,cases,hosps1,hosps2,hosps3,hosps4,Hospitalizations,tot hosp,deaths1,deaths2,deaths3,deaths4,daily deaths,deaths
#
# Make sure the 1st date is 2/26 (not 1/14) and replace "NA"s with 0s before proceeding (except cumulative deaths!)
# The NAs come from joins of the files by date on days when there were no deaths!
#
kc_data = read.csv("../data/king_county_by_age.csv", stringsAsFactors = FALSE)
kc_data$date = ymd(kc_data$date)
# calculate negative tests
kc_data = cbind(kc_data, kc_data[,paste0("tests", 1:4)] - kc_data[,paste0("pos", 1:4)])
names(kc_data)[(ncol(kc_data) - 3):ncol(kc_data)] = paste0("neg", 1:4)
# do the moving average on the full data as it will be more accurate that way
kc_data = kc_data %>%
mutate_at(vars(contains("tests")), .funs = list(ma = ~moving_avg(.))) %>%
mutate_at(vars(contains("hosps")), .funs = list(ma = ~moving_avg(.))) %>%
mutate_at(vars(contains("pos")), .funs = list(ma = ~moving_avg(.))) %>%
mutate_at(vars(contains("neg")), .funs = list(ma = ~moving_avg(.))) %>%
mutate_at(vars(contains("deaths")), .funs = list(ma = ~moving_avg(.))) %>%
rename_at( vars( contains( "_ma") ), list( ~paste("ma", gsub("_ma", "", .), sep = "_") ) )
# new data off for calibration is Dec 31th
end_date = ymd("2020-12-31")
kc_data_calib = filter(kc_data, date <= end_date)
mean_tests = function(date){
start = yday(ymd(kc_data$date[1]))
now = yday(ymd(date))
range = seq(-3,3) + (now - start + 1)
mean(kc_data$tests[range])
}
# these are the mid moth dates used for tests and other calculated parameters
test_dates = c("2020-03-15", "2020-04-15", "2020-05-15", "2020-06-15", "2020-07-15", "2020-08-15", "2020-09-15", "2020-10-15")
# this is the calculated data from DOH data
kc_data_monthly = list(
# number of tests performed
tests = data.frame( date = ymd(test_dates),
value = sapply(test_dates, mean_tests)), # Days at which to capture daily testing avg to use for the month
# TODO for now these monthly values are copy/pasted, but could move calculation to R like the tests?
# fractions based fraction of tests in each age group
# obtained by running "./get_tests_by_age.sh" on "IDM_king_county.csv"
age_test_frac = data.frame(date = ymd(test_dates),
value = matrix(c(c(0.0740358,0.509446,0.294448,0.12207), # based on tests taken 2/1/20 - 3/31/20
c(0.0640876,0.521905,0.297245,0.116763), # based on tests taken during April
c(0.0715118,0.471296,0.299394,0.157798), # based on tests taken during May
c(0.0594408,0.569942,0.245141,0.125476), # based on tests taken during June
c(0.100093,0.597033,0.220479,0.0823942), # based on tests taken during July
c(0.121455,0.559161,0.22471,0.0946729), # based on tests taken during Aug
c(0.13549,0.54648,0.226248,0.0917822), # based on tests taken during Sept
c(0.13058,0.562911,0.232331,0.0741788)), ncol = 4, byrow = TRUE)), # based on tests taken during Oct
# fractions based percent in an age group that are hospitalized (based on date of hospital admission)
# obtained by running "./get_hosp_data.sh" on "IDM_king_county.csv"
hosp_frac = data.frame(date = ymd(test_dates),
value = matrix(c(c(0.0833333,0.0870359,0.233359,0.614841), # based on hospital admissions during 2/1/20 - 3/31/20
c(0.0185185,0.0938338,0.236869,0.531697), # based on hospital admissions during April
c(0.0206186,0.065389,0.198324,0.435185), # based on hospital admissions during May
c(0.0108696,0.030285,0.130631,0.358974), # based on hospital admissions during June
c(0.00858896,0.030137,0.122622,0.318493), # based on hospital admissions during July
c(0.00256082,0.0264188,0.122503,0.377193), # based on hospital admissions during Aug
c(0.0105448,0.02547,0.102041,.333333), # based on hospital admissions during Sept
c(0.00735294,0.0158991,0.0797546,0.372694)), ncol = 4, byrow = TRUE)) # based on hospital admissions during Oct
)
# Set global starting parameters ####
source("kc_globalparams.R")
|
1017a60f09328a6f27cc264fa7d035bd8e9ff6bd | bde39c3923798ffd37486ca60532b8f3006a36b0 | /misc R scripts/QC computed indicators boxplots.R | 23ad8e60b72ac66183c9543b5dae282f6c6157c3 | [] | no_license | usubuglab/AIM_2016 | 0baa6eacb579e98ce9f2dc615bfa0647324200c2 | c634d37263ec4a8bd7ee1cf27d38edace4806b5a | refs/heads/master | 2021-07-19T21:18:38.268763 | 2021-03-24T19:51:43 | 2021-03-24T19:51:43 | 49,015,002 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,405 | r | QC computed indicators boxplots.R | #QC computed indicators by looking at boxplots by indicator and ecoregion
IndicatorsFinal=read.csv("Z:\\buglab\\Research Projects\\AIM\\Analysis\\QC\\2017\\AK\\IndicatorsAK8feb2018_Alldata_boxplots_for_workshop.csv")
IndicatorsFinal=read.csv("Z:\\buglab\\Research Projects\\AIM\\Analysis\\QC\\2017\\AK\\IndicatorsAK8feb2018_Alldata_boxplots_for_workshop.csv")
#IndicatorsFinal=read.csv("Z:\\buglab\\Research Projects\\AIM\\Analysis\\QC\\2017\\AK\\AK_points_ecoregions.csv")
#IndicatorsFinal$NAMC_Benchmark=IndicatorsFinal$US_L3NAME
str(IndicatorsFinal)
library (ggplot2)
IndicatorsFinalnum=IndicatorsFinalnum[,c(56:57,1:55)]
#for indicator by ecoregion & targeted
ecoregions=unique(unlist(IndicatorsFinal$EcoregionHybrid10))
for (s in 2:length(ecoregions)) {
Ecoregionsubset=subset(IndicatorsFinal,EcoregionHybrid10==ecoregions[s])
nums=sapply(Ecoregionsubset,is.numeric)
IndicatorsFinalnum=Ecoregionsubset[,nums]
IndicatorsFinalnum$EcoregionHybrid10=Ecoregionsubset$EcoregionHybrid10
IndicatorsFinalnum$Targeted=Ecoregionsubset$Targeted
for (f in 10:length(colnames(IndicatorsFinalnum))-2){
png(file=paste(ecoregions[s], colnames(IndicatorsFinalnum[f]),".png"), width=2000,height=700,pointsize=15)
boxplot(IndicatorsFinalnum[,f]~Targeted, data=IndicatorsFinalnum,xlab=paste(colnames(IndicatorsFinalnum[f]),names=paste0(Indicato$names, " (n=",b$n,")"))),lwd=3,cex.lab=2)
dev.off()
}
}
nums=sapply(IndicatorsFinal,is.numeric)
IndicatorsFinalnum=Ecoregionsubset[,nums]
IndicatorsFinalnum$EcoregionHybrid10=Ecoregionsubset$EcoregionHybrid10
IndicatorsFinalnum$Targeted=Ecoregionsubset$Targeted
#for indicator by ecoregion/stream size
for (f in 10:length(colnames(IndicatorsFinalnum))-2){
png(file=paste("boxplot",colnames(IndicatorsFinalnum[f]),".png"), width=2000,height=700,pointsize=10)
boxplot(IndicatorsFinalnum[,f]~Targeted, data=IndicatorsFinalnum,xlab=paste(colnames(IndicatorsFinalnum[f])),lwd=3,cex.lab=2)
dev.off()
}
#for indicator by ecoregion/stream size
for (f in 10:length(colnames(IndicatorsFinalnum))){
png(file=paste("boxplot",colnames(IndicatorsFinalnum[f]),".png"), width=2000,height=700,pointsize=10)
boxplot(IndicatorsFinalnum[,f]~NAMC_Benchmark, data=IndicatorsFinalnum,xlab=paste(colnames(IndicatorsFinalnum[f])),lwd=3,cex.lab=2)
dev.off()
}
#for all indicators
for (f in 10:length(colnames(IndicatorsFinalnum))){
png(file=paste("boxplot",colnames(IndicatorsFinalnum[f]),".png"), width=1000,height=700,pointsize=24)
boxplot(IndicatorsFinalnum[,f],xlab=paste(colnames(IndicatorsFinalnum[f])),lwd=3,cex.lab=2)
dev.off()
}
f <- function(x) {
r <- quantile(x, probs = c(0.05, 0.25, 0.5, 0.75, 0.95))
names(r) <- c("ymin", "lower", "middle", "upper", "ymax")
r
}
# define outlier as you want
o <- function(x) {
subset(x, x < quantile(x)[2] | quantile(x)[4] < x)
}
library(ggplot2)
#####works!!!!
IndicatorsFinal=read.csv("Z:\\buglab\\Research Projects\\AIM\\Workshops\\NAMF Data Analysis\\AK_2018\\BenchmarkScreeningDevelopment\\ALL_AK_data_for_workshop_16Feb2018_benchmark_boxplots_JC_exploration.csv")
IndicatorsFinal=subset(IndicatorsFinal, IndicatorsFinal$Targeted=='Random')
Indicators=c("NAMC_Benchmark","EcoregionHybrid10","OtherEcoregion","Targeted","BankCover","BankStability","BnkCover_Stab","BankfullWidth","BankOverheadCover","InstreamHabitatComplexity","FloodplainConnectivity","LWD_Freq","PctOverheadCover","pH","Slope","SpecificConductance","VegComplexity","PctFines","ThalwegDepthCV","Entrench")
IndicatorsFinalsub=IndicatorsFinal[,Indicators]
IndicatorsFinalsub=subset(IndicatorsFinalsub, EcoregionHybrid10 %in% c('Alaska Range','Interior Highlands','Arctic Coastal Plain',"Interior Forested Lowlands and Uplands"))
IndicatorsFinalsub=subset(IndicatorsFinalsub, EcoregionHybrid10 %in% c('Interior Highlands','Arctic Coastal Plain'))
#IndicatorsFinalsub$EcoregionHybrid10=factor(IndicatorsFinalsub$EcoregionHybrid10, levels=levels(IndicatorsFinalsub$EcoregionHybrid10)[c(1,2,6,7)])
IndicatorsFinalsub$NAMC_Benchmark=droplevels(IndicatorsFinalsub$NAMC_Benchmark)
levels(IndicatorsFinalsub$NAMC_Benchmark)
('Beaufort Coastal Plain','Alaska Range','Yukon-Tanana Uplands','Ray Mountains')
for (s in 5:length(colnames(IndicatorsFinalsub))){
png(file=paste("boxplot",colnames(IndicatorsFinalsub[s]),".png"), width=2000,height=700,pointsize=20)
b=boxplot(IndicatorsFinalsub[,s]~NAMC_Benchmark*Targeted, data=IndicatorsFinalsub,xlab=paste(colnames(IndicatorsFinalsub[s])),lwd=3,cex.lab=2)
boxplot(IndicatorsFinalsub[,s]~NAMC_Benchmark*Targeted, data=IndicatorsFinalsub,xlab=paste(colnames(IndicatorsFinalsub[s])),names=paste0(b$names,"\n(n=",b$n,")"),lwd=3,cex.lab=2)
dev.off()
}
###########
for (s in 4:length(Indicators)) {
ggplot(IndicatorsFinal, aes(EcoregionHybrid10,IndicatorsFinalsub[,5]))+ stat_summary(fun.data=f,geom="boxplot",fill='lightgray',colour="darkslategray") +
stat_summary(fun.y = o, geom="point")+
labs(x="", y = colnames(IndicatorsFinalsub[,5]))+
ylim(0.0,2.5)+
theme_classic()+
theme(axis.text=element_text(size=12),axis.title=element_text(size=12,face="bold") )#see http://ggplot2.tidyverse.org/reference/theme.html for more info on changing spacing and text size
}
#example new boxplot for vegComplexity
#create paired reference and targeted boxplot
png('VegComplex.png',width=1500, height=700, size=15)
IndicatorsFinal=subset(IndicatorsFinal,OtherEcoregion %in% c('Beaufort Coastal Plain','Alaska Range','Yukon-Tanana Uplands','Ray Mountains'))
ggplot(IndicatorsFinal, aes(OtherEcoregion,VegComplexity))+ stat_summary(fun.data=f,geom="boxplot",fill='lightgray',colour="darkslategray") +
stat_summary(fun.y = o, geom="point")+
labs(x="", y = "VegComplexity")+
ylim(0.0,2.5)+
theme_classic()+
theme(axis.text=element_text(size=12),axis.title=element_text(size=12,face="bold") )#see http://ggplot2.tidyverse.org/reference/theme.html for more info on changing spacing and text size
dev.off()
png('FloodplainConnect.png',width=1500, height=700, size=15)
ggplot(IndicatorsFinal, aes(OtherEcoregion,FloodplainConnectivity))+ stat_summary(fun.data=f,geom="boxplot",fill='lightgray',colour="darkslategray") +
stat_summary(fun.y = o, geom="point")+
labs(x="", y = "Floodplain Connectivity")+
#ylim(0.0,0.5)+
theme_classic()+
theme(axis.text=element_text(size=12),axis.title=element_text(size=12,face="bold"),axis.text.x=element_blank(),axis.ticks.x=element_blank(),axis.title.x=element_blank() )#see http://ggplot2.tidyverse.org/reference/theme.html for more info on changing spacing and text size
dev.off()
png('Entrench.png',width=1500, height=700, size=15)
ggplot(IndicatorsFinal, aes(OtherEcoregion,Entrench))+ stat_summary(fun.data=f,geom="boxplot",fill='lightgray',colour="darkslategray") +
stat_summary(fun.y = o, geom="point")+
labs(x="", y = "Entrench")+
#ylim(0.0,0.5)+
theme_classic()+
theme(axis.text=element_text(size=12),axis.title=element_text(size=12,face="bold") )#see http://ggplot2.tidyverse.org/reference/theme.html for more info on changing spacing and text size
dev.off()
png('LWD_freq.png',width=1500, height=700, size=15)
ggplot(IndicatorsFinal, aes(OtherEcoregion,LWD_Freq))+ stat_summary(fun.data=f,geom="boxplot",fill='lightgray',colour="darkslategray") +
stat_summary(fun.y = o, geom="point")+
labs(x="", y = "LWD_Freq")+
#ylim(0.0,0.5)+
theme_classic()+
theme(axis.text=element_text(size=12),axis.title=element_text(size=12,face="bold") )#see http://ggplot2.tidyverse.org/reference/theme.html for more info on changing spacing and text size
dev.off()
png('PctFines.png',width=1500, height=700, size=15)
ggplot(IndicatorsFinal, aes(OtherEcoregion,PctFines))+ stat_summary(fun.data=f,geom="boxplot",fill='lightgray',colour="darkslategray") +
stat_summary(fun.y = o, geom="point")+
labs(x="", y = "PctFines")+
ylim(0.0,2.5)+
theme_classic()+
theme(axis.text=element_text(size=12),axis.title=element_text(size=12,face="bold") )#see http://ggplot2.tidyverse.org/reference/theme.html for more info on changing spacing and text size
dev.off()
png('PctOverheadCover.png',width=1500, height=700, size=15)
ggplot(IndicatorsFinal, aes(OtherEcoregion,PctOverheadCover))+ stat_summary(fun.data=f,geom="boxplot",fill='lightgray',colour="darkslategray") +
stat_summary(fun.y = o, geom="point")+
labs(x="", y = "PctOverheadCover")+
#ylim(0.0,0.5)+
theme_classic()+
theme(axis.text=element_text(size=12),axis.title=element_text(size=12,face="bold"),axis.text.x=element_blank(),axis.ticks.x=element_blank(),axis.title.x=element_blank() )#see http://ggplot2.tidyverse.org/reference/theme.html for more info on changing spacing and text size
dev.off()
png(file='boxplotpH.png', width=1500,height=700,pointsize=15)
ggplot(IndicatorsFinal, aes(OtherEcoregion,pH))+ stat_summary(fun.data=f,geom="boxplot",fill='lightgray',colour="darkslategray") +
stat_summary(fun.y = o, geom="point")+
labs(x="", y = "pH")+
#ylim(0.0,0.5)+
theme_classic()+
theme(axis.text=element_text(size=12),axis.title=element_text(size=12,face="bold") )#see http://ggplot2.tidyverse.org/reference/theme.html for more info on changing spacing and text size
dev.off()
png('Slope.png',width=1500, height=700, size=15)
ggplot(IndicatorsFinal, aes(OtherEcoregion,Slope))+ stat_summary(fun.data=f,geom="boxplot",fill='lightgray',colour="darkslategray") +
stat_summary(fun.y = o, geom="point")+
labs(x="", y = "Slope")+
#ylim(0.0,0.5)+
theme_classic()+
theme(axis.text=element_text(size=12),axis.title=element_text(size=12,face="bold") )#see http://ggplot2.tidyverse.org/reference/theme.html for more info on changing spacing and text size
dev.off()
dev.off()
boxplot(FloodplainConnectivity~NAMC_Benchmark,data=IndicatorsFinal)
# # boxplot of Cal vs. Val OE scores:
# b = boxplot(OE.assess.cal$OE.scores$OoverE, OE.assess.vld$OE.scores$OoverE,names=c("calibration","validation"),border=c("red", "blue"),ylab="O/E")
#
# boxplot(OE.assess.cal$OE.scores$OoverE, OE.assess.vld$OE.scores$OoverE,border=c("red", "blue"),ylab="O/E",names=paste0(b$names, " (n=",b$n,")"))
# abline(h=1.0,col=8,lty=3)
#
# png(file=paste("boxplot",".png"), width=1000,height=700,pointsize=10)
# par(mfrow=c(10,10))
# for (f in 10:length(colnames(IndicatorsFinalnum))){
# boxplot(IndicatorsFinalnum[,f],xlab=paste(colnames(IndicatorsFinalnum[f])),lwd=3,cex.lab=2)
# }
# dev.off() |
6b66448dae67deac16d8dc1e3c7af19b465fc7e2 | 29af718d33105bceddd488326e53dab24e1014ef | /Satisfiability/Transformers/Generators/Queens/GenericSAT/data/exp_files/ExpQueens/ExperimentR2.R | d8c68e54c2a4dcc8084f6cf4f56b42f11b2c186a | [] | no_license | OKullmann/oklibrary | d0f422847f134705c0cd1eebf295434fe5ffe7ed | c578d0460c507f23b97329549a874aa0c0b0541b | refs/heads/master | 2023-09-04T02:38:14.642785 | 2023-09-01T11:38:31 | 2023-09-01T11:38:31 | 38,629 | 21 | 64 | null | 2020-10-30T17:13:04 | 2008-07-30T18:20:06 | C++ | UTF-8 | R | false | false | 6,209 | r | ExperimentR2.R | # ExpQueens: Version: 0.3.4, parameter r 0 ... 10
# run on csverify
# Remark OK 3.3.2019: used old version of shuffle, leaving first field untouched
N sol nds h munds msnds hts t
1 1 1 0 0 1 0 0.00
2 0 3 1 3 0 1 0.00
3 0 7 3 7 0 1 0.00
4 2 15 5 3 1 2 0.00
5 10 43 7 3 1 2 0.00
6 4 133 20 25 1 3 0.00
7 40 443 29 17 1 4 0.00
8 92 1479 37 169 3 4 0.00
9 352 6039 50 73 5 5 0.00
10 724 23821 61 221 5 6 0.02
11 2680 129345 78 477 11 7 0.08
12 14200 745781 89 1237 7 8 0.50
13 73712 4083199 123 975 11 9 2.59
14 365596 28673645 139 3171 11 10 19.33
15 2279184 153365233 171 7395 21 10 108.91
16 14772512 1033823919 189 6587 25 11 782.58
17 95815104 7876738773 226 15911 23 12 6184.95
18 666090624 68209557525 252 26307 33 13 55625.92
1 1 1 0 0 1 0 0.00
2 0 3 1 3 0 1 0.00
3 0 7 3 7 0 1 0.00
4 2 15 6 5 1 2 0.00
5 10 35 11 3 3 2 0.00
6 4 123 18 15 1 3 0.00
7 40 371 25 13 5 4 0.00
8 92 1375 38 49 5 4 0.00
9 352 6977 46 99 5 5 0.00
10 724 31091 64 313 5 6 0.02
11 2680 147207 79 431 5 7 0.10
12 14200 728543 105 1099 7 7 0.45
13 73712 4140313 124 1075 11 8 2.63
14 365596 27744423 141 3475 11 10 18.43
15 2279184 186933711 169 5419 15 10 129.43
16 14772512 1078230347 194 5079 17 11 805.62
17 95815104 8314998945 216 7445 21 12 6420.68
18 666090624 58027247941 258 23495 29 13 46911.63
1 1 1 0 0 1 0 0.00
2 0 3 1 3 0 1 0.00
3 0 7 3 7 0 1 0.00
4 2 13 5 3 1 2 0.00
5 10 43 9 3 1 2 0.00
6 4 97 16 17 1 3 0.00
7 40 449 29 25 3 4 0.00
8 92 1445 38 33 3 4 0.00
9 352 6445 55 97 5 5 0.00
10 724 25273 63 347 5 6 0.01
11 2680 143971 78 401 7 7 0.08
12 14200 712297 97 835 7 8 0.46
13 73712 3706699 118 939 13 8 2.38
14 365596 25139381 144 2047 11 9 16.75
15 2279184 143018573 162 3091 19 10 99.73
16 14772512 1128099237 187 7947 17 11 832.31
17 95815104 8418562963 219 9753 25 12 6483.94
18 666090624 51962386657 250 17061 31 13 42128.04
1 1 1 0 0 1 0 0.00
2 0 3 1 3 0 1 0.00
3 0 5 2 5 0 1 0.00
4 2 15 5 3 1 2 0.00
5 10 41 11 3 3 2 0.00
6 4 111 19 23 1 3 0.00
7 40 455 24 15 3 3 0.00
8 92 1681 39 63 3 4 0.00
9 352 6583 46 125 5 5 0.00
10 724 24561 66 169 5 6 0.02
11 2680 137981 89 1181 5 7 0.09
12 14200 781677 108 719 7 8 0.51
13 73712 3406445 121 1237 13 8 2.17
14 365596 28400221 139 3203 11 10 18.81
15 2279184 175478833 173 5391 15 10 121.55
16 14772512 986388265 189 7545 19 11 736.46
17 95815104 8598694069 220 16125 27 12 6611.98
18 666090624 67956000687 254 33341 31 13 55139.13
1 1 1 0 0 1 0 0.00
2 0 3 1 3 0 1 0.00
3 0 5 2 5 0 1 0.00
4 2 17 6 3 1 2 0.00
5 10 35 10 1 1 2 0.00
6 4 131 19 33 1 3 0.00
7 40 413 26 19 3 4 0.00
8 92 1079 32 31 3 4 0.00
9 352 7191 47 113 5 6 0.00
10 724 29071 65 297 5 6 0.02
11 2680 121663 82 463 5 7 0.08
12 14200 720313 104 799 9 7 0.48
13 73712 3862429 128 1237 11 8 2.48
14 365596 25002437 129 1877 13 9 16.65
15 2279184 140578465 168 3797 19 10 98.02
16 14772512 967017807 185 5735 21 11 716.31
17 95815104 5705417501 230 21489 25 12 4417.50
18 666090624 48674196089 251 17425 31 13 39597.29
1 1 1 0 0 1 0 0.00
2 0 3 1 3 0 1 0.00
3 0 5 2 5 0 1 0.00
4 2 11 4 3 3 2 0.00
5 10 47 13 7 3 2 0.00
6 4 125 16 29 1 3 0.00
7 40 413 32 31 1 3 0.00
8 92 1583 36 39 3 5 0.00
9 352 5647 44 61 5 5 0.00
10 724 26993 72 303 5 6 0.02
11 2680 152561 85 593 5 7 0.10
12 14200 624065 93 537 7 7 0.42
13 73712 4207971 108 1189 9 9 2.73
14 365596 22752753 138 1573 13 9 15.26
15 2279184 160600739 163 6009 15 10 111.31
16 14772512 1064030539 188 5085 19 11 796.20
17 95815104 7841371897 228 12517 25 12 6071.34
18 666090624 62335207057 246 17673 27 13 50465.61
1 1 1 0 0 1 0 0.00
2 0 3 1 3 0 1 0.00
3 0 5 2 5 0 1 0.00
4 2 13 5 3 1 2 0.00
5 10 33 8 3 3 2 0.00
6 4 97 14 25 1 3 0.00
7 40 355 25 15 1 3 0.00
8 92 1337 37 67 3 5 0.00
9 352 6087 48 75 5 5 0.00
10 724 28443 61 181 3 6 0.02
11 2680 105421 73 455 5 7 0.06
12 14200 718821 98 897 7 8 0.43
13 73712 4019329 127 1075 11 9 2.59
14 365596 24013435 133 1735 13 9 15.97
15 2279184 137389711 162 2449 15 10 95.99
16 14772512 1134328209 195 5893 17 11 843.87
17 95815104 8069726395 223 11721 23 12 6231.81
18 666090624 64895327775 252 20509 27 13 52285.70
1 1 1 0 0 1 0 0.00
2 0 3 1 3 0 1 0.00
3 0 5 2 5 0 1 0.00
4 2 17 7 3 1 2 0.00
5 10 49 12 13 3 2 0.00
6 4 107 16 19 1 3 0.00
7 40 351 25 15 3 3 0.00
8 92 1569 39 73 3 4 0.00
9 352 5993 46 93 5 6 0.00
10 724 26643 58 157 3 6 0.02
11 2680 117539 81 389 9 7 0.08
12 14200 581979 85 507 11 7 0.39
13 73712 4038751 122 1725 11 8 2.56
14 365596 25253437 135 1787 15 9 16.61
15 2279184 163998565 161 3465 15 10 114.24
16 14772512 999362085 195 6555 17 11 743.79
17 95815104 9230876305 209 19267 23 12 7099.00
18 666090624 66056945803 241 20343 25 13 53482.20
1 1 1 0 0 1 0 0.00
2 0 3 1 3 0 1 0.00
3 0 7 3 7 0 1 0.00
4 2 15 6 7 1 2 0.00
5 10 39 15 9 3 2 0.00
6 4 163 17 21 1 3 0.00
7 40 359 27 17 3 3 0.00
8 92 1427 35 51 5 5 0.00
9 352 5665 47 123 5 5 0.00
10 724 24483 60 321 7 6 0.02
11 2680 140849 80 609 5 7 0.10
12 14200 753657 98 1115 9 8 0.51
13 73712 4755901 117 1155 9 9 3.05
14 365596 28641817 140 4845 13 9 19.01
15 2279184 216527313 171 4477 17 11 150.25
16 14772512 1170339183 198 7929 17 11 879.23
17 95815104 8937716485 209 9913 29 12 6865.92
18 666090624 77627950497 263 23025 27 13 62734.40
1 1 1 0 0 1 0 0.00
2 0 3 1 3 0 1 0.00
3 0 5 2 5 0 1 0.00
4 2 13 5 5 1 2 0.00
5 10 37 12 1 3 2 0.00
6 4 113 16 31 1 3 0.00
7 40 381 25 23 3 3 0.00
8 92 1441 42 31 5 4 0.00
9 352 6177 47 85 5 5 0.00
10 724 23125 57 143 5 6 0.02
11 2680 133923 81 611 5 7 0.08
12 14200 721995 96 597 7 7 0.44
13 73712 3380527 118 885 9 8 2.22
14 365596 24267789 138 2427 13 9 16.32
15 2279184 133979673 171 3291 17 10 93.46
16 14772512 952437139 187 3783 21 11 710.39
17 95815104 7167225981 220 7469 23 12 5566.29
18 666090624 63901097405 256 27267 27 13 51730.70
1 1 1 0 0 1 0 0.00
2 0 3 1 3 0 1 0.00
3 0 7 3 7 0 1 0.00
4 2 13 5 3 1 2 0.00
5 10 39 11 7 3 2 0.00
6 4 119 15 19 1 3 0.00
7 40 353 25 27 3 4 0.00
8 92 1477 37 29 5 4 0.00
9 352 5323 45 55 7 5 0.00
10 724 23689 58 195 7 6 0.01
11 2680 141545 88 419 7 7 0.09
12 14200 781275 96 523 7 7 0.49
13 73712 3411869 130 899 9 8 2.19
14 365596 23144685 137 2379 11 9 15.57
15 2279184 144793591 160 4085 19 10 101.01
16 14772512 977223649 183 6917 21 11 726.97
17 95815104 7601344435 222 10477 27 12 5890.04
18 666090624 59268625561 251 16343 31 13 48057.21
|
dfc8700a7482c17c81c337acf0d3aa7dffe2bbfb | 257afcbc82d3610b68db6ad1227862a25f24e5d9 | /man/spectrolab.Rd | eca3f7e833dd2b18646ec6ae1c5ce922f44740b1 | [] | no_license | annakat/spectrolab | 061f6e9dfdfcf00431b8fea6164be3de1be052fd | c0a342b4a924bd48f051d8418df0b60a3d0dd9d7 | refs/heads/master | 2021-07-21T05:21:30.337824 | 2019-09-05T19:36:18 | 2019-09-05T19:36:18 | 142,489,204 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 250 | rd | spectrolab.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/spectrolab.R
\docType{package}
\name{spectrolab}
\alias{spectrolab}
\alias{spectrolab-package}
\title{Spectrolab}
\description{
Class and methods for hyperspectral data.
