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## Download the zip file and store in a temporary file,
## unzip it and read only the relvant data
temp <- tempfile()
download.file('https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip',
destfile = temp)
data <- read.table(unz(temp, 'household_power_consumption.txt'),
skip = 66637, nrow = 2880, sep = ';')
unlink(temp)
names(data) <- c('Date', 'Time', 'Global_active_power','Global_reactive_power',
'Voltage', 'Global_intensity', 'Sub_metering_1', 'Sub_metering_2',
'Sub_metering_3')
png(filename = 'plot1.png', width = 480, height = 480)
hist(data$Global_active_power, col = 'red',
xlab = 'Global Active Power (kilowatts)', main = 'Global Active Power')
dev.off()
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|
0dfef1c87eb94d2965ca002a765e8fc1e74a2dfd
|
28750d2d90cb45173a956cc8542ebda0d076e6dd
|
/Mincome/Codes/Regressions/eventhistory_final.R
|
e8b4c1adb7aeaa3b0eb48bab28b37033b55bd709
|
[] |
no_license
|
DrSnowtree/MINCOME
|
994e1060e84aba0d5944b8ead720b4eae8512534
|
aa8f41cae82b995e3290e56a6487dc9c71ca04e9
|
refs/heads/master
| 2021-07-25T20:26:21.767288
| 2020-09-22T13:50:57
| 2020-09-22T13:50:57
| 220,972,475
| 0
| 0
| null | 2019-11-19T13:51:34
| 2019-11-11T12:04:15
| null |
UTF-8
|
R
| false
| false
| 2,751
|
r
|
eventhistory_final.R
|
setwd("W:/WU/Projekte/mincome/Mincome/Data")
library("lme4")
library("stargazer")
library("ggstance")
library("jtools")
library("coefplot")
library(dplyr)
#remove the 5 women not included in the baseline analysis
bpid <- basepay[, 1]
bpid <- as.data.frame(bpid)
bpid <- bpid %>% rename(FAMNUM=bpid)
# only with women born after 1934 and never been divorced before
datapp2 <- merge(datapp2, bpid, by = "FAMNUM", all = F)
datapp2$treated <- 1
datapp2$treated[datapp2$plan == 9] <- 0
m1 <- glmer(formula = birth ~ treated*experiment + factor(age)
+ (1|OID) + factor(year) + factor(j) + married,
data = datapp2)
stargazer(m1)
m2 <- lmer(formula = birth ~ plan_1*experiment + plan_2*experiment
+ plan_3*experiment + plan_4*experiment + plan_5*experiment
+ plan_7*experiment + plan_8*experiment + factor(age)
+ (1|OID) + factor(year) + factor(j) + married,
data = datapp2)
stargazer(m2)
saveRDS(datapp2, "datapp2.rds")
##look only at married couples
bp2 <- basepay[which(!is.na(basepay$MAGE)), ]
bp2 <- bp2[which(!is.na(bp2$yrschm)), ]
bpid2 <- bp2[, 1]
bpid2 <- as.data.frame(bpid2)
bpid2 <- bpid2 %>% rename(FAMNUM=bpid2)
datapp4 <- merge(datapp2, bpid2, by = "FAMNUM", all = F)
datapp4$treated <- 1
datapp4$treated[datapp4$plan == 9] <- 0
m3 <- glmer(formula = birth ~ treated*experiment + factor(age)
+ (1|OID) + factor(year) + factor(j) + factor(married),
data = datapp4)
stargazer(m3)
m4 <- lmer(formula = birth ~ plan_1*experiment + plan_2*experiment
+ plan_3*experiment + plan_4*experiment + plan_5*experiment
+ plan_7*experiment + plan_8*experiment + factor(age)
+ (1|OID) + factor(year) + factor(j) + factor(married),
data = datapp4)
stargazer(m4)
saveRDS(datapp4, "datapp4.rds")
##third set of people
bp3 <- bp2[which(!is.na(bp2$fmotheduc)), ]
bpid3 <- bp3[, 1]
bpid3 <- as.data.frame(bpid3)
bpid3 <- bpid3 %>% rename(FAMNUM=bpid3)
datapp5 <- merge(datapp2, bpid3, by = "FAMNUM", all = F)
saveRDS(datapp5, "datapp5.rds")
m5 <- glmer(formula = birth ~ treated*experiment + factor(age)
+ (1|OID) + factor(year) + factor(j) + factor(married),
data = datapp5)
stargazer(m5)
m6 <- lmer(formula = birth ~ plan_1*experiment + plan_2*experiment
+ plan_3*experiment + plan_4*experiment + plan_5*experiment
+ plan_7*experiment + plan_8*experiment + factor(age)
+ (1|OID) + factor(year) + factor(j) + factor(married),
data = datapp5)
stargazer(m6)
stargazer(m1, m2, m3, m4, m5, m6)
|
aa5b5a6a69f34eaf37838c1cdb349b2910ef5120
|
178087fd666375abeb10fc4f9f23230d2438dc21
|
/R/peel.one.r
|
345254e60df24689601dfd46b98d95338c886859
|
[] |
no_license
|
cran/sdtoolkit
|
736adf447b9c59c9a795f7f4735d34985dfac0a2
|
8e9767f73b1266de8edf37744c7325a7e36c6497
|
refs/heads/master
| 2020-05-29T13:14:47.569985
| 2014-02-16T00:00:00
| 2014-02-16T00:00:00
| 17,699,521
| 3
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 6,261
|
r
|
peel.one.r
|
#peel.one -
#the main change I made was adding a new quantile function pquantile, that handles
#things more appropriately for PRIMs purposes (see that function).
`peel.one` <-
function(x, y, box, peel.alpha, mass.min, threshold, d, n,
peel_crit)
{
box.new <- box
mass <- length(y)/n
peel.alpha <- max(peel.alpha, 1/nrow(x)) #Added 2009-10-25
if (is.vector(x)) return(NULL)
y.mean <- mean(y)
#Matrix to store the means first row is for boxes restricted from below,
#second row is for boxes restricted from above:
y.mean.peel <- matrix(0, nrow=2, ncol=d)
#Equivalent for boxes that get peeled off, only for use in alternate peeling
#criteria:
y.mean.peeled <- matrix(0, nrow=2, ncol=d)
#Matrix to store volume of resulting boxes - same indexing as y.mean.peel
box.vol.peel <- matrix(NA, nrow=2, ncol=d)
#Matrix to store volume of the "little b" boxes that get peeled off, only for
#use in alternate peeling criteria:
box.vol.peeled <- matrix(0, nrow=2, ncol=d)
# box.supp.peeled <- matrix(NA, nrow=2, ncol=d)
box.supp.peeled <- matrix(0, nrow=2, ncol=d)
ranges <- apply(x,2,range)
spans <- ranges[2,]-ranges[1,]
# if(length(which(spans!=0))==0){
# #browser()
# stop("Out of variables with any variation.") #would be better to make it
# } #gracefully exit and end the
#peeling process...
#for (j in which(spans!=0)){
#print("peeled")
for (j in 1:d){
# print(j)
if(TRUE){
# if(j%in%which(spans!=0)){
box.min.new <- pquantile(x[,j], peel.alpha, ptype="lowend")
box.max.new <- pquantile(x[,j], 1-peel.alpha, ptype="highend")
#Possibly switch to this as faster version in the future, though seems to
#cause problems for some reason, even with type=2)
# box.min.new <- quantile(x[,j], peel.alpha, type=2)
# box.max.new <- quantile(x[,j], 1-peel.alpha, type=2)
} else {
box.min.new <- min(x[,j])
box.max.new <- max(x[,j])
if(box.min.new!=box.max.new) stop("what?")
}
y.mean.peel[1,j] <- mean(y[x[,j] >= box.min.new])
y.mean.peel[2,j] <- mean(y[x[,j] <= box.max.new])
y.mean.peeled[1,j] <- mean(y[x[,j] < box.min.new])
y.mean.peeled[2,j] <- mean(y[x[,j] > box.max.new])
box.supp.peeled[1,j] <- sum(x[,j] < box.min.new)
box.supp.peeled[2,j] <- sum(x[,j] > box.min.new)
#Initialize boxes that will be subset to equal the current full box:
#These are the boxes will hold the actual new box of interest:;
#(1 and 2) indicate restriction from below and above, respectively)
box.temp1 <- box
box.temp2 <- box
#These are the boxes that hold the boxes that get peeled away:
box.temp1.peeled <- box
box.temp2.peeled <- box
if (!is.na(box.min.new)){
box.temp1[1,j] <- box.min.new #new box
box.temp1.peeled[2,j] <- box.min.new #box to get peeled away
}
if (!is.na(box.max.new)){
box.temp2[2,j] <- box.max.new #new box
box.temp2.peeled[1,j] <- box.max.new #box to get peeled away
}
# box.vol.peel[1,j] <- vol.box(box.temp1)
# box.vol.peel[2,j] <- vol.box(box.temp2)
# box.vol.peeled[1,j] <- vol.box(box.temp1.peeled)
# box.vol.peeled[2,j] <- vol.box(box.temp2.peeled)
box.vol.peel[1,j] <- vol.box(box.temp1, x)
box.vol.peel[2,j] <- vol.box(box.temp2, x)
box.vol.peeled[1,j] <- vol.box(box.temp1.peeled, x)
box.vol.peeled[2,j] <- vol.box(box.temp2.peeled, x)
}
#Some code for starting to implement alternative peeling criteria --
#specifically Eq 14.5 of F&F 1999 paper.
toprint <- y.mean.peel
#colnames(toprint) <- colnames(x)
#print(toprint)
if(peel_crit==1){
y.mean.peel.max.ind <- which(y.mean.peel==max(y.mean.peel, na.rm=TRUE), arr.ind=TRUE)
#print(y.mean.peel.max.ind)
} else if(peel_crit==2){
#Final FF form: evaled.peel.crit2 <- y.mean.peel - y.mean.peeled
#How it is conceived, and original form:
evaled.peel.crit2 <- (y.mean.peel - y.mean)/box.supp.peeled
# if(all(is.na(evaled.peel.crit2))) evaled.peel.crit2[] <- 1
y.mean.peel.max.ind <- which(evaled.peel.crit2==max(evaled.peel.crit2, na.rm=TRUE), arr.ind=TRUE)
} else if(peel_crit==3){
evaled.peel.crit3 <- (y.mean.peel - y.mean)*
(nrow(x) - box.supp.peeled)/box.supp.peeled
# if(all(is.na(evaled.peel.crit3))) evaled.peel.crit3[] <- 1
y.mean.peel.max.ind <- which(evaled.peel.crit3==max(evaled.peel.crit3, na.rm=TRUE), arr.ind=TRUE)
} else {
stop("There is no implemented peeling criteria associated with the value
that was passed to the peel_crit argument")
}
## break ties by choosing box with largest volume
nrr <- nrow(y.mean.peel.max.ind)
if (nrr > 1){
box.vol.peel2 <- rep(0, nrr)
for (j in 1:nrr)
box.vol.peel2[j] <- box.vol.peel[y.mean.peel.max.ind[j,1],
y.mean.peel.max.ind[j,2]]
# print(box.vol.peel2)
row.ind <- which(max(box.vol.peel2)==box.vol.peel2)[1] #added 5/28/08 the [1] to deal with ties
# print(row.ind)
} else {
row.ind <- 1
}
# print("gotabove")
y.mean.peel.max.ind <- y.mean.peel.max.ind[row.ind,]
# print("gotbelow")
## peel along dimension j.max
j.max <- y.mean.peel.max.ind[2] #why is this [2]? Asked 2011-10-13
if (y.mean.peel.max.ind[1]==1){ ## peel lower
box.new[1,j.max] <- pquantile(x[,j.max], peel.alpha, ptype="lowend")
x.index <- x[,j.max] >= box.new[1,j.max]
} else if (y.mean.peel.max.ind[1]==2) { ## peel upper
box.new[2,j.max] <- pquantile(x[,j.max], 1-peel.alpha, ptype="highend")
x.index <- x[,j.max] <= box.new[2,j.max]
}
x.new <- x[x.index,]
y.new <- y[x.index]
mass.new <- length(y.new)/n
y.mean.new <- mean(y.new)
## if min. y mean and min. mass conditions are still true, update
## o/w return NULL
# cat("mm:",mass.min, mass.new,mass,y.mean,y.mean.new,"\n") #diagnostic
if ((y.mean.new >= threshold) & (mass.new >= mass.min) & (mass.new < mass) & (y.mean < 1))
return(list(x=x.new, y=y.new, y.mean=y.mean.new, box=box.new,
mass=mass.new))
}
|
c3592310caf66024a1754d1e55fc97883127b9b9
|
6a192ede793c1aa1c63c5dc388d87f704d4c8f02
|
/Unit3 Logistic Regression/Unit3_Framingham.R
|
8f3472d4c097bbc7de52991db727a4ca2f2ef322
|
[] |
no_license
|
jpalbino/-AnalyticsEdgeMITx
|
454341f8e69cc615ab7b8286ba9bbee9df66026d
|
9d6b152f5ac2ca9728e5363face0ba0e50da879c
|
refs/heads/master
| 2021-01-18T08:12:17.814468
| 2019-11-05T21:33:18
| 2019-11-05T21:33:18
| 57,457,758
| 0
| 0
| null | 2016-04-30T19:14:06
| 2016-04-30T19:14:05
| null |
UTF-8
|
R
| false
| false
| 1,260
|
r
|
Unit3_Framingham.R
|
# Unit 3, The Framingham Heart Study
# Video 3
# Read in the dataset
framingham = read.csv("framingham.csv")
# Look at structure
str(framingham)
# linear regression
fit<-lm(TenYearCHD~., data=framingham)
# Load the library caTools
library(caTools)
# Randomly split the data into training and testing sets
set.seed(1000)
split = sample.split(framingham$TenYearCHD, SplitRatio = 0.65)
# Split up the data using subset
train = subset(framingham, split==TRUE)
test = subset(framingham, split==FALSE)
# Logistic Regression Model
framinghamLog = glm(TenYearCHD ~ ., data = train, family=binomial)
summary(framinghamLog)
# Predictions on the test set
predictTest = predict(framinghamLog, type="response", newdata=test)
# Confusion matrix with threshold of 0.5
table(test$TenYearCHD, predictTest > 0.5)
# Accuracy
(1069+11)/(1069+6+187+11)
# Baseline accuracy
(1069+6)/(1069+6+187+11)
# Test set AUC
library(ROCR)
ROCRpred = prediction(predictTest, test$TenYearCHD)
as.numeric(performance(ROCRpred, "auc")@y.values)
ROCRperf = performance(ROCRpred, "tpr", "fpr")
# Plot ROC curve
plot(ROCRperf)
# Add colors
plot(ROCRperf, colorize=TRUE)
# Add threshold labels
plot(ROCRperf, colorize=TRUE, print.cutoffs.at=seq(0,1,by=0.1), text.adj=c(-0.2,1.7))
|
f5d5964fdc204b829076bdcc6dc414f7ea2e9e3b
|
c98d6f40abe3e3ad60569ae52e499de4ed6ab432
|
/man/shinySNPGene.Rd
|
abce4041492fe6bba0f8e950098cd3ca7bf654e2
|
[] |
no_license
|
byandell/qtl2shiny
|
6ad7309b7f4b6bf89147560e23102b4ac5f93620
|
e342ce39f2a30ea4df4010aac61822e448d43e20
|
refs/heads/master
| 2023-05-11T06:04:00.877301
| 2023-04-30T20:22:25
| 2023-04-30T20:22:25
| 78,020,416
| 2
| 3
| null | 2018-01-17T14:31:31
| 2017-01-04T14:00:43
|
R
|
UTF-8
|
R
| false
| true
| 850
|
rd
|
shinySNPGene.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/shinySNPGene.R
\name{shinySNPGene}
\alias{shinySNPGene}
\title{Shiny SNP Association}
\usage{
shinySNPGene(
input,
output,
session,
snp_par,
chr_pos,
pheno_names,
snp_scan_obj,
snpinfo,
top_snps_tbl,
gene_exon_tbl,
project_info,
snp_action = shiny::reactive({
"basic"
})
)
}
\arguments{
\item{input, output, session}{standard shiny arguments}
\item{snp_par, chr_pos, pheno_names, snp_scan_obj, snpinfo, top_snps_tbl, gene_exon_tbl, project_info, snp_action}{reactive arguments}
}
\value{
tbl with top SNPs
}
\description{
Shiny module for SNP association mapping, with interfaces \code{shinySNPGeneInput}, \code{shinySNPGeneUI} and \code{shinySNPGeneOutput}.
}
\author{
Brian S Yandell, \email{brian.yandell@wisc.edu}
}
\keyword{utilities}
|
26630ec32403bed953b66db01c0ee5fc21b75116
|
b4dc1ccbba0146fefb2a62bb6b557c27e4ec793e
|
/man/get_playlist.Rd
|
f035eb848ecbf88361511c4f4b15eca75e177cf1
|
[] |
no_license
|
Marcow12/antaresXpansion
|
863b7e5501412c8c85bb5f7353681e518d0a7bdf
|
1c53ebf646ef02f52c104a49d709972afcd0bdfd
|
refs/heads/master
| 2021-01-20T04:58:41.105372
| 2017-08-07T11:17:00
| 2017-08-07T11:17:00
| 101,404,694
| 0
| 0
| null | 2017-08-25T13:06:11
| 2017-08-25T13:06:11
| null |
UTF-8
|
R
| false
| true
| 640
|
rd
|
get_playlist.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/simulation_settings.R
\name{get_playlist}
\alias{get_playlist}
\title{Get playlist of simulated MC years}
\usage{
get_playlist(opts = antaresRead::simOptions())
}
\arguments{
\item{opts}{list of simulation parameters returned by the function
\code{antaresRead::setSimulationPath}}
}
\value{
Returns a vector of the identifier of the simulated MC year
}
\description{
\code{get_playlist} gives the identifier of the MC years which
will be simulated, taking into account the potential use of a
playlist which can disactivate some MC years
}
|
d9c5649cdcb49f157bb3d56e73a1a58d6b59ed97
|
2364af0d602947c317ec092c7c6d919624c303ae
|
/plot1.R
|
58d9998e1da485577ed3c8e3f0f9110ba2dfbf8b
|
[] |
no_license
|
leej3/dss_exploratory_analysis
|
fb77d2fd07137394f46400c10893072239ab77ea
|
bf42895af65d45bb16276936a28806b8021fd0f0
|
refs/heads/master
| 2021-01-15T17:37:07.013494
| 2015-09-13T21:30:54
| 2015-09-13T21:30:54
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,380
|
r
|
plot1.R
|
plot1 <- function() {
# Function plots a histogram of the global active power
# Data of electricity consumption as reported in the UC Irvine power consumption dataset. 1st and 2nd of February 2007 were assessed
#Data downloaded manually from :
#https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip
# on at Wed Sep 9th 17:23:38 2015
# Load data:
library(data.table)
house_power<- fread(input = "household_power_consumption.txt",
na.strings = c("NA","?","","N/A"))
# Convert date values
house_power[,Date := as.Date(Date,format = "%d/%m/%Y")]
# extract data for relevant dates
house_power<- house_power[Date == "2007-02-02" | Date == "2007-02-01"]
# Create a column for time in POSIXct format
house_power[, POSIXct_time :=
as.POSIXct(paste(house_power[,Date],house_power[,Time]),
format = "%Y-%m-%d %H:%M:%S")
]
# Convert other columns to numeric
house_power[ , names(house_power)[3:9] :=
lapply(.SD, function(x) as.numeric(x) ) , .SDcols=3:9]
#Create histogram for Global active power over the two days.
par(cex = 0.8)
hist(house_power[!is.na(Global_active_power), Global_active_power],
col = "red",
xlab = "Global active power (kilowatts)",
main = "Global active power")
#Generate png file
dev.copy(png, file = "plot1.png")
dev.off()
}
|
3faca4eb360dbf6a35d97d78a78c5a9ce9f7997d
|
ae45b1826a92420b6bf229e5b07e1f1a06ab622b
|
/ui.R
|
8ac3c1ef2878ecc053cfcb790718875fe726320f
|
[
"MIT"
] |
permissive
|
dathanasakoglou/clust-app
|
6a230b718205b9458d7a675ec8d7d449abd245a2
|
c78a83a2a2b8aa74fa907137f3623f1e3a8e3698
|
refs/heads/master
| 2021-08-14T22:50:10.277927
| 2017-11-16T23:16:12
| 2017-11-16T23:16:12
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 6,513
|
r
|
ui.R
|
library(shiny)
library(shinydashboard)
library(highcharter)
#---Header---#
header <- dashboardHeader(title = "Clustering App")
#---Sidebar---#
sidebar <- dashboardSidebar(collapsed = FALSE,
sidebarMenu(
# sidebarSearchForm(textId = "searchText", buttonId = "searchButton",
# label = "Search..."),
# sidebarSearchForm(textId = "searchText", buttonId = "searchButton",
# label = "Search..."),
menuItem("Dashboard", tabName = "dashboard", icon = icon("dashboard")),
menuItem("About", tabName = "about", icon = icon("info")),
menuItem("Charts", tabName = "charts", icon = icon("bar-chart")),
menuItem("Dataset", tabName = "view", icon = icon("table")),
menuItem("Clusters", tabName = "clusters", icon = icon("object-group")),
menuItem("Reports", tabName = "reports", icon = icon("file")),
h5("Dataset"),
downloadButton("downloadData", "Download iris.csv", class = NULL),
br(),
radioButtons('format', 'Document format', c('PDF', 'HTML', 'Word'),
inline = TRUE),
downloadButton("downloadReport", "Generate report"),
img(src = "iris.jpg", width = 200, alt = "logo.jpg")
))
#---Body---#
body <- dashboardBody(
tags$head(
tags$link(rel = "stylesheet", type = "text/css", href = "custom.css")
),
tabItems(
tabItem("dashboard", fluidRow(
valueBoxOutput("rate"),
valueBoxOutput("count"),
valueBoxOutput("users"),
box(
title = "Overview", solidHeader = TRUE,
width = 8,
status = "primary",
plotOutput("overview")
),
box(
title = "Inputs", status = "primary", width = 4,
selectInput("dataset", "Choose a dataset (or a subset) :",
choices = c("all iris data", "setosa", "versicolor", "virginica")),
selectInput("Xvar", "X variable",
choices = c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width")),
selectInput("Yvar", "Y variable",
choices = c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width"), selected = "Sepal.Width")
),
box(
title = "Boxplot", width = 8, solidHeader = TRUE,
status = "primary",
plotOutput("boxPlot")
),
box(
title = "Pie Chart", width = 4, solidHeader = FALSE,
status = "primary",
htmlOutput("pie")
),
box(
title = "Violin plot", width = 6, solidHeader = TRUE,
status = "primary",
plotOutput("violin")
),
box(
title = "Barplot", width = 6, solidHeader = TRUE,
status = "primary",
highchartOutput("bar")
),
box(
title = "Andrews Curves", width = 6, solidHeader = TRUE,
status = "primary",
plotOutput("line")
),
box(
title = "Density plot", width = 6, solidHeader = TRUE,
status = "primary",
plotOutput("dens")
)
)),
tabItem("about", fluidRow(
box(
title = "Iris Flower Dataset", width = 12, solidHeader = TRUE,
textOutput("text"),
img(src = "iris_types.jpg", width = 300, height = 100, alt = "iris types")
)
)),
tabItem("charts", fluidRow(
box(
title = "Sankey", solidHeader = TRUE, width = 4,
status = "primary",
htmlOutput("sankey")
),
box(
title = "Bubble", solidHeader = TRUE, width = 8,
status = "primary",
htmlOutput("bubble")
),
box(
title = "Scatter", solidHeader = TRUE, width = 12,
status = "primary",
htmlOutput("scatter")
)
)),
tabItem("view", fluidRow(
box(
title = "Dataset", solidHeader = TRUE, width = 5,
status = "primary",
dataTableOutput("view")
),
box(
title = "Summary Statistics", solidHeader = TRUE, width = 7,
status = "primary",
verbatimTextOutput("summary")
)
)),
tabItem("reports", fluidRow(
box(
title = "Saratoga Report", solidHeader = TRUE, width = 12,
status = "primary",
htmlOutput("inc")
)
)),
tabItem("clusters", fluidRow(
box(
title = "K-means", solidHeader = TRUE, width = 8,
textOutput("NbClust"), status = "primary",
plotOutput("kmeansPlot")
),
box(
title = "Inputs", status = "primary", width = 4,
numericInput("clusters", "Cluster count", 3, min = 1, max = 9)
),
box(
title = "Dbscan", width = 8, solidHeader = TRUE,
textOutput("dbscan_Param"), status = "primary",
plotOutput("dbscanPlot")
),
box(
title = "Inputs", width = 4, status = "primary",
sliderInput("eps", "Radius of neighborhood of each point", min = 0.0, max = 1.0, value = 0.2),
sliderInput("minPoints", "Number of neighbors within the eps radius", min = 0, max = 10, value = 3)
),
box(
title = "Decision Tree", solidHeader = TRUE, width = 12,
status = "primary",
plotOutput("treePlot")
)
))
)
)
#---Construct Page---#
dashboardPage(header, sidebar, body)
|
ad5ca46f8a34f8661598faa4e25da4baf0a5768e
|
e8733b08964cdec4a24ca5a6f8c60fab04dc4dd7
|
/scripts/testing.R
|
da45e74563559698a731be40ef126a947cd0a7f0
|
[] |
no_license
|
andreasolden/ssb-api-og-shiny
|
3befd81f3dc7a9df2f7016afbbe1aff5fa5d4f74
|
ce4bae88bf15a6e43c8b282f80898a243bff78df
|
refs/heads/master
| 2020-04-17T17:54:33.355885
| 2019-03-10T16:41:30
| 2019-03-10T16:41:30
| 166,803,955
| 1
| 1
| null | 2019-01-23T12:59:50
| 2019-01-21T11:36:59
|
R
|
UTF-8
|
R
| false
| false
| 2,138
|
r
|
testing.R
|
#Testing
options(scipen=10000)
library("checkpoint") #Checkpoint assures us that we use the same package versions
checkpoint("2019-01-21") #As they were at the date set in this function. i.e "2019-01-21"
library(stargazer)
library(scales)
library(skimr)
library(tidyverse)
library(stringr)
library(readr)
library(tidyverse)
library(here)
df_joined_wide <- readRDS(here("data/processed", "df_joined_wide.rds"))
df_joined_long <- readRDS(here("data/processed", "df_joined_long.rds"))
unique(df_joined_long$contents)
levels(df_joined_long$age)
#Notes:
#Faceting: contents: ave_home_price, home_transfers, inhabitants, oil_price, reg_unemployed, trans_val_in_1000
vec <- c("ave_home_price", "home_transfers", "inhabitants", "oil_price", "reg_unemployed", "trans_val_in_1000")
df_long_sub <- df_joined_long %>%
filter(contents %in% vec)
#Subsetting
#Sex: Females, Males, Population
# Population is in both inhabitants and reg_unemployed.
vec_unemp <- c("Population")
df_long_sub <- df_joined_long %>%
filter(sex %in% vec_unemp | is.na(sex))
#Filter on date
vec_date <- c( "2005-01-01", "2017-01-01" )
df_long_sub <- df_joined_long %>%
filter(date>=vec_date[1] & date<=vec_date[2])
#Filter on age 0-105 and sum them up. Drops age and gives bach inhabitants w/o
vec_age <- c(0,67)
df_long_sub <- df_joined_long %>%
mutate( age = parse_number(as.character(age))) %>%
filter(between(age, vec_age[1], vec_age[2]) | is.na(age)) %>%
group_by(region, sex, contents, date) %>%
summarise(value = sum(value)) %>%
ungroup()
# Plot
vec <- c("ave_home_price", "home_transfers")
df_long_sub <- df_joined_long %>%
filter(contents %in% vec)
p <- ggplot(data = df_long_sub,
aes(x = date, y = log(value), colour = region)) +
geom_point() +
facet_grid(contents~., scales = "free_y") +
geom_smooth(method ="lm") +
geom_line() +
ggtitle("Hello world")
p
#Testing logic
x1 <- c("a", "b", "c", "d")
x2 <- c("a", "b", "e", "f")
x1 %in% x2
any(x1 %in% x2)
#Testing paste
x3 <- c(1,2,3)
paste(x3[1],"-",x3[3])
|
5cea03785f41876efa703992c2f767085a67b3f6
|
cce287cfff55807ac72c5f43c24cddf83adbe4f4
|
/man/write_prms_dimension.Rd
|
a0445ab0b10d4ebf7b2847e1014b90fbe9604ff5
|
[
"LicenseRef-scancode-warranty-disclaimer",
"LicenseRef-scancode-public-domain-disclaimer",
"CC0-1.0"
] |
permissive
|
smwesten-usgs/prmsParameterizer
|
d55f362eff98482438d46786a81ba3c35613a8c1
|
ea00e68f24b615b87ccac0e6cb5a7f54c8a95521
|
refs/heads/master
| 2020-04-06T04:01:03.271356
| 2017-02-24T19:23:58
| 2017-02-24T19:23:58
| 83,072,983
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 612
|
rd
|
write_prms_dimension.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/write_prms_dimension.R
\name{write_prms_dimension}
\alias{write_prms_dimension}
\title{Write PRMS dimension information to a PRMS parameter file.}
\usage{
write_prms_dimension(fname = "", dimname = "one", dimlength = 1,
dHeader = FALSE)
}
\arguments{
\item{filename}{Name of the PRMS parameter file to write to.}
}
\value{
None.
}
\description{
This function is designed to be called once for each dimension written to the file.
For example it will be called once for the 'nmonth' dimension, again for the 'ntemp'
dimension, etc.
}
|
7c884b63c9606251111774cd2f1d4d8bd189963b
|
68249e521dca2c4ef3b6b4491abea093bf5e15d1
|
/project4/Q2-5.R
|
fee49fbee653adf129f60e8caebc487c791a514d
|
[] |
no_license
|
lx950627/Large-Scale-Network-Analysis-and-Mining
|
2194f9cef33a6b3dd6a3671e618dadd1e181c338
|
38d901ffae9f66087da503a601ae7f838d86c8b8
|
refs/heads/master
| 2020-03-15T14:40:21.087300
| 2018-06-13T01:29:59
| 2018-06-13T01:29:59
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,534
|
r
|
Q2-5.R
|
library(igraph)
library(data.table)
edge_list <- fread("C:\\Users\\IfyourRtheone\\Desktop\\UCLA\\2018Spring\\edge_list.txt", sep='\t', header=FALSE)
names(edge_list) <- c('from','to','weights')
edge_list <- edge_list[duplicated(edge_list[,1:2]) == F]
edge_list <- edge_list[edge_list$from != edge_list$to]
g = graph.data.frame(edge_list[,1:2],directed =TRUE)
degIn <- degree(g, mode = 'in')
hist(degIn, main = "In-degree Distribution of g")
degOut <- degree(g,mode = 'out')
hist(degOut, main = "Out-degree Distribution of g")
plot(degree.distribution(g, mode = 'in'),main = "In-degree Distribution of g",xlab="Degree",ylab="Frequency")
plot(degree.distribution(g, mode = 'out'),main ="Out-degree Distribution of g",xlab="Degree",ylab="Frequency")
pgrank = page.rank(g, directed = TRUE, damping = 0.85)
sorted = sort(pgrank$vector, decreasing = TRUE, index.return = TRUE)
ordered_idx = order(pgrank$vector, decreasing = TRUE)
cat(sorted$x[1:10])
V(g)[ordered_idx[1:10]]
name_list = c("Cruise, Tom", "Watson, Emma (II)", "Clooney, George","Hanks, Tom"
,"Johnson, Dwayne (I)","Depp, Johnny","Smith, Will (I)","Streep, Meryl",
"DiCaprio, Leonardo","Pitt, Brad","Roberts, Eric (I)","Tatasciore, Fred"
,"Jeremy, Ron","Trejo, Danny","Flowers, Bess","Hitler, Adolf","Riehle, Richard",
"Harris, Sam (II)","Jackson, Samuel L.","De Niro, Robert")
idx = c()
for (name in name_list){
print(name)
print(which(V(g)$name == name))
idx = c(idx, which(V(g)$name == name))
}
pgrank$vector[idx]
degree(g, v = V(g)[idx], mode = c("in"))
score = c()
for (i in 1:length(pgrank$vector)){
score = c(score, pgrank$vector[[i]])
}
pg_data = data.frame(V(g)$name,score)
write.csv (pg_data, file ="data2.csv")
actor_movie <- fread("actor_num_movies.txt",header = FALSE)
movie_num = c()
for (name in name_list){
print(name)
print(which(actor_movie$V1 == name))
movie_num = c(movie_num,actor_movie$V2[which(actor_movie$V1 == name)])
}
movie_num
name_list2 = c("Cruise, Tom", "Watson, Emma (II)", "Clooney, George","Hanks, Tom"
,"Johnson, Dwayne (I)","Depp, Johnny","Smith, Will (I)","Streep, Meryl",
"DiCaprio, Leonardo","Pitt, Brad")
for (name in name_list2){
idx = which(edge_list$from == name)
sortidx = sort(edge_list$weights[idx], decreasing = TRUE,index.return = TRUE)
print(name)
print(edge_list$to[idx[sortidx$ix[1]]])
print(edge_list$weights[idx[sortidx$ix[1]]])
}
|
ff2e16013db62726ef105d9309799798d2ef40e6
|
9c2296c877a283325c3998f4b4574cf7574c0512
|
/Test_DRIMSeq_0.3.3_sQTL_permutations.R
|
92dad39f500f1738a977d04b8a5aa241e6d1573d
|
[] |
no_license
|
gosianow/drimseq_package_devel_tests
|
e7bee32bc8111d295f4e3c8598aad98cf1a9a178
|
c555beaf708f38f984e015e6bf53114ce129481f
|
refs/heads/master
| 2021-01-18T15:08:32.488731
| 2017-07-18T13:04:03
| 2017-07-18T13:04:03
| 56,255,862
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 11,472
|
r
|
Test_DRIMSeq_0.3.3_sQTL_permutations.R
|
# R32dev
# Created 7 Apr 2016
rwd <- "/home/gosia/multinomial_project/package_devel/Test_DRIMSeq_0.3.3_sQTL_permutations"
dir.create(rwd, recursive = TRUE)
setwd(rwd)
library(DRIMSeq)
library(GenomicRanges)
library(rtracklayer)
library(ggplot2)
### Load sQTL data
data_dir <- system.file("extdata", package = "DRIMSeq")