}
|
5df4630b06c1c09c35578abb1174d669c3e58018 | 67a6f1af8a7e28e3e64f123ce48fff3017364094 | /man/getUCSC.Rd | 4bb581e1baf01f3239990bf5cf3cc062b40d9cdf | [] | no_license | tastanlab/NoRCE | 3257f0af8da9cbff2152313edc9df9e3b2b4dd1c | e8779cec9bdece0e71a8f85389259a8d1714465f | refs/heads/master | 2020-05-28T14:17:11.643250 | 2019-05-26T12:11:24 | 2019-05-26T12:11:24 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,016 | rd | getUCSC.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/getNearest.R
\name{getUCSC}
\alias{getUCSC}
\title{Get nearest genes for the window of the upstream/downstream region.}
\usage{
getUCSC(bedfile, upstream, downstream, hg)
}
\arguments{
\item{bedfile}{Bed formated input gene regions}
\item{upstream}{Maximum upstream distance from the transcription start region of the input gene}
\item{downstream}{Maximum downstream distance from the transcription end region of the input gene}
\item{hg}{genomee assembly of interest for the analysis. Possible assemblies are "mm10" for mouse, "dre10" for zebrafish, "rn6" for rat, "dm6" for fruit fly, "ce11" for worm, "sc3" for yeast, "hg19" and "hg38" for human}
}
\value{
genes
}
\description{
When downstream = 0 / upstream = 0, function converts bed formated regions to HUGO genes
}
\examples{
regionNC <- readbed(dm_file = ncRegion,isText = FALSE)
neighbour <- getUCSC(bedfile = regionNC, upstream = 1000, downstream = 1000,hg = 'hg38')
}
|
d633d0782183926f46cdd3f6ab14c15a16a27b76 | f5e66f86b5e89c21a54cd8a095de5752018a549e | /data-raw/NOAAetal2012.R | eda3e4e0499e8d185669ad49fafaecc88f017898 | [] | no_license | rjaneUCF/MultiHazard | 5279e4efc869e8745818fe62b37ce1d35534114c | 334b0e1aa6bd5f4dbd68876221fad3c499e691a2 | refs/heads/master | 2023-07-29T15:04:55.523801 | 2023-07-11T20:46:42 | 2023-07-11T20:46:42 | 239,541,679 | 11 | 4 | null | null | null | null | UTF-8 | R | false | false | 90 | r | NOAAetal2012.R | NOAAetal2012<-read.csv("NOAAetal2012.csv",header=TRUE)
devtools::use_data(NOAAetal2012)
|
ed58f8c36779b6c7dcbef4c52ce42a38d8938752 | 930a64ae51ba9c4052bcd2b6d4392ff70f98bde8 | /FORMULAS.R | 2bddc8934a33356b6ef0189f9cb0179292b25e7f | [] | no_license | statisticallyfit/RStatistics | 1e9f59a1ebef597d4c73f3cf10bed5170126d83b | 93915cc141c4cb2b465d301d44695b8ce0ad35f8 | refs/heads/master | 2020-06-26T02:29:51.113577 | 2019-10-18T18:00:14 | 2019-10-18T18:00:14 | 74,606,344 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 38,671 | r | FORMULAS.R | source('/datascience/projects/statisticallyfit/github/learningmathstat/RStatistics/Rfunctions.R')
# use DescTools
# Kurt
# Skew
#skewness <- function(x) {
# m3 <- mean((x - mean(x))^3)
# return(m3/(sd(x))^3)
#}
#kurtosis <- function(x) {
# m4 <- mean((x - mean(x))^4)
# return(m4/(sd(x))^4 - 3)
#}
# same SSE as for ANOVA as one from SUMMARY
SSE <- function(fit) {
y <- fit$model[[1]]
yhat <- fit$fitted
return(sum( (y - yhat)^2 ))
}
# can get this value with anova table too:
# (under MeanSq Column and Resids Row - that is s^2)
standardErrorOfRegression <- function(fit) {
sse.value <- invisible(SSE(fit))
n <- length(fit$fitted)
kplusOne <- ncol(fit$model)
return (sqrt((sse.value) / (n - kplusOne)))
}
MSE <- function(fit) {
s <- standardErrorOfRegression(fit)
return(s^2)
}
# Validation - training MSE
# Given a model fit on the training data, use the full Data set to calculate test info
# to calculate the test MSE
MSE <- function(train.fit, fullData){
trainIndices <- as.numeric(rownames(train.fit$model))
yName = colnames(train.fit$model)[1]
y <- fullData[,yName]
yHat = predict(train.fit, fullData)
return(mean( (y - yHat)[-trainIndices]^2 ))
}
# take only test index rows and the predictors used in the model
#testData <- fullData[-trainIndices, colnames(train.fit$model)]
#testY <- testData[, 1]
#preds = predict(train.fit, testData)
#overallMSE <- mean( (testY - preds)^2 )
standardErrorOfSlope <- function(fit) {
x <- fit$model[[2]]
s <- standardErrorOfRegression(fit)
return (s / sqrt(SSxx(x)))
}
SSyy <- function(fit) {
print("Warning: assuming quantitative response data")
y <- fit$model[[1]]
y.mean <- mean(y)
return ( sum( (y - y.mean)^2 ) )
}
SSxx <- function(x) {
#x <- fit$model[[xName]]
x.mean <- mean(x)
return ( sum( (x - x.mean)^2 ) )
}
SSxy <- function(x, y) {
x.mean <- mean(x)
y.mean <- mean(y)
return ( sum( (x - x.mean) * (y - y.mean) ) )
}
#standardErrorOfRegression <- function(sse.value, n) {
# return(sqrt(sse.value / (n - 2)))
#}
# OR
#a <- anova(lm)
#ss <- a$`Sum Sq`
#return(ss[length(ss)])
# OR
#s <- summary(r.lm)
#(1-s$r.squared)*SST(var$datamat$dc)
SST <- function(fit) {
return(SSyy(fit) - SSE(fit))
}
# TODO
# between group variance (equivalent to SST)
# input: list of xs and ys, where xs are the factor names and y = response
betweenGroupVariance <- function(xs, ys){
groups <- unique(xs)
grandMean <- mean(ys)
}
# within group variance (same as SSE)
# from page 636 in Allan Bluman
# TODO: BlockDesignTest (anova nested) that contains tests for both
# the treatments and blocks as in the notes.
# TODO RandomizedDesignTest (anova global F)
# TODO: TwoFactorialDesignTest (anova)
## Confidence interval for the Slope in Regression Model (multiple)
slopeCI <- function(fit, level=0.95) {
mat <- confint(fit, level=level)
df <- data.frame(as.numeric(mat[,1]), as.numeric(mat[,2]))
colnames(df) <- colnames(mat)
return(df)
}
#slopeCI <- function(fit, level = 0.95) {
# slope <- fit$coefficients[[2]]
# sB1 <- standardErrorOfSlope(fit)
#
# # Calculate alpha for the t-statistic, and df
# alpha <- 1 - level
# n <- length(y)
# df <- n - 2
# t.alpha2 <- abs(qt(alpha/2, df))
#
# # calculate conf.int
# leftCI <- slope - t.alpha2 * sB1
# rightCI <- slope + t.alpha2 * sB1
#
# return( c(leftCI, rightCI) )
#}
# Confidence interval for the Mean when x = xp
# E(y) = yhat(xp) +- t(alpha/2,df) * s * sqrt(1/n + (xp - xmean)^2/SSxx)
## NOTE: can be found using R with this:
# predict(model, new = data.frame(x.name=x.value), interval="confidence", level=0.95)
# TODODO use Rfunctions file betaCI to get many decimal places in output
meanCI <- function(fit, x.values=c(), level=0.95){
predictorNames <- names(fit$model)[-1]
df <- data.frame(x.values)
#rownames(df) <- rep("", length(predictorNames))
colnames(df) <- predictorNames
return(predict(fit, newdata = df, interval="confidence", level=level))
}
predictCI <- function(fit, x.values, level=0.95){
predictorNames <- names(fit$model)[-1]
df <- data.frame(cbind(x.values))
#rownames(df) <- ""
colnames(df) <- predictorNames
return(predict(fit, newdata = df, interval="prediction", level=level))
}
#meanCI <- function(fit, x.value, level=0.95) {
# # finding the yhat value at a particular x-.value
# yhat.at.xp <- fit$coeff[[2]] * x.value + fit$coeff[[1]]
# yhat <- fit$fitted
# x <- fit$model[[2]]
# y <- fit$model[[1]]
#
# # Finding the std error of regression, s
# s <- standardErrorOfRegression(fit)
#
# # Finding the t, alpha
# alpha <- 1 - level
# n <- length(y)
# df <- n - 2
# t.alpha2 <- abs(qt(alpha/2, df))
#
# # Calculating the confidence interval.
# leftCI <- yhat.at.xp - t.alpha2 * s * sqrt(1/n + (x.value - mean(x))^2 / SSxx(x))
# rightCI <- yhat.at.xp + t.alpha2 * s * sqrt(1/n + (x.value - mean(x))^2 / SSxx(x))
#
# return(c(leftCI, rightCI))
#}
# Confidence interval for the Predictor when x = xp
# yi = yhat(xp) +- t(alpha/2,df) * s * sqrt(1 + 1/n + (xp - xmean)^2/SSxx)
## NOTE: can be found using R with this:
# predict(model, new = data.frame(x.name=x.value), interval="prediction", level=0.95)
#predictCI <- function(fit, x.value, level=0.95) {
# # finding the yhat value at a particular x-.value
# yhat.at.xp <- fit$coeff[[2]] * x.value + fit$coeff[[1]]
# yhat <- fit$fitted
# x <- fit$model[[2]]
# y <- fit$model[[1]]
#
# # Finding the std error of regression, s
# s <- standardErrorOfRegression(fit)
#
# # Finding the t, alpha
# alpha <- 1 - level
# n <- length(y)
# df <- n - 2
# t.alpha2 <- abs(qt(alpha/2, df))
#
# # Calculating the confidence interval.
# leftCI <- yhat.at.xp - t.alpha2 * s * sqrt(1 + 1/n + (x.value - mean(x))^2 / SSxx(x))
# rightCI <- yhat.at.xp + t.alpha2 * s * sqrt(1 + 1/n + (x.value - mean(x))^2 / SSxx(x))
#
# return(c(leftCI, rightCI))
#}
# Given restricted and unrestricted models,
# do joint F-test but give Chi square instead of F
# k = number of restrictions = df1
# n - (k+1) = df2, n = sample size.
##### TODO: write the hypothesis tested: figure out the B# = B# = 0 etc.
# from the coefs that are missing in the reduced model.
####################################### TODO: check out EtaSq from DescTools
######################################## Use Anova() from car package.
#TODO: if the predictor is a factor THEN make the null hypothesis be stated
# as H0: mu_level1 = mu_level2 = ....
NestedFTest <- function(r.lm, u.lm, printNice=TRUE){
# g = number of parameter difference
g = length(u.lm$coef) - length(r.lm$coef)
# k = num parameters in the unrestricted/complete model
k = length(u.lm$coef) - 1
# n - k - 1
df2 = nrow(u.lm$model) - k - 1
df1 = g #
sse.r <- SSE(r.lm)
sse.c <- SSE(u.lm)
F.nested <- ((sse.r - sse.c) / df1) / ((sse.c / df2)); F.nested
chi.stat <- F.nested * df1
chi.p.value <- round(1 - pchisq(chi.stat, df=df1), 5)
f.p.value <- round(1 - pf(F.nested, df1=df1, df2=df2), 5)
result = data.frame(ChiSquare=chi.stat, ChiSqPValue=chi.p.value,
df.chi = df1,
FStatistic=F.nested, FPvalue=f.p.value,
df1=df1, df2=df2)
if(f.p.value < 0.05){
recommend = u.lm
recName = "complete model"
} else{
recommend = r.lm
recName = "reduced model"
}
result = list(RecommendedModel=recommend, RecommendedName=recName,Test=result)
if(printNice){
nu = formula(u.lm)
nr = formula(r.lm)
cat("\n")
cat("#############################################################\n")
cat("#### Analysis Of Variance ###\n")
cat("#### (F Test and Chi-Square Test for Nested Models) ###\n")
cat("#############################################################\n")
cat("\n")
cat("H0: reduced model is true: \n"); cat(paste(nr[[2]], nr[[1]], nr[3]))
cat("\n\nHA: complete model is true: \n"); cat(paste(nu[[2]], nu[[1]], nu[3]))
cat("\n\n")
cat("F statistic: ", F.nested, "\n")
cat("p-value: ", f.p.value, "\n")
cat("numerator df: ", df1, "\n")
cat("denominator df: ", df2, "\n\n")
return(invisible(result))
}else{
return(result)
}
}
# The Global F-test for regression model fit.
FTest <- function(fit) {
sst = SST(fit)
sse = SSE(fit)
k = length(fit$coefficients) - 1
n = nrow(fit$model)
Fcrit <- ((sst - sse)/k) / (sse / (n-k-1))
p.value <- 1 - pf(Fcrit, df1=k, df2= n-k-1)
cat("\n")
cat("#############################################################\n")
cat("#### Analysis Of Variance ###\n")
cat("#### (F Test The Regression Model) ###\n")
cat("#############################################################\n")
cat("\n")
cat("F statistic: ", Fcrit, "\n")
cat("p-value: ", p.value, "\n\n")
df <- data.frame(FStatistic=Fcrit, PValue=p.value,
DFNumerator=k, DFDenominator=n-k-1)
rownames(df) <- ""
return(invisible(df))
}
# REASONOING: method shown in section 8.4 Sinicich.
# We are splitting data determined by xSplit (by value of xs) and then
# doing F-variance test on regression stderroreg of both models (MSE's)
# xSplit = the x value such that x <= xSplit is sample 1 and x > xSplit is sample2
# variableName = the name of the variable that we want to split on.
# fit = the lm object
# alpha = sig level
# alternative = two-sided or one-sided (cif one sided then calcs which side)
# PREREQ: only for non-multiple regression (simple models)
HomoskedasticityRegressionTest.Split <- function(theFormula, data, xName, xSplit,
alternative="two-sided",
alpha=0.05){
sample1 <- data[data[[xName]] <= xSplit, ]
sample2 <- data[data[[xName]] > xSplit, ]
fit1 <- lm(theFormula, data=sample1)
fit2 <- lm(theFormula, data=sample2)
n1 <- nrow(sample1)
n2 <- nrow(sample2)
Fstat <- 0
k <- ncol(fit1$model) -1# should be equal to that of mod2, is num independent vars
# including transformations.
numerator.df <- 0
denominator.df <- 0
mse1 <- MSE(fit1)
mse2 <- MSE(fit2)
if(mse1 > mse2) {
Fstat <- mse1 / mse2
numerator.df <- n1 - (k+1)
denominator.df <- n2 - (k+1)
} else {
Fstat <- mse2 / mse1
numerator.df <- n2 - (k+1)
denominator.df <- n1 - (k+1)
}
# calculating F-crit
Fcrit <- 0
p.value <- 0
alternativeStr <- 0
if(alternative == "two-sided"){
alternativeStr <- "Ha: MSE.1 != MSE.2"
Fcrit <- qf(alpha/2, df1=numerator.df, df2=denominator.df, lower.tail=F)
p.value <- 2*pf(Fstat, df1=numerator.df, df2=denominator.df, lower.tail=F)
} else { # else it's one-sided
if(mse1 < mse2) alternativeStr <- "Ha: MSE.1 < MSE.2"
else alternativeStr <- "Ha: MSE.1 > MSE.2"
Fcrit <- qf(alpha, df1=numerator.df, df2=denominator.df, lower.tail=F)
p.value <- pf(Fstat, df1=numerator.df, df2=denominator.df, lower.tail=F)
}
cat("\n")
cat("#############################################################\n")
cat("######## Homoskedasticity of Regression Test ########\n")
cat("#############################################################\n")
cat(" H0: MSE.1 == MSE.2 \n")
cat(" ",alternativeStr," \n",sep="")
#cat("")
cat("F-statistic = ", Fstat, sep="", "\n")
cat("Numerator DF = ", numerator.df, sep="", "\n")
cat("Denominator DF = ", denominator.df, sep="", "\n")
cat("Critical F-value = ", Fcrit, sep="", "\n")
cat("P-value = ", p.value,sep="","\n")
df <- data.frame(Fstat=Fstat, FCritical=Fcrit, PValue=p.value)
return(invisible(df))
}
# Making the prediction equation, returning it as a function
# fit = the lm object
#predictionEquation <- function(fit) {
# intercept = fit$coef[[1]]
# slopes = as.numeric(fit$coef[-1])
#
# slopesSign = c() #if(slopes < 0) " - " else " + "
# newSlopes = c()
# for (i in 1:length(slopes)) {
# if(slopes[i] < 0){
# slopesSign = c(slopesSign, " - ")
# newSlopes = c(newSlopes, abs(slopes[i]))
# }
# else{
# slopesSign = c(slopesSign, " + ")
# newSlopes = c(newSlopes, slopes[i])
# }
#
# }
# # Making the x-names x1 x2 x3 ... etc
# xStrings <- names(fit$coefficients)[-1]
# #numXs = length(fit$coefficients) - 1
# #xStrings = c()
# #for(i in 1:numXs){
# # xStrings = c(xStrings, paste("x", i, sep=""))
# #}
# numCoefs <- length(fit$model)
# multiplySigns <- replicate(numCoefs, "*")
# allCoefsWithVars <- paste(intercept,
# paste(c(rbind(slopesSign, newSlopes,
# multiplySigns, xStrings)),
# collapse = ''), sep="")
#
# # args = find independent names first with 'stringr' package
# tup <- separateArgs(fit)
# args <- tup[[1]]
# extraArgs <- tup[[2]]
# ## TODO: body = detect types of patterns in the formula so I(RPM^2)
# # means square of the arg1=RPM, and RPM:CPRATIO is RPM*CPRATIO
# # Then put those variables in the formula.
# # PROCSS:
# # 1. get independent args
# # 2. for each independent arg, check for I-terms and get the matching
# # i-term( thing inside the Iterm must match the independent arg. Get
# # the poly-power (hint - split at ^))
# # 3. for each idnependent arg, if term is not I-term, then must be
# # interaction term so split at ";" and create variable x1 * x2 to put
# # in formula.
# patterns <- c("\\^", "\\:")
# bodyArgs <- c()
# for (p in patterns){
# testPattern <- str_detect(extraArgs, p)
# # for this pattern, get the simple arg with this pattern
# str_detect(args, p)
# ## TODO FINSIH
# }
#
# eval(parse(text = paste("yHat <- function(", args, ") { return(",
# body, ") }", sep="")))
#
# return(eval(theFormula))
#}
# Finds the independent, untransformed arguments of the lm object
# So if we have I(RPM^2), it returns RPM
separateArgs <- function(fit){
allNames <- names(fit$coefficients)[-1]
patterns <- c("\\^", "\\:")
extraArgs <- c()
for (p in patterns) {
# If no detecting of a pattern, then is INDEP ARG
testExtra <- str_detect(allNames, p)
extraArgs <- c(extraArgs, allNames[testExtra])
}
indicesNotExtraArgs <- which(!allNames %in% extraArgs)
args <- allNames[indicesNotExtraArgs]
return(list(args, extraArgs))
}
# OUTLIER POINTS = are those beyond (-2,2) of std residuals
outlier.outlierValues <- function(fit){
library(car)
srs = rstandard(fit)
isOutlier <- srs < -2 | srs > 2
print(outlierTest(fit)) # from car
df <- data.frame(Fitted=fit$fitted.values, StandardizedResiduals=srs,
IsOutlier=isOutlier)
return(invisible(df))
}
# INFLUENTIAL POINTS
# fit = the lm object
influence.leverageValues <- function(fit){
hs <- hatvalues(fit)
k <- length(fit$model) - 1
n <- nrow(fit$model)
h.mean <- (k+1)/n
h.cutoff <- 2*(k+1)/n
isInfluential <- hs > h.cutoff
return(data.frame(InfluentialPoints=hs, CutOff=h.cutoff, AvgLev=h.mean,
IsInfluential=isInfluential))
}
influence.cooksDistances <- function(fit) {
cks <- cooks.distance(fit)
p <- length(fit$coef) # p = number of parameters
n <- nrow(fit$model)
# Cooks distance Di follows an F-distribution: Di ~ F(df1=p, df2=n-p)
Fcrit <- qf(0.5, df1=p, df2=n-p)
cks.fprob <- pf(cks, df1=p, df2=n-p)
# TODO: check if my method here cks > Fcrit is correct:
# or do we use cks.fvalues > Fcrit???
isInfluential <- cks > Fcrit | cks >= 1
# https://newonlinecourses.science.psu.edu/stat501/node/340/
# An alternative method for interpreting Cook's distance that is sometimes
# used is to relate the measure to the F(p, n–p) distribution and to find the
# corresponding percentile value. If this percentile is less than about
# 10 or 20 percent, then the case has little apparent influence on the
# fitted values. On the other hand, if it is near 50 percent or even higher,
# then the case has a major influence. (Anything "in between" is more ambiguous.)
return(data.frame(CooksPoints=cks, FcritValue=Fcrit,
CooksFProb=cks.fprob, FcritProb=rep(0.5, n),
IsInfluential=isInfluential))
}
# NOTE
# pi.hat.Ho <- nullMod$fitted.values
#pi.hat.Ha <- altMod$fitted.values
#y <- placekick$good
# ******
# likRatio <- -2 * sum(y * log(pi.hat.Ho/pi.hat.Ha) + (1-y)*log((1-pi.hat.Ho)/(1-pi.hat.Ha)))
# NOTE: null model deviance is bigger (assuming) so the statistic
# will be negative if not.
# Equivalent to nested F-test between two models here, like anova(null, alt)
# Tests missing parameter significance.
NestedLikelihoodRatioTest <- function(reducedModel, fullModel, printNice=TRUE) {
y <- reducedModel$model[1]
pi.hat.Ho <- reducedModel$fitted.values
pi.hat.Ha <- fullModel$fitted.values
# don't know why this doesn't work when comparing null Mod with alternative
#likRatioStat <- -2 * sum(y * log(pi.hat.Ho/pi.hat.Ha) + (1-y)*log((1-pi.hat.Ho)/(1-pi.hat.Ha)))
likRatioStat <- reducedModel$deviance - fullModel$deviance
# note: df.null = n - 1, df.resid = n - k - 1, k = num params (not incl. B0)
df <- reducedModel$df.residual - fullModel$df.residual
pValue <- 1 - pchisq(likRatioStat, df)
#chi.crit <- qchisq(0.05, df=df, lower.tail=F)
result <- rbind(c(ResidualDev.Ho=reducedModel$deviance,
ResidualDev.Ha=fullModel$dev,
df.res.Ho=reducedModel$df.residual,
df.res.Ha=fullModel$df.residual,
df.dev=df,
Deviance=likRatioStat, PValue=pValue))
#ChiCrit=chi.crit,
rownames(result) <- ""
result <- data.frame(result)
statement <- ""
if(pValue < 0.05){
statement <- paste("Reject H0. Conclude at least one of the extra β ",
"coefficients \n",
"in the complete model is nonzero, so that the complete model is\n",
"better for predicting Y = ", names(reducedModel$model)[1], ".", sep="")
} else{
statement <-
paste("Insufficient evidence to reject Ho, that is, to conclude that",
"\nthe extra terms in the complete model are statistically useful",
"\nuseful predictors in the regression model.", sep="")
}
if(printNice){
#likRatioPrintNice(result, reducedModel, fullModel, statement)
nf = formula(reducedModel)
na = formula(fullModel)
cat("\n")
cat("#####################################################################\n")
cat("####### Nested Likelihood-Ratio Test #######\n")
cat("#####################################################################\n")
cat("\tH0: reduced model is true: "); cat(paste(nf[[2]], nf[[1]], nf[3]))
cat("\n")
cat("\tHA: complete model is true: "); cat(paste(na[[2]], na[[1]], na[3]))
cat("\n\n")
cat(" ΔG:\t\t ", result$Deviance, "\n")
#cat(" Critical Chi-square (α = 0.05): ", ChiCrit, "\n")
cat(" df:\t\t ", result$df, "\n")
cat(" p-value:\t\t ", result$PValue, "\n\n")
cat(statement)
return(invisible(result))
}else{
return(result)
}
}
# nested likelihood test between null model and fit model
# NOTE: use the count = cbind(success, total) y-value when fitting the fit model
# so that here tempData is made of two cols. Do not let y be a proportion
# otherwise we get weird results.
# TODO: make this work for binomial glm when response = proportion (beetle.glm in
# STAT320, topic7)
DevianceTest <- function(fit, printNice=TRUE){
# Check if the fitted model contains an offset. If so, we need to fit
# the null model with this same offset
# The nullData contains the response column observations and the
# offset in another column
nullData <- NULL
yName <- NULL
theFormula <- NULL
if(!is.null(fit$offset)){
nullData <- data.frame(fit$model[1], OFFSET=fit$offset)
yName <- colnames(nullData)[1]
theFormula <- as.formula(paste(yName, "~ offset(OFFSET)", sep=""))
} else {
nullData <- data.frame(fit$model[1])
yName <- colnames(nullData)[1]
theFormula <- as.formula(paste(yName, "~ 1", sep=""))
}
nullModel <- glm(theFormula, family=fit$family, data=nullData)
result <- NestedLikelihoodRatioTest(nullModel, fit, printNice = printNice)
if(printNice){
return(invisible(result))
} else {
return(result)
}
}
# Likelihood Ratio Test for the GLM Model
# Null hypothesis is that expected mu and observed ys are the same
# If low p-value then they are not.
# TESTS: the difference between expected successes with observed ones, and the
# expected failures with observed ones. (global F-test)
# Equivalent to global F-test, tests overall model fit.
# TODO: make this work for binomial glm when response = proportion (beetle.glm in
# STAT320, topic7)
ResidualDevianceTest <- function(fit, printNice=TRUE) {
# residualdeviance has chi-square distribution on n - k - 1 degrees freedom.
df <- fit$df.residual # always n - k - 1, where k+1 = num params/coefs
res.dev <- fit$deviance
result <- data.frame(LikRatio=res.dev, df=df,
PValue= 1 - pchisq(res.dev, df=df))
row.names(result) <- ""
# Ho: deviance = 0 p = big fail reject H0 = good model fit = deviance small
statement <- ""
if(result$PValue < 0.05){
# testing if G = 0
# ni <- gen$total; yi <- gen$male; mui <- ni *ratio.glm$fitted.values
# G <- 2*sum( yi*log(yi/mui) + (ni-yi)*log((ni-yi)/(ni-mui)) )
statement <-
paste("Reject H0. Conclude the residual deviance is large and\n",
"different from zero. So expected successes are not equal to\n",
"observed successes and expected failures are not equal to \n",
"observed failures. The model is not a good fit for the data.", sep="")
} else{
statement <-
paste("Fail to reject H0. Not enough evidence to conclude that the\n",
"residual deviance is not 0, so we say the residual deviance is small.\n",
"So expected and observed successes/failures are similar. \n",
"Thus the model is a good fit for the data.", sep="")
}
# TODO: update null hypothesis, is not the same of the nested test H0.
if(printNice){
yName <- names(fit$model)[1]
form = as.formula(paste(yName, " ~ 1", sep=""))
nullModel <- glm(form, data=fit$model, family=fit$family)
#likRatioPrintNice(result, nullModel, fit, statement)
nf = formula(nullModel)
na = formula(fit)
cat("\n")
cat("#####################################################################\n")
cat("####### Likelihood-Ratio Residual Deviance Test #######\n")
cat("#####################################################################\n")
cat("\tH0: residual deviance G = 0 \n") #; cat(paste(nf[[2]], nf[[1]], nf[3]))
#cat("\n")
cat("\tHA: residual deviance G != 0\n\n") #; cat(paste(na[[2]], na[[1]], na[3]))
cat(" G:\t\t ", result$LikRatio, "\n")
cat(" df:\t\t ", result$df, "\n")
cat(" p-value:\t\t ", result$PValue, "\n\n")
cat(statement)
return(invisible(result))
} else{
return(result)
}
}
NullDevianceTest <- function(model){
# null deviance has chi-square distribution on n - 1 degrees freedom.
df <- model$df.null
dev <- model$null.deviance
result <- data.frame(NullDeviance=dev, df=df,
PValue= 1 - pchisq(dev, df=df))
row.names(result) <- ""
return(result)