# gene_ranges with names!
gene_ranges <- import(paste0(data_dir, "/genes_subset.bed"))
names(gene_ranges) <- mcols(gene_ranges)$name
counts <- read.table(paste0(data_dir, "/TrQuantCount_CEU_subset.tsv"),
header = TRUE, sep = "\t", as.is = TRUE)
genotypes <- read.table(paste0(data_dir, "/genotypes_CEU_subset.tsv"),
header = TRUE, sep = "\t", as.is = TRUE)
# snp_ranges with names!
snp_ranges <- GRanges(Rle(genotypes$chr), IRanges(genotypes$start,
genotypes$end))
names(snp_ranges) <- genotypes$snpId
## Check if samples in count and genotypes are in the same order
all(colnames(counts[, -(1:2)]) == colnames(genotypes[, -(1:4)]))
## [1] TRUE
sample_id <- colnames(counts[, -(1:2)])
d <- dmSQTLdataFromRanges(counts = counts[, -(1:2)], gene_id = counts$geneId,
feature_id = counts$trId, gene_ranges = gene_ranges,
genotypes = genotypes[, -(1:4)], snp_id = genotypes$snpId,
snp_ranges = snp_ranges, sample_id = sample_id, window = 5e3,
BPPARAM = BiocParallel::MulticoreParam(workers = 1))
d
d <- dmFilter(d, min_samps_gene_expr = 70, min_samps_feature_expr = 5,
min_samps_feature_prop = 5, minor_allele_freq = 5,
BPPARAM = BiocParallel::MulticoreParam(workers = 1))
d1 <- dmDispersion(d, common_dispersion = FALSE, speed = TRUE, BPPARAM = BiocParallel::MulticoreParam(workers = 5))
plotDispersion(d1, out_dir = "speed_TRUE_")
ggp1 <- plotDispersion(d1)
###########################################################################
### Comapre dispersion when speed = TRUE and speed = FALSE
###########################################################################
d2 <- dmDispersion(d, common_dispersion = FALSE, speed = FALSE, BPPARAM = BiocParallel::MulticoreParam(workers = 5))
plotDispersion(d2, out_dir = "speed_FALSE_")
ggp2 <- plotDispersion(d2)
### Plot both on one figure
ggp3 <- ggp2 +
geom_point(data = ggp1$data, aes(x = mean_expression, y = dispersion), color = "black", size = 0.5)
pdf("speed_both_dispersion_vs_mean.pdf")
print(ggp3)
dev.off()
### Expectation that there are NAs for the cases where null theta is higher than full theta
### It is not the case.
disp1 <- unlist(d1@genewise_dispersion)
disp2 <- unlist(d2@genewise_dispersion)
table(disp1 <= disp2)
table(disp1 <= disp2, rowSums(is.na(d1@genotypes@unlistData)) == 0)
###########################################################################
### Implement permutations based on all genes
###########################################################################
library(devtools)
load_all("/home/gosia/R/package_devel/DRIMSeq")
d <- d1
d <- dmFit(d, BPPARAM = BiocParallel::MulticoreParam(workers = 5))
d <- dmTest(d, permutations = "all_genes", verbose = TRUE, BPPARAM = BiocParallel::MulticoreParam(workers = 10))
d <- dmTest(d, permutations = "per_gene", verbose = TRUE, BPPARAM = BiocParallel::MulticoreParam(workers = 10))
plotTest(d)
x <- d
test = "lr"
prop_mode = "constrOptimG"
prop_tol = 1e-12
verbose = TRUE
BPPARAM = BiocParallel::MulticoreParam(workers = 10)
fit_null <- dmSQTL_fitOneModel(counts = x@counts, genotypes = x@genotypes, dispersion = slot(x, x@dispersion), model = "null", prop_mode = prop_mode, prop_tol = prop_tol, verbose = verbose, BPPARAM = BPPARAM)
### Number of samples used for the analysis
n <- unlist(lapply(1:length(x@counts), function(g){
sum(!is.na(x@counts[[g]][1, ]))
}))
results <- dmSQTL_test(fit_full = x@fit_full, fit_null = fit_null, test = test, n = n, verbose = verbose, BPPARAM = BPPARAM)
# library(gtools)
# gtools::permutations(4, 4)
max_nr_perm_cycles <- 10
max_nr_min_nr_sign_pval <- 1e3
dmSQTL_permutations_all_genes <- function(x, fit_null, results, max_nr_perm_cycles = 10, max_nr_min_nr_sign_pval = 1e3, prop_mode, prop_tol, n, test, verbose, BPPARAM){
fit_full <- x@fit_full
nr_perm_tot <- 0
nr_perm_cycles <- 0
min_nr_sign_pval <- 0
n <- ncol(x@counts)
pval <- results$pvalue
nas <- is.na(pval)
pval <- pval[!nas]
pval <- factor(pval)
sum_sign_pval <- rep(0, length(pval))
# ds_genes <- results$adj_pvalue < 0.1
while(nr_perm_cycles < max_nr_perm_cycles && min_nr_sign_pval < max_nr_min_nr_sign_pval){
permutation <- sample(n, n)
### Permute counts for all genes
counts <- x@counts[, permutation, drop = FALSE]
fit_full_perm <- dmSQTL_fitOneModel(counts = counts, genotypes = x@genotypes, dispersion = slot(x, x@dispersion), model = "full", prop_mode = prop_mode, prop_tol = prop_tol, verbose = verbose, BPPARAM = BPPARAM)
fit_null_perm <- dmSQTL_fitOneModel(counts = counts, genotypes = x@genotypes, dispersion = slot(x, x@dispersion), model = "null", prop_mode = prop_mode, prop_tol = prop_tol, verbose = verbose, BPPARAM = BPPARAM)
results_perm <- dmSQTL_test(fit_full = fit_full_perm, fit_null = fit_null_perm, test = test, n = n, verbose = verbose, BPPARAM = BPPARAM)
nr_perm <- nrow(results_perm)
nr_perm_tot <- nr_perm_tot + nr_perm
nr_perm_cycles <- nr_perm_cycles + 1
### Count how many pval_permuted is lower than pval from the model
pval_perm <- results_perm$pvalue
nas_perm <- is.na(pval_perm)
pval_perm <- pval_perm[!nas_perm]
pval_perm_cut <- cut(pval_perm, c(-1, levels(pval), 2), right=FALSE)
pval_perm_sum <- table(pval_perm_cut)
pval_perm_cumsum <- cumsum(pval_perm_sum)[-length(pval_perm_sum)]
names(pval_perm_cumsum) <- levels(pval)
sum_sign_pval <- sum_sign_pval + pval_perm_cumsum[pval]
pval_adj <- (sum_sign_pval + 1) / (nr_perm_tot + 1)
min_nr_sign_pval <- min(sum_sign_pval)
}
pval_out <- rep(NA, nrow(results))
pval_out[!nas] <- pval_adj
return(pval_out)
}
pval_permutations <- dmSQTL_permutations_all_genes(x, fit_null, results, max_nr_perm_cycles = 3, max_nr_min_nr_sign_pval = 1e3, prop_mode, prop_tol, verbose, n, test, BPPARAM)
ggp <- dm_plotPvalues(pvalues = results$pvalue)
pdf(paste0("hist_pvalues_dm.pdf"))
print(ggp)
dev.off()
ggp <- dm_plotPvalues(pvalues = pval_permutations)
pdf(paste0("hist_pvalues_permutations_all_genes.pdf"))
print(ggp)
dev.off()
###########################################################################
### Implement permutations foe each gene individually
###########################################################################
library(devtools)
load_all("/home/gosia/R/package_devel/DRIMSeq")
d <- d1
d <- dmFit(d, BPPARAM = BiocParallel::MulticoreParam(workers = 5))
# d <- dmTest(d, BPPARAM = BiocParallel::MulticoreParam(workers = 5))
x <- d
test = "lr"
prop_mode = "constrOptimG"
prop_tol = 1e-12
verbose = 0
BPPARAM = BiocParallel::MulticoreParam(workers = 5)
fit_null <- dmSQTL_fitOneModel(counts = x@counts, genotypes = x@genotypes, dispersion = slot(x, x@dispersion), model = "null", prop_mode = prop_mode, prop_tol = prop_tol, verbose = verbose, BPPARAM = BPPARAM)
### Number of samples used for the analysis
n <- unlist(lapply(1:length(x@counts), function(g){
sum(!is.na(x@counts[[g]][1, ]))
}))
results <- dmSQTL_test(fit_full = x@fit_full, fit_null = fit_null, test = test, n = n, verbose = verbose, return_list = TRUE, BPPARAM = BPPARAM)
max_nr_perm = 100
max_nr_sign_pval = 10
verbose = TRUE
dmSQTL_permutations_per_gene <- function(x, fit_null, results, max_nr_perm = 1e6, max_nr_sign_pval = 1e2, prop_mode, prop_tol, n, test, verbose = TRUE, BPPARAM){
fit_full <- x@fit_full
n <- ncol(x@counts)
pval <- lapply(results, function(x){
pval_tmp <- x$pvalue
pval_tmp <- pval_tmp[!is.na(pval_tmp)]
pval_tmp <- factor(pval_tmp)
return(pval_tmp)
})
results_width <- unlist(lapply(pval, length))
nas <- results_width == 0
genes2permute <- which(!nas)
sum_sign_pval <- vector("list", length(pval))
sum_sign_pval[!nas] <- split(rep(0, sum(results_width)), factor(rep(1:length(results_width), times = results_width)))
nr_perm_tot <- rep(0, length(x@counts))
nr_perm_tot[nas] <- NA
min_nr_sign_pval <- rep(0, length(x@counts))
min_nr_sign_pval[nas] <- NA
while(length(genes2permute) > 0){
if(verbose)
message(paste0(length(genes2permute), " genes left for permutation..\n"))
permutation <- sample(n, n)
### Permute counts for all genes that need additional permutations
counts <- x@counts[genes2permute, permutation]
genotypes <- x@genotypes[genes2permute, ]
dispersion <- slot(x, x@dispersion)
if(is.list(dispersion))
dispersion <- dispersion[genes2permute]
fit_full_perm <- dmSQTL_fitOneModel(counts = counts, genotypes = genotypes, dispersion = dispersion, model = "full", prop_mode = prop_mode, prop_tol = prop_tol, verbose = verbose, BPPARAM = BPPARAM)
fit_null_perm <- dmSQTL_fitOneModel(counts = counts, genotypes = genotypes, dispersion = dispersion, model = "null", prop_mode = prop_mode, prop_tol = prop_tol, verbose = verbose, BPPARAM = BPPARAM)
results_perm <- dmSQTL_test(fit_full = fit_full_perm, fit_null = fit_null_perm, test = test, n = n, return_list = TRUE, verbose = verbose, BPPARAM = BPPARAM)
### Count how many pval_permuted is lower than pval from the model
update_nr_sign_pval <- lapply(1:length(results_perm), function(i, results_perm, pval, genes2permute){
# i = 1
pval_perm <- results_perm[[i]]$pvalue
pval_perm <- pval_perm[!is.na(pval_perm)]
pval_perm_cut <- cut(pval_perm, c(-1, levels(pval[[genes2permute[i]]]), 2), right=FALSE)
pval_perm_sum <- table(pval_perm_cut)
pval_perm_cumsum <- cumsum(pval_perm_sum)[-length(pval_perm_sum)]
names(pval_perm_cumsum) <- levels(pval[[genes2permute[i]]])
nr_sign_pval <- pval_perm_cumsum[pval[[genes2permute[i]]]]
return(nr_sign_pval)
}, results_perm = results_perm, pval = pval, genes2permute = genes2permute)
### Update values in sum_sign_pval
for(i in 1:length(update_nr_sign_pval)){
sum_sign_pval[[genes2permute[i]]] <- sum_sign_pval[[genes2permute[i]]] + update_nr_sign_pval[[i]]
}
nr_perm <- unlist(lapply(results_perm, nrow))
nr_perm_tot[genes2permute] <- nr_perm_tot[genes2permute] + nr_perm
min_nr_sign_pval[genes2permute] <- unlist(lapply(sum_sign_pval[genes2permute], min))
### Update genes2permute
genes2permute <- which(nr_perm_tot < max_nr_perm & min_nr_sign_pval < max_nr_sign_pval)
}
### Calculate permutation adjusted p-values
pval_adj <- lapply(1:length(results), function(i, results, sum_sign_pval, nr_perm_tot){
pval_tmp <- results[[i]]$pvalue
nas <- is.na(pval_tmp)
if(sum(!nas) == 0)
return(pval_tmp)
pval_tmp[!nas] <- (sum_sign_pval[[i]] + 1) / (nr_perm_tot[i] + 1)
return(pval_tmp)
}, results = results, sum_sign_pval = sum_sign_pval, nr_perm_tot = nr_perm_tot)
pval_out <- unlist(pval_adj)
return(pval_out)
}
pval_permutations <- dmSQTL_permutations_per_gene(x, fit_null, results, max_nr_perm = 1e6, max_nr_sign_pval = 1e2, prop_mode, prop_tol, n, test, verbose = TRUE, BPPARAM)
|
345f4fb2afbad20c57ffeb1527c5244a0cc4dbd3
|
62df50b1f31e9330bd42d853674dc6d40ee383a8
|
/baypass_daph_interp.R
|
82eb5c17e8a6d9cc7968bc52025b1985d4ff6f83
|
[] |
no_license
|
andbeck/BayPass
|
2d9cb9c566bbe130768d5d4baaa514f371454393
|
af336af08b133e9d95399f047ead1dffe1118143
|
refs/heads/master
| 2020-07-09T14:05:28.604565
| 2017-03-22T16:29:49
| 2017-03-22T16:29:49
| 66,954,036
| 3
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 5,497
|
r
|
baypass_daph_interp.R
|
# setups
library(corrplot)
library(ape)
library(ggplot2)
library(gridExtra)
library(dplyr)
library(ggthemes)
library(qqman)
source('baypass_utils.R') # this is in the repo
setwd('/Volumes/TTYLMF')
# Covariance matrix
omega<-as.matrix(read.table("anaprEnvfile__mat_omega.out"))
omega<-matrix(omega, 8,8, byrow = TRUE, dimnames=list(c(paste("pop:",1:8)), c(paste("pop:",1:8))))
omega_plot<-cov2cor(omega)
par(mfrow = c(2,1))
corrplot(omega_plot,method = "square", order = 'hclust',mar = c(2,1,1,0))
plot(hclust(dist(omega_plot), 'complete'))
# # This needs to be developed to establish the criteria for detecting XtX outlier
# # DETAIL provided by Gautier
# # omega read in above
# pi.params<-as.numeric(read.table("anaprEnvfile__summary_beta_params.out",h=T)[,2]) ##the parmeters of the Beta distribution
# poolsize<-as.numeric(read.table("SAMPLEFILE")) #the original pool sizes
# readcounts<-geno2YN("ALLELEFILE") # the orginal read counts (needed to sample from the observed coverages)
# simulate.baypass(omega,nsnp=250000,
# beta.pi = pi.params,
# sample.size = poolsize,
# coverage=readcounts$NN,
# pi.maf = 0.01,
# suffix="pods",
# remove.fixed.loci = F)
# The real games begin here.
## =========================================================
## read in ALL baypass output and processing
## to be use in identification of outliers and plotting
## =========================================================
load('BayPassInterpret.RData') # this should eliminate data reading and prep for
# # Step 1: use the pods analysis to identify the 99% ile of XtX
# XtX_Emp<-read.table('anaprEnvfile__summary_pi_xtx.out', header = TRUE)
# XtX_Sim<-read.table('anaPOD__summary_pi_xtx.out', header = TRUE)
# # calcuate the threshold XtX using quantile = 0.99, for the top 1%
# XtX_threshold<-quantile(XtX_Sim$M_XtX, 0.99)
# XtX_Sig<-XtX_Emp[XtX_Emp$M_XtX>=XtX_threshold,]
# # percent exceeding threshold
# dim(XtX_Sig)[1]/dim(XtX_Emp)[1]
# # Step 2: use the pods analysis to identify the 99% ile of betas
# # The tables are indexed by covariable 1-3
# # 1 = lat/pH
# # 2 = temp
# # 3 = predation
# beta_Emp<-read.table('anaprEnvfile__summary_betai_reg.out', header = TRUE)
# beta_Sim<-read.table('anaPOD__summary_betai_reg.out', header = TRUE)
# # the B
# one<-filter(beta_Emp, COVARIABLE == '1')
# two<-filter(beta_Emp, COVARIABLE == '2')
# three<-filter(beta_Emp, COVARIABLE == '3')
# betaThreshold<-beta_Sim %>%
# group_by(COVARIABLE) %>%
# summarise(
# betaThreshold = quantile(BF.dB., 0.99))
# betaThreshold$COVARIABLE<-c("Lat/pH", "Temp", "Predation")
# betaThreshold
# # scaf positions and ID to add to dfs
# scaf_pos<-read.csv('scaf_pos.csv')
# # create df of bayes factors and XtX and scaf positions
# df_latpH<-data.frame(one, XtX = XtX_Emp$M_XtX, scaf_pos)
# df_temp<-data.frame(two, XtX = XtX_Emp$M_XtX, scaf_pos)
# df_predation<-data.frame(three, XtX = XtX_Emp$M_XtX, scaf_pos)
# these are the outliers based on XtX and bayes factor for each
latpH_keyptsID<-filter(df_latpH, BF.dB. > as.numeric(betaThreshold[1,2]) & XtX > XtX_threshold) # 2890
temp_keyptsID<-filter(df_temp, BF.dB. > as.numeric(betaThreshold[2,2]) & XtX > XtX_threshold) # 2531
predation_keyptsID<-filter(df_predation, BF.dB. > as.numeric(betaThreshold[3,2]) & XtX > XtX_threshold) # 3094
# use VennDiagram and gplots
library(VennDiagram)
library(gplots)
# create list of MRKs
All<-list(latpH = latpH_keyptsID$MRK,temp = temp_keyptsID$MRK, predation = predation_keyptsID$MRK)
# build plot
vennPlot<-venn.diagram(All, NULL, fill = c('blue','orange','green'), alpha = c(0.5,0.5,0.5), cex = 3, cat.cex = 2)
# draw plot
grid.draw(vennPlot)
# Hmmm - bit different numbers...
# # These are common to all three
# length(Reduce(intersect, list(latpH_keyptsID$MRK, temp_keyptsID$MRK, predation_keyptsID$MRK)))
#
# # THese are common to pairs
# length(Reduce(intersect, list(latpH_keyptsID$MRK, predation_keyptsID$MRK)))
# length(Reduce(intersect, list(temp_keyptsID$MRK, predation_keyptsID$MRK)))
#save(latpH_keyptsID, temp_keyptsID, predation_keyptsID, file = 'outliers.RData')
# Produce outlier figures
par(mfrow = c(1,3))
#latitude/pH
plot(BF.dB. ~ XtX, data = df_latpH, pch = '.', col = '#00000033', ylim = c(-10,60))
points(BF.dB. ~ XtX, data = latpH_keyptsID, pch = 21, col = 'grey')
abline(v = XtX_threshold, lty = 3, lwd = 2, col = 'red')
abline(h = betaThreshold[1,2], lty = 3, lwd = 2, col = 'green')
legend(5,60, legend = c("XtX Threshold", "BayesFactor Threshold", "Outliers"), lty=c(3,3,NA), pch = c(NA, NA, 21), col = c('red', 'green', 'grey'))
title('Latitude/pH')
# temp
plot(BF.dB. ~ XtX, data = df_temp, pch = '.', col = '#00000033', ylim = c(-10,60))
points(BF.dB. ~ XtX, data = temp_keyptsID, pch = 21, col = 'grey')
abline(v = XtX_threshold, lty = 3, lwd = 2, col = 'red')
abline(h = betaThreshold[2,2], lty = 3, lwd = 2, col = 'green')
legend(5,60, legend = c("XtX Threshold", "BayesFactor Threshold", "Outliers"), lty=c(3,3,NA), pch = c(NA, NA, 21), col = c('red', 'green', 'grey'))
title('Temperature')
# predation
plot(BF.dB. ~ XtX, data = df_predation, pch = '.', col = '#00000033', ylim = c(-10,60))
points(BF.dB. ~ XtX, data = predation_keyptsID, pch = 21, col = 'grey')
abline(v = XtX_threshold, lty = 3, lwd = 2, col = 'red')
abline(h = betaThreshold[3,2], lty = 3, lwd = 2, col = 'green')
legend(5,60, legend = c("XtX Threshold", "BayesFactor Threshold", "Outliers"), lty=c(3,3,NA), pch = c(NA, NA, 21), col = c('red', 'green', 'grey'))
title('Predation')
|
c92f491f8677a4079f5b2c89a70d2cb7fa44dbc6
|
ffdea92d4315e4363dd4ae673a1a6adf82a761b5
|
/data/genthat_extracted_code/fechner/examples/regMin.Rd.R
|
b96abb9833ced11cc6a773e622c1463b4fe6e864
|
[] |
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
| 289
|
r
|
regMin.Rd.R
|
library(fechner)
### Name: regMin
### Title: Artificial Data: Regular Minimality In Non-canonical Form
### Aliases: regMin
### Keywords: datasets
### ** Examples
## dataset regMin satisfies regular minimality in non-canonical form
regMin
check.regular(regMin, type = "reg.minimal")
|
a838be60502310044ae76480ecce0282ddce6ec5
|
30cf71cda7b873411ed66ad0eb78a0ed21bb26ba
|
/webgestalt.R
|
53da1c58cc3b3f306b68ca8ab7d92d653cfdc7b7
|
[] |
no_license
|
TNO/immune_health_textmining
|
74050cb82c19d7e1818ee34488b90c1a24d68352
|
4190c86191c80dd0abd228ac230a237b23939729
|
refs/heads/master
| 2020-09-09T11:42:21.133609
| 2020-05-15T12:02:11
| 2020-05-15T12:02:11
| 221,438,037
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,725
|
r
|
webgestalt.R
|
webgestalt <- function(topic){
library(devtools)
install_github("bzhanglab/WebGestaltR")
install_bitbucket("ibi_group/disgenet2r")
install.packages("WebGestaltR")
library("WebGestaltR")
library("disgenet2r")
library(httr)
library(jsonlite)
library(lubridate)
options(stringsAsFactors = FALSE)
library("readxl")
diseaseIDtab <- read_excel("C:\\TNO\\2019\\20191003_Exposome\\disease_mappings_converted.xlsx", sheet = 1, col_names = TRUE, col_types = "text")
## Arguments
args = commandArgs(trailingOnly=TRUE)
topic <- args[1]
## Gene list
#topic <- "asthma"
geneList0 <- (((read.csv(paste("C:\\TNO\\2019\\20191003_Exposome\\output\\",topic,"_protein_list.txt", sep = ""), header = FALSE, stringsAsFactors = FALSE))))
geneList2 <- as.data.frame(geneList0)
## Output directory WebGestalt if "isOutput" = TRUE
outputdir <- paste("C:\\TNO\\2019\\20191003_Exposome\\output\\webgestalt\\","outputWebGestaltR\\", sep = "")
## WebGestalt
outputWebGestalt <- WebGestaltR(enrichMethod = "ORA", organism = "hsapiens",
enrichDatabase = "disease_Disgenet", enrichDatabaseFile = NULL,
enrichDatabaseType = "genesymbol", enrichDatabaseDescriptionFile = NULL,
interestGeneFile = NULL, interestGene = geneList2[,1],
interestGeneType = "genesymbol", collapseMethod = "mean",
referenceGeneFile = NULL, referenceGene = NULL,
referenceGeneType = NULL, referenceSet = "genome", minNum = 5,
maxNum = 2000, sigMethod = "fdr", fdrMethod = "BH", fdrThr = 0.05,
topThr = 100, reportNum = 20, perNum = 1000, isOutput = TRUE,
outputDirectory = outputdir, projectName = NULL,
dagColor = "continuous", setCoverNum = 10,
networkConstructionMethod = NULL, neighborNum = 10,
highlightType = "Seeds", highlightSeedNum = 10, nThreads = 1,
hostName = "http://www.webgestalt.org/")
diseaseIDtab <- diseaseIDtab[which(diseaseIDtab$vocabulary=="MSH" | diseaseIDtab$vocabulary=="DO" | diseaseIDtab$vocabulary=="HPO"),]
colnames(outputWebGestalt)[1] <- "diseaseId"
outputWebGestalt2 <- merge(x = outputWebGestalt, y = diseaseIDtab, by = "diseaseId", all.x = TRUE)
write.table(outputWebGestalt2, file = paste("C:\\TNO\\2019\\20191003_Exposome\\output\\webgestalt\\Gene_Disease_relation_MESH_DO_HPO3_",topic,".csv", sep = ""), sep = "\t", row.names = FALSE, col.names = TRUE,quote=TRUE)
}
|
7d487735dbc51f252aadb46344401a9f0afca49b
|
2f15b2dc16de0471e7bee43f6739b6ad8522c81d
|
/man/selection_bit_map.Rd
|
67494e44d6ea386c8cc285791c2e07dd508275be
|
[
"MIT"
] |
permissive
|
billster45/starschemar
|
45566be916c95778727a3add3239143d52796aa9
|
5f7e0201494a36f4833f320e4b9535ad02b9bdc1
|
refs/heads/master
| 2022-12-20T13:45:06.773852
| 2020-09-26T03:44:12
| 2020-09-26T03:44:12
| 298,796,838
| 1
| 0
|
NOASSERTION
| 2020-09-26T11:10:30
| 2020-09-26T11:10:30
| null |
UTF-8
|
R
| false
| true
| 593
|
rd
|
selection_bit_map.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/fact_table_incremental_refresh.R
\name{selection_bit_map}
\alias{selection_bit_map}
\title{Generate a record selection bitmap}
\usage{
selection_bit_map(table, values, names)
}
\arguments{
\item{table}{A \code{tibble}, table to select.}
\item{values}{A \code{tibble}, set of values to search.}
\item{names}{A vector of column names to consider.}
}
\value{
A vector of boolean.
}
\description{
Obtain a vector of boolean to select the records in the table that have the
combination of values.
}
\keyword{internal}
|
e98b96736514cd2c5af5b3bd3d111cab5711e5ee
|
f3079beec1719b26e22114da21a8612f725bd8c5
|
/2015_codeClinic/2_CodeClinic_ImageAnalysis/isthisacroppedversionofthat.R
|
4b123d11fcce931b55622290ee9c8b8ce6208374
|
[] |
no_license
|
CheolsoonIm/CodeClinicR
|
d7712b510f2bf4325be71acafbe4874d74799c49
|
c2664ebad2fe2bded13a0f204343938f4044865a
|
refs/heads/master
| 2021-09-13T04:14:08.280424
| 2018-04-24T22:37:21
| 2018-04-24T22:37:21
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 4,485
|
r
|
isthisacroppedversionofthat.R
|
# function to test if one image is a cropped version of another
# needle - image that might be a cropped version
# haystack - image that might be a parent version
# returns TRUE or FALSE
isthisacroppedversionofthat <- function(needle,haystack) {
# needle <- paste("imagesToAnalyze/","460249177a.jpg",sep="")
# haystack <- paste("imagesToAnalyze/","460249177.jpg",sep="")
# assumes needle and haystack are jpeg images
needle.raster <- readJPEG(needle)
needle.width <- ncol(needle.raster) # width and height are used a lot
needle.height <- nrow(needle.raster)
haystack.raster <- readJPEG(haystack)
haystack.width <- ncol(haystack.raster)
haystack.height <- nrow(haystack.raster)
red.layer <- 1 # We're only interested in one layer
# if needle is larger than or same size as haystack,
# then needle can't be a cropped version.
if ((needle.height >= haystack.height)
&& (needle.width >= haystack.width)) return(c(0,0))
# I'm going to assume that a match in one (of RGB) layers between
# needle and haystack is a match between all layers. This reduces the
# amount of data we need to correlate by 2/3
# This requires versions of needle and haystack with just one layer
needle.red <- needle.raster[,,red.layer]
haystack.red <- haystack.raster[,,red.layer]
# points.of.interest is an array of x,y coordinates that are possible
# starting points for a subset image. Any points outside of this range
# are either too narrow or too shallow to fit needle.
# remember that we are talking about rows and columns of the IMAGE...
# ...NOT the graph. Images start with 0,0 at upper left.
# Graphs start with 0,0 in lower left
diff.haystack.needle.height <- haystack.height-needle.height
poi.rows <- (1:diff.haystack.needle.height)
diff.haystack.needle.width <- haystack.width-needle.width
poi.columns <- (1:diff.haystack.needle.width)
xpos <<- numeric()
ypos <<- numeric()
ccf.max <<- numeric()
stepSize <- 100
for (rowIndex in seq(from=poi.rows[1],
to=poi.rows[length(poi.rows)],
by=stepSize)) {
for (columnIndex in seq(from=poi.columns[1],
to=poi.columns[length(poi.columns)],
by=stepSize)) {
# if (rowIndex+needle.height < haystack.height &
# columnIndex+needle.width < haystack.width) {
haystack.subset.to.be.correlated <- haystack.red[
(rowIndex:(rowIndex+needle.height)),
(columnIndex:(columnIndex+needle.width))
]
ccf.object <- ccf(as.vector(haystack.subset.to.be.correlated),
as.vector(needle.red),
plot=FALSE)
max.ccf <- max(ccf.object$acf)
ccf.max <<- append(ccf.max,max.ccf)
xpos <<- append(xpos,columnIndex)
ypos <<- append(ypos,rowIndex)
###########
# creates pretty graphics - and slows things down
maintitle <- paste("ccf=",round(max.ccf,2),"xpos=",columnIndex,"ypos=",rowIndex)
maintitle2 <- paste("ccf=",round(max.ccf,2))
saveHere <- paste0("Plot",sprintf("%04d",rowIndex),sprintf("%04d",columnIndex),"_x",columnIndex,"_y",rowIndex,".png")
png(filename=saveHere)
# nf <- layout(matrix(c(1,2),1,2,byrow=T),c(1,1),c(1,1),T)
#layout.show(nf)
#boxplot(ccf.object$acf,ylim=c(0,1),main=maintitle2)
plot(c(0, haystack.width), c(0, haystack.height), main=maintitle,type = "n")
# rasterImage(haystack.subset.to.be.correlated,
# xleft=0,
# xright=ncol(haystack.subset.to.be.correlated),
# ytop=nrow(haystack.subset.to.be.correlated),
# ybottom=0)
rasterImage(haystack.raster,
xleft=0,
xright=haystack.width,
ytop=haystack.height,
ybottom=0)
rasterImage(needle.red,
xleft=columnIndex,
xright=(needle.width+columnIndex),
ytop=(haystack.height-(rowIndex+needle.height)),
ybottom=(haystack.height-rowIndex))
dev.off()
###########
cat("rowIndex=",rowIndex,"columnIndex=",columnIndex," \r")
# }
}
}
ccf.results <- matrix(c(xpos,ypos,ccf.max),ncol=3)
colnames(ccf.results) <- c("xpos","ypos","ccf.max")
}
|
768d2d175e20fdb1f2ada194e906d7950dc67cfc
|
4789c9f646348cee93918b3edce7ad81e8b92b40
|
/man/correlations.to.adjacencies.Rd
|
9fb3afe03f4e12e6d3476cf63c1609a805a0de5d
|
[] |
no_license
|
cran/brainwaver
|
19316a65e8b358efd5441acb9fa8f86a407ab16d
|
6f0d05b26b1270a2de63b0c86e36b4e23c1f30d3
|
refs/heads/master
| 2021-01-16T19:14:15.079321
| 2010-08-09T00:00:00
| 2010-08-09T00:00:00
| 17,694,878
| 0
| 1
| null | 2014-09-06T01:50:20
| 2014-03-13T04:10:12
|
R
|
UTF-8
|
R
| false
| false
| 2,265
|
rd
|
correlations.to.adjacencies.Rd
|
\name{correlations.to.adjacencies}
\alias{correlations.to.adjacencies}
\alias{ideal.wavelet.levels}
\alias{distance}
\title{Produce adjencency matrices for a given number of edges}
\description{
Given a correlations thingy as produced by \code{const.cor.list},
produce a list of adjacency matrices fiddled to have a preferred number of edges
Actually this is not quite possible, but come as close as \code{choose.thresh.nbedges}
will allow us. A functional parameter allows us to say things like produce the graphs with n log n edges where n is the number of nodes
}
\usage{
correlations.to.adjacencies(correlations, edge.func)
ideal.wavelet.levels(brain)
distance(x,y,z)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{correlations}{a list of correlation matrices produced by \code{const.cor.list}}
\item{edge.func}{a function to mention the way to choose the number of edges given the number of nodes in the graph. In the companion scripts files, the small-limit is used and by default \code{edge.func=(function(x){x*log(x)})}}
\item{brain}{ matrix containing the data time series. Each column of the matrix represents one time series.}
\item{x}{x coordinate}
\item{y}{y coordinate}
\item{z}{z coordinate}
}
\details{
Functions produced to manipulate better nice outputs of the package
}
\value{
\item{correlations.to.adjacencies}{Description of 'comp1'}
\item{ideal.wavelets.levels}{number indicating up to each wavelet scale it is possible to go given the length of the time series}
\item{disctance}{the euclidean distance in 3D}
}
\references{ S. Achard, R. Salvador, B. Whitcher, J. Suckling, Ed Bullmore (2006)
A Resilient, Low-Frequency, Small-World Human Brain Functional Network with Highly Connected Association Cortical Hubs. \emph{Journal of Neuroscience}, Vol. 26, N. 1, pages 63-72.