}
#DevianceResiduals <- function(obsData, expData=NULL) {
# # doing chi-test just to get expected values.
# if(!is.null(expData)) chi.test <- chisq.test(obsData, p=expData/sum(expData))
# else chi.test <- chisq.test(obsData)
# os <- obsData
# es <- chi.test$exp
# return(sign(os - es) * sqrt(abs(2 * os * log(os/es))))
#}
#
# GOAL: computes the deviance residuals for a poisson glm
# TODO: how to check that family == poisson? (string-based comparison?)
# if this is not a poisson glm, this result will be wrong
# must be the same as residuals(fit)
DevianceResiduals.Poisson <- function(fit) {
yi <- fit$model[1]
mui <- fit$fitted.values
si <- sign(yi - mui)
di <- si * sqrt(2 * (yi * log(yi/mui) - (yi - mui)))
return(di[,])
}
# TODO: cannot calculate mui since the counts n_i are not always given - sometimes
# we are just given binary 1,0 data.
# TODO: this still won't work when we get NaN's like when ni's and yi's are the same.
#DevianceResiduals.Binomial <- function(fit) {
# yi <- fit$model[1]
# mui <-
#yi <- beetleData$r #fit$model[1]
# ni <- beetleData$n
# mui <- ni * beetle.glm$fitted.values
# si <- sign(yi - mui)
# di <- si * sqrt(2 * (yi * log(yi/mui) + (ni - yi) * log((ni-yi)/(ni - mui))))
##}
ResidualDevianceTest.Poisson <- function(fit){
devianceRes <- DevianceResiduals.Poisson(fit)
deviance <- sum(devianceRes^2)
result <- data.frame(ResidualDeviance=deviance,
PValue=1-pchisq(deviance, df=fit$df.residual),
DegreesFreedom = fit$df.residual)
return(result)
}
# ---------------------- Row/Col Probability Estimates -----------------------------------------
rowProbabilityHat <- function(tbl){prop.table(tbl, margin=1)}
colProbabilityHat <- function(tbl){prop.table(tbl, margin=2)}
# ---------------------- Returns table with marginal row/col sums ------------------------------
################ TODO make for k-way tables too, not just two-way
marginalTable <- function(tbl) {
t = tbl
dimnames(t) <- NULL
t <- cbind(t, margin.table(t, margin=1))
t <- rbind(t, margin.table(t, margin=2))
dms <- dimnames(tbl)
dms[[1]] <- c(dms[[1]], "") # c(dms[[1]], "ColTotals")
dms[[2]] <- c(dms[[2]], "")
dimnames(t) <- dms
return(t)
}
# ----------------------------------------------------------------------------------------------
#################################################################################
######## Measures of Association for 1,2-way Tables ########
#################################################################################
# odds ratio, relative risk, diff of proportions test independence of two variables
# in the two-way table.
# ---------------------- Odds Ratio ----------------------------
#################################### TODO make it work with ftable
#################################### problem because no names in ftable
# odds ratio for n x m table (two-way) is the same anyway you place the coeffs
# Returns odds ratio for all combinations of pairings between variable levels.
# CAN TEST WITH THIS DATA:
#eyeTable <- as.table(matrix(c(20,30,10,15,10,85,25,15,12,20,10,82,45,45,22,35,20,167),byrow = TRUE, nrow=3))
#rownames(eyeTable) <- c("Female", "Male", "Total")
#colnames(eyeTable) <- c("Black", "Brown", "Blue", "Green", "Gray", "Total")
#oddsRatio(eyeTable)
colOdds <- function(tbl) {
if(length(dim(tbl)) > 2){
cat("Error: Needs to be 2-dimensional table")
return(invisible(NULL))
}
### make names combos
combosColnames <- combn(colnames(tbl), m=2)
combosColnames.forward <- paste(combosColnames[1,], "/", combosColnames[2,], sep="")
combosColnames.backward <- paste(combosColnames[2,], "/", combosColnames[1,], sep="")
### make odds for each col, holding rows constant
combosCol <- lapply(1:nrow(tbl), function(i) combn(tbl[i,], m=2))
cOdds.forward <- lapply(1:nrow(tbl), function(i){combosCol[[i]][1, ] / combosCol[[i]][2, ]})
cOdds.backward <- lapply(1:nrow(tbl), function(i){combosCol[[i]][2, ] / combosCol[[i]][1, ]})
# adjusting
library(plyr)
cOdds.forward <- ldply(cOdds.forward)
cOdds.backward <- ldply(cOdds.backward)
#naming
colnames(cOdds.forward) <- combosColnames.forward
rownames(cOdds.forward) <- rownames(tbl)
colnames(cOdds.backward) <- combosColnames.backward
rownames(cOdds.backward) <- rownames(tbl)
return(cbind(cOdds.forward, cOdds.backward))
}
rowOdds <- function(tbl){
if(length(dim(tbl)) > 2){
cat("Error: Needs to be 2-dimensional table")
return(invisible(NULL))
}
### make names combos
combosRownames <- combn(rownames(tbl), m=2)
combosRownames.forward <- paste(combosRownames[1, ], "/", combosRownames[2,], sep="")
combosRownames.backward <- paste(combosRownames[2, ], "/", combosRownames[1,], sep="")
### make odds for each col, holding rows constant
combosRow <- lapply(1:ncol(tbl), function(i) combn(tbl[,i], m=2))
rOdds.forward <- lapply(1:ncol(tbl), function(i){combosRow[[i]][1, ] / combosRow[[i]][2, ]})
rOdds.backward <- lapply(1:ncol(tbl), function(i){combosRow[[i]][2, ] / combosRow[[i]][1, ]})
# adjusting a bit
library(plyr)
rOdds.forward <- t(ldply(rOdds.forward))
rOdds.backward <- t(ldply(rOdds.backward))
#naming
colnames(rOdds.forward) <- colnames(tbl)
rownames(rOdds.forward) <- combosRownames.forward
colnames(rOdds.backward) <- colnames(tbl)
rownames(rOdds.backward) <- combosRownames.backward
return(rbind(rOdds.forward, rOdds.backward))
}
oddsRatio <- function(tbl) {
if(length(dim(tbl)) > 2){
cat("Error: Needs to be 2-dimensional table")
return(invisible(NULL))
}
### make names combos
combosColnames <- combn(colnames(tbl), m=2)
combosColnames <- paste(combosColnames[1,], "/", combosColnames[2,], sep="")
combosRownames <- combn(rownames(tbl), m=2)
combosRownames <- paste(combosRownames[1, ], "/", combosRownames[2,], sep="")
### make odds for each col, holding rows constant
combosCol <- lapply(1:nrow(tbl), function(i) combn(tbl[i,], m=2))
cOdds <- lapply(1:nrow(tbl), function(i){combosCol[[i]][1, ] / combosCol[[i]][2, ]})
library(plyr)
cOdds <- ldply(cOdds)
colnames(cOdds) <- combosColnames
rownames(cOdds) <- rownames(tbl)
oddsPairs <- lapply(1:ncol(cOdds), function(i) combn(cOdds[,i], m=2))
oddsRatios <- lapply(1:ncol(cOdds), function(i) {oddsPairs[[i]][1,] / oddsPairs[[i]][2,]})
oddsRatios <- t(ldply(oddsRatios))
colnames(oddsRatios) <- combosColnames
rownames(oddsRatios) <- combosRownames
return(oddsRatios)
}
# TODO: normality subset tests for the completely randomized design # and rest
# factorials etc... within each population, do shapiro wilk normality test
# and report p-values in a table. Also graph densities like in the
# Leaf data set (logistic)
# TODO: equal variance subset tests for all the randomized/block/factorial
# designs (levens and bartlet) and report p-vals in table.
# -------------------------------------------------------------------------------
bootstrapMean <- function(data, R, level=0.95){
# Get n samples from the known population distribution. (this is the data given)
n = length(data) # observed data, original data
# Sample from F-hat (instead of rexp) to sample with replacement from F-hat
H.star = rep(NA, R)
for(i in 1:R){
# resample from original data with replacement.
dataStar = sample(data, size=n, replace=TRUE) #this is the bootstrap sample
H.star[i] = mean(dataStar)
}
# MEAN(H*) should be close to MU_XBAR should be close to MU
# SD(H*) should be close to SD_XBAR should be close to S/SQRT(N)
cat("boot.mu_xbar (hstar) = ", mean(H.star), "\n", sep="")
cat("xbar (mean of data) = ", mean(data), "\n\n", sep="")
B.hat = mean(H.star) - mean(data) # estimated bias
cat("B.hat = mean(hstar) - mean(data) = ", B.hat, "\n", sep="")
mu.hat.B = mean(data) - B.hat # bias-corrected version of mu
cat("theta.hat.B = mean(data) - bias = ", mu.hat.B, "\n\n",sep="")
se.mean = sd(H.star) # standard error of the mean
cat("boot.se.mean (hstar) = ", se.mean, "\n", sep="") # call this se.mean
#print(sqrt(var(H.star))) # same as sd(H.star)
cat("se.mean = sigma/sqrtn(n) = ", theta/sqrt(n), "\n", sep="")
cat("se.mean = s / sqrt(n) = ", sd(data)/sqrt(n), "\n\n", sep="")
# 95 % confidence interval methods.
# calculate critical value first
lower = abs( (1-level)/2)
upper = lower + level
z.crit = abs(qnorm(lower))
cat("CI by normal approximation: ")
cat("(",mu.hat.B - z.crit*se.mean,", ",mu.hat.B + z.crit*se.mean,")","\n",sep="")
cat("CI by percentile method: ")
qs = quantile(H.star, c(lower, upper))
cat("(", qs[1],", ", qs[2],")", "\n", sep="")
cat("CI by basic method:", "")
cat("(", 2*xbar - qs[2],", ", 2*xbar - qs[1],")", "\n", sep="")
return(invisible(H.star))
}
# STAT330 Learning -----------------------------------------------------------------------
# GOAL: fit lda model but attach also the observed data in a separate column.
# ldaFit <- function(formula())
# TODO: how to get formula in arguments
#ldaFit <- function(response, predictors, data){
# library(MASS)
# formulaToFit <- reformulate(response=response, termlables=predictors)
# lda.fit <- lda(formulaToFit, data=data)
# return (lda.fit)
#}
# lda.fit = the model fit by lda (MASS package)
# GOAL: converts output of lda.fit (list) to data frame to look nicer
ldaToDataFrame <- function(lda.fit){
pred.list <- predict(lda.fit)
# note: posterior in pred.list may be multidim array? depending on num classes
# so that changes num cols of this df ??
ldaValues <- pred.list$x
colnames(ldaValues) <- "LDAClassifier"
pred.df <- data.frame(PredictedClass = pred.list$class, ldaValues,
Posterior=pred.list$posterior)
# note: LDA classifier == linear discriminant fits
return(pred.df)
}
# observedY = original observed y-values (categorical)
# lda.fit = the model fit by lda (MASS package)
# GOAL: calculate lda error rate, which is estimation of Bayes error rate.
ldaErrorRate <- function(lda.fit, observedY) {
return(1 - mean(predict(lda.fit)$class == observedY))
}
# GOAL: calculate confusion matrix of the lda fit
# observedY = vector of observed y values (categorical)
# lda.fit = model fit by lda
# link to another type of confusion matrix:
# https://maths-people.anu.edu.au/~johnm/courses/mathdm/2008/pdf/r-exercisesVI.pdf
twoWayConfusionMatrix <- function(lda.fit, observedY){
df = data.frame(PredictedResponse=predict(lda.fit)$class,
ObservedResponse=observedY)
# note: returning transpose so positions of sensitivity and specificity
# match my notes
tbl = marginalTable(table(df))
# rename "ColTotals" to total population counts per class
rownames(tbl)[3] <- "TotalPopulationCounts"
colnames(tbl)[3] <- "TotalPredictedCounts"
# calculate sensitivity (true positive) = lower right corner rate: TP / P
# TODO: apply to multivariate case
# calculate specificity (true negative) = upper left corner rate: TN / N
return(tbl)
}
|
fac6778abef5010ce5bc1cbf8efb20d2c0892857 | 0ea852e85df22dde661279d2738f229e2f8511e1 | /R/gra.R | 69416b86957c6197e60f49c0a0d5aad89cc29c87 | [] | no_license | ekarbowiak/SuperFarmerPKEK | 6666768970e682de58a1d7136b744c0a3f642f4f | e660a8a5ef72834d77a64bd8ce7290e9e2c0af8e | refs/heads/master | 2021-01-13T05:31:16.125472 | 2017-01-27T09:08:00 | 2017-01-27T09:08:00 | 80,165,451 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,051 | r | gra.R | #' Gra w SuperFarmera zadana strategia
#'
#' Funkcja gra() oblicza czas gry w grę SupreFarmer zadana strategia
#'
#' @param strategia strategia, ktora chcemy zagrac. W pakiecie do wyboru: strategy1, strategy2, strategy3
#' @param kostka1 zwierzeta znajdujace sie na kostce nr 1
#' @param kostka2 zwierzeta znajdujace sie na kostce nr 2
#' @param StadoGracza stan stada gracza, zapisany jako nazwany wektor
#' @return funkcja zwraca czas gry dana strategia
#'
#' @export
#'
#'
gra = function(strategia=SuperFarmerPKEK::strategy2, kostka1 = SuperFarmerPKEK::kostka1,
kostka2 = SuperFarmerPKEK::kostka2,StadoGracza = SuperFarmerPKEK::StadoGracza) {
czas = 0
while (!czy_wygrana(StadoGracza)) {
czas = czas + 1
StadoGracza = strategia(StadoGracza)
if (!czy_wygrana(StadoGracza)) {
kostka1.wynik = sample(kostka1, 1)
kostka2.wynik = sample(kostka2, 1)
StadoGracza = reproduction(kostka1.wynik, kostka2.wynik, StadoGracza)}
}
return(czas)
}
|
18f8d934168fb4283542605213ba6edba053fc98 | b119d79e8a8790fd6ce300bab406a2b67f7e70ec | /tests/testthat/test-parser.R | c2eb67d6c3df5d60b650689ea21289dffa0168eb | [
"BSD-3-Clause"
] | permissive | gadenbuie/selectr | f8a499c8aad67e3dfacb312e155947b8a6dd8f93 | 7bd1b0f834fc7eaa1b6dde749a1b847884663dc0 | refs/heads/master | 2022-04-27T22:02:58.729128 | 2019-04-02T08:42:08 | 2019-04-02T08:43:19 | 202,213,707 | 0 | 0 | NOASSERTION | 2019-08-13T19:47:56 | 2019-08-13T19:47:56 | null | UTF-8 | R | false | false | 5,984 | r | test-parser.R | context("parser")
test_that("parser parses canonical test expressions", {
parse_many <- function(css) {
selectors <- lapply(css, function(x) parse(x))
n <- length(selectors)
results <- list()
for (i in seq_len(n)) {
selector <- selectors[[i]]
if (is.list(selector)) {
results[[i]] <- unlist(lapply(selector, function(x) x$repr()))
} else {
results[[i]] <- selector$repr()
}
}
if (n)
unlist(results)
else
character(0)
}
expect_that(parse_many("*"), equals("Element[*]"))
expect_that(parse_many("*|*"), equals("Element[*]"))
expect_that(parse_many("*|foo"), equals("Element[foo]"))
expect_that(parse_many("|foo"), equals("Element[foo]"))
expect_that(parse_many("foo|*"), equals("Element[foo|*]"))
expect_that(parse_many("foo|bar"), equals("Element[foo|bar]"))
expect_that(parse_many('foo[lang|="zh"]'), equals("Attrib[Element[foo][lang |= 'zh']]"))
# This will never match, but it is valid:
expect_that(parse_many("#foo#bar"),
equals("Hash[Hash[Element[*]#foo]#bar]"))
expect_that(parse_many(c("div>.foo",
"div> .foo",
"div >.foo",
"div > .foo",
"div > .foo",
"div \n> \t \t .foo",
"div\r>\n\n\n.foo",
"div\f>\f.foo")),
equals(rep("CombinedSelector[Element[div] > Class[Element[*].foo]]", 8)))
expect_that(parse_many(c("td.foo,.bar",
"td.foo, .bar",
"td.foo\t\r\n\f ,\t\r\n\f .bar")),
equals(rep(c("Class[Element[td].foo]",
"Class[Element[*].bar]"), 3)))
expect_that(parse_many(c("div, td.foo, div.bar span")),
equals(c("Element[div]",
"Class[Element[td].foo]",
"CombinedSelector[Class[Element[div].bar] <followed> Element[span]]")))
expect_that(parse_many("div > p"),
equals("CombinedSelector[Element[div] > Element[p]]"))
expect_that(parse_many("td:first"),
equals("Pseudo[Element[td]:first]"))
expect_that(parse_many("td :first"),
equals("CombinedSelector[Element[td] <followed> Pseudo[Element[*]:first]]"))
expect_that(parse_many(c("a[name]", "a[ name\t]")),
equals(rep("Attrib[Element[a][name]]", 2)))
expect_that(parse_many("a [name]"),
equals("CombinedSelector[Element[a] <followed> Attrib[Element[*][name]]]"))
expect_that(parse_many(c('a[rel="include"]', 'a[rel = include]')),
equals(rep("Attrib[Element[a][rel = 'include']]", 2)))
expect_that(parse_many(c("a[hreflang |= 'en']", "a[hreflang|=en]")),
equals(rep("Attrib[Element[a][hreflang |= 'en']]", 2)))
expect_that(parse_many("div:nth-child(10)"),
equals("Function[Element[div]:nth-child(['10'])]"))
expect_that(parse_many(":nth-child(2n+2)"),
equals("Function[Element[*]:nth-child(['2', 'n', '+2'])]"))
expect_that(parse_many("div:nth-of-type(10)"),
equals("Function[Element[div]:nth-of-type(['10'])]"))
expect_that(parse_many("div div:nth-of-type(10) .aclass"),
equals("CombinedSelector[CombinedSelector[Element[div] <followed> Function[Element[div]:nth-of-type(['10'])]] <followed> Class[Element[*].aclass]]"))
expect_that(parse_many("label:only"),
equals("Pseudo[Element[label]:only]"))
expect_that(parse_many("a:lang(fr)"),
equals("Function[Element[a]:lang(['fr'])]"))
expect_that(parse_many('div:contains("foo")'),
equals("Function[Element[div]:contains(['foo'])]"))
expect_that(parse_many("div#foobar"),
equals("Hash[Element[div]#foobar]"))
expect_that(parse_many("div:not(div.foo)"),
equals("Negation[Element[div]:not(Class[Element[div].foo])]"))
expect_that(parse_many("td ~ th"),
equals("CombinedSelector[Element[td] ~ Element[th]]"))
# handle comments
expect_that(parse_many("a /* test */"),
equals("Element[a]"))
expect_that(parse_many("a/* test */"),
equals("Element[a]"))
expect_that(parse_many("/* test */ a"),
equals("Element[a]"))
expect_that(parse_many("/* test */a"),
equals("Element[a]"))
expect_that(parse_many("a /* test */ b"),
equals("CombinedSelector[Element[a] <followed> Element[b]]"))
expect_that(parse_many("a /* test "),
equals("Element[a]"))
})
test_that("parsed elements print correctly", {
shw <- function(x) trimws(capture.output(parse(x)[[1]]$show()))
expect_that(shw("a"), equals("Element[a]"))
expect_that(shw(".test"), equals("Class[Element[*].test]"))
expect_that(shw(":active"), equals("Pseudo[Element[*]:active]"))
expect_that(shw("a:not(.toggle)"), equals("Negation[Element[a]:not(Class[Element[*].toggle])]"))
expect_that(shw("[href]"), equals("Attrib[Element[*][href]]"))
expect_that(shw("#id"), equals("Hash[Element[*]#id]"))
})
test_that("compiled regex parsing functions behave as expected", {
m_whitespace <- compile_('[ \t\r\n\f]+')
m_number <- compile_('[+-]?(?:[0-9]*\\.[0-9]+|[0-9]+)')
m_hash <- compile_(paste0("^#([_a-zA-Z0-9-]|", nonascii, "|\\\\(?:", delim_escapes, "))+"))
m_ident <- compile_(paste0("^([_a-zA-Z0-9-]|", nonascii, "|\\\\(?:", delim_escapes, "))+"))
expect_that(m_whitespace("a b"), equals(match_whitespace("a b")))
expect_that(m_number("a 1"), equals(match_number("a 1")))
expect_that(m_hash("a #test"), equals(match_hash("a #test")))
expect_that(m_ident(" test"), equals(match_ident(" test")))
})
|
ab3214b976903edc7adda28eea07c05c8ceadcb1 | d692ef9a915e8d901d6c17567a632f555c70d5c9 | /YATAWebModels/R/generators.R | bada7e4ad93635605bdc20d759267398a8f42514 | [
"CC0-1.0"
] | permissive | cryptobuks1/YATA2 | 386f0cc47e6bfeda8c626d6d44e033708f06c404 | 04d7c4fefea3babc0d0f30ee15c9a52602623055 | refs/heads/main | 2023-04-26T16:05:54.430701 | 2021-05-27T15:00:58 | 2021-05-27T15:00:58 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,761 | r | generators.R | makePoints = function(size=100) {
rpar = 50
# Start
r1 = sample(100:150, 10, replace=TRUE)
idx = sample(1:10, 1)
# Plus/Minus
ud = sample(c(-1,1),size,replace=TRUE)
#values
v1 = runif(n = size, min = 0, max = 1)
v1 = round(v1,3)
var = v1 * ud / 10
dat = rep(r1[idx], size)
for (i in 1:(size-1)) {
dat[i+1] = dat[i] * (1 + var[i])
}
data.frame(x=seq(1,size), y=dat)
}
makePlot = function(df, color, base) {
p = ggplot(data=df) + geom_point(aes(x,y)) + theme_bw()
if (!missing(base)) p = base + p
m = nrow(df)
p = p + geom_segment(aes(x=df[1,1], y=df[1,2], xend=df[m,1], yend=df[m,2], colour=color))
plot = Plot$new(df,p,calcRect(df))
plot$tg = calcSlope(plot)
plot
}
addSegment = function (model) {
m = nrow(model$dfw)
p = model$plots[[model$step + 1]]
model$plots[[model$step + 2]] = p + geom_segment(aes(x=model$dfw[1,1], y=model$dfw[1,2], xend=model$dfw[m,1], yend=model$dfw[m,2], colour=model$color))
eq = calcRect(model$dfw)
model$equs[[model$step + 2]] = eq
}
calcRect = function(df) {
x1 = df[1,1]
y1 = df[1,2]
x2 = df[nrow(df),1]
y2 = df[nrow(df),2]
xr = x2 - x1
yr = y2 - y1
B = ((y1 * xr) - (x1 * yr)) / xr
A = yr / xr
list(a=A,b=B)
}
calcSlope = function(model) {
dfw = model$df
dfw$y = dfw$y - model$beta
size = nrow(dfw)
dfw$x = dfw$x / size
dfw$y = dfw$y / size
parms = calcRect(dfw)
atan(parms$a) * 180 / pi
}
rotate = function() {
cang = cos(alpha * pi / 180) * 180 / pi
sang = sin(alpha * pi / 180) * 180 / pi
df$x1 = (df$x * cang) + (df$y * sang)
df$y1 = (df$x * sang * -1) + (df$y * cang)
# grados * pi / 180
}
# x'=x\cos \theta +y\sin \theta
# y ′ = − x sin θ + y cos
|
431ed021c69925bc4d25a3afe415b08248d55b41 | aee78785ad6670a0fab9a6ae0b43710076f4f67f | /R/cra3.R | a4e96c57e43ceca019fbddf65cf874d99c421658 | [] | no_license | cran/PowerUpR | 86f9b9210dbfa5645b5780c448c75d0e339e7749 | ac7e9ab6b727f9536a0245441642b17bdbb3f54e | refs/heads/master | 2021-11-10T04:28:44.972166 | 2021-10-25T15:10:02 | 2021-10-25T15:10:02 | 72,525,187 | 11 | 7 | null | 2018-04-25T18:56:54 | 2016-11-01T10:12:25 | R | UTF-8 | R | false | false | 40,291 | r | cra3.R | mdes.cra3r3 <- function(power=.80, alpha=.05, two.tailed=TRUE,