}
\author{John Aspden, external collaborator of the brainwaver package}
\seealso{\code{\link{const.cor.list}}}
\examples{
data(brain)
brain<-as.matrix(brain)
# WARNING : To process only the first five regions
brain<-brain[,1:5]
n.levels<-4
wave.cor.list<-const.cor.list(brain,n.levels=n.levels)
adj.mat<-correlations.to.adjacencies(wave.cor.list,edge.func=(function(x){x*log(x)}))
}
\keyword{multivariate}
\keyword{ts}
|
d48b183d352d2067ae15ab466b1045bfd3bccd00
|
29585dff702209dd446c0ab52ceea046c58e384e
|
/lfl/R/tail.fsets.R
|
1eceafa39de78498d4deb1d6286df24be360ff88
|
[] |
no_license
|
ingted/R-Examples
|
825440ce468ce608c4d73e2af4c0a0213b81c0fe
|
d0917dbaf698cb8bc0789db0c3ab07453016eab9
|
refs/heads/master
| 2020-04-14T12:29:22.336088
| 2016-07-21T14:01:14
| 2016-07-21T14:01:14
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 250
|
r
|
tail.fsets.R
|
tail.fsets <- function(x, n = 6L, ...) {
if (!is.fsets(x)) {
stop("'x' is not a valid 'fsets' object")
}
v <- vars(x)
s <- specs(x)
class(x) <- setdiff(class(x), 'fsets')
return(fsets(tail(x, n=n), vars=v, specs=s))
}
|
20125db2ca70df4bb063e600b86e329c7ad02a1e
|
58c16d88f72cdbd25567464d26f028849ec07768
|
/cachematrix.R
|
79d8afb0f4bff8aece2f0b3e7534bb9af8ff72cf
|
[] |
no_license
|
rfquah/ProgrammingAssignment2
|
9a183abb23d8e01312bbb0ef9eff587b8a970ed6
|
fcaf9b91ddaaedd1069fe93083e1ea68a97baf92
|
refs/heads/master
| 2020-12-26T00:53:44.894633
| 2014-11-23T14:18:23
| 2014-11-23T14:18:23
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,965
|
r
|
cachematrix.R
|
## makeCacheMatrix is a function that takes a matrix as its argument,
## and sets im (the inverse matrix) to NULL in the function environment.
## Contained within the makeCacheMatrix function is the function set,
## which sets im to NULL in the global environment. But the function set
## is only defined within makeCacheMatrix. it is actually executed within
## the cacheSolve function.
makeCacheMatrix <- function(x = matrix()) {
im <- NULL # inverse matrix, set to NULL every time the makeCacheMatrix function is called
## NOTE: functions defined below (set, get, setinv, and getinv) are only defined here
## they run when called from the cacheSolve function
set <- function(y) {
x <<- y
im <<- NULL
}
get <- function() x # this function returns the value of the original matrix
setinv <- function(inv) im <<- inv # this function is called by cacheSolve() during the first cacheSolve() access,
# and will store the value using the superassignment <<-
getinv <- function() im # this function will return the cached inverse matrix to cacheSolve() on
# subsequent accesses
list(set = set, get = get, setinv = setinv, getinv = getinv)
# this list is created each time makeCacheMatrix() is called. it is a list
# of the internal functions ('methods') so a calling function knows how to
# access those methods.
}
## cacheSolve is a function that takes a matrix (x) as its argument and
## returns the matrix inverse from the cache if found in cache,
## otherwise calculates the inverse and returns the newly calculated
## result.
cacheSolve <- function(x, ...) {
## Return a matrix that is the inverse of 'x'
im <- x$getinv()
if (!is.null(im)) {
message("getting cached data")
return(im)
}
data <- x$get()
im <- solve(data)
x$setinv(im)
im
}
|
72ec46f335798a8f3886004ce7b774152315da5f
|
c0a08d09bb804cbfd9c186ba4e5c75dfb3e4faee
|
/R/TSGMM_PP.R
|
59362e0a69c57881daedab16110fc34e4aef8f00
|
[] |
no_license
|
lalondetl/GMM
|
bd1b21f694a35c09484dfb6e99aa974a6f6a48f6
|
628889324529b0b86f55a4d2054bed5442af0b40
|
refs/heads/master
| 2018-07-05T06:26:23.000514
| 2018-05-31T20:57:15
| 2018-05-31T20:57:15
| 119,891,979
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 6,626
|
r
|
TSGMM_PP.R
|
#' Two-Step Generalized Method of Moments, Truncated Count Component of Longitudinal Hurdle Model
#'
#' This function calculates the Generalized Method of Moments (GMM) parameter estimates and standard errors for the zero-truncated count component ("positive Poisson") of a hurdle model for longitudinal excess zero count responses. This is modeled similarly to a Positive Poisson Zero-Truncated Regression using a log link. The function allows for unbalanced data, meaning subjects can have different numbers of times of observation. Both time-independent covariates and time-dependent covariates can be accommodated. Time-dependent covariates can be handled either by specifying the type of each time-dependent covariate, or by allowing the data to determine appropriate moment conditions through the extended classification method. Data must be organized by subject, and an intercept term is assumed. The function outputs a list with parameter estimates betaHat along with parameter covariance estimates covEst.
#' @param y The vector of positive count responses. This vector must be organized by subject, and by time within subject ((sum(Tvec)) x 1).
#' @param subjectID The vector of subject ID values for each response ((sum(Tvec)) x 1).
#' @param Zmat The design matrix for time-independent covariates ((sum(Tvec)) x K0).
#' @param Xmat The design matrix for time-dependent covariates ((sum(Tvec)) x Ktv).
#' @param Tvec The vector of times for each subject.
#' @param N The number of subjects.
#' @param mc The method of identifying appropriate moment conditions, either 'EC' for extended classification (default) or 'Types' for user-identified types.
#' @param covTypeVec The vector indicating the type of each time-dependent covariate, according to the order of the columns of Xmat.
#' @param betaI The initial parameter estimates.
#' r_c The vector of residuals from hurdle GEE with independent working correlation structure.
#' @keywords GMM
#' @export
#' @examples
#' TSGMM_PP()
TSGMM_PP = function(yvec,subjectID,Zmat,Xmat,Tvec,N,mc='EC',covTypeVec=c(-1),betaI=c(1),r_c=c(-1)){
####################
# DEFINE CONSTANTS #
####################
if(!is.matrix(Zmat)){K0 = 0}
else if(is.matrix(Zmat)){K0 = ncol(Zmat)}
Ktv = ncol(Xmat)
K = 1+K0+Ktv # TOTAL NUMBER OF PARAMETERS #
Tmax = max(Tvec)
K1 = 0
K2 = 0
K3 = 0
K4 = 0
if(covTypeVec[1] != -1)
{
for(k in 1:Ktv)
{
if(covTypeVec[k]==1){K1 = K1+1}
if(covTypeVec[k]==2){K2 = K2+1}
if(covTypeVec[k]==3){K3 = K3+1}
if(covTypeVec[k]==4){K4 = K4+1}
}
}
####################
####################
# CONSTRUCT betaI AND r_c IF NECESSARY #
if(betaI == c(-1)){betaI = rep(0,(1+ncol(Zmat)+ncol(Xmat)))}
##########################################################
## MAXIMUM NUMBER OF ESTIMATING EQUATIONS (PER SUBJECT) ##
##########################################################
if(mc=='Types'){Lmax = 1*Tmax + K0*Tmax + (Tmax^2)*K1 + Tmax*(Tmax+1)/2*K2 + Tmax*K3 + Tmax*(Tmax+1)/2*K4}
##########################################
# IF NECESSARY, FIND TYPES OF PARAMETERS #
##########################################
if(mc=='EC')
{
alpha = 0.05/(Tmax^2)
types = validComb_EC_PP(yvec,Zmat,Xmat,betaI,Tvec,alpha,r_c)
Lmax = 1*Tmax + K0*Tmax + sum(types)
}
##########################################################
##########################################################
#############################################################
# DEFINE QUADRATIC FORM FUNCTION TAKING ONLY BETAI AS INPUT #
#############################################################
QuadForm = function(beta){
G = rep(0,Lmax)
VN = matrix(0,Lmax,Lmax)
Count = rep(0,Lmax) # Vector of denominators for G and VN #
# FILL G AND VN USING VALIDMCPP FOR EACH SUBJECT #
for(i in 1:N)
{
subjectIndex = sum(Tvec[0:(i-1)])+1
if(mc=='EC'){Est_i = validMCPP_EC(yvec,subjectIndex,Zmat,Xmat,beta,Tvec[i],Tmax,Count,types)}
if(mc=='Types'){Est_i = validMCPP_Types(yvec,subjectIndex,Zmat,Xmat,covTypeVec,beta,Tvec[i],Tmax,Count)}
gEst_i = Est_i[[1]]
Count = Est_i[[2]]
G = G + gEst_i
VN = VN + gEst_i%*%t(gEst_i)
}
G = G / Count
# CREATE DIVISOR MATRIX D FROM COUNT #
D = matrix(0,Lmax,Lmax)
for(i in 1:Lmax)
{
for(j in 1:Lmax)
{
D[i,j] = min(Count[i],Count[j])
}
}
W = MASS::ginv(VN / D)
# QUADRATIC FUNCTION TO BE OPTIMIZED #
QF = t(G) %*% W %*% G
QF
} # END QUADFORM #
#############################################################
#############################################################
######################################################################################
# GMM COEFFICIENTS ARE OBTAINED BY MINIMIZING QUADFORM, WITH BETAI AS INITIAL VALUES #
######################################################################################
betahat = optim(betaI, QuadForm)$par
######################################################################################
######################################################################################
########################################################################
# VARIANCE ESTIMATE IS OBTAINED USING DERIVATIVES WITH RESPECT TO BETA #
########################################################################
dBetaG = matrix(0,Lmax,K)
VN = matrix(0,Lmax,Lmax)
Count = rep(0,Lmax) # Vector of denominators for G and VN #
for(i in 1:N)
{
subjectIndex = sum(Tvec[0:(i-1)])+1
if(mc=='EC'){Est_i = validMCPP_EC(yvec,subjectIndex,Zmat,Xmat,betahat,Tvec[i],Tmax,Count,types)}
if(mc=='Types'){Est_i = validMCPP_Types(yvec,subjectIndex,Zmat,Xmat,covTypeVec,betahat,Tvec[i],Tmax,Count)}
gEst_i = Est_i[[1]]
Count = Est_i[[2]]
VN = VN + gEst_i%*%t(gEst_i)
if(mc=='EC'){dBetagEst_i = validMDPP_EC(yvec,subjectIndex,Zmat,Xmat,betahat,Tvec[i],Tmax,types)}
if(mc=='Types'){dBetagEst_i = validMDPP_Types(yvec,subjectIndex,Zmat,Xmat,covTypeVec,betahat,Tvec[i],Tmax)}
dBetaG = dBetaG + dBetagEst_i
}
# CREATE DIVISOR MATRIX D FROM COUNT #
D = matrix(0,Lmax,Lmax)
for(i in 1:Lmax)
{
for(j in 1:Lmax)
{
D[i,j] = min(Count[i],Count[j])
}
}
## CREATE DIVISOR MATRIX FOR DBETAG FROM COUNT ##
Divisor = matrix(c(rep(Count,K)),length(Count),K)
dBetaG = dBetaG / Divisor
W = MASS::ginv(VN / D)
AsymptoticWeight = t(dBetaG) %*% W %*% dBetaG
AsymptoticCovariance = (1/N)*MASS::ginv(AsymptoticWeight)
########################################################################
########################################################################
list(betaHat=betahat, covEst = AsymptoticCovariance)
} # END TSGMM_PP #
|
b718936b316fba85246b6031b065d80fee946c7e
|
4bedace4382076f899e3969043a4e22a1c5da62c
|
/Testing 2 file.R
|
749dea5983c319286eced40da7cbe2b4c9261cc8
|
[] |
no_license
|
muchandifungam/Test-data
|
5bef5bd5a118569d6abe77a456347392ea19acd9
|
d64e975d998639afdd8a774c98272f1d63453b0d
|
refs/heads/master
| 2023-03-29T02:53:34.299863
| 2021-04-01T18:37:07
| 2021-04-01T18:37:07
| 352,073,933
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 44
|
r
|
Testing 2 file.R
|
a <- (1:10)
b <- c(1:10, nrol=2,ncol=5)
a
b
|
5b00e0d3327050c2401ceddd8ea37f50f94bf05f
|
75ec5fea203bbe5b46867bd3da5d85480bd9f71d
|
/High-dimension/Cross Validation Steps.Work.R.R
|
5d096a1bb4b33d9bbf19540a76c733bcd4faffe2
|
[] |
no_license
|
mshasan/EmLassoSCAD
|
a4db9c16eabd16b42693b07d0f97b327ebf9c57a
|
517c5d3fa7db94eee964a687591a666c0b250eed
|
refs/heads/master
| 2021-01-20T09:57:19.429087
| 2017-05-05T01:20:20
| 2017-05-05T01:20:20
| 90,310,856
| 1
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 6,185
|
r
|
Cross Validation Steps.Work.R.R
|
## Cross Validation for Glasso
## Row Elimination (every row considered as a fold) for one rho
set.seed(100)
cv.gl<-function(p,q) # p rows and q culumns
{
rho<-.01
y<-matrix(rnorm(p*q),ncol=q)
cv.s<-numeric(p)
for(k in 1:p)
{
if (k >= p) {x<-y[0:(k-1), ]} else # Elimination of kth-fold
{if (k < p) {x<-rbind(y[0:(k-1), ] , y[(k+1):p, ])}}
s<-var(x)
cv.s[k]<-glasso(s,rho)$loglik # Single Cross Validation
}
sum(cv.s) # Sum of all corss validation for one rho
list(data=y,fold=x,var=s,ind.cv=cv.s,sum=sum(cv.s))
}
cv.gl(5,2)
## Fold elimination for one rho
set.seed(100)
cv.gl<-function(p,q,m,n) # p rows,q columns,m fold and n rows in each fold
{
rho<-.01
y<-matrix(rnorm(p*q),ncol=q)
cv.s<-numeric(m)
for(k in 1:m)
{ # Elimination of kth-fold
if (k == 1) {x<-y[(n*k+1):20, ]} else
{if (k > 1 && k < m) {x<-rbind(y[1:(n*(k-1)), ] , y[(n*k+1):20, ])}}
{if (k == m) {x<-y[1:(n*(k-1)), ]}}
s<-var(x)
cv.s[k]<-glasso(s,rho)$loglik # Single Cross Validation
}
sum(cv.s) # Sum of all corss validation for one rho
list(data=y,fold=x,var=s,ind.cv=cv.s,sum=sum(cv.s))
}
cv.gl(20,2,4,5)
## Glasso Final Cross Validation
library(glasso)
library(MASS)
set.seed(100)
cv.gl<-function(n,p,k,m,r,d) # n rows,p columns,k fold,m rows in each fold
{ # r rho(s) and d diff. between two rho(s)
rho<-0
cv.tot<-numeric(r)
for (i in 1:r)
{
rho<-rho + d
y<-matrix(rnorm(n*p),ncol=p)
cv.s<-numeric(k)
for(j in 1:k)
{ # Elimination of kth-fold
if (j == 1) {x<-y[(m*j+1):n, ]} else
{if (j > 1 && j < k) {x<-rbind(y[1:(m*(j-1)), ] , y[(m*j+1):20, ])}}
{if (j == k) {x<-y[1:(m*(j-1)), ]}}
s<-var(x)
cv.s[j]<-glasso(s,rho)$loglik # Single Cross Validation
}
cv.tot[i]<-sum(cv.s) # Crosso Validation for different rho
}
list(data=y,fold=x,var=s,ind.cv=cv.s,CV=cv.tot)
}
cv.gl(20,3,5,4,10,.1)
## Adaptive Glasso Final Cross Validation
library(glasso)
library(MASS)
cv.al1<-function(n,p,k,m,r,d,gamma)
{
y<-matrix(rnorm(n*p),ncol=p)
esti.L1.out<-NULL
n<-dim(y)[1]
p<-dim(y)[2]
lambda<-0
cv.tot<-numeric(r)
for(i in 1:r)
{
lambda<-lambda+d
cov.sa<-cov(y)
esti.L1.out<-glasso(cov.sa,lambda)
rhomat<-lambda*matrix(1,p,p)/(pmax(abs(esti.L1.out$wi)^gamma,1e-5))
cv.s.aL1<-numeric(k)
for(j in 1:k)
{ # Elimination of kth-fold
if (j == 1) {x<-y[(m*j+1):n, ]} else
{if (j > 1 && j < k) {x<-rbind(y[1:(m*(j-1)), ] , y[(m*j+1):20, ])}}
{if (j == k) {x<-y[1:(m*(j-1)), ]}}
s<-cov(x)
cv.s.aL1[j]<-glasso(s,rhomat)$loglik # Single Cross Validation
}
cv.tot[i]<-sum(cv.s.aL1) # Crosso Validation for different rho
}
list(data=y,ind.cv=cv.s.aL1,CV=cv.tot)
}
cv.al1(20,3,5,4,10,.01,0.5)
## SCAD Final Cross Validation
library(glasso)
library(MASS)
cv.scad<-function(n,p,k,m,r,d,a)
{
y<-matrix(rnorm(n*p),ncol=p)
est.L1.out<-NULL
n<-dim(y)[1]
p<-dim(y)[2]
lam<-0
cv.tot<-numeric(r)
for(i in 1:r)
{
lam<-lam+d
cov.sa<-cov(y)
est.L1.out<-glasso(cov.sa,lam)
rhomat<-pmax(lam*((abs(est.L1.out$wi)<=lam)
+pmax(a*lam-abs(est.L1.out$wi),0)
*abs((est.L1.out$wi)>lam)/(a-1)/lam),1e-4)
cv.s.scad<-numeric(k)
for(j in 1:k)
{ # Elimination of kth-fold
if (j == 1) {x<-y[(m*j+1):n, ]} else
{if (j > 1 && j < k) {x<-rbind(y[1:(m*(j-1)), ] , y[(m*j+1):20, ])}}
{if (j == k) {x<-y[1:(m*(j-1)), ]}}
s<-cov(x)
cv.s.scad[j]<-glasso(s,rhomat)$loglik # Single Cross Validation
}
cv.tot[i]<-sum(cv.s.scad) # Crosso Validation for different rho
}
list(data=y,ind.cv=cv.s.scad,CV=cv.tot)
}
cv.scad(20,3,5,4,10,.01,3.7)
## SCAD Final Cross Validation(multiple steps)
library(glasso)
library(MASS)
cv.scad<-function(n,p,k,m,r,d,a,g)
{
y<-matrix(rnorm(n*p),ncol=p)
est.L1.out<-NULL
cv.scad<-NULL
n<-dim(y)[1]
p<-dim(y)[2]
lam<-0
CV<-numeric(r)
for(i in 1:r)
{
lam<-lam+d
cov.sa<-cov(y)
cv.scad<-numeric(g)
for (l in 1:g)
{
est.L1.out<-glasso(cov.sa,lam)
rhomat<-pmax(lam*((abs(est.L1.out$wi)<=lam)
+pmax(a*lam-abs(est.L1.out$wi),0)
*abs((est.L1.out$wi)>lam)/(a-1)/lam),1e-4)
cv.s.scad<-numeric(k)
for(j in 1:k)
{ # Elimination of kth-fold
if (j == 1) {x<-y[(m*j+1):n, ]} else
{if (j > 1 && j < k) {x<-rbind(y[1:(m*(j-1)), ] , y[(m*j+1):20, ])}}
{if (j == k) {x<-y[1:(m*(j-1)), ]}}
s<-cov(x)
cv.s.scad[j]<-glasso(s,rhomat)$loglik # Single Cross Validation
}
cv.scad[l]<-cv.s.scad[j]
}
CV[i]<-sum(cv.scad) # Cross Validation for different rho
}
list(data=y,cv.s.scad=cv.s.scad,cv.scad=cv.scad,CV=CV)
}
cv.scad(20,3,5,4,10,.01,3.7,5)
## SCAD Final Cross Validation (multiple steps with while statement and convergence)
library(glasso)
library(MASS)
cv.scad<-function(n,p,k,m,r,d,a)
{
y<-matrix(rnorm(n*p),ncol=p)
est.L1.out<-NULL
n<-dim(y)[1]
p<-dim(y)[2]
lam<-0
cv.tot<-numeric(r)
for(i in 1:r)
{
lam<-lam+d
cov.sa<-cov(y)
est.L1.out<-glasso(cov.sa,lam)
rhomat<-pmax(lam*((abs(est.L1.out$wi)<=lam)
+pmax(a*lam-abs(est.L1.out$wi),0)
*abs((est.L1.out$wi)>lam)/(a-1)/lam),1e-4)
cv.s.scad<-numeric(k)
for(j in 1:k)
{ # Elimination of kth-fold
if (j == 1) {x<-y[(m*j+1):n, ]} else
{if (j > 1 && j < k) {x<-rbind(y[1:(m*(j-1)), ] , y[(m*j+1):20, ])}}
{if (j == k) {x<-y[1:(m*(j-1)), ]}}
s<-cov(x)
est.scad.old<-NULL
est.scad.new<-est.L1.out$wi
eps<-1
count<-1
while(eps >1e-4)
{
est.scad.old<-est.scad.new
est.scad.new<-glasso(s,rhomat)$wi
count<-count+1
eps<-max(abs(est.scad.old-est.scad.new))
}
cat(" Iterration number ")
cv.s.scad[j]<-glasso(est.scad.old, rhomat)$loglik # Single Cross Validation
}
cv.tot[i]<-sum(cv.s.scad) # Crosso Validation for different rho
}
list(data=y,ind.cv=cv.s.scad,CV=cv.tot,iteration=count)
}
cv.scad(20,3,5,4,10,.01,3.7)
mu0<-c(3, 1, 4);mu0
sig0<-matrix(c(6,1,-2,1,13,4,-2,4,4), nrow=3, byrow=T);sig0
Y<-mvrnorm(20,mu0,sig0);Y
##K-fold cross-validation
library(DAAG)
cv.lm(df=mydata, fit, m=4) # 3 fold cross-validation
|
9d980c567eb013deb292e4267a9583e108347380
|
37d272c2e369a1eb1e55ca7d38ceb9911d42dd05
|
/man/extract_from_cellranger.Rd
|
1cc6b660a46dc54cca814b0c0431fbd947ab4a06
|
[] |
no_license
|
shambam/scgex_navigator
|
3afc69c769442707c496b5f1ad6fbb0bb52c77a0
|
361407a6848c972ed117b5f4d3b6399a3b944930
|
refs/heads/master
| 2021-01-22T23:54:18.501033
| 2017-05-31T09:12:26
| 2017-05-31T09:12:26
| 85,680,902
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 551
|
rd
|
extract_from_cellranger.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dataprep_funcs.R
\name{extract_from_cellranger}
\alias{extract_from_cellranger}
\title{Extracts the components need from a cellRanger aggrergated file}
\usage{
extract_from_cellranger(path, build = c("mm10", "hg38"))
}
\arguments{
\item{path}{A path to the cellranger output folder}
\item{build}{The version of the build, default is "mm10", alternative is "hg38"}
}
\description{
lkjfl;ksjd lksjdf lksdfj lksd fjslkdf slkdjf lskjd
}
\keyword{cellranger}
\keyword{extract}
|
9313c73dfcbf8323a7f6617d48a14deff9132cd1
|
295b502d7e367edfa0ee4017f1a7b6a4135211d3
|
/R/predict.ELMCoxBoost.R
|
06e34a5a40f27a8e594556ade9d8c246bc5bf7c3
|
[] |
no_license
|
whcsu/SurvELM
|
e5b09b504af20ad5e322c687504090cf19689cb9
|
c9297f6bd29ff3448e84d1420aeed1215e611ba9
|
refs/heads/master
| 2021-05-11T02:52:36.034952
| 2020-01-28T08:57:32
| 2020-01-28T08:57:32
| 117,897,717
| 10
| 2
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,755
|
r
|
predict.ELMCoxBoost.R
|
##' Predicting from An Extreme Learning Machine Cox Model with Likelihood Based Boosting
##' @title SurvELM predict.ELMCoxBoost
##' @param object An object that inherits from class ELMCoxBoost.
##' @param testx A data frame in which to look for variables with which to predict.
##' @param ... Additional arguments for \code{CoxBoost}.
##' @return produces a vector of predictions or a matrix of predictions
##' @seealso \code{predict.CoxBoost}
##' @author Hong Wang
##' @examples
##' set.seed(123)
##' library(SurvELM)
##' library(survival)
##' #Lung DATA
##' data(lung)
##' lung=na.omit(lung)
##' lung[,3]=lung[,3]-1
##' n=dim(lung)[1]
##' L=sample(1:n,ceiling(n*0.5))
##' trset<-lung[L,]
##' teset<-lung[-L,]
##' rii=c(2,3)
##' elmsurvmodel=ELMCoxBoost(x=trset[,-rii],y=Surv(trset[,rii[1]], trset[,rii[2]]))
##' #THE predicted linear predictor
##' testpre=predict(elmsurvmodel,teset[,-c(rii)])
##' #The predicted cumulative incidence function
##' testprecif=predict(elmsurvmodel,teset[,-c(rii)],type="CIF")
##' # The predicted partial log-likelihood
##' testprellk=predict(elmsurvmodel,teset[,-c(rii)],newtime=teset[,rii[1]],
##' newstatus=teset[,rii[2]],type="logplik")
##' uniquetimes=sort(unique(trset$time))
##' # The predicted probability of not yet having had the event at the time points given in times
##' testprerisk=predict(elmsurvmodel,teset[,-c(rii)],times=uniquetimes,type="risk")
##' @export
predict.ELMCoxBoost<- function(object, testx,...) {
Kernel_para = object$Kernel_para
kerneltype = object$kerneltype
trainx = object$trainx
H = kernmat(trainx,kerneltype, Kernel_para,testx)
#elmcoxpre = survFit(mbelmcox$mbelm_cox,as.matrix(H))
elmcoxpre = predict(object$elmcoxboost,as.matrix(H),...)
return(elmcoxpre)
}
|
6ca58b6159703592173cfae73c22a8d8c25709b4
|
e56c98512229172467f1f4f99870ed2aac5324cd
|
/man/sdm.Rd
|
54c5e1d8363b0940d3ab63f53b8f736ef4bcde6f
|
[] |
no_license
|
babaknaimi/sdm
|
6de65e769562adedca326adaaf66518870830e62
|
63ec623526e3867158a4847e3eff1c5d3350552b
|
refs/heads/master
| 2021-11-25T10:59:02.624170
| 2021-11-11T05:35:37
| 2021-11-11T07:19:16
| 39,352,874
| 18
| 7
| null | null | null | null |
UTF-8
|
R
| false
| false
| 3,676
|
rd
|
sdm.Rd
|
\name{sdm}
\alias{sdm}
\alias{sdm,ANY,sdmdata,character-method}
\alias{sdm,sdmdata,.sdmCorSetting,ANY-method}
\alias{sdm,ANY,sdmdata,.sdmCorSetting-method}
\title{Fit and evaluate species distribution models}
\description{
Fits sdm for single or multiple species using single or multiple methods specified by a user in \code{methods} argument, and evaluates their performance.
}
\usage{
sdm(formula, data, methods,...)
}
\arguments{
\item{formula}{Specifies the structure of the model, types of features, etc.}
\item{data}{a \code{sdmdata} object created using \code{\link{sdmData}} function}
\item{methods}{Character. Specifies the methods, used to fit the models}
\item{...}{additional arguments}
}
\details{
sdm fits multiple models and can be used to generate multiple runs (replicates) of each method through partitioning (using one or several partitioning methods including: \code{subsampling}, \code{cross-validation}, and \code{bootstrapping}.
Each model is evaluated against training data, and if available, splitted data (through partitioning; called dependent test data as well, i.e., "dep.test") and/or indipendent test data ("indep.test").
User should make sure that the methods are available and the required packages for them are installed before putting their names in the function, otherwise, the methods that cannot be run for any reason, are excluded by the function. It is a good practice to call \code{\link{installAll}} function (just one time when the sdm is installed), that tries to install all the packages that may be needed somewhere in the \code{sdm} package.
A new method can be adopted and added to the package by a user using \code{\link{add}} function. It is also possible to get an instance of an existing method, override the setting and definition, and then add it with a new name (e.g., my.glm).
The output would be a single object (\code{sdmModels}) that can be read/reproduced everywhere (e.g., on a new machine). A setting object can also be taken (exported) out of the output \code{sdmModels} object, that can be used to reproduce the same practice but given new conditions (i.e., new dataset, area. etc.)
}
\value{
an object of class \code{sdmModels}
}
\references{
Naimi, B., Araujo, M.B. (2016) sdm: a reproducible and extensible R platform for species distribution modelling, Ecography, 39:368-375, DOI: 10.1111/ecog.01881
}
\author{Babak Naimi \email{naimi.b@gmail.com}
\url{https://www.r-gis.net/}
\url{https://www.biogeoinformatics.org}
}
\examples{
\dontrun{
file <- system.file("external/pa_df.csv", package="sdm")
df <- read.csv(file)
head(df)
d <- sdmData(sp~b15+NDVI,train=df)
d
#----
# Example 1: fit using 3 models, and no evaluation (evaluation based on training dataset):
m <- sdm(sp~b15+NDVI,data=d,methods=c('glm','gam','gbm'))
m
# Example 3: fit using 5 models, and
# evaluates using 10 runs of subsampling replications taking 30 percent as test:
m <- sdm(sp~b15+NDVI,data=d,methods=c('glm','gam','gbm','svm','rf'),
replication='sub',test.percent=30,n=10)
m
# Example 3: fits using 5 models, and
# evaluates using 10 runs of both 5-folds cross-validation and bootsrapping replication methods
m <- sdm(sp~.,data=d,methods=c('gbm','tree','mars','mda','fda'),
replication=c('cv','boot'),cv.folds=5,n=10)
m
# Example 4: fit using 3 models; evaluate the models using subsampling,
# and override the default settings for the method brt:
m <- sdm(sp~b15+NDVI,data=d,methods=c('glm','gam','brt'),test.p=30,
modelSettings=list(brt=list(n.trees=500,train.fraction=0.8)))
m
}
}
\keyword{spatial}
\keyword{model}
\keyword{data}
\keyword{species}
|
b895df96ad71d67acdac1b6bcd3feda3018bfe70
|
bad016da9e32faadd8800ff5e65f57cb6909479c
|
/ui.R
|
0d266877650adf147d1647cbba94fbc5ba363e6f
|
[] |
no_license
|
kevindaymath/NCEmissionsShiny
|
a6636c3ec9602b5f95c76380f610e21461186a8d
|
0692faa1284e544cb5093dff37e0b950cdb6ec97
|
refs/heads/master
| 2020-03-21T01:26:02.603890
| 2018-06-20T15:25:08
| 2018-06-20T15:25:08
| 137,942,356
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,309
|
r
|
ui.R
|
options(warn=-1)
library(shiny)
library(leaflet)
library(DT)
load("data\\data.RData")
# Define UI for dataset viewer application
fluidPage(
# Application title
titlePanel("EPA Emissions Data (in tons)"),
# sidebarLayout(
# sidebarPanel(
# helpText("Select a county below"),
# selectInput("county",label="Choose a county",
# choices=NCCountyList$County.Name, selected="Alamance"),
# width = 3,
# br()
# ),
mainPanel(
tabsetPanel(
tabPanel("Map", selectInput("variable",label="Choose a varsiable",
choices=var, selected=var[1]),
htmlOutput("County"),leafletOutput("CountyMap"),
htmlOutput("Region"),leafletOutput("RegionMap")),
tabPanel("Table", sidebarPanel(
radioButtons("Level",label="Choose a level",
choices=list("County"=1,"Region"=2),selected =1), width = 3),
checkboxGroupInput("show_vars","Display",unique(categories$Category),selected = "Agriculture",inline=TRUE),
DT::dataTableOutput("tableCounty")
)
)
)
)
|
5cc219aa3f04c735d84503be238277f24c890db4
|
8a03ba8694baec69d38cb30f3198fc1d770b467b
|
/R/graph_mwra_sewage_data.R
|
38ecd92d9bfdae03678e8cdf47efa6a47586c7e4
|
[] |
no_license
|
smach/WrangleMACovidData
|
c7817e841d16698bb84e9566fefe08dfc5c941a1
|
cdbb0f9cb1aa22447155aca520db241ee3b6d46d
|
refs/heads/main
| 2023-02-20T08:11:01.434747
| 2021-01-22T00:32:18
| 2021-01-22T00:32:18
| 287,988,156
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 925
|
r
|
graph_mwra_sewage_data.R
|
#' Generate interactive plotly graph of MWRA sewage testing data
#'
#' Data from http://www.mwra.com/biobot/biobotdata.htm generated by import_mwra_sewage_data() function. See mwra-data vignette for details.