rho2, rho3, p=.50, g3=0, r21=0, r22=0, r23=0,
n, J, K){
user.parms <- as.list(match.call())
.error.handler(user.parms)
df <- K - g3 - 2
SSE <- sqrt(rho3*(1-r23)/(p*(1-p)*K) +
rho2*(1-r22)/(p*(1-p)*J*K) +
(1-rho3-rho2)*(1-r21)/(p*(1-p)*J*K*n))
mdes <- .mdes.fun(power = power, alpha = alpha, sse = SSE, df = df, two.tailed = two.tailed)
.summ.mdes(effect = "main", power = power, alpha = alpha, sse = SSE, df = df, two.tailed = two.tailed, mdes = mdes)
mdes.out <- list(fun = "mdes.cra3r3",
parms = list(power=power, alpha=alpha, two.tailed=two.tailed,
rho2=rho2, rho3=rho3,
g3=g3, r21=r21, r22=r22, r23=r23,
p=p, n=n, J=J, K=K),
df = df,
ncp = mdes[1]/SSE,
mdes = mdes)
class(mdes.out) <- c("main", "mdes")
return(invisible(mdes.out))
}
# example
# mdes.cra3r3(rho3=.06, rho2=.17, n=15, J=3, K=60)
mdes.cra3 <- mdes.cra3r3
mdesd.mod331 <- mdes.mod331 <- function(power=.80, alpha=.05, two.tailed=TRUE,
rho2, rho3, omegam2, omegam3,
r21=0, r2m3=0,
p=.50, q=NULL, n, J, K){
user.parms <- as.list(match.call())
.error.handler(user.parms)
if(omegam2 == 0) {
df <- n*J*K - J*K - K - 2
if(omegam3 != 0 || r2m3 != 1) {
omegam3 <- 0
r2m3 <- 1
message("Arguments 'omegam3' and/or 'r2m3' are ignored")
}
cat("\nModerator effect: Non-randomly varying (level 2 and 3) \nModerator type:",
ifelse(is.null(q), "Continuous\n", "Binary\n"))
} else if(omegam3 == 0 || r2m3 == 1) {
df <- J*K - K - 2
cat("\nModerator effect: Non-randomly varying (level 3) \nModerator type:",
ifelse(is.null(q), "Continuous\n", "Binary\n"))
} else {
df <- K - 2
cat("\nModerator effect: Randomly varying \nModerator type:",
ifelse(is.null(q), "Continuous\n", "Binary\n"))
}
SSE <- ifelse(is.null(q),
sqrt(rho3*omegam3*(1-r2m3)/(p*(1-p)*K) +
rho2*omegam2/(p*(1-p)*K*J) +
(1-rho2-rho3)*(1-r21)/(p*(1-p)*K*J*n)), # continuous mod
sqrt(rho3*omegam3*(1-r2m3)/(p*(1-p)*K) +
rho2*omegam2/(p*(1-p)*K*J) +
(1-rho2-rho3)*(1-r21)/(p*(1-p)*q*(1-q)*K*J*n)) # binary mod
)
mdes <- .mdes.fun(power = power, alpha = alpha, sse = SSE, df = df, two.tailed = two.tailed)
.summ.mdes(effect = "mod", power = power, alpha = alpha, sse = SSE, df = df, two.tailed = two.tailed, mdes = mdes)
mdes.out <- list(fun = "mdes.mod331",
parms = list(power=power, alpha=alpha, two.tailed=two.tailed,
rho2=rho2, rho3=rho3, omegam2=omegam2, omegam3=omegam3,
r21=r21, r2m3=r2m3,
p=p, q=q, n=n, J=J, K=K),
df = df,
ncp = mdes[1]/SSE,
mdes = mdes)
class(mdes.out) <- c("mod331", "mdes")
return(invisible(mdes.out))
}
# examples
# mdes.mod331(rho3=.05, rho2=.12, omegam2=.08, omegam3=.07, p=.4, q=.7, r21=.20, r2m3=0, n=20, J=4, K=60)
# mdes.mod331(rho3=.05, rho2=.12, omegam2=.08, omegam3=.07, p=.4, r21=.20, r2m3=0, n=20, J=4, K=60)
# mdes.mod331(rho3=.05, rho2=.12, omegam2=0.1, omegam3=0, p=.4, q=.7, r21=.20, r2m3=0, n=20, J=4, K=60)
# mdes.mod331(rho3=.05, rho2=.12, omegam2=0, omegam3=0, p=.4, r21=.20, r2m3=1, n=20, J=4, K=60)
mdesd.mod332 <- mdes.mod332 <- function(power=.80, alpha=.05, two.tailed=TRUE,
rho2, rho3, omegam3, r21=0, r22=0, r2m3=0,
p=.50, q=NULL, n, J, K){
user.parms <- as.list(match.call())
.error.handler(user.parms)
if(omegam3 == 0 || r2m3 == 1) {
df <- J*K - K - 2
cat("\nModerator effect: Non-randomly varying \nModerator type:",
ifelse(is.null(q), "Continuous\n", "Binary\n"))
} else {
df <- K - 2
cat("\nModerator effect: Randomly varying \nModerator type:",
ifelse(is.null(q), "Continuous\n", "Binary\n"))
}
SSE <- ifelse(is.null(q),
sqrt(rho3*omegam3*(1-r2m3)/(p*(1-p)*K) +
rho2*(1-r22)/(p*(1-p)*K*J) +
(1-rho2-rho3)*(1-r21)/(p*(1-p)*K*J*n)), # continuous mod
sqrt(rho3*omegam3*(1-r2m3)/(p*(1-p)*K) +
rho2*(1-r22)/(p*(1-p)*q*(1-q)*K*J) +
(1-rho2-rho3)*(1-r21)/(p*(1-p)*q*(1-q)*K*J*n)) # binary mod
)
mdes <- .mdes.fun(power = power, alpha = alpha, sse = SSE, df = df, two.tailed = two.tailed)
.summ.mdes(effect = "mod", power = power, alpha = alpha, sse = SSE, df = df, two.tailed = two.tailed, mdes = mdes)
mdes.out <- list(fun = "mdes.mod332",
parms = list(power=power, alpha=alpha, two.tailed=two.tailed,
rho2=rho2, rho3=rho3, omegam3=omegam3,
r21=r21, r22=r22, r2m3=r2m3,
p=p, q=q, n=n, J=J, K=K),
df = df,
ncp = mdes[1]/SSE,
mdes = mdes)
class(mdes.out) <- c("mod332", "mdes")
return(invisible(mdes.out))
}
# examples
# mdes.mod332(rho3=.1, rho2=.1, omegam3=.05, q=.5, r21=.30, r22=.4, r2m3=0, n=20, J=4, K=60)
# mdes.mod332(rho3=.1, rho2=.1, omegam3=.05, r21=.30, r22=.4, r2m3=0, n=20, J=4, K=60)
# mdes.mod332(rho3=.1, rho2=.1, omegam3=0, q=.5, r21=.30, r22=.4, r2m3=0, n=20, J=4, K=60)
# mdes.mod332(rho3=.1, rho2=.1, omegam3=0, r21=.30, r22=.4, r2m3=0, n=20, J=4, K=60)
mdesd.mod333 <- mdes.mod333 <- function(power=.80, alpha=.05, two.tailed=TRUE,
rho2, rho3, r21=0, r22=0, r23=0,
p=.50, q=NULL, n, J, K){
user.parms <- as.list(match.call())
.error.handler(user.parms)
cat(ifelse(is.null(q), "\nModerator type: Continuous\n", "\nModerator type: Binary\n"))
df <- K - 5
SSE <- ifelse(is.null(q),
sqrt(rho3*(1-r23)/(p*(1-p)*(K-5)) +
rho2*(1-r22)/(p*(1-p)*J*(K-5)) +
(1-rho3-rho2)*(1-r21)/(p*(1-p)*J*(K-5)*n)), # continuous mod
sqrt(rho3*(1-r23)/(p*(1-p)*q*(1-q)*(K-5)) +
rho2*(1-r22)/(p*(1-p)*q*(1-q)*J*(K-5)) +
(1-rho3-rho2)*(1-r21)/(p*(1-p)*q*(1-q)*J*(K-5)*n)) # binary mod
)
mdes <- .mdes.fun(power = power, alpha = alpha, sse = SSE, df = df, two.tailed = two.tailed)
.summ.mdes(effect = "mod", power = power, alpha = alpha, sse = SSE, df = df, two.tailed = two.tailed, mdes = mdes)
mdes.out <- list(fun = "mdes.mod333",
parms = list(power=power, alpha=alpha, two.tailed=two.tailed,
rho2=rho2, rho3=rho3, r21=r21, r22=r22, r23=r23,
p=p, q=q, n=n, J=J, K=K),
df = df,
ncp = mdes[1]/SSE,
mdes = mdes)
class(mdes.out) <- c("mod333", "mdes")
return(invisible(mdes.out))
}
# examples
# mdes.mod333(rho3=.1, rho2=.1, q=.5, r21=.3, r22=.4, r23=.5, n=20, J=4, K=60)
# mdes.mod333(rho3=.1, rho2=.1, r21=.3, r22=.4, r23=.5, n=20, J=4, K=60)
power.cra3r3 <- function(es=.25, alpha=.05, two.tailed=TRUE,
rho2, rho3, g3=0, r21=0, r22=0, r23=0,
p=.50, n, J, K){
user.parms <- as.list(match.call())
.error.handler(user.parms)
df <- K - g3 - 2
SSE <- sqrt(rho3*(1-r23)/(p*(1-p)*K) +
rho2*(1-r22)/(p*(1-p)*J*K) +
(1-rho3-rho2)*(1-r21)/(p*(1-p)*J*K*n))
power <- .power.fun(es = es, alpha = alpha, sse = SSE, df = df, two.tailed = two.tailed)
.summ.power(power = power, alpha = alpha, sse = SSE, df = df, two.tailed = two.tailed, es = es)
power.out <- list(fun = "power.cra3r3",
parms = list(es=es, alpha=alpha, two.tailed=two.tailed,
rho2=rho2, rho3=rho3,
g3=g3, r21=r21, r22=r22, r23=r23,
p=p, n=n, J=J, K=K),
df = df,
ncp = es/SSE,
power = power)
class(power.out) <- c("main", "power")
return(invisible(power.out))
}
# example
# power.cra3r3(es=.269, rho3=.06, rho2=.17, n=15, J=3, K=60)
power.cra3 <- power.cra3r3
power.mod331 <- function(es=.25, alpha=.05, two.tailed=TRUE,
rho2, rho3, omegam2, omegam3,
r21=0, r2m3=0,
p=.50, q=NULL, n, J, K){
user.parms <- as.list(match.call())
.error.handler(user.parms)
if(omegam2 == 0) {
df <- n*J*K - J*K - K - 2
if(omegam3 != 0 || r2m3 != 1) {
omegam3 <- 0
r2m3 <- 1
message("Arguments 'omegam3' and/or 'r2m3' are ignored")
}
cat("\nModerator effect: Non-randomly varying (level 2 and 3) \nModerator type:",
ifelse(is.null(q), "Continuous\n", "Binary\n"))
} else if(omegam3 == 0 || r2m3 == 1) {
df <- J*K - K - 2
cat("\nModerator effect: Non-randomly varying (level 3) \nModerator type:",
ifelse(is.null(q), "Continuous\n", "Binary\n"))
} else {
df <- K - 2
cat("\nModerator effect: Randomly varying \nModerator type:",
ifelse(is.null(q), "Continuous\n", "Binary\n"))
}
SSE <- ifelse(is.null(q),
sqrt(rho3*omegam3*(1-r2m3)/(p*(1-p)*K) +
rho2*omegam2/(p*(1-p)*K*J) +
(1-rho2-rho3)*(1-r21)/(p*(1-p)*K*J*n)), # continuous mod
sqrt(rho3*omegam3*(1-r2m3)/(p*(1-p)*K) +
rho2*omegam2/(p*(1-p)*K*J) +
(1-rho2-rho3)*(1-r21)/(p*(1-p)*q*(1-q)*K*J*n)) # binary mod
)
power <- .power.fun(es = es, alpha = alpha, sse = SSE, df = df, two.tailed = two.tailed)
.summ.power(power = power, alpha = alpha, sse = SSE, df = df, two.tailed = two.tailed, es = es)
power.out <- list(fun = "power.mod331",
parms = list(es=es, alpha=alpha, two.tailed=two.tailed,
rho2=rho2, rho3=rho3, omegam2=omegam2, omegam3=omegam3,
r21=r21, r2m3=r2m3,
p=p, q=q, n=n, J=J, K=K),
df = df,
ncp = es/SSE,
power = power)
class(power.out) <- c("mod331", "power")
return(invisible(power.out))
}
# examples
# power.mod331(es=.16, rho3=.05, rho2=.12, omegam2=.08, omegam3=.07, p=.4, q=.7, r21=.20, r2m3=0, n=20, J=4, K=60)
# power.mod331(es=.09, rho3=.05, rho2=.12, omegam2=.08, omegam3=.07, p=.4, r21=.20, r2m3=0, n=20, J=4, K=60)
# power.mod331(es=.16, rho3=.05, rho2=.12, omegam2=0, omegam3=.07, p=.4, q=.7, r21=.20, r2m3=0, n=20, J=4, K=60)
# power.mod331(es=.09, rho3=.05, rho2=.12, omegam2=0, omegam3=.07, p=.4, r21=.20, r2m3=0, n=20, J=4, K=60)
# power.mod331(es=.16, rho3=.05, rho2=.12, omegam2=.08, omegam3=0, p=.4, q=.7, r21=.20, r2m3=0, n=20, J=4, K=60)
# power.mod331(es=.09, rho3=.05, rho2=.12, omegam2=.08, omegam3=0, p=.4, r21=.20, r2m3=0, n=20, J=4, K=60)
power.mod332 <- function(es=.25, alpha=.05, two.tailed=TRUE,
rho2, rho3, omegam3, r21=0, r22=0, r2m3=0,
p=.50, q=NULL, n, J, K){
user.parms <- as.list(match.call())
.error.handler(user.parms)
if(omegam3 == 0 || r2m3 == 1) {
df <- J*K - K - 2
cat("\nModerator effect: Non-randomly varying \nModerator type:",
ifelse(is.null(q), "Continuous\n", "Binary\n"))
} else {
df <- K - 2
cat("\nModerator effect: Randomly varying \nModerator type:",
ifelse(is.null(q), "Continuous\n", "Binary\n"))
}
SSE <- ifelse(is.null(q),
sqrt(rho3*omegam3*(1-r2m3)/(p*(1-p)*K) +
rho2*(1-r22)/(p*(1-p)*K*J) +
(1-rho2-rho3)*(1-r21)/(p*(1-p)*K*J*n)), # continuous mod
sqrt(rho3*omegam3*(1-r2m3)/(p*(1-p)*K) +
rho2*(1-r22)/(p*(1-p)*q*(1-q)*K*J) +
(1-rho2-rho3)*(1-r21)/(p*(1-p)*q*(1-q)*K*J*n)) # binary mod
)
power <- .power.fun(es = es, alpha = alpha, sse = SSE, df = df, two.tailed = two.tailed)
.summ.power(power = power, alpha = alpha, sse = SSE, df = df, two.tailed = two.tailed, es = es)
power.out <- list(fun = "power.mod332",
parms = list(es=es, alpha=alpha, two.tailed=two.tailed,
rho2=rho2, rho3=rho3, omegam3=omegam3,
r21=r21, r22=r22, r2m3=r2m3,
p=p, q=q, n=n, J=J, K=K),
df = df,
ncp = es/SSE,
power = power)
class(power.out) <- c("mod332", "power")
return(invisible(power.out))
}
# examples
# power.mod332(es=.22, rho3=.1, rho2=.1, omegam3=.05, q=.5, r21=.30, r22=.4, r2m3=0, n=20, J=4, K=60)
# power.mod332(es=.11, rho3=.1, rho2=.1, omegam3=.05, r21=.30, r22=.4, r2m3=0, n=20, J=4, K=60)
# power.mod332(es=.22, rho3=.1, rho2=.1, omegam3=0, q=.5, r21=.30, r22=.4, r2m3=0, n=20, J=4, K=60)
# power.mod332(es=.11, rho3=.1, rho2=.1, omegam3=0, r21=.30, r22=.4, r2m3=0, n=20, J=4, K=60)
power.mod333 <- function(es=.25, alpha=.05, two.tailed=TRUE,
rho2, rho3, r21=0, r22=0, r23=0,
p=.50, q=NULL, n, J, K){
user.parms <- as.list(match.call())
.error.handler(user.parms)
cat(ifelse(is.null(q), "\nModerator type: Continuous\n", "\nModerator type: Binary\n"))
df <- K-5
SSE <- ifelse(is.null(q),
sqrt(rho3*(1-r23)/(p*(1-p)*(K-5)) +
rho2*(1-r22)/(p*(1-p)*J*(K-5)) +
(1-rho3-rho2)*(1-r21)/(p*(1-p)*J*(K-5)*n)), # continuous mod
sqrt(rho3*(1-r23)/(p*(1-p)*q*(1-q)*(K-5)) +
rho2*(1-r22)/(p*(1-p)*q*(1-q)*J*(K-5)) +
(1-rho3-rho2)*(1-r21)/(p*(1-p)*q*(1-q)*J*(K-5)*n)) # binary mod
)
power <- .power.fun(es = es, alpha = alpha, sse = SSE, df = df, two.tailed = two.tailed)
.summ.power(power = power, alpha = alpha, sse = SSE, df = df, two.tailed = two.tailed, es = es)
power.out <- list(fun = "power.mod333",
parms = list(es=es, alpha=alpha, two.tailed=two.tailed,
rho2=rho2, rho3=rho3,
r21=r21, r22=r22, r23=r23,
p=p, q=q, n=n, J=J, K=K),
df = df,
ncp = es/SSE,
power = power)
class(power.out) <- c("mod333", "power")
return(invisible(power.out))
}
# examples
# power.mod333(es=.30, rho3=.1, rho2=.1, q=.5, r21=.3, r22=.4, r23=.5, n=20, J=4, K=60)
# power.mod333(es=.15, rho3=.1, rho2=.1, r21=.3, r22=.4, r23=.5, n=20, J=4, K=60)
power.med331 <- function(esa, esB, two.tailed = TRUE, alpha = .05,
mc = TRUE, nsims = 1000, ndraws = 1000,
rho2, rho3, gm3 = 4, r2m3 = 0,
r21 = 0, r22 = 0, g3 = 5, r23 = 0,
p = .50, n, J, K) {
user.parms <- as.list(match.call())
.error.handler(user.parms)
# standard errors for 3-3-1 mediation
.se.a331 <- function(r2m3, gm3, p, K) {
sig2m <- 1
var.a331 <- sig2m * (1 - r2m3) /
(p * (1 - p) * (K - gm3 - 1))
if(is.nan(var.a331) | var.a331 <= 0) {
stop("Design is not feasible", call. = FALSE)
}
return(invisible(sqrt(var.a331)))
}
.se.B331 <- function(rho2, rho3, r2m3, r21, r22, g3, r23, n, J, K) {
sig2m <- 1
var.B331 <- (rho3 * (1 - r23) + rho2 * (1 - r22) / J + (1 - rho3 - rho2) * (1 - r21) / (n * J)) /
((K - g3 - 1) * sig2m * (1 - r2m3))
if(is.nan(var.B331) | var.B331 <= 0) {
stop("Design is not feasible", call. = FALSE)
}
return(invisible(sqrt(var.B331)))
}
dfa <- K - gm3 - 1
dfB <- K - g3 - 1
df <- rbind(dfa, dfB)
colnames(df) <- "df"
rownames(df) <- c("a", "B")
sea331 <- .se.a331(r2m3 = r2m3, gm3 = gm3, p = p, K = K)
seB331 <- .se.B331(rho2 = rho2, rho3 = rho3, r2m3 = r2m3,
r21 = r21, r22 = r22, g3 = g3, r23 = r23,
n = n, J = J, K = K)
ncpa <- esa/sea331
ncpB <- esB/seB331
ncp <- rbind(ncpa, ncpB)
colnames(ncp) <- "ncp"
rownames(ncp) <- c("a", "B")
powera <- .power.fun(es = esa, alpha = alpha, sse = sea331, two.tailed = two.tailed, df = dfa)
powerB <- .power.fun(es = esB, alpha = alpha, sse = seB331, two.tailed = two.tailed, df = dfB)
power.sobel.aB <- .power.sobel(x = esa, y = esB, sex = sea331, sey = seB331, alpha = alpha, two.tailed = two.tailed)
power.joint.aB <- .power.jt(x = esa, y = esB, sex = sea331, sey = seB331, alpha = alpha, dfx = dfa, dfy = dfB, two.tailed = two.tailed)
power.mc.aB <- ifelse(mc, .power.mc(nsims = nsims, ndraws = ndraws, x = esa, y = esB, sex = sea331, sey = seB331, alpha = alpha, two.tailed = two.tailed), NA)
power <- rbind(
c(round(powera, 3), NA, NA, NA),
c(round(powerB, 3), NA, NA, NA),
c(NA, round(power.sobel.aB, 3), round(power.joint.aB, 3), round(power.mc.aB, 3))
)
colnames(power) <- c("t", "sobel", "joint", "mc")
rownames(power) <- c("a", "B", "aB")
power.out <- list(fun = "power.med331",
parms = list(esa = esa, esB = esB,
two.tailed = two.tailed, alpha = alpha,
mc = mc, nsims = nsims, ndraws = ndraws,
rho2 = rho2, rho3 = rho3, gm3 = gm3, r2m3 = r2m3,
r21 = r21, r22 = r22, g3 = g3, r23 = r23,
p = p, n = n, J = J, K = K),
df = df,
ncp = ncp,
power = round(power, 3))
cat("Statistical power: \n")
cat("------------------------------------ \n")
print(power)
cat("------------------------------------ \n")
cat("Degrees of freedom for path a:", dfa,
"\nDegrees of freedom for path B:", dfB,
"\nStandardized standard error for path a:", round(sea331, 3),
"\nStandardized standard error for path B:", round(seB331, 3),
"\nType I error rate:", alpha,
"\nTwo-tailed test:", two.tailed, "\n")
class(power.out) <- c("power", "med331")
return(invisible(power.out))
}
# example
# power.med331(esa= .5, esB = .3, rho2 = .15, rho3 = .15,
# r21 = .2, r22 = .2, g3 = 4,
# n = 20, J = 4, K = 80, p = .5, mc = TRUE)
power.med321 <- function(esa, esB, two.tailed = TRUE, alpha = .05,
mc = TRUE, nsims = 1000, ndraws = 1000,
rhom3, rho2, rho3,
r2m2 = 0, gm3 = 4, r2m3 = 0,
r21 = 0, r22 = 0, g3 = 5, r23 = 0,
p = .50, n, J, K) {
user.parms <- as.list(match.call())
.error.handler(user.parms)
# standard errors for 3-2-1 mediation
.se.a321 <- function(rhom3, r2m2, r2m3, gm3, p, J, K) {
var.a321 <- (rhom3 * (1 - r2m3) + (1 - rhom3) * (1 - r2m2) / J) /
(p * (1 - p) * (K - gm3 - 1))
if(is.nan(var.a321) | var.a321 <= 0) {
stop("Design is not feasible", call. = FALSE)
}
return(invisible(sqrt(var.a321)))
}
.se.B321 <- function(rhom3, rho2, rho3, r2m2, r2m3, r21, r22, g3, r23, n, J, K) {
var.B321 <- (rho3 * (1 - r23) + rho2 * (1 - r22) / J + (1 - rho3 - rho2) * (1 - r21) / (n * J)) /
((K - g3 - 1) * (rhom3 * (1 - r2m3) + (1 - rhom3) * (1 - r2m2) / J))
if(is.nan(var.B321) | var.B321 <= 0) {
stop("Design is not feasible", call. = FALSE)
}
return(invisible(sqrt(var.B321)))
}
dfa <- K - gm3 - 1
dfB <- K - g3 - 1
df <- rbind(dfa, dfB)
colnames(df) <- "df"
rownames(df) <- c("a", "B")
sea321 <- .se.a321(rhom3 = rhom3, r2m2 = r2m2, gm3 = gm3, r2m3 = r2m3, p = p, J = J, K = K)
seB321 <- .se.B321(rhom3 = rhom3, rho2 = rho2, rho3 = rho3, r2m2 = r2m2, g3 = g3, r2m3 = r2m3,
r21 = r21, r22 = r22, r23 = r23, n = n, J = J, K = K)
ncpa <- esa/sea321
ncpB <- esB/seB321
ncp <- rbind(ncpa, ncpB)
colnames(ncp) <- "ncp"
rownames(ncp) <- c("a", "B")
powera <- .power.fun(es = esa, alpha = alpha, sse = sea321, two.tailed = two.tailed, df = dfa)
powerB <- .power.fun(es = esB, alpha = alpha, sse = seB321, two.tailed = two.tailed, df = dfB)
power.sobel.aB <- .power.sobel(x = esa, y = esB, sex = sea321, sey = seB321, alpha = alpha, two.tailed = two.tailed)
power.joint.aB <- .power.jt(x = esa, y = esB, sex = sea321, sey = seB321, alpha = alpha, dfx = dfa, dfy = dfB, two.tailed = two.tailed)
power.mc.aB <- ifelse(mc, .power.mc(nsims = nsims, ndraws = ndraws, x = esa, y =esB, sex = sea321, sey = seB321, alpha = alpha, two.tailed = two.tailed), NA)
power <- rbind(
c(round(powera, 3), NA, NA, NA),
c(round(powerB, 3), NA, NA, NA),
c(NA, round(power.sobel.aB, 3), round(power.joint.aB, 3), round(power.mc.aB, 3))
)
colnames(power) <- c("t", "sobel", "joint", "mc")
rownames(power) <- c("a", "B", "aB")
power.out <- list(fun = "power.med321",
parms = list(esa = esa, esB = esB,
two.tailed = two.tailed, alpha = alpha,
mc = mc, nsims = nsims, ndraws = ndraws,
rhom3 = rhom3, rho2 = rho2, rho3 = rho3,
r2m2 = r2m2, gm3 = gm3, r2m3 = r2m3,
r21 = r21, r22 = r22, g3 = g3, r23 = r23,
p = p, n = n, J = J, K = K),
df = df,
ncp = ncp,
power = round(power, 3))
cat("Statistical power: \n")
cat("------------------------------------ \n")
print(power)
cat("------------------------------------ \n")
cat("Degrees of freedom for path a:", dfa,
"\nDegrees of freedom for path B:", dfB,
"\nStandardized standard error for path a:", round(sea321, 3),
"\nStandardized standard error for path B:", round(seB321, 3),
"\nType I error rate:", alpha,
"\nTwo-tailed test:", two.tailed, "\n")
class(power.out) <- c("power", "med321")
return(invisible(power.out))
}
# example
# power.med321(esa= .49, esB = .30, rhom3 = 0.26, rho2 = .15, rho3 = .20,
# r2m2 = .07, r2m3 = .17, r21 = .02, r22 = .41, r23 = .38,
# p = .50, n = 20, J = 4, K = 30)
power.med311 <- function(esa, esB, two.tailed = TRUE, alpha = .05,
mc = TRUE, nsims = 1000, ndraws = 1000,
rhom2, rhom3, rho2, rho3,
r2m1 = 0, r2m2 = 0, gm3 = 4, r2m3 = 0,
r21 = 0, r22 = 0, g3 = 5, r23 = 0,
p = .50, n, J, K) {
user.parms <- as.list(match.call())
.error.handler(user.parms)
# standard errors for 3-1-1 mediation
.se.a311 <- function(rhom2, rhom3, r2m1, gm3, r2m2, r2m3, p, n, J, K) { # r2m3z,
var.a311 <- (rhom3 * (1 - r2m3) + rhom2 * (1 - r2m2) / J + (1 - rhom3 - rhom2) * (1 - r2m1) / (n*J)) /
(p * (1 - p) * (K - gm3 - 1))
if(is.nan(var.a311) | var.a311 <= 0) {
stop("Design is not feasible", call. = FALSE)
}
return(invisible(sqrt(var.a311)))
}
.se.B311 <- function(rho2, rho3, rhom2, rhom3,
r2m1, r2m2, r2m3, r21, r22, g3, r23,
n, J, K) {
var.B311 <- (rho3 * (1 - r23) + rho2 * (1 - r22) / J + (1 - rho3 - rho2) * (1 - r21) / (n * J)) /
((K - g3 - 1) * (rhom3 * (1 - r2m3) + rhom2 * (1 - r2m2) / J + (1 - rhom3 - rhom2) * (1 - r2m3) / (n*J)))
if(is.nan(var.B311) | var.B311 <= 0) {
stop("Design is not feasible", call. = FALSE)
}
return(invisible(sqrt(var.B311)))
}
dfa <- K - gm3 - 1
dfB <- K - g3 - 1
df <- rbind(dfa, dfB)
colnames(df) <- "df"
rownames(df) <- c("a", "B")
sea311 <- .se.a311(rhom2 = rhom2, rhom3 = rhom3, r2m1 = r2m1, r2m2 = r2m2, r2m3 = r2m3,
gm3 = gm3, p = p, n = n, J = J, K = K)
seB311 <- .se.B311(rho2 = rho2, rho3 = rho3, rhom2 = rhom2, rhom3 = rhom3,
r2m1 = r2m1, r2m2 = r2m2, r2m3 = r2m3, r21 = r21, r22 = r22, g3 = g3, r23 = r23,
n = n, J = J, K = K)
ncpa <- esa/sea311
ncpB <- esB/seB311
ncp <- rbind(ncpa, ncpB)
colnames(ncp) <- "ncp"
rownames(ncp) <- c("a", "B")
powera <- .power.fun(es = esa, alpha = alpha, sse = sea311, two.tailed = two.tailed, df = dfa)
powerB <- .power.fun(es = esB, alpha = alpha, sse = seB311, two.tailed = two.tailed, df = dfB)
power.sobel.aB <- .power.sobel(x = esa, y = esB, sex = sea311, sey = seB311, alpha = alpha, two.tailed = two.tailed)
power.joint.aB <- .power.jt(x = esa, y = esB, sex = sea311, sey = seB311, alpha = alpha, dfx = dfa, dfy = dfB, two.tailed = two.tailed)
power.mc.aB <- ifelse(mc, .power.mc(nsims = nsims, ndraws = ndraws, x = esa, y = esB, sex = sea311, sey = seB311, alpha = alpha, two.tailed = two.tailed), NA)
power <- rbind(
c(round(powera, 3), NA, NA, NA),
c(round(powerB, 3), NA, NA, NA),
c(NA, round(power.sobel.aB, 3), round(power.joint.aB, 3), round(power.mc.aB, 3))
)
colnames(power) <- c("t", "sobel", "joint", "mc")
rownames(power) <- c("a", "B", "aB")
power.out <- list(fun = "power.med311",
parms = list(esa = esa, esB = esB,
two.tailed = two.tailed, alpha = alpha,
mc = mc, nsims = nsims, ndraws = ndraws,
rhom2 = rhom2, rhom3 = rhom3, rho2 = rho2, rho3 = rho3,
r2m1 = r2m1, r2m2 = r2m2, r2m3 = r2m3, gm3 = gm3,
r21 = r21, r22 = r22, r23 = r23, g3 = g3,
p = p, n = n, J = J, K = K),
df = df,
ncp = ncp,
power = round(power, 3))
cat("Statistical power: \n")
cat("------------------------------------ \n")
print(power)
cat("------------------------------------ \n")
cat("Degrees of freedom for path a:", dfa,
"\nDegrees of freedom for path B:", dfB,
"\nStandardized standard error for path a:", round(sea311, 3),
"\nStandardized standard error for path B:", round(seB311, 3),
"\nType I error rate:", alpha,
"\nTwo-tailed test:", two.tailed, "\n")
class(power.out) <- c("power", "med311")
return(invisible(power.out))
}
# example
# power.med311(esa= .49 , esB = .30, two.tailed = TRUE, alpha = .05,
# mc = TRUE, nsims = 1000, ndraws = 1000,
# rhom2 = .05, rhom3 = .26, rho2 = .15, rho3 = .20,
# r2m1 = .10, r2m2 = .07, r2m3 = .17, r21 = .02, r22 = .41, r23 = .38,
# p = .50, n = 20, J = 4, K = 30)
mrss.cra3r3 <- function(es=.25, power=.80, alpha=.05, two.tailed=TRUE,
n, J, K0=10, tol=.10,
rho2, rho3, p=.50, g3=0, r21=0, r22=0, r23=0){
user.parms <- as.list(match.call())
.error.handler(user.parms)
i <- 0
conv <- FALSE
while(i<=100 & conv==FALSE){
df <- K0-g3-2
if(df<= 0 | is.infinite(df)){break}
T1 <- ifelse(two.tailed==TRUE,abs(qt(alpha/2,df)),abs(qt(alpha,df)))
T2 <- abs(qt(power,df))
M <- ifelse(power>=.5,T1+T2,T1-T2)
K1 <- (M/es)^2 * (rho3*(1-r23)/(p*(1-p)) +
rho2*(1-r22)/(p*(1-p)*J) +
(1-rho3-rho2)*(1-r21)/(p*(1-p)*J*n))
K0 <- (K1+K0)/2
i <- i+1
}
K <- ifelse(df>0,round(K0),NA)
mrss.out <- list(fun = "mrss.cra3r3",
parms = list(es=es, power=power, alpha=alpha, two.tailed=two.tailed,
n=n, J=J, K0=K0, tol=tol,
rho2=rho2, rho3=rho3,
p=p, r21=r21, r22=r22, r23=r23, g3=g3),
df = df,
ncp = M,
K = K)
class(mrss.out) <- c("main", "mrss")
cat("K =", K, "\n")
return(invisible(mrss.out))
}
# example
# mrss.cra3r3(rho3=.06, rho2=.17, n=15, J=3)
mrss.cra3 <- mrss.cra3r3
mrss.mod331 <- function(es=.25, power=.80, alpha=.05, two.tailed=TRUE,
rho2, rho3, omegam2, omegam3,
r21=0, r2m3=0,
p=.50, q=NULL, n, J, K0=10, tol=.10){
user.parms <- as.list(match.call())
.error.handler(user.parms)
i <- 0
conv <- FALSE
while(i<=100 & conv==FALSE){
if(omegam2 == 0) {
df <- n*J*K0 - J*K0 - K0 - 2
if(omegam3 != 0 || r2m3 != 1) {
omegam3 <- 0
r2m3 <- 1
}
} else if(omegam3 == 0 || r2m3 == 1) {
df <- J*K0 - K0 - 2
} else {
df <- K0 - 2
}
if(df<= 0 | is.infinite(df)){break}
T1 <- ifelse(two.tailed==TRUE,abs(qt(alpha/2,df)),abs(qt(alpha,df)))
T2 <- abs(qt(power,df))
M <- ifelse(power>=.5,T1+T2,T1-T2)
K1 <- ifelse(is.null(q),
(M/es)^2 * (rho3*omegam3*(1-r2m3)/(p*(1-p)) +
rho2*omegam2/(p*(1-p)*J) +
(1-rho2-rho3)*(1-r21)/(p*(1-p)*J*n)), # continuous mod
(M/es)^2 * (rho3*omegam3*(1-r2m3)/(p*(1-p)) +
rho2*omegam2/(p*(1-p)*J) +
(1-rho2-rho3)*(1-r21)/(p*(1-p)*q*(1-q)*J*n)) # binary mod
)
K0 <- (K1+K0)/2
i <- i+1
}
K <- ifelse(df>0,round(K0),NA)
mrss.out <- list(fun = "mrss.mod331",
parms = list(es=es, power=power, alpha=alpha, two.tailed=two.tailed,
rho2=rho2, rho3=rho3, omegam2=omegam2, omegam3=omegam3,
r21=r21, r2m3=r2m3,
p=p, q=q, n=n, J=J, K0=K0, tol=tol),
df = df,
ncp = M,
K = K)
if(omegam2 == 0) {
if(omegam3 != 0 || r2m3 != 1) {
message("Arguments 'omegam3' and/or 'r2m3' are ignored")
}
cat("\nModerator effect: Non-randomly varying (level 2 and 3) \nModerator type:",
ifelse(is.null(q), "Continuous\n", "Binary\n"),
"\nK =", K)
} else if(omegam3 == 0 || r2m3 == 1) {
cat("\nModerator effect: Non-randomly varying (level 3) \nModerator type:",
ifelse(is.null(q), "Continuous\n", "Binary\n"),
"\nK =", K)
} else {
cat("\nModerator effect: Randomly varying \nModerator type:",
ifelse(is.null(q), "Continuous\n", "Binary\n"),
"\nK =", K)
}
class(mrss.out) <- c("mod331", "mrss")
return(invisible(mrss.out))
}
# examples
# mrss.mod331(es=.16, rho3=.05, rho2=.12, omegam2=.08, omegam3=.07, p=.4, q=.7, r21=.20, r2m3=0, n=20, J=4)
# mrss.mod331(es=.09, rho3=.05, rho2=.12, omegam2=.08, omegam3=.07, p=.4, r21=.20, r2m3=0, n=20, J=4)
# mrss.mod331(es=.16, rho3=.05, rho2=.12, omegam2=0, omegam3=.07, p=.4, q=.7, r21=.20, r2m3=0, n=20, J=4)
# mrss.mod331(es=.09, rho3=.05, rho2=.12, omegam2=0, omegam3=.07, p=.4, r21=.20, r2m3=0, n=20, J=4)
# mrss.mod331(es=.16, rho3=.05, rho2=.12, omegam2=.08, omegam3=0, p=.4, q=.7, r21=.20, r2m3=0, n=20, J=4)
# mrss.mod331(es=.09, rho3=.05, rho2=.12, omegam2=.08, omegam3=0, p=.4, r21=.20, r2m3=0, n=20, J=4)
mrss.mod332 <- function(es=.25, power=.80, alpha=.05, two.tailed=TRUE,
rho2, rho3, omegam3, r21=0, r22=0, r2m3=0,
p=.50, q=NULL, n, J, K0=10, tol=.10){
user.parms <- as.list(match.call())
.error.handler(user.parms)
i <- 0
conv <- FALSE
while(i<=100 & conv==FALSE){
if(omegam3 == 0 || r2m3 == 1) {
df <- J*K0 - K0 - 2
} else {
df <- K0 - 2
}
if(df<= 0 | is.infinite(df)){break}
T1 <- ifelse(two.tailed==TRUE,abs(qt(alpha/2,df)),abs(qt(alpha,df)))
T2 <- abs(qt(power,df))
M <- ifelse(power>=.5,T1+T2,T1-T2)
K1 <- ifelse(is.null(q),
(M/es)^2 * (rho3*omegam3*(1-r2m3)/(p*(1-p)) +
rho2*(1-r22)/(p*(1-p)*J) +
(1-rho2-rho3)*(1-r21)/(p*(1-p)*J*n)), # continuous mod
(M/es)^2 * (rho3*omegam3*(1-r2m3)/(p*(1-p)) +
rho2*(1-r22)/(p*(1-p)*q*(1-q)*J) +
(1-rho2-rho3)*(1-r21)/(p*(1-p)*q*(1-q)*J*n)) # binary mod
)
K0 <- (K1+K0)/2
i <- i+1
}
K <- ifelse(df>0,round(K0),NA)
mrss.out <- list(fun = "mrss.mod332",
parms = list(es=es, power=power, alpha=alpha, two.tailed=two.tailed,
rho2=rho2, rho3=rho3, omegam3=omegam3,
r21=r21, r22=r22, r2m3=r2m3,
p=p, q=q, n=n, J=J, K0=K0, tol=tol),
df = df,
ncp = M,
K = K)
if(omegam3 == 0 || r2m3 == 1) {
cat("\nModerator effect: Non-randomly varying \nModerator type:",
ifelse(is.null(q), "Continuous\n", "Binary\n"),
"\nK =", K)
} else {
cat("\nModerator effect: Randomly varying \nModerator type:",
ifelse(is.null(q), "Continuous\n", "Binary\n"),
"\nK =", K)
}
class(mrss.out) <- c("mod332", "mrss")
return(invisible(mrss.out))
}
# examples
# mrss.mod332(es=.22, rho3=.1, rho2=.1, omegam3=.05, q=.5, r21=.30, r22=.4, r2m3=0, n=20, J=4)
# mrss.mod332(es=.11, rho3=.1, rho2=.1, omegam3=.05, r21=.30, r22=.4, r2m3=0, n=20, J=4)
# mrss.mod332(es=.22, rho3=.1, rho2=.1, omegam3=0, q=.2, r21=.30, r22=.4, r2m3=0, n=20, J=4)
# mrss.mod332(es=.11, rho3=.1, rho2=.1, omegam3=0, r21=.30, r22=.4, r2m3=0, n=20, J=4)
mrss.mod333 <- function(es=.25, power=.80, alpha=.05, two.tailed=TRUE,
rho2, rho3, r21=0, r22=0, r23=0,
p=.50, q=NULL, n, J, K0=10, tol=.10){
user.parms <- as.list(match.call())
.error.handler(user.parms)
i <- 0
conv <- FALSE
while(i<=100 & conv==FALSE){
df <- K0 - 5
if(df<= 0 | is.infinite(df)){break}
T1 <- ifelse(two.tailed==TRUE,abs(qt(alpha/2,df)),abs(qt(alpha,df)))
T2 <- abs(qt(power,df))
M <- ifelse(power>=.5,T1+T2,T1-T2)
K1 <- ifelse(is.null(q),
5 + (M/es)^2 * (rho3*(1-r23)/(p*(1-p)) +
rho2*(1-r22)/(p*(1-p)*J) +
(1-rho3-rho2)*(1-r21)/(p*(1-p)*J*n)), # continuous mod
5 + (M/es)^2 * (rho3*(1-r23)/(p*(1-p)*q*(1-q)) +
rho2*(1-r22)/(p*(1-p)*q*(1-q)*J) +
(1-rho3-rho2)*(1-r21)/(p*(1-p)*q*(1-q)*J*n)) # binary mod
)
K0 <- (K1+K0)/2
i <- i+1
}
K <- ifelse(df>0,round(K0),NA)
mrss.out <- list(fun = "mrss.mod333",
parms = list(es=es, power=power, alpha=alpha, two.tailed=two.tailed,
rho2=rho2, rho3=rho3,
r21=r21, r22=r22, r23=r23,
p=p, q=q, n=n, J=J, K0=K0, tol=tol),
df = df,
ncp = M,
K = K)
cat(ifelse(is.null(q), "\nModerator type: Continuous\n", "\nModerator type: Binary\n"),
"\nK =", K)
class(mrss.out) <- c("mod333", "mrss")
return(invisible(mrss.out))
}
# examples
# mrss.mod333(es=.30, rho3=.1, rho2=.1, q=.5, r21=.3, r22=.4, r23=.5, n=20, J=4)
# mrss.mod333(es=.15, rho3=.1, rho2=.1, r21=.3, r22=.4, r23=.5, n=20, J=4)
mdes.bcra4f3 <- function(power=.80, alpha=.05, two.tailed=TRUE,
rho2, rho3, p=.50, r21=0, r22=0, r23=0, g3=0,
n, J, K, L){
user.parms <- as.list(match.call())
.error.handler(user.parms)
df <- L * (K - 2) - g3
SSE <- sqrt(rho3*(1-r23)/(p*(1-p)*K*L) +
rho2*(1-r22)/(p*(1-p)*J*K*L) +
(1-rho3-rho2)*(1-r21)/(p*(1-p)*J*K*L*n))
mdes <- .mdes.fun(power = power, alpha = alpha, sse = SSE, df = df, two.tailed = two.tailed)
.summ.mdes(effect = "main", power = power, alpha = alpha, sse = SSE, df = df, two.tailed = two.tailed, mdes = mdes)
mdes.out <- list(fun = "mdes.bcra4f3",
parms = list(power=power, alpha=alpha, two.tailed=two.tailed,
rho2=rho2, rho3=rho3,
p=p, r21=r21, r22=r22, r23=r23, g3=g3,
n=n, J=J, K=K, L=L),
df=df,
ncp = mdes[1]/SSE,
mdes = mdes)
class(mdes.out) <- c("main", "mdes")
return(invisible(mdes.out))
}
# example
# mdes.bcra4f3(alpha=.05, two.tailed=TRUE, power=.80, rho3=.15, rho2=.15, n=10, J=4, K=4, L=15)
power.bcra4f3 <- function(es=.25, alpha=.05, two.tailed=TRUE,
rho2, rho3, p=.50, r21=0, r22=0, r23=0, g3=0,
n, J, K, L){
user.parms <- as.list(match.call())
.error.handler(user.parms)
df <- L * (K - 2) - g3
SSE <- sqrt(rho3*(1-r23)/(p*(1-p)*K*L) +
rho2*(1-r22)/(p*(1-p)*J*K*L) +
(1-rho3-rho2)*(1-r21)/(p*(1-p)*J*K*L*n))
power <- .power.fun(es = es, alpha = alpha, sse = SSE, df = df, two.tailed = two.tailed)
.summ.power(power = power, alpha = alpha, sse = SSE, df = df, two.tailed = two.tailed, es = es)
power.out <- list(fun = "power.bcra4f3",
parms = list(es=es, alpha=alpha, two.tailed=two.tailed,
rho2=rho2, rho3=rho3,
p=p, r21=r21, r22=r22, r23=r23, g3=g3,
n=n, J=J, K=K, L=L),
df=df,
ncp = es/SSE,
power = power)
class(power.out) <- c("main", "power")
return(invisible(power.out))
}
# example
# power.bcra4f3(es=0.24, alpha=.05, two.tailed=TRUE, rho3=.15, rho2=.15, n=10, J=4, K=4, L=15)
mrss.bcra4f3 <- function(es=.25, power=.80, alpha=.05, two.tailed=TRUE,
n, J, L, K0=10, tol=.10,
rho2, rho3, p=.50, g3=0, r21=0, r22=0, r23=0){
user.parms <- as.list(match.call())
.error.handler(user.parms)
i <- 0
conv <- FALSE
while(i<=100 & conv==FALSE){
df <- L*(K0-2)-g3
if(df<=0 | is.infinite(df)){break}
T1 <- ifelse(two.tailed==TRUE,abs(qt(alpha/2,df)),abs(qt(alpha,df)))
T2 <- abs(qt(power,df))
M <- ifelse(power>=.5,T1+T2,T1-T2)
K1 <- (M/es)^2 * (rho3*(1-r23)/(p*(1-p)*L) +
rho2*(1-r22)/(p*(1-p)*L*J) +
(1-rho3-rho2)*(1-r21)/(p*(1-p)*J*L*n))
if(abs(K1-K0)<tol){conv <- TRUE}
K0 <- (K1+K0)/2
i <- i+1
}
K <- ifelse(df>0,round(K0),NA)
mrss.out <- list(fun = "mrss.bcra4f3",
parms = list(es=es, power=power, alpha=alpha, two.tailed=two.tailed,
n=n, J=J, L=L, K0=K0, tol=tol,
rho2=rho2, rho3=rho3,
p=p, r21=r21, r22=r22, r23=r23, g3=g3),
df=df,
ncp = M,
K = K)
class(mrss.out) <- c("main", "mrss")
cat("K =", K, "(per block)\n")
return(invisible(mrss.out))
}
# example
# mrss.bcra4f3(rho3=.15, rho2=.15, g3=1, p=.5, r23=.5, r22=.5, r21=.5, n=10, J=4, L=4)
|
c5485ffbbdca33e8721007ce76e4820c3b9fc18a | aa01dbbf23cbea8b1575dc9b7b5f04717a13796e | /Project5.R | 95e8aeab1dde8a8d856ce93cf44ebd29a353f0a5 | [] | no_license | Priyanshi73/R_projects | 21a66f1db3a05f52fc80f3cd69438a1f67060381 | f7b5352aae088e0c6fa1801f7a70cbd0676ab3fb | refs/heads/master | 2022-12-14T20:08:24.688877 | 2020-09-14T13:28:29 | 2020-09-14T13:28:29 | 295,216,317 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,364 | r | Project5.R | getwd()
setwd("C:\\Users\\Priyanshi\\Downloads\\Data\\project_5")
bank_full_test=read.csv("bank-full_test.csv",stringsAsFactors = F)
bank_full_train=read.csv("bank-full_train.csv",stringsAsFactors = F)
View(bank_full_train)
dim(bank_full_test)
dim(bank_full_train)
setdiff(names(bank_full_train),names(bank_full_test))
bank_full_test$y=NA
bank_full_test$data="test"
bank_full_train$data="train"
bank <- rbind(bank_full_train,bank_full_test)
dim(bank)
library(dplyr)
glimpse(bank)
##character variable
table(bank$job)#dummy encoding
table(bank$marital) ## 0-divorced 1-single 2-married
table(bank$education) #0-pri 1-sec 2-tert 3-unknown
table(bank$default) #0 1
table(bank$housing) #0 1
table(bank$loan) # 0 1
table(bank$contact) ## 0-cellu 1-tele 3-unknown
table(bank$month)#dummy
table(bank$poutcome) ##0-fail 1-success 2-other 3-unknown
table(bank$y) #0 1
bank=bank %>%
mutate(default=as.numeric(default=="yes"),
housing=as.numeric(housing=="yes"),
loan=as.numeric(loan=="yes"),
y=as.numeric(y=="yes"))
glimpse(bank)
##education #0-pri 1-sec 2-tert 3-unknown
##contact # 0-cellu 1-tele 3-unknown
#failure ##0-fail 1-success 2-other 3-unknown
#marital ## 0-divorced 1-single 2-married
bank=bank %>%
mutate(education=ifelse(education=="primary",0,
ifelse(education=="secondary",1,
ifelse(education=="tertiary",2,3))),
contact=ifelse(contact=="cellular",0,
ifelse(contact=="telephone",1,2)),
poutcome=ifelse(poutcome=="failure",0,
ifelse(poutcome=="success",1,
ifelse(poutcome=="other",2,3))),
marital=ifelse(marital=="divorced",0,
ifelse(marital=="single",1,2)))
glimpse(bank)
#job-- group by mean
bank %>%
group_by(job) %>%
summarise(mean_job=mean(y,na.rm=T))
bank=bank %>%
mutate(job_11=as.numeric(job %in% c("admin","self-employed","technician","unknown")),
job_13=as.numeric(job %in% c("management","unemployed")),
job_07=as.numeric(job %in% c(" blue-collar","housemaid","entrepreneur","services")),
job_20=as.numeric(job %in% c("retired","student"))) %>%
select(-job)
##month -- group by mean
bank %>%
group_by(month) %>%
summarise(mean_job=mean(y,na.rm=T))
bank=bank %>%
mutate(month_4=as.numeric(month %in% c("dec","oct","sep","marc")),
month_06=as.numeric(month %in% c("may","jul")),
month_09=as.numeric(month %in% c("jun","nov","jan","aug")),
month_16=as.numeric(month %in% c("feb","apr"))) %>%
select(-month)
glimpse(bank)
##
table(bank$age)
sort(table(bank$balance),decreasing = T)[1:5]
table(bank$day)
table(bank$duration)
table(bank$campaign)
table(bank$pdays)
table(bank$previous)
table(bank$ID)
##-----------
#checking for NA
colSums(is.na(bank))
dim(bank)
bank_full_test=bank %>% filter(data=="test") %>% select(-data,-y)
bank_full_train=bank %>% filter(data=="train") %>% select(-data)
glimpse(bank_full_train)
dim(bank_full_train)
##
s=sample(1:nrow(bank_full_train),.8*nrow(bank_full_train))
bank_train1=bank_full_train[s,]
bank_train2=bank_full_train[-s,]
##
library(car)
for_vif=lm(y~.-ID,data=bank_train1)
sort(vif(for_vif),decreasing = T)
#removed vif>4
for_vif=lm(y~.-ID-month_06,data=bank_train1)
sort(vif(for_vif),decreasing = T)
#glm
log_fit=glm(y~.-ID-month_06,data=bank_train1,family = "binomial")
log_fit=step(log_fit)
formula(log_fit)
log_fit=glm(y ~ marital + education + balance + housing +
loan + contact + duration + campaign + previous +
poutcome + job_07 + job_20 + month_4+
month_16,data=bank_full_train,family = "binomial")
summary(log_fit)
##scoring auc
library(pROC)
train1.score=predict(log_fit,newdata = bank_train1,type="response")
auc(roc(bank_train1$y,train1.score))
train2.score=predict(log_fit,newdata = bank_train2,type="response")
auc(roc(bank_train2$y,train2.score))
train.score=predict(log_fit,newdata = bank_full_train,type="response")
real=bank_full_train$y #positivies
cutoffs=seq(0.001,0.999,0.001)
cutoff_data=data.frame(cutoff=99,Sn=99,Sp=99,KS=99,F5=99,F.1=99,M=99)
for(cutoff in cutoffs){
print(paste0("cutoff is:", cutoff))
predicted=as.numeric(train.score>cutoff) #call it 1
TP=sum(real==1 & predicted==1)
TN=sum(real==0 & predicted==0)
FP=sum(real==0 & predicted==1)
FN=sum(real==1 & predicted==0)
P=TP+FN
N=TN+FP
Sn=TP/P #sensitivity
Sp=TN/N #specificity
precision=TP/(TP+FP)
recall=Sn
KS=(TP/P)-(FP/N)
F5=(26*precision*recall)/((25*precision)+recall)
F.1=(1.01*precision*recall)/((.01*precision)+recall)
M=(4*FP+FN)/(5*(P+N))
cutoff_data=rbind(cutoff_data,
c(cutoff,Sn,Sp,KS,F5,F.1,M))
}
cutoff_data=cutoff_data[-1,]
View(cutoff_data)
#### visualise how these measures move across cutoffs
library(ggplot2)
ggplot(cutoff_data,aes(x=cutoff,y=Sp))+geom_line()
## to see whr is ur maxx cutoff and KS
my_cutoff=cutoff_data$cutoff[which.max(cutoff_data$KS)]
my_cutoff
View(cutoff_data) #look KS at cotoff=.121
#ks=.624
#now we have KS we will predict with train and test
train.score
train.predicted=as.numeric(train.score>my_cutoff)
test.score=predict(log_fit,newdata = bank_full_test,type="response")
test.predicted=as.numeric(test.score>my_cutoff)
head(test.predicted)
dim(bank_full_test)
length(test.predicted)
write.csv(test.predicted,"Priyanshi_Agarwal_P5_part2.csv",row.names = F)
##
##---------------------------------Quiz-------------------------------------
View(bank_full_train)
#1 40.91
mean(bank_full_train$age,na.rm = T)
#2 3329
table(bank_full_train$balance)
#upper
quantile(bank_full_train$balance,.75)+1.5*IQR(bank_full_train$balance)
sum(bank_full_train$balance>3427) #3315
#lower
quantile(bank_full_train$balance,.25)-1.5*IQR(bank_full_train$balance)
sum(bank_full_train$balance<(-1941)) #14
#3
var(bank_full_train$balance)
#4-- vif
#5-- lower AIC
#6-- no
#7-- yes
#8-- balanced
#9-- Q3+1.5*IQR
#10-- adjusted R2
##------------------------------------------------------------------ |
dcc6668152ab63bd0f7c2c316a8586dd0f561f3e | 7a513b603765226575b01d439530715b1519d74c | /man/convert.temp.Rd | 704fb833e264b4d67deed97591dd0e3bb982769f | [] | no_license | jsugarelli/tempconvertr | 00ce29b5312cceee4e11a590be8b68cd4ced1ae9 | c0960f6e83bbaa0bc81a783b0ccd387fcf3e32ee | refs/heads/master | 2021-03-01T18:37:07.693329 | 2020-03-08T12:11:09 | 2020-03-08T12:11:09 | 245,806,456 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,397 | rd | convert.temp.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/tempconv.r
\name{convert.temp}
\alias{convert.temp}
\title{Converting Temperatures}
\usage{
convert.temp(from.temp, from.unit = "Celsius", to.unit = "Kelvin",
as.json = FALSE)
}
\arguments{
\item{from.temp}{Temperature value to be converted.}
\item{from.unit}{Scale of the temperature value to be converted. Either
\code{"Celsius"} (default), \code{"Fahrenheit"} or \code{"Kelvin"}.}
\item{to.unit}{Target temperature scale. Either \code{"Celsius"} (default),
\code{"Fahrenheit"} or \code{"Kelvin"}.}
\item{as.json}{Indicates if function returns JSON object describing the
conversion instead of just the converted temperature value.}
}
\value{
Either the converted temperature value or a JSON object (see
details).
}
\description{
Converts temperatures temperatures between degrees Celsius,
degrees Fahrenheit and Kelvin.
}
\details{
If \code{as.json==TRUE} then \code{convert.temp()} returns a JSON
object containing two sub-object \code{from} and \code{to} each of which
has a \code{temp} and a \code{unit} field to describe the temperature. The
\code{from.unit} and \code{to.unit} arguments are case-insensitive.