#'
#' @param mydf dataframe of MWRA sewage test data from import_mwra_sewage_data()
#' @param mytitle character string of desired graph title
#'
#' @return plotly graph
#' @export
#'
graph_sewage_data <- function(mydf, mytitle = "") {
plotly::ggplotly(
ggplot2::ggplot(mydf, ggplot2::aes(x = SampleDate, y = DetectedCovid, colour = Region, Group = Region)) +
ggplot2::geom_line() +
ggplot2::geom_point() +
ggplot2::scale_color_manual(values = c("#377eb8", "#4daf4a")) +
ggplot2::xlab("") +
ggplot2::ylab("RNA copies/mL") +
ggplot2::theme_minimal() +
ggplot2::scale_x_date(date_breaks = "28 days", date_labels = "%b %e") +
ggplot2::labs(title = mytitle)
)
}
|
0d685a41b32d1b2d07aeb02b53802e021259538e
|
6dcd7c7215b226abf5ed6736b9dc118af31e7466
|
/man/all_object_size.Rd
|
3a50b744ea8096e14eb59feaaf88f273de3e3d0f
|
[
"MIT"
] |
permissive
|
adrientaudiere/MiscMetabar
|
b31a841cdac749a0074a0c24c7f8248348c64a22
|
2cb7839d26668836aac129af7115dea0a52385c0
|
refs/heads/master
| 2023-08-31T06:57:15.546756
| 2023-08-23T09:10:52
| 2023-08-23T09:10:52
| 268,765,075
| 7
| 0
|
NOASSERTION
| 2023-09-06T10:12:57
| 2020-06-02T10:02:00
|
R
|
UTF-8
|
R
| false
| true
| 487
|
rd
|
all_object_size.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/miscellanous.R
\name{all_object_size}
\alias{all_object_size}
\title{List the size of all objects of the GlobalEnv.}
\usage{
all_object_size()
}
\value{
a list of size
}
\description{
\ifelse{html}{\href{https://lifecycle.r-lib.org/articles/stages.html#stable}{\figure{lifecycle-stable.svg}{options: alt='[Stable]'}}}{\strong{[Stable]}}
Code from https://tolstoy.newcastle.edu.au/R/e6/help/09/01/1121.html
}
|
734f5489eab1d5272b3d54f2e7ca3ee27dc0d58f
|
5307b4b351ab14d178ce00850f3b336ceb58c8e6
|
/markov_smoking_probabilistic_novgam.R
|
1b6542e0c025b31ab21e00a1827b7b892acc5bb5
|
[] |
no_license
|
dearku/Markov-model-without-VGAM
|
02e1f9bc27ba124250889ca2609f57a07cc590b2
|
590398230db8c90d0ee6d316726c08a9e5459ce3
|
refs/heads/main
| 2023-05-23T10:31:21.294633
| 2021-06-17T15:52:27
| 2021-06-17T15:52:27
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 10,476
|
r
|
markov_smoking_probabilistic_novgam.R
|
# Smoking Cessation Markov model
# Edited to use rbeta() instead of rdiric() to avoid dependency on VGAM
# Howard Thom
# Load necessary libraries
# If not installed use the following line first
# install.packages("BCEA)
library(BCEA)
# Set a random number seed so results are reproducible
set.seed(1002435)
# Define the number and names of treatments
# These are Standard of Care with website
# and Standard of Care without website
n_treatments <- 2
treatment_names <- c("SoC with website", "SoC")
# Define the number and names of states of the model
# This is two and they are "Smoking" and "Not smoking"
n_states <- 2
state_names <- c("Smoking", "Not smoking")
# Define the number of cycles
# This is 10 as the time horizon is 5 years and cycle length is 6 months
# The code will work for any even n_cycles (need to change the discounting code if
# an odd number of cycles is desired)
n_cycles <- 10
# Define simulation parameters
# This is the number of PSA samples to use
n_samples <- 1000
#############################################################################
## Input parameters #########################################################
#############################################################################
# The transition matrix is a 2x2 matrix
# Rows sum to 1
# Top left entry is transition probability from smoking to smoking
# Top right is transition probability from smoking to not smoking
# Bottom left is transition probability from not smoking to smoking
# Bottom right is transition probability from not smoking to not smoking
# There is one transition matrix for each treatment option and each PSA sample
# Store them in an array with (before filling in below) NA entries
transition_matrices <- array(dim = c(n_treatments, n_samples, n_states, n_states),
dimnames = list(treatment_names, NULL, state_names, state_names))
# First the transition matrix for Standard of Care with website
# Transitions from smoking
temp <- rbeta(n_samples, 85, 15)
transition_matrices["SoC with website", , "Smoking", ] <- matrix(c(temp, 1-temp), ncol = 2)
# Transitions from not smoking
temp <- rbeta(n_samples, 8, 92)
transition_matrices["SoC with website", , "Not smoking", ] <- matrix(c(temp, 1-temp), ncol = 2)
# Second the transition matrix for Standard of Care
# Transitions from smoking
temp <- rbeta(n_samples, 88, 12)
transition_matrices["SoC", , "Smoking", ] <- matrix(c(temp, 1-temp), ncol = 2)
# Transitions from not smoking
# These should be the same as the transition probabilities from not smoking for SoC with website
# as the website has no impact on probability of relapse
transition_matrices["SoC", , "Not smoking", ] <- transition_matrices["SoC with website", , "Not smoking", ]
# Now define the QALYS associated with the states per cycle
# There is one for each PSA sample and each state
# Store in an NA array and then fill in below
state_qalys <- array(dim = c(n_samples, n_states), dimnames = list(NULL, state_names))
# QALY associated with 1-year in the smoking state is Normal(mean = 0_95, SD = 0_01)
# Divide by 2 as cycle length is 6 months
state_qalys[, "Smoking"] <- rnorm(n_samples, mean = 0.95, sd = 0.01) / 2
# QALY associated with 1-year in the not smoking state is 1 (no uncertainty)
# So all PSA samples have the same value
# Again divide by 2 as cycle length is 6 months
state_qalys[, "Not smoking"] <- 1 / 2
# And finally define the state costs
# These are all zero as the only cost is a one-off subscription fee of ?50
# to the smoking cessation website
state_costs<-array(0, dim = c(n_samples, n_states), dimnames = list(NULL, state_names))
# Define the treatment costs
# One for each PSA sample and each treatment
# Treatment costs are actually fixed but this allows flexibility if we
# want to include uncertainty / randomness in the cost
treatment_costs <- array(dim = c(n_treatments, n_samples), dimnames = list(treatment_names, NULL))
# Cost of the smoking cessation website is a one-off subscription fee of ?50
treatment_costs["SoC with website", ] <- 50
# Zero cost for standard of care
treatment_costs["SoC", ] <- 0
#############################################################################
## Simulation ###############################################################
#############################################################################
# Build an array to store the cohort vector at each cycle
# Each cohort vector has 2 ( = n_states) elements: probability of being in smoking state,
# and probability of being in the not smoking state
# There is one cohort vector for each treatment, for each PSA sample, for each cycle_
cohort_vectors <- array(dim = c(n_treatments, n_samples, n_cycles, n_states),
dimnames = list(treatment_names, NULL, NULL, state_names))
# Assume that everyone starts in the smoking state no matter the treatment
cohort_vectors[, , 1, "Smoking"] <- 1
cohort_vectors[, , 1, "Not smoking"] <- 0
# Build an array to store the costs and QALYs accrued per cycle
# One for each treatment, for each PSA sample, for each cycle
# These will be filled in below in the main model code
# Then discounted and summed to contribute to total costs and total QALYs
cycle_costs <- array(dim = c(n_treatments, n_samples, n_cycles),
dimnames = list(treatment_names, NULL, NULL))
cycle_qalys <- array(dim = c(n_treatments, n_samples, n_cycles),
dimnames = list(treatment_names, NULL, NULL))
# Build arrays to store the total costs and total QALYs
# There is one for each treatment and each PSA sample
# These are filled in below using cycle_costs,
# treatment_costs, and cycle_qalys
total_costs <- array(dim = c(n_treatments, n_samples),
dimnames = list(treatment_names, NULL))
total_qalys <- array(dim = c(n_treatments, n_samples),
dimnames = list(treatment_names, NULL))
# The remainder of the cohort_vectors will be filled in by Markov updating below
# Main model code
# Loop over the treatment options
for (i_treatment in 1:n_treatments)
{
# Loop over the PSA samples
for (i_sample in 1:n_samples)
{
# Loop over the cycles
# Cycle 1 is already defined so only need to update cycles 2:n_cycles
for (i_cycle in 2:n_cycles)
{
# Markov update
# Multiply previous cycle's cohort vector by transition matrix
# i_e_ pi_j = pi_(j-1)*P
cohort_vectors[i_treatment, i_sample, i_cycle, ] <-
cohort_vectors[i_treatment, i_sample, i_cycle-1, ]%*%
transition_matrices[i_treatment, i_sample, , ]
}
# Now use the cohort vectors to calculate the
# total costs for each cycle
cycle_costs[i_treatment, i_sample, ] <-
cohort_vectors[i_treatment, i_sample, , ] %*% state_costs[i_sample, ]
# And total QALYs for each cycle
cycle_qalys[i_treatment, i_sample, ] <-
cohort_vectors[i_treatment, i_sample, , ] %*% state_qalys[i_sample, ]
# Combine the cycle_costs and treatment_costs to get total costs
# Apply the discount factor
# (1 in first year, 1_035 in second, 1_035^2 in third, and so on)
# Each year acounts for two cycles so need to repeat the discount values
total_costs[i_treatment, i_sample] <- treatment_costs[i_treatment, i_sample] +
cycle_costs[i_treatment, i_sample, ] %*%
(1 / 1.035)^rep(c(0:(n_cycles / 2-1)), each = 2)
# Combine the cycle_qalys to get total qalys
# Apply the discount factor
# (1 in first year, 1_035 in second, 1_035^2 in third, and so on)
# Each year acounts for two cycles so need to repeat the discount values
total_qalys[i_treatment, i_sample] <- cycle_qalys[i_treatment, i_sample, ]%*%
(1 / 1.035)^rep(c(0:(n_cycles / 2-1)), each = 2)
}
}
#############################################################################
## BCEA Analysis of results #################################################
#############################################################################
#Note costs and QALYs need to be transposed in this example for BCEA to run
smoking_bcea <- bcea(e = t(total_qalys), c = t(total_costs), ref = 1, interventions = treatment_names)
#get summary statistics
summary(smoking_bcea, wtp = 20000)
#plot the cost-effectiveness plane
ceplane.plot(smoking_bcea, wtp = 20000)
#plot a CEAC
smoking_multi_ce <- multi.ce(smoking_bcea)
mce.plot(smoking_multi_ce, pos = c(1,0))
#############################################################################
## Base R Analysis of results ###############################################
#############################################################################
# Average costs
# These are ?50 on the website and 0 on standard of care as there are no
# costs other than the website subscription cost
average_costs <- rowMeans(total_costs)
# Average effects (in QALY units)
# These are slightly higher on the website as higher probability of
# quitting smoking
average_effects <- rowMeans(total_qalys)
# Incremental costs and effects relative to standard of care
# No uncertainty in the costs as the website cost is fixed at ?50
incremental_costs <- total_costs["SoC with website", ]-total_costs["SoC", ]
# In some samples the website leads to higher QALYs but in others it is negative
# There is uncertainty as to whether the website is an improvement over SoC
incremental_effects <- total_qalys["SoC with website", ]-total_qalys["SoC", ]
# The ICER comparing Standard of care with website to standard of care
# This is much lower than the ?20,000 willingness-to-pay threshold indicating
# good value for money
ICER <- mean(incremental_costs) / mean(incremental_effects)
# Incremental net benefit at the ?20,000 willingness-to-pay
# Sometimes positive (website more cost-effective) and sometimes negative (SoC more cost-effective)
# Need to look at averages and consider probabilities of cost-effectiveness
incremental_net_benefit <- 20000*incremental_effects-incremental_costs
# Average incremental net benefit
# This is positive indicating cost-effectiveness at the ?20,000 threshold
average_inb <- mean(incremental_net_benefit)
# Probability cost-effective
# This is the proportion of samples for which the incremental net benefit is positive
# It is close to 72%, representing good degree of certainty
# in recommendation to adopt the smoking cessation website
probability_cost_effective <- sum(incremental_net_benefit>0) / n_samples
|
c50434076e713a9cf75f241d6e3e4e93777e1ecb
|
6fb04083c9d4ee38349fc04f499a4bf83f6b32c9
|
/R/wilcox.test.R
|
253541fd0f101a9644451b158ee0fed57648d416
|
[] |
no_license
|
phani-srikar/AdapteR
|
39c6995853198f01d17a85ac60f319de47637f89
|
81c481df487f3cbb3d5d8b3787441ba1f8a96580
|
refs/heads/master
| 2020-08-09T10:33:28.096123
| 2017-09-07T09:39:25
| 2017-09-07T09:39:25
| 214,069,176
| 0
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 6,724
|
r
|
wilcox.test.R
|
#' Wilcox Test.
#'
#' An S3 class to represent wilcox signed Rank test Performs one- and
#' two-sample Wilcoxon tests on vectors of data; the latter is also
#' known as \code{Mann-Whitney} test.
#' If only x is given, or if both x and y are given and paired is
#' TRUE, a Wilcoxon signed rank test of the null that the distribution
#' of x (in the one sample case) or of x - y (in the paired two sample
#' case) is symmetric about mu is performed. Otherwise, if both x and
#' y are given and paired is FALSE, a Wilcoxon rank sum test
#' (equivalent to the Mann-Whitney test: see the Note) is carried out.
#' In this case, the null hypothesis is that the distributions of x
#' and y differ by a location shift of mu and the alternative is that
#' they differ by some other location shift (and the one-sided
#' alternative "greater" is that x is shifted to the right of y).
#' @param x FLvector of data values. Non-finite (e.g., infinite or
#' missing) values will be omitted.
#' @param y an optional FLVector of data values: as with x non-finite values will be omitted.
#' @param paired a logical indicating whether you want a paired test.
#' @section Constraints: conf.level, conf.int is not supported currently for FL objects.
#' @seealso \code{\link[stats]{wilcox.test}} for R reference implementation.
#' @return A list with class "htest".
#' @examples
#' Wilcoxon Signed Rank test:
#' a <- as.FLVector(c(85,70,40,65,80,75,55,20))
#' b <- as.FLVector(c(75,50,50,40,20,65,40,25))
#' res <- wilcox.test(a, b, paired = TRUE)
#'
#' Mann-Whitney test:
#' a <- as.FLVector(c(6, 8, 2, 4, 4, 5))
#' b <- as.FLVector(c(7, 10, 4, 3, 5, 6))
#' res <- wilcox.test(a, b, paired = FALSE)
#' @export
setGeneric("wilcox.test",function(x, ...)
standardGeneric("wilcox.test"))
setMethod("wilcox.test",signature(x="ANY"),
function(x,...){
return(stats::wilcox.test(x,...))
})
setMethod("wilcox.test",signature(x="FLVector"),
function(x,y = NULL,paired = TRUE, mu = 0,...)