}
\examples{
result.fahrh.json <- convert.temp(24.5, from.unit="celsius", to.unit="fahrenheit", as.json=TRUE)
result.kelv <- convert.temp(24.5)
}
|
317741497b389110c55939f7d40b74b3417afd87 | 7350b810110e86c3418ed68556a1922cfdc0ac17 | /script.R | 3fece8c423c39bfc45b761489946715ae9302319 | [] | no_license | mattpiskorz/image_processing_r | dd808403628912191a70be4684f8631627aef8c2 | a64c13543f5646aaa5a7a208e784c7d3fb2b2620 | refs/heads/master | 2020-05-30T18:56:01.647357 | 2014-12-04T14:07:36 | 2014-12-04T14:07:36 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 271 | r | script.R | library("EBImage")
library("png", lib.loc="~/R/x86_64-pc-linux-gnu-library/3.1")
file_source <- "/home/matt/Projects/image_processing/test_images"
file_name <- "6_6_black_and_white.png"
image = readPNG(paste(file_source,file_name, sep="/"))
display(image)
print(image)
|
0869086682d473af1e9ee052fc66b6c4b3b24c97 | 04ab1edfb4b08adabbf89eb8011b78c35c6f4e0f | /scripts/10_parseProteomicsSpectronaut.R | b84d60029a8659fbd770e377883ae20f785412ad | [] | no_license | alanlorenzetti/tlr_v2 | b4bc9d7c00580dd08015273957e919a35cc9b4df | ea4c3e2701cbd5e6357fac83b4f764e6b7969d30 | refs/heads/master | 2023-05-27T11:57:20.296063 | 2021-06-08T22:17:02 | 2021-06-08T22:17:02 | 290,974,388 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,257 | r | 10_parseProteomicsSpectronaut.R | # alorenzetti 202007
# description ####
# this script will parse proteomics abundance data
# generated by Spectronaut software
# loading files ####
file = "data/Halo_WholeLysate_analysis_HaloIonLibraryVNG_merged_final_2018-04-07_SW100_Protein_Report_20200731.xls"
file2 = "data/Halo_Ribo_analysis_HaloIonLibraryVNG_merged_final_2018-04-07_SW100_Protein_Report.xls"
# they will throw a warning saying
# cols were parsed as.character
# but we ll deal with that later
spectro1 = read_delim(file = file, delim = "\t")
spectro2 = read_delim(file = file2, delim = "\t")
# in this version I am going to use
# only the lysate fraction
spectro = spectro1
# full join of both dataframes
#spectro = full_join(spectro1, spectro2, by = "PG.ProteinAccessions")
# wrangling ####
# adjusting colnames
colnames(spectro)[-1] = sub("^\\[.* ", "", colnames(spectro)[-1])
colnames(spectro)[-1] = sub("^.*_?(BR[1-3])(TP[1-4]).*_.*_.*_(r0[1-3]).*$",
"\\2_\\1_\\3",
colnames(spectro)[-1],
perl = T)
colnames(spectro)[-1] = paste0(rep("lysate_", 36),
colnames(spectro)[-1])
colnames(spectro)[1] = "locus_tag"
# removing first entry line
spectro = spectro[-1, ]
# warnings will be thrown due to
# missing entries ("Filtered" value)
spectro = spectro %>%
mutate_at(.vars = vars(contains("BR")),
.funs = as.numeric)
# selection of entries ####
# we have to filter the table due to the existence
# of peptides matching more than one protein
# our simplification is:
# the members of a protein group can exist in
# the form of an individual observation.
# if only one member of a protein group exists individually,
# protein group values and individual protein values
# are tallied up;
# if more than two members of a protein group exist individually
# the protein group is discarded
# if no member of a protein group exists individually
# the protein group is kept (first locus_tag is preserved)
# finding out solos and protein groups
solo = spectro %>%
filter(!str_detect(locus_tag, ","))
pg = spectro %>%
filter(str_detect(locus_tag, ","))
ex = NULL
pgvspg = NULL
for(i in 1:dim(pg)[1]){
curline = pg$locus_tag[i] %>%
str_split(pattern = ",") %>%
unlist()
for(j in 1:length(curline)){
ex[j] = curline[j] %in% solo$locus_tag
names(ex)[j] = curline[j]
}
if(sum(ex) == 0){pass = "pass0"; repres = names(ex)[1]}
if(sum(ex) == 1){pass = "pass1"; repres = names(which(ex == TRUE))}
if(sum(ex) > 1){pass = "fail"; repres = NA_character_}
y = 1:dim(pg)[1]
for(k in y[-i]){
curpg = pg$locus_tag[k] %>%
str_split(pattern = ",") %>%
unlist()
pgvspg = c(pgvspg, sum(names(ex) %in% curpg))
}
npg = sum(pgvspg > 0)
if(sum(pgvspg) == 0){passpg = "pass"}
if(sum(pgvspg) > 0){passpg = paste0("fail", npg)}
if((pass == "pass0" | pass == "pass1") & passpg == "pass"){
pg$locus_tag[i] = repres
}
ex = NULL
pgvspg = NULL
}
# filtering out those protein groups
# that failed our simplification
pg = pg %>%
filter(!str_detect(locus_tag, ","))
# VNG5030G (GvpA) and VNG5033G (GvpN) were manually included
# since they are protein groups contained within
# another protein group but satisfy the nonambiguity criterion
# e.g. the protein groups come from isoforms of GvpA1a, GvpA1b, and GvpA2
gvpAN = spectro %>%
filter(str_detect(string = locus_tag, pattern = "^VNG5030G|^VNG5033G")) %>%
mutate(locus_tag = str_replace(string = locus_tag, pattern = ",.*", replacement = ""))
# unifying individual proteins to
# simplified protein groups
soloPlusSPG = bind_rows(solo, pg, gvpAN) %>%
arrange(locus_tag) %>%
group_by(locus_tag) %>%
dplyr::summarise(across(.cols = starts_with("lysate"),
.fns = ~ sum(.x, na.rm = T))) %>%
ungroup() %>%
dplyr::mutate(across(.cols = starts_with("lysate"),
.fns = ~ case_when(.x == 0 ~ NA_real_,
TRUE ~ as.numeric(.x))))
spectro = soloPlusSPG
# pivoting data frame
# we will take run means and annotate number of runs used
# I would say using six (of nine) observations is fair enough
spectroLong = spectro %>%
pivot_longer(cols = contains("BR"),
names_to = c("libType", "timepoint", "bioRep", "run"),
names_sep = "_",
values_to = "abundance") %>%
group_by(locus_tag, libType, timepoint) %>%
summarise(nruns = 9 - sum(is.na(abundance)),
mean_abundance = mean(abundance, na.rm = T),
se_abundance = (sd(abundance, na.rm = T) / sqrt(nruns))) %>%
mutate(mean_abundance = case_when(is.nan(mean_abundance) ~ NA_real_,
TRUE ~ as.numeric(mean_abundance)),
se_abundance = case_when(is.nan(se_abundance) ~ NA_real_,
TRUE ~ as.numeric(se_abundance))) %>%
ungroup() %>%
filter(nruns >= 6)
# using longer dataframe, I will
# create a wider version to be able
# to plot heat maps using ComplxHeatmaps
# and furthermore to unify it with
# RNA-Seq data
spectroWide = spectroLong %>%
dplyr::select(-nruns) %>%
pivot_wider(names_from = c("libType", "timepoint"),
names_sep = "_",
values_from = c("mean_abundance", "se_abundance"))
colnames(spectroWide)[-1] = sub("abundance_", "abundance_protein_", colnames(spectroWide)[-1])
# getting representative locus_tags
# representative entries with more than
# one corresponding protein entry will get the mean
spectroWide = left_join(x = spectroWide,
y = nrtxsep %>% select(-product),
by = c("locus_tag" = "locus_tag")) %>%
filter(!is.na(representative)) %>%
relocate(representative) %>%
select(-locus_tag) %>%
group_by(representative) %>%
summarise(across(.cols = everything(),
.fns = ~ mean(.x))) %>%
rename(locus_tag = "representative")
# exploratory plots #####
# plotting densities
# normalization doesnt seem necessary
# distributions are quite similar
# and look like poisson
# ggplot(spectroLong,
# aes(x = mean_abundance,
# color = timepoint)) +
# geom_density() +
# xlim(c(0, 50000))
|
3a0010d2473f7a3cafd6a3e49678b667e282fedc | f8698ecfccdb58bb284f364108e93cd0d4c8355e | /functions/utility.R | 944cd0b2e239250aa4f752aa0965d2db8326d484 | [] | no_license | martin-vasilev/DEVS2 | 5e46cb702ed06e6ee927907fcd8606a4e5277baf | f47f5d19d5c1d5b13bf64c128afb56aa205dd347 | refs/heads/master | 2023-01-11T22:06:28.078862 | 2020-11-11T22:44:46 | 2020-11-11T22:44:46 | 121,888,621 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 26,048 | r | utility.R |
# Martin R. Vasilev, 2017
########################
# UTILITY FUNCTIONS #
########################
# Extract text containing stimuli info:
get_text<- function(file){ ## extracts the loaded text material #
start<- which(grepl("DISPLAY TEXT", file))+1 # start point
end<- which(grepl("START", file))
if(length(start)==0){ # no start of text detected
return(0) # don't map fixations to stimuli
} else{ # extract text containing stimuli info
text<- file[start:end] # subset file
a= which(gregexpr(pattern ='BUTTON', text)==1)
if(length(a)>0){
text<- text[-a]
# text<- text[-a] # because one line is removed
}
#input<- which(grepl("INPUT", text)); input<- input[1]-1 # end point
end2<- which(grepl("REGION", text))
end2<- end2[length(end2)]
text<- text[1:end2]
#end<- which(grepl("INPUT", file)); end<- end[1]-1 # end point
#text<- file[start:end] # subset file
return(text)
}
}
# Create a database with position of text on screen
get_coord<- function(string){ # extracts text coordinates from trial info
# check to make sure there are no button press flags here..
a= which(gregexpr(pattern ='BUTTON', string)==1)
if(length(a)>0){
string<- string[-a]
}
#if(which(grepl(' ', string))==1){
# loc<- as.numeric(gregexpr("CHAR", string))
# part1<- substr(string, 1, loc+7)
# part2<- substr(string, loc+9, nchar(string))
# string2<- paste(part1, part2, sep="")
#}
#else{
#out <- data.frame( do.call( rbind, strsplit( string, ' ' ) ) )
out <- do.call( rbind, strsplit( string, '\t' ) )
out<- out[,2]
out <- data.frame(do.call( rbind, strsplit( out, ' ' ) ) )
#}
out<- subset(out, X2!="DELAY") # Remove DELAY 1ms lines
out$X7<- as.numeric(as.character(out$X7))
out$X8<- as.numeric(as.character(out$X8))
out$X9<- as.numeric(as.character(out$X9))
out$X10<- as.numeric(as.character(out$X10))
out$X11<- as.numeric(as.character(out$X11))
fix_spaces<- function(out){
out$space<- NULL
a<- which(out$X6=="") # find position of empty spaces
out$space<- NA
out$space[a]<- 1
# Re-arrange misplaced coords
out$X7[a]<- out$X8[a]
out$X8[a]<- out$X9[a]
out$X9[a]<- out$X10[a]
out$X10[a]<- out$X11[a]
out<- out[,-11]
}
out<- fix_spaces(out)
# clean_MSG<- function(msg){as.numeric(unlist(gsub("[^0-9]", "", unlist(msg)), ""))}
# out$X1<- clean_MSG(out$X1) # get rid of MSG text
out<- out[,-c(1,2,3,5)] # remove useless columns
# map sentences:
map_sent<- function(out){
sent_bnd<- which(out$X6=="."| out$X6=="?");
sent<- NULL
if(length(sent_bnd)>0){
for(i in 1:length(sent_bnd)){
if(i==1){
sent[1:sent_bnd[i]]<- 1
}
else{
sent<- c(sent, c(rep(i, length(sent_bnd[i-1]+1:sent_bnd[i])- length(sent))))
# sent[sent_bnd[i-1]+1:sent_bnd[i]]<- i
}
if(i==length(sent_bnd)){
sent<- c(sent, c(rep(i+1, length(sent_bnd[i]+1:nrow(out))- length(sent))))
}
}
out$sent<- sent
} else{
out$sent<- 1
}
return(out)
}
out<- map_sent(out)
# map lines:
map_line<- function(out){
line<- NULL
lines<- sort(unique(out$X8));
# as.numeric.factor <- function(x) {as.numeric(levels(x))[x]}
#lines<- as.numeric.factor(lines)
for(i in 1:length(lines)){
loc_lines<- which(out$X8==lines[i])
line<- c(line, rep(i, length(loc_lines)))
out$space[length(line)]<- 2
}
out$line<- line
return(out)
}
out<- map_line(out)
# map words:
map_words<- function(out){
out$X6<- as.character(out$X6)
out$word<- NULL
curr_sent=1
curr_word=1
sent_line<- which(out$space==2); sent_line<- sent_line+1
for(i in 1:nrow(out)){
newSent<- curr_sent!= out$sent[i]
out$word[i]<- curr_word
if(out$X6[i]== ""& !newSent){
curr_word<- curr_word+1
out$word[i]<- curr_word
}
if(is.element(i, sent_line)){
if(out$X6[i]!="."){
curr_word<- curr_word+1
}
out$word[i]<- curr_word
}
if(newSent){
curr_sent<- curr_sent+1
curr_word<- 1
out$word[i]<- curr_word
}
}
return(out)
}
out<- map_words(out)
# change column names:
colnames(out)<- c("char", "letter", "x1", "y1", "x2", "y2", "space", "sent",
"line", "word")
# map characters per line (for Eye Doctor):
out$line_char<- NA
unq_line<- unique(out$line)
for(i in 1:length(unq_line)){
line<- which(out$line==unq_line[i])
out$line_char[line[1]:line[length(line)]]<- 1:length(line)
}
return(out)
}
# Use stimuli information to create a coordinate map for each pixel on the screen
# This makes it possible to find exactly what participants were fixating
coord_map<- function(coords, x=ResX, y= ResY){
#as.numeric.factor <- function(x) {as.numeric(levels(x))[x]}
#coords$x1<- as.numeric.factor(coords$x1); coords$x2<- as.numeric.factor(coords$x2)
#coords$y1<- as.numeric.factor(coords$y1); coords$y2<- as.numeric.factor(coords$y2)
coords$id<- 1:nrow(coords)
map<- data.frame(matrix(NA, nrow = y, ncol = x))
for(i in 1:nrow(coords)){
map[coords$y1[i]:coords$y2[i],coords$x1[i]:coords$x2[i]]<- coords$id[i]
}
return(map)
}
# Create a data frame with information about trials, to be used for pre-processing
trial_info<- function(file, maxtrial, data){ # extracts information for processing trials
### get trial names:
ID<- which(grepl('TRIALID', file));
trial_text<- file[ID]
trials<- substr(trial_text, unlist(gregexpr(pattern =' ',trial_text[1]))[2]+1, nchar(trial_text))
#trials<- trials[which(letter!="P" & letter!="F")] # remove practice items and questions
trials<- gsub(" ", "", trials)
# sometimes there is an extra empty space that can mess up detection of duplicates
### get condition:
I<- unlist(gregexpr(pattern ='I',trials)) # start of item info
cond<- as.numeric(substr(trials, 2, I-1)) # extract condition number
### get item:
D<- unlist(gregexpr(pattern ='D',trials)) # start of dependent info
item<- as.numeric(substr(trials, I+1, D-1)) # extract condition number
depend<- as.numeric(substr(trials, nchar(trials), nchar(trials)))
### get sequence:
#seq<- 1:length(trials)
### get start & end times
start<- which(grepl('DISPLAY ON', file))
end <- which(grepl('DISPLAY OFF', file))
duplicated<- trials[duplicated(trials)]
if(length(duplicated)>0){ # if there were aborted trials..
# message(paste(" Diplicated trial", duplicated, "for file:", data, "\n"))
# message("Analysing only last attempt at the trial!")
toBeRemoved<- NULL
uniqueDupl<- unique(duplicated)
dup_rem<- NULL
for(i in 1:length(uniqueDupl)){
dup_rem_T<- which(trials==uniqueDupl[i])
dup_rem<- c(dup_rem, dup_rem_T[1:length(dup_rem_T)-1])
} # end of i
start<- start[-dup_rem]
#end<- end[-dup_rem]
cond<- cond[-dup_rem]
item<- item[-dup_rem]
# seq<- seq[-toBeRemoved]
depend<- depend[-dup_rem]
ID<- ID[-dup_rem]
} # end of aborted conditional
trial_db<- data.frame(cond, item, depend, start, end, ID)
trial_db<- subset(trial_db, depend==0 & item< maxtrial+1)
trial_db$seq<- 1:nrow(trial_db)
###
# trials<- trials[which(!is.element(trials, duplicated))]
return(trial_db)
}
# Basic pre-processing and extraction of fixations from data file:
parse_fix<- function(file, map, coords, trial_db, i, ResX, ResY, keepLastFix, hasText=TRUE){
get_FIX_stamp<- function(string){as.numeric(substr(string, 1, unlist(gregexpr(pattern ='\t', string))[1]-1))}
loc_blinks<- function(blink_time, s_time){findInterval(blink_time, s_time)+1 }
# +1 because it affects the next fixation
get_x_pixel<- function(string){
#tab_loc<- unlist(gregexpr(pattern ='\t', string))[3:4]
#sub_str<- substr(string, tab_loc[1], tab_loc[2])
#x<- as.numeric(unlist(gsub("[^0-9.]", "", unlist(sub_str)), ""))
#x<- floor(x)
a<- data.frame(do.call( rbind, strsplit( string, '\t' ) ) )
x<- as.numeric(as.character(unlist(a$X4)))
return(x)
}
get_y_pixel<- function(string){
#tab_loc<- unlist(gregexpr(pattern ='\t', string))[4:5]
#sub_str<- substr(string, tab_loc[1], tab_loc[2])
#y<- as.numeric(unlist(gsub("[^0-9.]", "", unlist(sub_str)), ""))
#y<- floor(y)
a<- data.frame(do.call( rbind, strsplit( string, '\t' ) ) )
y<- as.numeric(as.character(unlist(a$X5)))
return(y)
}
# trial_db<- trial_info(file)
# for(i in 1:nrow(trial_db)){
# get position of fixation stamps:
SFIX_stamps<- which(grepl('SFIX', file[trial_db$start:trial_db$end]))
EFIX_stamps<- which(grepl('EFIX', file[trial_db$start:trial_db$end]))
if(length(SFIX_stamps)==0 | length(EFIX_stamps)==0){
# means that there was no complete fixation on this trial (i.e,
# participant likely pressed end button by mistake)
raw_fix<- NULL
return(raw_fix)
message(sprintf("No fixations in Trial %d: excluded", j))
}
if(EFIX_stamps[1]<SFIX_stamps[1]){ # removes fixation that triggered gaze box
EFIX_stamps<- EFIX_stamps[-1]
}
if(EFIX_stamps[length(EFIX_stamps)]< SFIX_stamps[length(SFIX_stamps)]){
SFIX_stamps<- SFIX_stamps[-length(SFIX_stamps)]
} # fixation was not terminated before the end of trial
# get start and end time of fixations:
s_time<- get_FIX_stamp(file[SFIX_stamps+ trial_db$start]) # start time of fixation
e_time<- get_FIX_stamp(file[EFIX_stamps+ trial_db$start-2]) # end time of fixation
# calculate fixation durations:
fixDur<- e_time- s_time
# get x pixel position:
x<- get_x_pixel(file[EFIX_stamps+ trial_db$start-1])
# get y pixel position:
y<- get_y_pixel(file[EFIX_stamps+ trial_db$start-1])
# find blinks:
blink_stamp<- which(grepl('EBLINK', file[trial_db$start:trial_db$end]))
blink_time<- get_FIX_stamp(file[blink_stamp+ trial_db$start])-1
blink_out<- which(blink_time<s_time[1]| blink_time>e_time[length(e_time)])
if(length(blink_out>0)){ # blinks outside time window that is analysed
blink_time<- blink_time[-blink_out] # remove them
}
blink_pos<- loc_blinks(blink_time, s_time)
blink<- rep(0, length(s_time))
blink[blink_pos]<-1
#if(length(blink)>length(e_time)){
# blink<- blink[-length(blink)]
#}
# merge into a dataframe:
fix<- data.frame(s_time, e_time, fixDur, x, y, blink)
#-----------------------------------------------#
# map fixations to stimuli on the screen: #
#-----------------------------------------------#
loc<- NULL; raw_fix<- NULL; temp<- NULL; sub<- NULL
s_time<- NULL; e_time<- NULL; xPos<- NULL; yPos<- NULL
item<- NULL; cond<- NULL; seq<- NULL; fix_num<- NULL; fix_dur<- NULL
sent<- NULL; line<- NULL; word<- NULL; char_trial<- NULL
max_sent<- NULL; max_word<- NULL; intersent_regr<- NULL
intrasent_regr<- NULL; blink<- NULL; outOfBnds<- NULL; outsideText<- NULL
if(hasText){
# max word for each sentence:
curr_sent<- matrix(0, max(coords$sent),2)
curr_sent[,1]<- c(1:max(coords$sent))
}
for(j in 1:nrow(fix)){
if(hasText){
loc<- map[fix$y[j], fix$x[j]] # locate fixation
if(is.list(loc)){
loc<- NULL
}
}
# general info:
sub[j]<- i
item[j]<- trial_db$item
cond[j]<- trial_db$cond
seq[j]<- trial_db$seq
fix_num[j]<- j
fix_dur[j]<- fix$fixDur[j]
# info from asc file:
s_time[j]<- fix$s_time[j];
e_time[j]<- fix$e_time[j]
xPos[j]<- fix$x[j];
yPos[j]<- fix$y[j]
blink[j]<- fix$blink[j]
if(xPos[j]<1 | xPos[j]> ResX | yPos[j]< 1 | yPos[j]>ResY){ # fixation is out of bounds
outOfBnds[j]<- 1
} else{
outOfBnds[j]<- 0
if(hasText){
# outside of text area?
if(is.na(loc)){
outsideText[j]<- 1
} else{
outsideText[j]<- 0
}
}
}
if(fix$x[j]<0){
loc<- NA
outOfBnds[j]<- 1
outsideText[j]<- 1
}
if(hasText){
# stimuli info:
if(!is.null(loc)){
sent[j]<- coords$sent[loc]
line[j]<- coords$line[loc]
word[j]<- coords$word[loc]
char_trial[j]<- as.numeric(as.character(levels(coords$char[loc])[coords$char[loc]]))+1
# +1 bc Eyetrack counts from 0
} else{
sent[j]<- NA; line[j]<- NA; word[j]<- NA; char_trial[j]<- NA
}
# saccade stuff:
if(j==1){
max_sent[j]<- sent[j]
} else{
max_sent[j]<- max_sent[j-1]
if(!is.na(max_sent[j])& !is.na(sent[j]) & sent[j]> max_sent[j]){
max_sent[j]<- sent[j]
}
}
# maximum word:
if(j==1){
max_word[j]<- abs(word[j])
curr_sent[sent[j],2]<- abs(word[j])
} else{
max_word[j]<- curr_sent[sent[j],2]
if(!is.na(word[j])& abs(word[j])>curr_sent[sent[j],2]){
max_word[j]<- abs(word[j])
curr_sent[sent[j],2]<- abs(word[j]) # replace new max word
}
}
# Regression stuff:
if(!is.na(max_sent[j])& !is.na(sent[j]) & sent[j]< max_sent[j]){
intersent_regr[j]<- 1
} else{
intersent_regr[j]<- 0
}
# intra-sentence regressions:
if(!is.na(word[j])& abs(word[j])<max_word[j]){
intrasent_regr[j]<- 1
}else{
intrasent_regr[j]<- 0
}
} else{ # end of if hasText
sent[j]=NA; max_sent[j]=NA; line[j]=NA; word[j]=NA; max_word[j]=NA;
char_trial[j]=NA; intrasent_regr[j]=NA; intersent_regr[j]=NA; outsideText[j]=NA
}
} # end of j loop
if(length(outsideText)!= length(item)){
outsideText[length(outsideText):length(item)]<- NA
}
raw_fix<- data.frame(sub,item, cond, seq, s_time, e_time,xPos, yPos, fix_num, fix_dur,
sent, max_sent, line, word, max_word, char_trial, intrasent_regr, intersent_regr, blink,
outOfBnds, outsideText)
if(keepLastFix==FALSE){
# remove last fixation on trial (due to participants' making a decision to press the button)
raw_fix<- raw_fix[-nrow(raw_fix),]
}
if(hasText==TRUE){
raw_fix$hasText<-1
}else{
raw_fix$hasText<-0
}
return(raw_fix)
}
#############
# PLOTFIX.R #
#############
plot_fix<- function(coords, raw_fix_temp, i, j, ResX, ResY, hasText=TRUE, printOriginal= TRUE){
remap_letters<- function(letter, y){ # adjusted y position for easier reading
letter<- as.character(letter)
ascenders<- c("b", "d", "f", "h", "i", "k", "l", "t")
descenders<- c("g", "j", "p", "q", "y")
punct<- c(",", ".")
caps<- which(grepl("[A-Z]",letter))
which_asc<- which(is.element(letter, ascenders))
which_desc<- which(is.element(letter, descenders))
which_punct<- which(is.element(letter, punct))
y[which_desc]<- y[which_desc]- 2
y[which_asc]<- y[which_asc]+1
y[which_punct]<- y[which_punct]-4
y[caps]<- y[caps]+1
return(y)
}
add.alpha <- function(col, alpha=1){
if(missing(col))
stop("Please provide a vector of colours.")
apply(sapply(col, col2rgb)/255, 2,
function(x)
rgb(x[1], x[2], x[3], alpha=alpha))
}
# create new directory for images:
mainDir <- getwd()
imgDir <- "img"
subDir<- paste("s", i, sep= "")
if (!file.exists(imgDir)){
dir.create(file.path(mainDir, imgDir), showWarnings = FALSE)
}
if (!file.exists(paste(imgDir, "/", subDir, sep=""))){
dir.create(file.path(imgDir, subDir), showWarnings = FALSE)
}
# create output string
output= paste(imgDir, "/", subDir, "/", "Sub_", i, "_", "item_", j, ".png", sep="")
# open file for plotting:
png(filename = output, width = ResX, height = ResY,
units = "px", pointsize = 12, bg="white", res = 100)
par(mar=c(0,0,0,0)) # set plot margins to 0
# create empty plot:
plot(NA, axes=F, main="", xlab="" ,ylab="", col="black", ylim=c(0, ResY), xlim=c(0, ResX), xaxs="i", yaxs="i")
if(hasText){
# convert coordinates to numbers:
coords$y1<- as.numeric(as.character(coords$y1))
coords$y2<- as.numeric(as.character(coords$y2))
coords$x1<- as.numeric(as.character(coords$x1))
coords$x2<- as.numeric(as.character(coords$x2))
# Plot stimuli that appeared on screen:
rect(coords$x1, ResY-coords$y1, coords$x2, ResY-coords$y2, col= "white", border= "#CFCBCB")
xpos= coords$x1+ (coords$x2- coords$x1)/2
ypos= ResY-coords$y1- (coords$y2- coords$y1)/2
# correct y pos of some letter for better readibility:
ypos<- remap_letters(coords$letter, ypos)
# print letters:
text(xpos, ypos, coords$letter, col= "black")
#symbols(raw_fix$xPos, raw_fix$yPos, circles=rep(0.1, nrow(raw_fix)))
# add saccades
# for (k in 1:nrow(raw_fix_temp)){
# if(k==1){ next}
# x1<- raw_fix_temp$xPos[k-1]+6
# y1<- ResY-raw_fix_temp$yPos[k-1]
# if(x2>x1){
# x2<- raw_fix_temp$xPos[k]-10
# } else{
# x2<- raw_fix_temp$xPos[k]+10
# }
# y2<- ResY-raw_fix_temp$yPos[k]
# fixPosX<- x1+ (x2-x1)/2
# fixPosY<- y1+ (y2-y1)/2
# arrows(x1, y1, x2, y2, col = add.alpha("blue",0.2), lty= 1, lwd=2, length=0.10)
#text(fixPosX, fixPosY, raw_fix_temp$fix_num, col= add.alpha("blue",0.3))
# }
first<- subset(raw_fix_temp, intrasent_regr==0 & intersent_regr==0)
second<- subset(raw_fix_temp, intrasent_regr==1 | intersent_regr==1)
# first-pass:
points(x= first$xPos, y=ResY-first$yPos, pch = 16, col= add.alpha("green",0.20),
cex= 0.7*(first$fix_dur/75))
points(x= first$xPos, y=ResY-first$yPos, pch = 16, cex=0.7, col="green")
# second-pass:
points(x= second$xPos, y=ResY-second$yPos, pch = 16, col= add.alpha("orange",0.20),
cex= 0.7*(second$fix_dur/75))
points(x= second$xPos, y=ResY-second$yPos, pch = 16, cex=0.7, col="orange")
# fixation numbers:
text(raw_fix_temp$xPos, ResY-raw_fix_temp$yPos+15, raw_fix_temp$fix_num, col= add.alpha("red",0.6))
} else{ # end of hasText
# plot fixations:
points(x= raw_fix_temp$xPos, y=ResY-raw_fix_temp$yPos, pch = 16, col= add.alpha("blue",0.20),
cex= 0.7*(raw_fix_temp$fix_dur/75))
points(x= raw_fix_temp$xPos, y=ResY-raw_fix_temp$yPos, pch = 16, cex=0.7, col="blue")
# fixation numbers:
text(raw_fix_temp$xPos, ResY-raw_fix_temp$yPos+15, raw_fix_temp$fix_num, col= add.alpha("red",0.6))
}
# print original label:
if(printOriginal){
text(ResX - 0.05*ResX, ResY -ResY*0.02, 'ORIGINAL', col= 'black', face=2)
}
dev.off() # close file
}
reAlign<- function(rawfix, coords, map, ResX, ResY){
#------------------------------------------
# Functions:
#------------------------------------------
# Check for a return sweep (RS):
RS<- function(i, rawfix, coords, reqYthresh=TRUE, reqXthresh=TRUE, Ythresh= 1/4, Xthresh= 8, threshSimilar= 2/3){
if(i==1){ # first fixation can't be a return sweep
return(0)
}
### settings:
lw<- coords$x2[1]-coords$x1[1] # letter width
lh<- coords$y2[1]-coords$y1[1] # letter height
meetXthresh<- FALSE
meetYthresh<- FALSE
nextSimilar<- FALSE
#############################
# conditions
# leftward saccade?
leftSacc<- rawfix$xPos[i]< rawfix$xPos[i-1]
# downward saccade?
downSacc<- rawfix$yPos[i]> rawfix$yPos[i-1]
if(downSacc & reqYthresh){ # x pixels downwards required for RS
Ydiff<- lh*Ythresh # threshold to be met
trueYdiff<- rawfix$yPos[i]- rawfix$yPos[i-1] # Y distance traveled
meetYthresh<- trueYdiff >= Ydiff
}
if(leftSacc & reqXthresh){ # x pixels leftwards required for RS
Xdiff<- lw*Xthresh # threshold to be met
trueXdiff<- rawfix$xPos[i-1]- rawfix$xPos[i] # X distance traveled
meetXthresh<- trueXdiff >= Xdiff
}
# next fixation with 'similar' Y pos?
if(i!= nrow(rawfix)){ # only if not the last fixation in trial..
howSimilar<- threshSimilar*lh # within x/yth of a letter height
nextSimilar<- rawfix$yPos[i+1]>= rawfix$yPos[i]- howSimilar | rawfix$yPos[i+1]<= rawfix$yPos[i]+ howSimilar
}
###### Point system:
# - leftward saccade: 1 point
# - leftward saccade meeting threshold: 1 point
# - downward saccade: 2 points
# - downward saccade meeting threshold: 1 point
# - next Fix. with 'similar' Ypos: 1 point
# = 6 max points
maxPoints<- 1 +2 +1
if(reqYthresh){
maxPoints<- maxPoints+1
}
if(reqXthresh){
maxPoints<- maxPoints+1
}
#----------------------------
currPoints<- 0 # start with 0
if(leftSacc){
currPoints<- currPoints + 1/maxPoints
if(meetXthresh){
currPoints<- currPoints+ 1/maxPoints
}
}
if(downSacc){
currPoints<- currPoints +2/maxPoints
if(meetYthresh){
currPoints<- currPoints+ 1/maxPoints
}
}
if(nextSimilar){
currPoints<- currPoints +1/maxPoints
}
# prob<- currPoints/maxPoints
return(round(currPoints, 2))
}
# Check if fixation is still on the same line (SL):
SL<- function(i, rawfix, coords, curr_line, xstart, xend, RSthresh= 0.8){
if(i==1){ # first fixation will be on the first line (assuming gaze box)
return(1)
}
# Fixation is already assigned to the current line?
isAssigned<- rawfix$outsideText[i]==0 & rawfix$line[i]== curr_line
# Is current fixation a likely return sweep?
likelyRS<- rawfix$pRS[i]>= RSthresh
#how much skipped info if fixation is no longer on the same line?
ppl<- coords$x2[1]- coords$x1[1] # pixels per letter
pixLeft<- xend[curr_line, 2] - rawfix$xPos[i] # pixels left until end of line
letLeft<- pixLeft/ppl # letters left until end of line
totalLetters<- (xend[curr_line, 2]- xstart[curr_line, 2])/ ppl # num of letters on line
textLeft<- totalLetters- (totalLetters- letLeft) # num letters from current xPos until end of line
propLeft<- textLeft/totalLetters # prop of text left on screen
###### Point system:
# - Fixation is already assigned to the current line: 2 points
# - Fixation is not likely a return sweep: 1 point
# - Discourage skipping text on the line: 1 points max (function of remaining text)
# = max 4 points
maxPoints<- 2+1+1
currPoints<- 0
if(isAssigned){ # already assigned to same line
currPoints<- currPoints + 2/maxPoints
}
if(!likelyRS){ # not likely a RS
currPoints<- currPoints + 1/maxPoints
}
# weight points by amount of text left on the line (max 1)
currPoints<- currPoints+ (1-propLeft)/maxPoints
currPoints<- round(currPoints,2)
return(currPoints)
}
# Re-map fixation info after re-aligning it:
reMap<- function(rawfix, i, map, coords, newX=NULL, newY=NULL){
rawfix$reAligned[i]<- 1
rawfix$prevLine[i]<- rawfix$line[i]
rawfix$prevX[i]<- rawfix$xPos[i]
rawfix$prevY[i]<- rawfix$yPos[i]
if(hasArg(newX)){ # if new x to be replaced..
rawfix$xPos[i]<- newX
}
if(hasArg(newY)){ # if new y to be replaced..
rawfix$yPos[i]<- newY
}
# new location on the screen:
loc<- map[rawfix$yPos[i], rawfix$xPos[i]]
rawfix$sent[i]<- coords$sent[loc]
rawfix$word[i]<- coords$word[loc]
rawfix$line[i]<- coords$line[loc]
return(rawfix)
}
#---------------------------------------
# get some info about position of text:
#---------------------------------------
ystart<- coords$y1[1] # start of first line on y-axis
yend<- coords$y2[nrow(coords)] # end of last line on y-axis
nlines<- max(coords$line) # number of lines in trial
# start position of each line
xstart<- matrix(nrow = nlines, ncol = 2, data = 0)
xstart[1:nlines,1]<- 1:nlines
ystart<- matrix(nrow = nlines, ncol = 2, data = 0)
ystart[1:nlines,1]<- 1:nlines
# end position of each line
xend<- matrix(nrow = nlines, ncol = 2, data = 0)
xend[1:nlines,1]<- 1:nlines
yend<- matrix(nrow = nlines, ncol = 2, data = 0)
yend[1:nlines,1]<- 1:nlines
for(i in 1:nlines){
a<- subset(coords, line==i)
xstart[i,2]<- a$x1[1]
xend[i,2]<- a$x2[nrow(a)]
ystart[i,2]<- a$y1[1]
yend[i,2]<- a$y2[1]
}
####################################################
# Process fixations here: #
####################################################
rawfix$pRS<- NULL
rawfix$pSL<- NULL # prob fixation is still on the same line
rawfix$reAligned<- 0
rawfix$prevX<- NA
rawfix$prevY<- NA
rawfix$prevLine<- NA
# check first fixation:
curr_line<- 1 # start at line 1 (assuming gaze box)
if(rawfix$line[1]!=1){
if(rawfix$yPos[1]< ystart[1,2]){ # first fix is above first one
rawfix<- reMap(rawfix, 1, map, coords, newY= ystart[1,2]+1)
}
if(rawfix$yPos[1]> yend[1,2]){ # first fix is below first line
rawfix<- reMap(rawfix, 1, map, coords, newY= yend[1,2]-1)
}
}
## Calculate probability of return sweep for each fixation:
for(i in 1:nrow(rawfix)){
rawfix$pRS[i]<- RS(i, rawfix, coords)
}
for(i in 1:nrow(rawfix)){
rawfix$pSL[i]<- SL(i, rawfix, coords, curr_line, xstart, xend)
if(!is.na(rawfix$line[i])){
curr_line<- rawfix$line[i]
}
}
return(rawfix)
}
|
9a1968c2f4af0c9233aae705ce9d034b3e9d11ff | 9edbb0f6dab00623bd0b0042062e0201042090f0 | /man/plot.detectOutlier.Rd | 7aeee4cff5a07698374e73bb3d9f9fa03567ce3a | [] | no_license | bozenne/butils | 87161bb1e31570984f5a80e00d783f59297516fb | 358a62cb656bf0b8ac335e71bf8b79102b458d8e | refs/heads/master | 2023-07-23T10:19:58.756236 | 2023-07-18T13:00:10 | 2023-07-18T13:00:10 | 63,180,644 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 385 | rd | plot.detectOutlier.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plot.detectOutlier.R
\name{plot.detectOutlier}
\alias{plot.detectOutlier}
\title{plot function for detectOutlier objects}
\usage{
\method{plot}{detectOutlier}(x, ...)
}
\arguments{
\item{x}{an object of class detectOutlier.}
\item{...}{not used.}
}
\description{
plot function for detectOutlier objects.
}
|
adc9e204ff79d031db95fff6c6064583bf63034c | f57f1f4c562cde9d39539bc3186c8eed3b6f54e0 | /man/selectLambda.Rd | c4d7e05b0a09056e99a8a0057d5bd1b2e0c02501 | [] | no_license | ChristianGoueguel/rospca | 0a8905c686ad83a24ad4b081c924bd4ad1539a44 | 6631abbd32845558160bf682ac992be018768643 | refs/heads/master | 2023-03-19T03:37:37.298457 | 2018-10-20T15:46:39 | 2018-10-20T15:46:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,311 | rd | selectLambda.Rd | \name{selectLambda}
\alias{selectLambda}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Selection of sparsity parameter using IC
}
\description{
Selection of the sparsity parameter for ROSPCA and SCoTLASS using BIC of Hubert et al. (2016), and for SRPCA using BIC of Croux et al. (2013).
}
\usage{
selectLambda(X, k, kmax = 10, method = "ROSPCA", lmin = 0, lmax = 2, lstep = 0.02,
alpha = 0.75, stand = TRUE, skew = FALSE, multicore = FALSE,
mc.cores = NULL, P = NULL, ndir = "all")
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{X}{An \eqn{n} by \eqn{p} matrix or data matrix with observations in the rows and variables in the columns.}
\item{k}{
Number of Principal Components (PCs).
}
\item{kmax}{
Maximal number of PCs to be computed, only used when \code{method = "ROSPCA"} or \code{method = "ROSPCAg"}. Default is 10.}
\item{method}{
PCA method to use: ROSPCA (\code{"ROSPCA"} or \code{"ROSPCAg"}), SCoTLASS (\code{"SCoTLASS"} or \code{"SPCAg"}) or SRPCA (\code{"SRPCA"}). Default is \code{"ROSPCA"}.
}
\item{lmin}{
Minimal value of \eqn{\lambda} to look at, default is 0.
}
\item{lmax}{
Maximal value of \eqn{\lambda} to look at, default is 2.
}
\item{lstep}{
Difference between two consecutive values of \eqn{\lambda}, i.e. the step size, default is 0.02.
}
\item{alpha}{
Robustness parameter for ROSPCA, default is 0.75.
}
\item{stand}{
Logical indicating if the data should be standardised, default is \code{TRUE}.
}
\item{skew}{
Logical indicating if the skewed version of ROSPCA should be applied, default is \code{FALSE}.
}
\item{multicore}{
Logical indicating if multiple cores can be used, default is \code{TRUE}. Note that this is not possible for the Windows platform, so \code{multicore} is always \code{FALSE} there.
}
\item{mc.cores}{
Number of cores to use if \code{multicore=TRUE}, default is \code{NULL} which corresponds to the number of cores minus 1.
}
\item{P}{
True loadings matrix, a numeric matrix of size \eqn{p} by \eqn{k}. The default is \code{NULL} which means that no true loadings matrix is specified.
}
\item{ndir}{Number of directions used when computing the outlyingness (or the adjusted outlyingness when \code{skew=TRUE}) in \code{rospca}, see \code{\link[mrfDepth]{outlyingness}} and \code{\link[mrfDepth]{adjOutl}} for more details.
}
}
\details{
We select an optimal value of \eqn{\lambda} for a certain method on a certain dataset by looking at an equidistant grid of \eqn{\lambda} values. For each value of \eqn{\lambda}, we apply the method on the dataset using this sparsity parameter, and compute an Information Criterion (IC). The optimal value of \eqn{\lambda} is then the one corresponding to the minimal IC. The ICs we consider are the BIC of for Hubert et al. (2016) for ROSPCA and SCoTLASS, and the BIC of Croux et al. (2013) for SRPCA.
The BIC of Hubert et al. (2016) is defined as
\deqn{BIC(\lambda)=\ln(1/(h_1p)\sum_{i=1}^{h_1} OD^2_{(i)}(\lambda))+df(\lambda)\ln(h_1p)/(h_1p),}
where \eqn{h_1} is the size of \eqn{H_1} (the subset of observations that are kept in the non-sparse reweighting step) and \eqn{OD_{(i)}(\lambda)} is the \eqn{i}th smallest orthogonal distance for the model when using \eqn{\lambda} as the sparsity parameter. The degrees of freedom \eqn{df(\lambda)} are the number of non-zero loadings when \eqn{\lambda} is used as the sparsity parameter.
}
\value{
A list with components:\cr
\item{opt.lambda}{Value of \eqn{\lambda} corresponding to minimal IC.}
\item{min.IC}{Minimal value of IC.}
\item{Lambda}{Numeric vector containing the used values of \eqn{\lambda}.}
\item{IC}{Numeric cector containing the IC values corresponding to all values of \eqn{\lambda} in \code{Lambda}.}
\item{loadings}{Loadings obtained using method with sparsity parameter \code{opt.lambda}, a numeric matrix of size \eqn{p} by \eqn{k}.}
\item{fit}{Fit obtained using method with sparsity parameter \code{opt.lambda}. This is a list containing the loadings (\code{loadings}), the eigenvalues (\code{eigenvalues}), the standardised data matrix used as input (\code{Xst}), the scores matrix (\code{scores}), the orthogonal distances (\code{od}) and the score distances (\code{sd}).}
\item{type}{Type of IC used: \code{BICod} (BIC of Hubert et al. (2016)) or \code{BIC} (BIC of Croux et al. (2013)).}
\item{measure}{A numeric vector containing the standardised angles between the true and the estimated loadings matrix for each value of \eqn{\lambda} if a loadings matrix is given. When no loadings matrix is given as input (\code{P=NULL}), \code{measure} is equal to \code{NULL}.}
}
\references{
Hubert, M., Reynkens, T., Schmitt, E. and Verdonck, T. (2016). ``Sparse PCA for High-Dimensional Data with Outliers,'' \emph{Technometrics}, 58, 424--434.
Croux, C., Filzmoser, P., and Fritz, H. (2013), ``Robust Sparse Principal Component Analysis,'' \emph{Technometrics}, 55, 202--214.
}
\author{
Tom Reynkens
}
\seealso{
\link{selectPlot}, \link[parallel]{mclapply}, \link{angle}
}
\examples{
X <- dataGen(m=1, n=100, p=10, eps=0.2, bLength=4)$data[[1]]
sl <- selectLambda(X, k=2, method="ROSPCA", lstep=0.1)
selectPlot(sl)
}
% Add one or more standard keywords, see file 'KEYWORDS' in the
% R documentation directory.
\keyword{ optimize }
|
11d39c244a78f8767bb383fb873d58cb11758b7f | 22057bf4f2eb001f739761e53a5578de578e6920 | /2D_model_initial_setup/field.statistics.R | a90fed6b288f36408f3512475ce4929608602a8a | [] | no_license | mrubayet/archived_codes_for_sfa_modeling | 3e26d9732f75d9ea5e87d4d4a01974230e0d61da | f300fe8984d1f1366f32af865e7d8a5b62accb0d | refs/heads/master | 2020-07-01T08:16:17.425365 | 2019-08-02T21:47:18 | 2019-08-02T21:47:18 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 20,521 | r | field.statistics.R | rm(list = ls())
library(rhdf5)
library(BurStFin) #for partial.rainbow
H5close()
load("results/general.r")
case.name='heat2.1'
obs.time = seq(1,960)
ntime = length(obs.time)
load(paste("results/",case.name,".x.velocity",sep=''))
load(paste("results/",case.name,".z.velocity",sep=''))
load(paste("results/",case.name,".hydraulic.head",sep=''))
#load(paste("results/",case.name,".ch2o.conc",sep=''))
#load(paste("results/",case.name,".h.conc",sep=''))
#load(paste("results/",case.name,".hco3.conc",sep=''))
#load(paste("results/",case.name,".n2.conc",sep=''))
#load(paste("results/",case.name,".no3.conc",sep=''))
#load(paste("results/",case.name,".o2.conc",sep=''))
load(paste("results/",case.name,".tracer.conc",sep=''))
tracer.conc = tracer.conc/35.453
load(paste("results/",case.name,".elevation.field",sep=''))
river.gradients=as.matrix(read.table(paste("field.5/",case.name,"/Gradients_River2015.txt",sep='')))
river.level=as.matrix(read.table(paste("field.5/",case.name,"/DatumH_River2015.txt",sep='')))
#river.gradients = river.gradients[(10*24+1):dim(river.gradients)[1],]
#river.level = river.level[(10*24+1):dim(river.level)[1],]
east.level=array(2*dim(river.level)[1],dim=c(dim(river.level)[1],2))
##east.level[,1]=river.level[,1]/3600/24-9
east.level[,1]=river.level[,1]/3600-240
east.level[,2]=(y.new-river.level[,3])*river.gradients[,3]+river.level[,4]
well.level=as.matrix(read.table(paste("field.5/",case.name,"/DatumH_West2015.txt",sep="")))
#well.level = well.level[(10*24+1):dim(river.level)[1],]
west.level=array(2*dim(well.level)[1],dim=c(dim(well.level)[1],2))
##west.level[,1]=well.level[,1]/3600/24-9
west.level[,1]=well.level[,1]/3600-240
west.level[,2]=well.level[,4]
#creat new material id
material_ids.new=h5read(paste("field.5/",case.name,"/Plume_Slice_AdaptiveRes_material.h5",sep=''),"Materials/Material Ids")
material_ids.new=array(material_ids.new,c(nx.new,ny.new,nz.new))
material_ids.reverse=material_ids.new
#find river.bank
mud.hanford=NULL
for (iz in nz.new:2)
{
for (ix in nx.new:2)
{
if (((material_ids.new[ix,1,iz]-material_ids.new[ix,1,(iz-1)])==4) ||
((material_ids.new[ix,1,iz]-material_ids.new[(ix-1),1,iz])==4))
{
mud.hanford=rbind(mud.hanford,c(ix,iz))
}
}
}
mud.hanford = mud.hanford[order(mud.hanford[,1]),]
hanford.ringold=NULL
for (iz in nz.new:2)
{
for (ix in (nx.new-1):2)
{
if (((material_ids.new[ix,1,iz]-material_ids.new[ix,1,(iz-1)])==-3) ||
((material_ids.new[ix,1,iz]-material_ids.new[(ix-1),1,iz])==-3) ||
((material_ids.new[ix,1,iz]-material_ids.new[(ix+1),1,iz])==-3))
{
hanford.ringold=rbind(hanford.ringold,c(ix,iz))
}
}
}
hanford.ringold = hanford.ringold[order(hanford.ringold[,1]),]
## header = names(h5.data[[obs.time[1]]][[3]])
## simu.col=grep("Material_ID",header)
## simu.results = h5.data[[obs.time[1]]][[3]][[simu.col]]
## simu.results = aperm(simu.results,c(3,1,2))
## simu.results = simu.results[,,]
#elevation.field=t(replicate(nx.new,z.new))
#elevation.field[simu.results==0]=0
obs.type = c("X-Velocity",
"Y-Velocity",
"Z-Velocity",
"Liquid_Pressure",
"Liquid_Saturation",
"Material_ID",
"Total_CH2O(aq)", #12.0107
"Total_H+", #1.00794
"Total_HCO3-", #12.0107
"Total_N2(aq)", #28.0134
"Total_NO3- [M]", #62.005
"Total_Total_O2(aq)", #31.9988
"Tracer ")
for (itime in 1:ntime){
print(itime)
jpeg.name = paste("figures/",case.name,".",formatC((itime),width = 3,flag = "0"),
"_hydraulic.head",".jpg",sep = "")
jpeg(jpeg.name,width = 12,height = 2.58,units = "in",res = 300, quality = 100)
par(xpd=TRUE,mgp=c(2.,0.6,0),mar=c(4.1,4.1,2.1,2.1))
filled.contour(x.new,z.new,hydraulic.head[,,itime],
ylim = c(bottom.new,top.new),
xlim = c(west.new,east.new),
zlim = range(hydraulic.head[hydraulic.head>100]),
color.palette = terrain.colors,
xlab = "Rotated east-west direction (m)",
ylab = "Elevation (m)",
main = paste("Hydraulic head : t =",itime,"hour"),
asp = 1.0,
plot.axes = {
axis(1,cex.axis = 1.)
axis(2,cex.axis = 1.)
lines(x.new[river.bank.x]-0.5*0.1,z.new[river.bank.z]-0.5*0.05,
col = 'black',lwd = 0.5)
lines(x.new[mud.hanford[,1]]-0.5*0.1,z.new[mud.hanford[,2]]-0.5*0.05,
col = "black", lwd = 0.5)
lines(x.new[hanford.ringold[,1]],z.new[hanford.ringold[,2]],lwd = 0.5)
x.index = rep(seq(1,nx.new),nz.new)
z.index = rep(seq(1,nz.new),each = nx.new)
## x.index = x.index[hydraulic.head[,,itime]<0][which(elevation.field[hydraulic.head[,,itime]<0]>0)]
## z.index = z.index[hydraulic.head[,,itime]<0][which(elevation.field[hydraulic.head[,,itime]<0]>0)]
x.index = x.index[intersect(which(hydraulic.head[,,itime]<elevation.field),which(elevation.field>0))]
z.index = z.index[intersect(which(hydraulic.head[,,itime]<elevation.field),which(elevation.field>0))]
# z.index = z.index[hydraulic.head[,,itime]<elevation.field]
x.vadose = unique(x.index)
x.vadose = sort(x.vadose)
z.vadose = rep(0,length(x.vadose))
j=1
for (i in 1:length(x.vadose))
{
z.vadose[i] = min(z.index[x.index==x.vadose[i]])
j=j+1
}
lines(x.new[x.vadose],z.new[z.vadose],lty = 2,lwd = 0.5)
current.west.level = west.level[which.min(abs(west.level[,1]-obs.time[itime])),2]
current.east.level = east.level[which.min(abs(east.level[,1]-obs.time[itime])),2]
current.east.interface = x.new[river.bank.x[which.min(abs(z.new[river.bank.z]-
current.east.level))]]
lines(c(current.east.interface,east.new),rep(current.east.level,2),col = "blue",lwd = 0.8)
points(east.new,current.west.level,col = "black",pch = 16)
text((east.new-10),current.east.level+2,
paste("dh =",
formatC(current.east.level-current.west.level,digits=2,format="e"),"m"),
col="black")
}
)
dev.off()
## jpeg.name = paste("figures/",case.name,formatC((itime),width = 3,flag = "0"),
## "_CH2O",".jpg",sep = "")
## jpeg(jpeg.name,width = 12,height = 2.58,units = "in",res = 300, quality = 100)
## par(xpd=TRUE,mgp=c(2.,0.6,0),mar=c(4.1,4.1,2.1,2.1))
## filled.contour(x.new,z.new,ch2o.conc[,,itime],
## ylim = c(bottom.new,top.new),
## xlim = c(west.new,east.new),
## zlim= range(ch2o.conc),
## color = function(x)rev(heat.colors(x)),
## xlab = "Rotated east-west direction (m)",
## ylab = expression("CH"[2]*"O (mg/L)"),
## main = bquote("CH"[2]*"O : t =" ~.(obs.time[itime]) ~ "day" ),
## asp = 1.0,
## plot.axes = {
## axis(1,cex.axis = 1.)
## axis(2,cex.axis = 1.)
## lines(x.new[river.bank.x]-0.5*0.1,z.new[river.bank.z]-0.5*0.05,
## col = 'black',lwd = 0.5)
## lines(x.new[mud.hanford[,1]]-0.5*0.1,z.new[mud.hanford[,2]]-0.5*0.05,
## col = "black", lwd = 0.5)
## lines(x.new[hanford.ringold[,1]],z.new[hanford.ringold[,2]],lwd = 0.5)
## lines(x.new[x.vadose],z.new[z.vadose],lty = 2,lwd = 0.5)
## lines(c(current.east.interface,east.new),rep(current.east.level,2),col = "blue",lwd = 0.8)
## points(east.new,current.west.level,col = "black",pch = 16)
## text((east.new-10),current.east.level+2,
## paste("dh =",
## formatC(current.east.level-current.west.level,digits=2,format="e"),"m"),
## col="black")
## }
## )
## dev.off()
## jpeg.name = paste("figures/",case.name,formatC((itime),width = 3,flag = "0"),
## "_H+",".jpg",sep = "")
## jpeg(jpeg.name,width = 12,height = 2.58,units = "in",res = 300, quality = 100)
## par(xpd=TRUE,mgp=c(2.,0.6,0),mar=c(4.1,4.1,2.1,2.1))
## filled.contour(x.new,z.new,h.conc[,,itime],
## ylim = c(bottom.new,top.new),
## xlim = c(west.new,east.new),
## zlim = range(h.conc),
## color = function(x)rev(heat.colors(x)),
## xlab = "Rotated east-west direction (m)",
## ylab = expression("H+ (mg/L)"),
## main = bquote("H+ : t =" ~.(obs.time[itime]) ~ "day" ),
## asp = 1.0,
## plot.axes = {
## axis(1,cex.axis = 1.)
## axis(2,cex.axis = 1.)
## lines(x.new[river.bank.x]-0.5*0.1,z.new[river.bank.z]-0.5*0.05,
## col = 'black',lwd = 0.5)
## lines(x.new[mud.hanford[,1]]-0.5*0.1,z.new[mud.hanford[,2]]-0.5*0.05,
## col = "black", lwd = 0.5)
## lines(x.new[hanford.ringold[,1]],z.new[hanford.ringold[,2]],lwd = 0.5)
## lines(x.new[x.vadose],z.new[z.vadose],lty = 2,lwd = 0.5)
## lines(c(current.east.interface,east.new),rep(current.east.level,2),col = "blue",lwd = 0.8)
## points(east.new,current.west.level,col = "black",pch = 16)
## text((east.new-10),current.east.level+2,
## paste("dh =",
## formatC(current.east.level-current.west.level,digits=2,format="e"),"m"),
## col="black")
## }
## )
## dev.off()
## jpeg.name = paste("figures/",case.name,formatC((itime),width = 3,flag = "0"),
## "_HCO3-",".jpg",sep = "")
## jpeg(jpeg.name,width = 12,height = 2.58,units = "in",res = 300, quality = 100)
## par(xpd=TRUE,mgp=c(2.,0.6,0),mar=c(4.1,4.1,2.1,2.1))
## filled.contour(x.new,z.new,hco3.conc[,,itime],
## ylim = c(bottom.new,top.new),
## xlim = c(west.new,east.new),
## zlim = range(hco3.conc),
## color = function(x)rev(heat.colors(x)),
## xlab = "Rotated east-west direction (m)",
## ylab = expression("HCO"[3]*"- (mg/L)"),
## main = bquote("HCO"[3]*"- : t =" ~.(obs.time[itime]) ~ "day" ),
## asp = 1.0,
## plot.axes = {
## axis(1,cex.axis = 1.)
## axis(2,cex.axis = 1.)
## lines(x.new[river.bank.x]-0.5*0.1,z.new[river.bank.z]-0.5*0.05,
## col = 'black',lwd = 0.5)
## lines(x.new[mud.hanford[,1]]-0.5*0.1,z.new[mud.hanford[,2]]-0.5*0.05,
## col = "black", lwd = 0.5)
## lines(x.new[hanford.ringold[,1]],z.new[hanford.ringold[,2]],lwd = 0.5)
## lines(x.new[x.vadose],z.new[z.vadose],lty = 2,lwd = 0.5)
## lines(c(current.east.interface,east.new),rep(current.east.level,2),col = "blue",lwd = 0.8)
## points(east.new,current.west.level,col = "black",pch = 16)
## text((east.new-10),current.east.level+2,
## paste("dh =",
## formatC(current.east.level-current.west.level,digits=2,format="e"),"m"),
## col="black")
## }
## )
## dev.off()
## jpeg.name = paste("figures/",case.name,formatC((itime),width = 3,flag = "0"),
## "_N2",".jpg",sep = "")
## jpeg(jpeg.name,width = 12,height = 2.58,units = "in",res = 300, quality = 100)
## par(xpd=TRUE,mgp=c(2.,0.6,0),mar=c(4.1,4.1,2.1,2.1))
## filled.contour(x.new,z.new,n2.conc[,,itime],
## ylim = c(bottom.new,top.new),
## xlim = c(west.new,east.new),
## zlim = range(n2.conc),
## color = function(x)rev(heat.colors(x)),
## xlab = "Rotated east-west direction (m)",
## ylab = expression("N"[2]*" (mg/L)"),
## main = bquote("N"[2]*" concentration : t =" ~.(obs.time[itime]) ~ "day" ),
## asp = 1.0,
## plot.axes = {
## axis(1,cex.axis = 1.)
## axis(2,cex.axis = 1.)
## lines(x.new[river.bank.x]-0.5*0.1,z.new[river.bank.z]-0.5*0.05,
## col = 'black',lwd = 0.5)
## lines(x.new[mud.hanford[,1]]-0.5*0.1,z.new[mud.hanford[,2]]-0.5*0.05,
## col = "black", lwd = 0.5)
## lines(x.new[hanford.ringold[,1]],z.new[hanford.ringold[,2]],lwd = 0.5)
## lines(x.new[x.vadose],z.new[z.vadose],lty = 2,lwd = 0.5)
## lines(c(current.east.interface,east.new),rep(current.east.level,2),col = "blue",lwd = 0.8)
## points(east.new,current.west.level,col = "black",pch = 16)
## text((east.new-10),current.east.level+2,
## paste("dh =",
## formatC(current.east.level-current.west.level,digits=2,format="e"),"m"),
## col="black")
## }
## )
## dev.off()
## jpeg.name = paste("figures/",case.name,formatC((itime),width = 3,flag = "0"),
## "_NO3-",".jpg",sep = "")
## jpeg(jpeg.name,width = 12,height = 2.58,units = "in",res = 300, quality = 100)
## par(xpd=TRUE,mgp=c(2.,0.6,0),mar=c(4.1,4.1,2.1,2.1))
## filled.contour(x.new,z.new,no3.conc[,,itime],
## ylim = c(bottom.new,top.new),
## xlim = c(west.new,east.new),
## zlim = range(no3.conc),
## color = function(x)rev(heat.colors(x)),
## xlab = "Rotated east-west direction (m)",
## ylab = expression("NO"[3]*"- (mg/L)"),
## main = bquote("NO"[3]*"- : t =" ~.(obs.time[itime]) ~ "day" ),
## asp = 1.0,
## plot.axes = {
## axis(1,cex.axis = 1.)
## axis(2,cex.axis = 1.)
## lines(x.new[river.bank.x]-0.5*0.1,z.new[river.bank.z]-0.5*0.05,
## col = 'black',lwd = 0.5)
## lines(x.new[mud.hanford[,1]]-0.5*0.1,z.new[mud.hanford[,2]]-0.5*0.05,
## col = "black", lwd = 0.5)
## lines(x.new[hanford.ringold[,1]],z.new[hanford.ringold[,2]],lwd = 0.5)
## lines(x.new[x.vadose],z.new[z.vadose],lty = 2,lwd = 0.5)
## lines(c(current.east.interface,east.new),rep(current.east.level,2),col = "blue",lwd = 0.8)
## points(east.new,current.west.level,col = "black",pch = 16)
## text((east.new-10),current.east.level+2,
## paste("dh =",
## formatC(current.east.level-current.west.level,digits=2,format="e"),"m"),
## col="black")
## }
## )
## dev.off()
## jpeg.name = paste("figures/",case.name,formatC((itime),width = 3,flag = "0"),
## "_O2",".jpg",sep = "")
## jpeg(jpeg.name,width = 12,height = 2.58,units = "in",res = 300, quality = 100)
## par(xpd=TRUE,mgp=c(2.,0.6,0),mar=c(4.1,4.1,2.1,2.1))
## filled.contour(x.new,z.new,o2.conc[,,itime],
## ylim = c(bottom.new,top.new),
## xlim = c(west.new,east.new),
## zlim = range(o2.conc),
## color = function(x)rev(heat.colors(x)),
## xlab = "Rotated east-west direction (m)",
## ylab = expression("O"[2]*" (mg/L)"),
## main = bquote("O"[2]*" concentration : t =" ~.(obs.time[itime]) ~ "day" ),
## asp = 1.0,
## plot.axes = {
## axis(1,cex.axis = 1.)
## axis(2,cex.axis = 1.)
## lines(x.new[river.bank.x]-0.5*0.1,z.new[river.bank.z]-0.5*0.05,
## col = 'black',lwd = 0.5)
## lines(x.new[mud.hanford[,1]]-0.5*0.1,z.new[mud.hanford[,2]]-0.5*0.05,
## col = "black", lwd = 0.5)
## lines(x.new[hanford.ringold[,1]],z.new[hanford.ringold[,2]],lwd = 0.5)
## lines(x.new[x.vadose],z.new[z.vadose],lty = 2,lwd = 0.5)
## lines(c(current.east.interface,east.new),rep(current.east.level,2),col = "blue",lwd = 0.8)
## points(east.new,current.west.level,col = "black",pch = 16)
## text((east.new-10),current.east.level+2,
## paste("dh =",
## formatC(current.east.level-current.west.level,digits=2,format="e"),"m"),
## col="black")
## }
## )
## dev.off()
jpeg.name = paste("figures/",case.name,".",formatC((itime),width = 3,flag = "0"),
"_Tracer",".jpg",sep = "")
jpeg(jpeg.name,width = 12,height = 2.58,units = "in",res = 300, quality = 100)
par(xpd=TRUE,mgp=c(2.,0.6,0),mar=c(4.1,4.1,2.1,2.1))
filled.contour(x.new,z.new,tracer.conc[,,itime],
ylim = c(bottom.new,top.new),
xlim = c(west.new,east.new),
zlim = range(tracer.conc),
color = function(x)rev(heat.colors(x)),
xlab = "Rotated east-west direction (m)",
ylab = expression("Tracer (mg/l)"),
main = bquote("Tracer : t =" ~.(obs.time[itime]) ~ "hour" ),
asp = 1.0,
plot.axes = {
axis(1,cex.axis = 1.)
axis(2,cex.axis = 1.)
lines(x.new[river.bank.x]-0.5*0.1,z.new[river.bank.z]-0.5*0.05,
col = 'black',lwd = 0.5)
lines(x.new[mud.hanford[,1]]-0.5*0.1,z.new[mud.hanford[,2]]-0.5*0.05,
col = "black", lwd = 0.5)
lines(x.new[hanford.ringold[,1]],z.new[hanford.ringold[,2]],lwd = 0.5)
lines(x.new[x.vadose],z.new[z.vadose],lty = 2,lwd = 0.5)
lines(c(current.east.interface,east.new),rep(current.east.level,2),col = "blue",lwd = 0.8)
points(east.new,current.west.level,col = "black",pch = 16)
text((east.new-10),current.east.level+2,
paste("dh =",
formatC(current.east.level-current.west.level,digits=2,format="e"),"m"),
col="black")
}
)
dev.off()
}
|
16bfa68e757cbac33ddaeadff5505489e2b24379 | e55359349183a5c8547f624c628e59f8d378f618 | /SLX-12998_mmhs/peaks/SLX-12998.D707_D507.HH772BBXX.s_2.r_1.fq.gz_model.r | 112de3c8d12f1200e265370cee0440c18c75f4c4 | [] | no_license | andrewholding/BrundleDevelopment | 701d011546cdce0c3033a9a4f91357fa629e609a | 46034028558886f0c442f329ded61969b12c023d | refs/heads/master | 2021-01-17T19:15:48.543303 | 2018-02-20T16:28:53 | 2018-02-20T16:28:53 | 95,553,226 | 5 | 0 | null | null | null | null | UTF-8 | R | false | false | 78,110 | r | SLX-12998.D707_D507.HH772BBXX.s_2.r_1.fq.gz_model.r | # R script for Peak Model
# -- generated by MACS
p <- 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m <- 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ycorr <- 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altd <- c(191)
x <- seq.int((length(p)-1)/2*-1,(length(p)-1)/2)
pdf('SLX-12998.D707_D507.HH772BBXX.s_2.r_1.fq.gz_model.pdf',height=6,width=6)
plot(x,p,type='l',col=c('red'),main='Peak Model',xlab='Distance to the middle',ylab='Percentage')
lines(x,m,col=c('blue'))
legend('topleft',c('forward tags','reverse tags'),lty=c(1,1,1),col=c('red','blue'))
plot(xcorr,ycorr,type='l',col=c('black'),main='Cross-Correlation',xlab='Lag between + and - tags',ylab='Correlation')
abline(v=altd,lty=2,col=c('red'))
legend('topleft','alternative lag(s)',lty=2,col='red')
legend('right','alt lag(s) : 191',bty='n')
dev.off()
|
640746c63f7be8aa735cf18230c88536ef222b54 | 5476725a7a3ea2c8e4396edd59e6d8d565f8d28d | /0.Tidy_data_boss.R | f80c205c1b674100437d14a5d72e55fdaf5b5678 | [] | no_license | UWA-Marine-Ecology-Group-projects/GB_Habitat_Classification | 49ecff1424abb1654f88ba5855b34d7528d8e702 | 9392d5e89ea60a440c6f5831ab571d594a09f077 | refs/heads/master | 2023-07-09T18:10:31.794091 | 2021-08-19T04:40:07 | 2021-08-19T04:40:07 | 375,555,452 | 0 | 0 | null | 2021-06-10T03:15:21 | 2021-06-10T03:15:20 | null | UTF-8 | R | false | false | 5,670 | r | 0.Tidy_data_boss.R |
###
# Project: Geographe Bay
# Data: BOSS
# Task: Cleaning boss habitat data and adding spatial covariates
# author: Kingsley Griffin
# date: Aug 2021
# modified: Anita Giraldo 13/08/2021 # just added lines for directories and to save xy
##
# library(reshape2)
library(dplyr)
# library(googlesheets4)
library(raster)
library(sp)
# Clear memory ----
rm(list=ls())
### Set directories ----
w.dir<- dirname(rstudioapi::getActiveDocumentContext()$path)
d.dir <- paste(w.dir, "data", sep ='/')
dt.dir <- paste(w.dir, "data/tidy", sep ='/')
dr.dir <- paste(w.dir, "data/raw", sep ='/')
setwd(w.dir)
# get metadata from survey tracker googlesheet
# bossd <- as.data.frame(read_sheet("https://docs.google.com/spreadsheets/d/1ZfW-XJKP0BmY2UXPNquTxnO5-iHnG9Kw3UuJbALCcrs/edit#gid=814068592",
# sheet = "2021-03_Geographe_BOSS"))
# saveRDS(bossd, 'data/2103_Geographe_BOSS_metadata.rds')
# read in data
bosmet <- readRDS('data/2103_Geographe_BOSS_metadata.rds') # boss metadata
habdat <- read.table('data/raw/2021-03_Geographe_BOSS_Habitat_Dot Point Measurements.txt',
skip = 5, sep = "\t") # habitat annotations
downdat <- read.table('data/raw/2021-03_Geographe_BOSS_Downwards_Habitat_Dot Point Measurements.txt',
skip = 5, sep = "\t") # downwards habitat annotations
# clean all and merge to combine with metadata
bosmet <- bosmet[, colnames(bosmet) %in% c("Date", "Time.bottom", "Latitude",
"Longitude", "Site", "Sample",
"Location", "Status", "Depth",
"Type")] # only cols of interest
summary(habdat)
habdat <- habdat[ , c(1, 4, 5, 18:21, 23, 26)] # omit bare columns
colnames(habdat) <- c("Filename", "Image row", "Image col", "Broad",
"Morphology", "Type", "FOV", "CODE", "Radius") # fix colnames
habdat$Site <- gsub(".jpg", "", habdat$Filename)
downdat <- downdat[ , c(1, 4, 5, 18:21, 23, 26)] # omit bare columns
colnames(downdat) <- c("Filename", "Image row", "Image col", "Broad",
"Morphology", "Type", "FOV", "CODE", "Radius") # fix colnames
downdat$Site <- gsub(".jpg", "", downdat$Filename)
allhab <- merge(bosmet, habdat,by.x = "Sample", by.y = "Site")
allhab$pa <- c(1)
head(allhab)
alldwn <- merge(bosmet, downdat,by.x = "Sample", by.y = "Site")
alldwn$pa <- c(1)
head(alldwn)
# save
#write.csv(alldwn, paste(dr.dir, "BOSS_downwards.xy.csv", sep ='/'))
# convert to wide percent cover data
allhabw <- dcast(allhab, Sample + Latitude + Longitude + Depth ~ Broad,
value.var = "pa", fun.aggregate = sum, drop = TRUE)
allhabw$totpts <- rowSums(allhabw[, 6:14]) - (allhabw$Unknown + allhabw$Var.5)
head(allhabw)
allhabw_pc <- allhabw
allhabw_pc[6:14] <- sapply(allhabw_pc[, 6:14],
FUN = function(x){
round((x/allhabw_pc$totpts)*100, 2)}) # calculate percent cover
alldwnw <- dcast(alldwn, Sample + Latitude + Longitude + Depth ~ Broad,
value.var = "pa", fun.aggregate = sum, drop = TRUE)
alldwnw$totpts <- rowSums(alldwnw[, 5:12]) - (alldwnw$Unscorable + alldwnw$Var.5)
alldwnw_pc <- alldwnw
alldwnw_pc[6:11] <- sapply(alldwnw_pc[, 6:11],
FUN = function(x){
round((x/alldwnw_pc$totpts)*100, 2)}) # calculate percent cover
# bring in bathy raster and check alignment
b2 <- raster('spatial_data/GBmultib_lidar_CMR.tif')
allhab_sp <- SpatialPointsDataFrame(coords = cbind(allhabw_pc$Longitude,
allhabw_pc$Latitude),
data = allhabw_pc)
alldwn_sp <- SpatialPointsDataFrame(coords = cbind(alldwnw_pc$Longitude,
alldwnw_pc$Latitude),
data = alldwnw_pc)
plot(b2)
plot(allhab_sp, add = T)
# calculate bathy derivatives
slope4 <- terrain(b2, "slope", unit = "degrees", neighbors = 4)
slope8 <- terrain(b2, "slope", unit = "degrees", neighbors = 8)
aspect4 <- terrain(b2, "aspect", unit = "degrees", neighbors = 4)
aspect8 <- terrain(b2, "aspect", unit = "degrees", neighbors = 8)
tpi <- terrain(b2, "TPI")
tri <- terrain(b2, "TRI")
rough <- terrain(b2, "roughness")
flowdir <- terrain(b2, "flowdir")
preds <- stack(b2, slope4, slope8, aspect4, aspect8,
tpi, tri, roughness, flowdir)
names(preds)[c(1:4, 8)] <- c("slope4", "slope8", "aspect4",
"aspect8", "roughness")
namesp <- names(preds)
# extract predictor data
allhab <- cbind(allhabw_pc, extract(preds, allhab_sp))
head(allhab)
summary(allhab)
alldwn <- cbind(alldwnw_pc, extract(preds, alldwn_sp))
head(alldwn)
summary(alldwn)
# exclude sites outside of bathy area
allhab <- na.omit(allhab)
alldwn <- na.omit(alldwn)
# # if we want presence/absence at the image scale
# allhab[, 6:14] <- sapply(allhab[, 6:14],
# FUN = function(x){ifelse(x > 1, 1, 0)}) # convert to binomial
# alldwn[, 6:11] <- sapply(alldwn[, 6:11],
# FUN = function(x){ifelse(x > 1, 1, 0)}) # convert to binomial
#
saveRDS(allhab, "data/tidy/2103_Geographe_BOSS_habitat.rds")
saveRDS(alldwn, "data/tidy/2103_Geographe_BOSS_downwardshabitat.rds")
|
fbd95b008caa1e107365a3dbd9b0ad424373c36b | a0ceb8a810553581850def0d17638c3fd7003895 | /scripts/scripts_analysis/all_merged_analysis/2-downstream_LDA.R | 662d5955b5d5eec1d9528c92517919480d5670ab | [] | no_license | jakeyeung/sortchicAllScripts | 9e624762ca07c40d23e16dbd793ef9569c962473 | ecf27415e4e92680488b6f228c813467617e7ee5 | refs/heads/master | 2023-04-15T22:48:52.272410 | 2022-10-24T10:45:24 | 2022-10-24T10:45:24 | 556,698,796 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,612 | r | 2-downstream_LDA.R | # Jake Yeung
# Date of Creation: 2019-12-07
# File: ~/projects/scchic/scripts/scripts_analysis/all_merged_analysis/2-downstream_LDA.R
# Downstream LDA
rm(list=ls())
library(dplyr)
library(tidyr)
library(ggplot2)
library(data.table)
library(Matrix)
library(hash)
library(igraph)
library(umap)
library(scchicFuncs)
# Load -------------------------------------------------------------------
# all marks
jmark <- "H3K27me3"
inf <- paste0("/Users/yeung/data/scchic/from_cluster/ldaAnalysisBins_B6BM_All_allmarks_mergedtagged_dedupfixed_redo/lda_outputs.B6BM_AllMerged_", jmark, ".TAcutoff_0.5.countscutoff_1000.binfilt_cellfilt.2019-12-05.K-30.binarize.TRUE/ldaOut.B6BM_AllMerged_", jmark, ".TAcutoff_0.5.countscutoff_1000.binfilt_cellfilt.2019-12-05.K-30.Robj")
assertthat::assert_that(file.exists(inf))
load(inf, v=T)
tm.result <- posterior(out.lda)
topics.mat <- tm.result$topics
jsettings <- umap.defaults
jsettings$n_neighbors <- 30
jsettings$min_dist <- 0.1
jsettings$random_state <- 123
umap.out <- umap(topics.mat, config = jsettings)
dat.umap.long <- data.frame(cell = rownames(umap.out$layout), umap1 = umap.out$layout[, 1], umap2 = umap.out$layout[, 2], stringsAsFactors = FALSE) %>%
rowwise() %>%
mutate(experi = strsplit(cell, split = "-")[[1]][[5]])
# ggplot(dat.umap.long, aes(x = umap1, y = umap2, color = experi)) + geom_point() + theme_bw() + theme(aspect.ratio=1, panel.grid.major = element_blank(), panel.grid.minor = element_blank()) +
# theme_bw() + theme(aspect.ratio=1, panel.grid.major = element_blank(), panel.grid.minor = element_blank())
ggplot(dat.umap.long, aes(x = umap1, y = umap2, color = experi)) + geom_point() + theme_bw() + theme(aspect.ratio=1, panel.grid.major = element_blank(), panel.grid.minor = element_blank()) +
theme_bw() + theme(aspect.ratio=1, panel.grid.major = element_blank(), panel.grid.minor = element_blank()) +
facet_wrap(~experi)
# get variance
jchromos <- paste("chr", c(seq(19), "X", "Y"), sep = "")
dat.impute.log <- log2(t(tm.result$topics %*% tm.result$terms))
dat.var <- CalculateVarAll(dat.impute.log, jchromos)
dat.merge <- left_join(dat.umap.long, dat.var)
ggplot(dat.merge, aes(x = umap1, y = umap2, color = cell.var.within.sum.norm)) + geom_point() + theme_bw() + theme(aspect.ratio=1, panel.grid.major = element_blank(), panel.grid.minor = element_blank()) +
theme_bw() + theme(aspect.ratio=1, panel.grid.major = element_blank(), panel.grid.minor = element_blank()) +
scale_color_viridis_c(direction = -1) +
facet_wrap(~experi)
# Find topics ------------------------------------------------------------
|
f5619a9af1828da5d92f5c9be7e5830779c0ba97 | ab1730e13eb49478aa0cc443d40c68921cbe1662 | /man/StockChanges.Rd | 0dfd309de691316adc7632356f583f9234a676f5 | [] | no_license | cran/Lock5Data | 688fa73667eacea4f4a325763907bba30f55c030 | 770f5fd2b8956dbb0c822133223f9c811561f130 | refs/heads/master | 2021-08-02T06:23:39.102769 | 2021-07-22T21:40:10 | 2021-07-22T21:40:10 | 17,680,339 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 727 | rd | StockChanges.Rd | \name{StockChanges}
\alias{StockChanges}
\docType{data}
\title{
Stock Changes
}
\description{
Stock price change for a sample of stocks from the S&P 500 (August 2-6, 2010)
}
%\usage{data(StockChanges)}
\format{
A dataset with 50 observations on the following variable.
\tabular{rl}{
\code{SPChange} \tab {Change in stock price (in dollars)} \cr
}
}
\details{
A random sample of 50 companies from Standard & Poor's index of 500 companies was selected. The change in the price of the stock (in dollars)
over the 5-day period from August 2 - 6, 2010 was recorded for each company in the sample.
}
\source{
Data obtained from \emph{http://money.cnn.com/data/markets/sandp/}
}
\keyword{datasets}
|
4afc8950caa21de07e084c5ae739b2ed7cdb5525 | 261e86602bc68e647ce1c2335694bb186a18952d | /man/tor_ppo.Rd | 5775180585ca0c2d4931ba5d1519e13114fb839a | [] | no_license | vullioud/torpor | 9b600eebe41f3904f72a7b6c7c2c9bbc7743cb1c | f7fbf134e1ae7fd7c9b6d2ee31c803e79d59c91b | refs/heads/master | 2023-06-24T11:42:09.732894 | 2023-06-16T09:01:00 | 2023-06-16T09:01:00 | 139,414,327 | 0 | 0 | null | 2023-06-16T08:36:40 | 2018-07-02T08:33:03 | R | UTF-8 | R | false | true | 856 | rd | tor_ppo.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/tor_summarise.R
\name{tor_ppo}
\alias{tor_ppo}
\title{Check priors/posteriors overlap}
\usage{
tor_ppo(tor_obj)
}
\arguments{
\item{tor_obj}{a fitted model from \code{\link[=tor_fit]{tor_fit()}}}
}
\value{
a data.frame
}
\description{
\code{\link[=tor_ppo]{tor_ppo()}} generates prior/posterior overlap values for Tlc,
Tt and Betat. Values larger than 0.3 should lead to the conclusion that conforming
torpor, regulated torpor or thermoregulation respectively could not be modeled
with the data provided (Fasel et al. (in prep)). \code{\link[=tor_ppo]{tor_ppo()}} can be used independently but is also used internally in
\code{\link[=tor_summarise]{tor_summarise()}}.
}
\seealso{
Other summary:
\code{\link{get_parameters}()},
\code{\link{tor_summarise}()}
}
\concept{summary}
|
8e6131efce72582aeb1c50f20a544b14c02754a7 | 859a2bdab8ba9943fffde77a0a930ba877c80fd9 | /R/create_background_panel_instance.R | a0f5c0fe6cb9957b246dd635c84328b04cb35a56 | [
"MIT"
] | permissive | alkodsi/ctDNAtools | e0ed01de718d3239d58f1bfd312d2d91df9f0f0d | 30bab89b85951282d1bbb05c029fc333a139e044 | refs/heads/master | 2022-03-20T11:47:06.481129 | 2022-02-20T11:25:48 | 2022-02-20T11:25:48 | 208,288,617 | 30 | 9 | null | null | null | null | UTF-8 | R | false | false | 4,027 | r | create_background_panel_instance.R |
#' Creates a background panel instance from a bam file (e.g. healthy samples)
#'
#' This function scans the targets regions in one bam file, and reports the number of reference, non-reference reads for each loci
#' in addition to the non-reference (VAF) allele frequency. Loci with VAF higher than vaf_threshold are masked with NA. This function is
#' used internally by create_background_panel
#' @param bam A character specifying the path to bam file.
#' @param targets The targets data frame must have the columns chr, start and end.
#' @param reference The reference genome as BSgenome object.
#' @param vaf_threshold Loci with the fraction of non-reference reads above this value are masked with NA.
#' @param tag The RG tag in the bam file. Empty string defaults to using the whole bam.
#' @param min_base_quality The minimum base quality to count a read for a loci.
#' @param max_depth Maximum depth for the pileup
#' @param min_mapq The minimum mapping quality to count a read for a loci
#' @param substitution_specific logical, whether to have the loci by substitutions.
#' @return A named list having depth, alt and vaf data frames. Each has the same order of loci in rows and the input sample in columns.
#' @keywords internal
create_background_panel_instance <- function(bam, targets, reference, vaf_threshold = 0.05, tag = "",
min_base_quality = 20, max_depth = 1e+05, min_mapq = 30, substitution_specific = TRUE) {
gr <- GenomicRanges::reduce(GenomicRanges::GRanges(targets$chr, IRanges::IRanges(targets$start, targets$end)))
if (tag == "") {
sbp <- Rsamtools::ScanBamParam(which = gr)
} else {
sbp <- Rsamtools::ScanBamParam(which = gr, tagFilter = list(RG = tag))
}
pileup_param <- Rsamtools::PileupParam(
max_depth = max_depth, min_base_quality = min_base_quality,
min_mapq = min_mapq, distinguish_strands = FALSE, include_deletions = FALSE, include_insertions = FALSE
)
p <- Rsamtools::pileup(bam, scanBamParam = sbp, pileupParam = pileup_param) %>%
tidyr::pivot_wider(
names_from = .data$nucleotide, values_from = .data$count,
values_fill = list(count = 0)
) %>%
as.data.frame() %>%
dplyr::mutate(ref = as.character(BSgenome::getSeq(
reference,
GenomicRanges::GRanges(.data$seqnames, IRanges::IRanges(.data$pos, .data$pos))))
) %>%
dplyr::mutate(depth = .data$A + .data$C + .data$G + .data$T)
if (substitution_specific) {
p_ann <- p %>%
dplyr::mutate(
VAFC = .data$C / .data$depth,
VAFT = .data$T / .data$depth,
VAFG = .data$G / .data$depth,
VAFA = .data$A / .data$depth
) %>%
tidyr::pivot_longer(names_to = "alt", cols = dplyr::starts_with("VAF"), values_to = "vaf") %>%
dplyr::mutate(alt = gsub("VAF", "", .data$alt)) %>%
dplyr::filter(.data$ref != .data$alt) %>%
dplyr::mutate(
Locus = paste(.data$seqnames, .data$pos, .data$ref, .data$alt, sep = "_"),
nonRefCount = .data$depth * .data$vaf
) %>%
dplyr::select(.data$Locus, .data$depth, .data$nonRefCount, .data$vaf)
} else {
p_ann <- dplyr::mutate(p,
refCount = purrr::map2_dbl(c(1:nrow(p)), p$ref, ~ p[.x, .y]),
nonRefCount = .data$depth - .data$refCount, vaf = .data$nonRefCount / .data$depth
) %>%
dplyr::mutate(
nonRefCount = ifelse(.data$vaf >= vaf_threshold, NA, .data$nonRefCount),
depth = ifelse(.data$vaf >= vaf_threshold, NA, .data$depth),
vaf = ifelse(.data$vaf >= vaf_threshold, NA, .data$vaf),
Locus = paste(.data$seqnames, .data$pos, sep = "_")
) %>%
dplyr::select(.data$Locus, .data$depth, .data$nonRefCount, .data$vaf)
}
out <- list(
depth = data.frame(Locus = p_ann$Locus, depth = p_ann$depth, stringsAsFactors = FALSE),
alt = data.frame(Locus = p_ann$Locus, alt = p_ann$nonRefCount, stringsAsFactors = FALSE),
vaf = data.frame(Locus = p_ann$Locus, vaf = p_ann$vaf, stringsAsFactors = FALSE)
)
return(out)
}
|
467d3504cf26b28bfe72299d5b153362fa0650db | 0c6032eb0f9711b6de7ef08e7349197c620952f0 | /STabundance/man/d_logP_Z.Rd | 16592368ead9f2c8e0de51232d706f9635a96569 | [] | no_license | pconn/STabundance | 75c3b8efc27112b086711442a854ce8d27a914e3 | ef6ccccf0fae635d7a9f6b1a49d3c1bc34dd6f44 | refs/heads/master | 2021-01-01T05:35:05.392653 | 2016-01-29T21:45:07 | 2016-01-29T21:45:07 | 15,171,892 | 3 | 1 | null | 2016-01-29T21:45:07 | 2013-12-13T19:19:16 | TeX | UTF-8 | R | false | false | 923 | rd | d_logP_Z.Rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/util_funcs.R
\name{d_logP_Z}
\alias{d_logP_Z}
\title{Calculate partial derivative vector for for Langevin Z MH updates (for OPRS model)
(Assuming function is called separately for each multinomial update)}
\usage{
d_logP_Z(Z, Log.offset, Counts, Mu, tau)
}
\arguments{
\item{Z}{Current values of Z}
\item{Log.offset}{The log of offset values (e.g. proportion of area surveyed)}
\item{Counts}{Animal counts for current time step being updated}
\item{Mu}{Expected value for Omega}
\item{tau}{precision for exchangable errors}
}
\value{
Vector of partial derivates evaluated at Omega
}
\description{
Calculate partial derivative vector for for Langevin Z MH updates (for OPRS model)
(Assuming function is called separately for each multinomial update)
}
\author{
Paul Conn \email{paul.conn@noaa.gov}
}
\keyword{expit}
\keyword{logit,}
|
e386af4957dccd2409a42df3bc5510c567c8d077 | aad2ba18223ae709bd1ae8546a8cbf1cc8e1af4a | /scripts/functions/cache_RDS.R | bf00914d9e5a5e2c2c44d8874ed9dd2f06497cdc | [
"MIT"
] | permissive | rubysaltbush/pollination-macroevolution | b20c3d43d4db18ef61e49a02b323705b0db3750f | 1693b63a60f0d4fdb27e9fe2627af83b2239950f | refs/heads/main | 2023-06-25T01:37:12.459831 | 2023-06-20T00:57:55 | 2023-06-20T00:57:55 | 551,184,048 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 412 | r | cache_RDS.R | # function to cache pre-prepared R data. If RDS already in cache will read data
# from cache, if not will run defined script to produce and then cache data
cache_RDS <- function(path, fetch_function, read_function = readRDS,
save_function = saveRDS) {
if (file.exists(path)) {
read_function(path)
} else {
data <- fetch_function()
save_function(data, path)
data
}
}
|
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