{
if(!is.FLVector(x) || !is.FLVector(y))
stop("Must be FLVector")
else {
if(paired) {
vviewName <- gen_view_name("wsrTest")
if(length(x)> length(y))
res <- sqlSendUpdate(connection, createHypoView(y,x,vviewName))
else
res <- sqlSendUpdate(connection, createHypoView(x,y,vviewName))
##
vcall <- as.list(sys.call())
dname = paste0(vcall[2]," and ",vcall[3])
## Using Stored Proc Query.
ret <- sqlStoredProc(connection,
"FLWSRTest",
TableName = vviewName,
Val1ColName = "Num_Val1",
Val2ColName = "Num_Val2",
WhereClause = "NULL" ,
GroupBy = "DatasetID",
TableOutput = 1,
outputParameter = c(ResultTable = 'a'))
colnames(ret) <- tolower(colnames(ret))
if(!is.null(ret$resulttable)){
sqlstr <- paste0( "SELECT q.W_STAT AS w_stat,
q.P_VALUE AS p_value,
q.W_STAT_Neg AS w_stat_neg,
q.W_STAT_Posi AS w_stat_posi
FROM ",ret$resulttable," AS q")
result <- sqlQuery(connection,sqlstr)
}
else result <- ret
stats <- c(V = result$w_stat_posi)
##
res <- list(statistic = stats,
parameter = NULL,
p.value = result$p_value,
null.value = c("location shift"=0),
alternative = "two.sided",
method = "Wilcoxon signed rank test",
data.name =dname
# call=vcall
)
class(res) <- "htest"
dropView(vviewName)
return(res)
} else {
vviewName <- gen_view_name("MWTest")
t <- constructUnionSQL(pFrom = c(a = constructSelect(x),
b = constructSelect(y)),
pSelect = list(a = c(DatasetID=1,
GroupID = 1,
Num_Val = "a.vectorValueColumn"),
b = c(DatasetID=1,
GroupID = 2,
Num_Val = "b.vectorValueColumn")))
q <- createView(vviewName,t)
vcall <- as.list(sys.call())
dname = paste0(vcall[2]," and ",vcall[3])
ret <- sqlStoredProc(connection,
"FLMWTest",
TableName = vviewName,
ValColName = "Num_Val",
GroupColName = "GroupID",
WhereClause = "NULL" ,
GroupBy = "DatasetID",
TableOutput = 1,
outputParameter = c(ResultTable = 'a'))
colnames(ret) <- tolower(colnames(ret))
if(!is.null(ret$resulttable)){
sqlstr <- paste0("SELECT U_STAT AS u_stat, \n ",
" P_VALUE AS p_value \n ",
" FROM ",ret$resulttable)
result <- sqlQuery(connection, sqlstr)
}
else result <- ret
res <- list(statistic = c(W = result$u_stat),
parameter = NULL,
p.value = result$p_value,
null.value = c("location shift"=0),
alternative = "two.sided",
method = "Wilcoxon rank sum test",
data.name = dname
)
class(res) <- "htest"
dropView(vviewName)
return(res)
}
}
})
|
9d2fb41b4fd15ba85423613fd0f5babc3e329f6a
|
2e37e4e3506c814f0449fae6971c4e10440e5e01
|
/tests/testthat/testsummary.zoonWorkflow.R
|
725e72d716bf594b7b41a7b1f44b20e0ac50d8d8
|
[] |
no_license
|
cran/zoon
|
f5c03c31d417238c9be03c7a2bca133c71749fa3
|
cd5903ef805b4ed31fb4c2b338c32f02f1fee1e3
|
refs/heads/master
| 2020-04-06T21:25:50.085542
| 2020-02-28T15:30:02
| 2020-02-28T15:30:02
| 48,091,536
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,252
|
r
|
testsummary.zoonWorkflow.R
|
context("summary.zoonWorkflow")
test_that("summary.zoonWorkflow tests", {
skip_on_cran()
set.seed(1)
expect_is(
summary(workflow(
occurrence = UKAnophelesPlumbeus,
covariate = UKAir,
process = Background(n = 70),
model = LogisticRegression,
output = SameTimePlaceMap
)),
class = "character"
)
set.seed(1)
expect_is(
summary(workflow(
occurrence = UKAnophelesPlumbeus,
covariate = Chain(UKAir, UKAir, UKAir, UKAir, UKAir, UKAir),
process = Background(n = 70),
model = LogisticRegression,
output = SameTimePlaceMap
)),
class = "character"
)
set.seed(1)
expect_is(
summary(workflow(
occurrence = UKAnophelesPlumbeus,
covariate = list(UKAir, UKAir),
process = Background(n = 70),
model = LogisticRegression,
output = SameTimePlaceMap
)),
class = "character"
)
set.seed(1)
expect_is(
summary(workflow(
occurrence = UKAnophelesPlumbeus,
covariate = UKAir,
process = Background(n = 70),
model = LogisticRegression,
output = SameTimePlaceMap
)),
class = "character"
)
set.seed(1)
expect_is(
summary(workflow(
occurrence = UKAnophelesPlumbeus,
covariate = UKAir,
process = list(Background(n = 70), NoProcess),
model = LogisticRegression,
output = SameTimePlaceMap
)),
class = "character"
)
set.seed(1)
expect_is(
summary(workflow(
occurrence = UKAnophelesPlumbeus,
covariate = UKAir,
process = Background(n = 70),
model = list(Domain, LogisticRegression),
output = SameTimePlaceMap
)),
class = "character"
)
suppressWarnings({SDMTools_check <- requireNamespace('SDMTools', quietly = TRUE)})
if(!SDMTools_check) skip(message = 'SDMTools required for some tests')
set.seed(1)
expect_is(
summary(workflow(
occurrence = UKAnophelesPlumbeus,
covariate = UKAir,
process = BackgroundAndCrossvalid,
model = Domain,
output = list(
SameTimePlaceMap,
PerformanceMeasures
)
)),
class = "character"
)
})
|
7ed38451def847b1ced89ec383c3acad5ed812bc
|
269eff99a621cde7e6f26e9fd27a031a4a66ea38
|
/R-language-projects/K means clustering-Wine/Kmeans-Wine.R
|
16189748ee66c53fd1fe6e2d3f64d5bd230eacb0
|
[] |
no_license
|
KrushnaWakode12/Data-Science
|
d7e1734bdff99e5a2d5203259cd9ee1b0280b6fa
|
9f6695e8fc8520bce249a8bc04f074222b9728c9
|
refs/heads/master
| 2020-10-01T22:12:53.780791
| 2020-02-25T20:24:22
| 2020-02-25T20:24:22
| 227,634,547
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,481
|
r
|
Kmeans-Wine.R
|
#define libraries required for program
library(ggplot2)
library(cluster)
#Read input files
df1 <- read.csv('winequality-red.csv', sep = ';')
df2 <- read.csv('winequality-white.csv', sep = ';')
#add wine labels
df1$label <- sapply(df1$pH, function(x){'red'})
df2$label <- sapply(df2$pH, function(x){'white'})
#COmbine both datasets
wine <- rbind(df1,df2)
#Plot Histograms and Scatterplot between different parameters to understand co-relation between them
print(ggplot(wine,aes(x=residual.sugar)) + geom_histogram(aes(fill=label),color='black',bins = 50) + scale_fill_manual(values = c('#ae4554','white')) + theme_gray())
print(ggplot(wine,aes(x=citric.acid)) + geom_histogram(aes(fill=label),color='black',bins = 50) + scale_fill_manual(values = c('#ae4554','white')) + theme_gray())
print(ggplot(wine,aes(x=alcohol)) + geom_histogram(aes(fill=label),color='black',bins = 50) + scale_fill_manual(values = c('#ae4554','white')) + theme_gray())
print(ggplot(wine,aes(x=citric.acid,y=residual.sugar)) + geom_point(aes(color=label), alpha = 0.3) + scale_color_manual(values = c('red','white')) + theme_dark())
#Build K-means model and observe outcome
wine.cluster <- kmeans(wine[1:12],2)
print(wine.cluster)
#Compare Predicted clusters with given data outcome to find effeciency of model
print(table(wine$label,wine.cluster$cluster))
write.table(table(wine$label,wine.cluster$cluster), file='conf_table.csv', sep=',',col.names = c('white','red'))
|
5653e5a8eb9213da1a14c265e0f559cc39d84cff
|
959c359d37be00c71250fcc8f82d47382ad60637
|
/cachematrix.R
|
5059b3fb8f4ebd735e177fc6c73102b7a6abe8eb
|
[] |
no_license
|
harinimukund/ProgrammingAssignment2
|
ff93864e14087b204fc834fe7cd5ece978e2e04c
|
a6f1ab4035d040511617274a2d0d65236a9928e5
|
refs/heads/master
| 2021-01-17T15:34:14.356221
| 2015-12-23T23:23:05
| 2015-12-23T23:23:05
| 48,455,639
| 0
| 0
| null | 2015-12-22T21:50:10
| 2015-12-22T21:50:09
| null |
UTF-8
|
R
| false
| false
| 1,243
|
r
|
cachematrix.R
|
## The program below is divided in 2 parts.
## 1) The program makeCacheMatrix keeps a list of values in memory
## 2) The program cacheSolve calculates the Inverse of an matrix if it is not cached already.
## The makeCacheMatrix takes in a matrix and returns a list
## which is used later used by the function cacheSolve.
makeCacheMatrix <- function(x=matrix(data=NA,nrow=1,ncol=1)) {
m <- NULL
set <- function(y) {
x <<- y
m <<- NULL
}
get <- function() x
setInverse <- function(inv) m <<- inv
getInverse <- function() m
list(set = set,
get = get,
setInverse = setInverse,
getInverse = getInverse)
}
## The function cacheSolve takes in a matrix and checks if
## the inverse of the matrix is already calculated or not by calling the
## function makeCacheMatrix. If the values returned is Null it uses the SOLVE() in R
## to calculated the Inverse of a matrix and again called the makeCacheMatrix.setInverse() to save this value.
cacheSolve <- function(x, ...) {
## Return a matrix that is the inverse of 'x'
m <- x$getInverse()
if(!is.null(m)) {
message("getting cached data")
return(m)
}
data <- x$get()
m <- solve(data, ...)
x$setInverse(m)
m
}
|
1b297616b288f667bff9c1643ce9fd047c37306e
|
8e4e6474a8fce97066e9231c0fe4b0a83ab841ff
|
/Ridgelineattempts/20191016_jmcastagnetto_ridgelines.R
|
b6231da8fee6981d6c7bc07c20507777f7a303dc
|
[] |
no_license
|
microbesandmud/dataviz
|
51d51fae8e797701347a381d1472d0f6db57b0a0
|
c2ea09ece34068b3776671a681f3feefd80d9fef
|
refs/heads/master
| 2020-09-01T09:47:42.489507
| 2020-05-16T07:14:48
| 2020-05-16T07:14:48
| 218,933,769
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,715
|
r
|
20191016_jmcastagnetto_ridgelines.R
|
install.packages("lubridate")
install.packages("gganimate")
install.packages("extrafont")
install.packages("ggdark")
install.packages("ggridges")
library(tidyverse)
library(lubridate)
library(extrafont)
library(ggdark)
library(ggridges)
library(tidyverse)
df <- read_csv(
"20191016_simple_biopiles.csv",
col_types = "fnnnnn")
p1 <- ggplot(df,
aes(y = Biopile, x = Months, fill = ..x..)) +
geom_density_ridges_gradient(
quantile_lines = TRUE, quantiles = 2,
jittered_points = TRUE, alpha = 0.5,
point_size = 0.6, point_color = "black") +
scale_fill_viridis_c() +
labs(
title = "Age Distribution of Amusement Park Injuries in Texas",
subtitle = "Young men seem to be more injury prone\n(Median value shown as a vertical line)\n#TidyTuesday, 2019-09-10",
caption = "Source: data.world\n@jmcastagnetto / Jesus M. Castagnetto",
y = "",
x = "",
fill = "Months"
) +
xlim(-10, 80) +
dark_theme_minimal() +
theme(
legend.position = "bottom",
legend.text = element_text(size = 10),
legend.title = element_text(size = 12),
legend.justification = c(0, 0),
axis.text.y = element_text(size = 18, color = "yellow"),
axis.text.x = element_text(size = 12, color = "yellow"),
strip.text = element_text(family = "Inconsolata", size = 36, face = "bold.italic"),
plot.margin = unit(rep(1, 4), "cm"),
plot.title = element_text(size = 24, face = "bold"),
plot.subtitle = element_text(size = 18, face = "bold"),
plot.caption = element_text(family = "Inconsolata", size = 14)
)
p1
ggsave(
plot = p1,
filename = "2019-09-10.png",
width = 12,
height = 9
)
|
34fbaf0757a0c3866435fa44235e0e4bc78c83b0
|
8bc0348da53579f6d7cb45d7a60db9eafd04b7eb
|
/R/supp_rarefaction_curves.R
|
ceb9d6b318cafd5a5c9976b4ed963bea60e8cb59
|
[
"MIT"
] |
permissive
|
RadicalCommEcol/multitrophic_feasibility
|
24fef12cfbd4b59af1c9837ee40b52c3c40122f7
|
fd98486f0638cc280da79b648727a344f38bd4ee
|
refs/heads/main
| 2023-04-15T18:36:02.170317
| 2023-02-07T09:18:58
| 2023-02-07T09:18:58
| 540,769,909
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 8,165
|
r
|
supp_rarefaction_curves.R
|
# Calculate rarefaction curves of species interactions
# for each type of interaction observed (plant-plant, plant-herb, plant-pol)
# INPUT
# interaction matrices (/data/*_matrices.RData)
# OUTPUT
# rarefaction curves for every interaction and local community
# -------------------------------------------------------------------------
library(tidyverse)
library(vegan)
library(iNEXT)
library(patchwork)
# -------------------------------------------------------------------------
# add a version suffix?
vers.out <- ""
vers <- ""
# -------------------------------------------------------------------------
years <- c(2019,2020)
plots <- 1:9
pp.all.years <- list()
ph.all.years <- list()
pfv.all.years <- list()
for(i.year in 1:length(years)){
load(paste("./data/plant_plant_matrices_",
years[i.year],vers,".RData",sep=""))
load(paste("./data/plant_floral_visitor_matrices_",
years[i.year],vers,".RData",sep=""))
load(paste("./data/plant_herbivore_matrices_",
years[i.year],vers,".RData",sep=""))
pp.all.years[[i.year]] <- p_p
ph.all.years[[i.year]] <- p_h
pfv.all.years[[i.year]] <- p_fv
}
names(pp.all.years) <- years
names(ph.all.years) <- years
names(pfv.all.years) <- years
# remove empty rows and columns -------------------------------------------
# keep only species that appear in a given year and plot
for(i.year in 1:length(years)){
for(i.plot in 1:length(plots)){
for(i.guild in c("pp","ph","pfv")){
if(i.guild == "pp"){
my.matrix <- pp.all.years[[i.year]][[i.plot]]
my.valid.rows <- apply(my.matrix,1,sum)
my.valid.rows <- names(my.valid.rows)[which(my.valid.rows != 0)]
my.valid.cols <- apply(my.matrix,2,sum)
my.valid.cols <- names(my.valid.cols)[which(my.valid.cols != 0)]
# slightly different from ph,pfv, because this is a plant-plant
# matrix, so one sp cannot be only on rows/cols.
my.valid.sp <- intersect(my.valid.rows,my.valid.cols)
my.matrix <- my.matrix[my.valid.sp,my.valid.sp]
pp.all.years[[i.year]][[i.plot]] <- my.matrix
}else if(i.guild == "ph"){
my.matrix <- ph.all.years[[i.year]][[i.plot]]
my.valid.rows <- apply(my.matrix,1,sum)
my.valid.rows <- names(my.valid.rows)[which(my.valid.rows != 0)]
my.valid.cols <- apply(my.matrix,2,sum)
my.valid.cols <- names(my.valid.cols)[which(my.valid.cols != 0)]
ph.all.years[[i.year]][[i.plot]] <- my.matrix[my.valid.rows,
my.valid.cols]
}else if(i.guild == "pfv"){
my.matrix <- pfv.all.years[[i.year]][[i.plot]]
my.valid.rows <- apply(my.matrix,1,sum)
my.valid.rows <- names(my.valid.rows)[which(my.valid.rows != 0)]
my.valid.cols <- apply(my.matrix,2,sum)
my.valid.cols <- names(my.valid.cols)[which(my.valid.cols != 0)]
pfv.all.years[[i.year]][[i.plot]] <- my.matrix[my.valid.rows,
my.valid.cols]
}# if i.guild
}# for i.guild
}# for i.plot
}# for i.year
# -------------------------------------------------------------------------
# rarefaction/extrapolation
# first, convert matrices to 1d vectors, usable by iNEXT, and store them in lists
pp.interaction.list <- pp.all.years
ph.interaction.list <- ph.all.years
pfv.interaction.list <- pfv.all.years
for(i.year in 1:length(years)){
for(i.plot in plots){
# plant-plant interactions
my.pp.vec <- c(t(pp.all.years[[i.year]][[i.plot]]))
my.pp.names.grid <- expand.grid(colnames(pp.all.years[[i.year]][[i.plot]]),
rownames(pp.all.years[[i.year]][[i.plot]]))
names(my.pp.vec) <- sprintf('%s_%s', my.pp.names.grid[,1], my.pp.names.grid[,2])
pp.interaction.list[[i.year]][[i.plot]] <- my.pp.vec
# plant-herb interactions
my.ph.vec <- c(t(ph.all.years[[i.year]][[i.plot]]))
my.ph.names.grid <- expand.grid(colnames(ph.all.years[[i.year]][[i.plot]]),
rownames(ph.all.years[[i.year]][[i.plot]]))
names(my.ph.vec) <- sprintf('%s_%s', my.ph.names.grid[,1], my.ph.names.grid[,2])
ph.interaction.list[[i.year]][[i.plot]] <- my.ph.vec
# plant-pol interactions
my.pfv.vec <- c(t(pfv.all.years[[i.year]][[i.plot]]))
my.pfv.names.grid <- expand.grid(colnames(pfv.all.years[[i.year]][[i.plot]]),
rownames(pfv.all.years[[i.year]][[i.plot]]))
names(my.pfv.vec) <- sprintf('%s_%s', my.pfv.names.grid[,1], my.pfv.names.grid[,2])
pfv.interaction.list[[i.year]][[i.plot]] <- my.pfv.vec
}# for i.plot
}# for i.year
# second, generate iNEXT curves
# these lists will hold the iNEXT plots
pp.plot.list <- list()
ph.plot.list <- list()
pfv.plot.list <- list()
community.sc <- expand.grid(year = years,plot = plots,
pp = NA, pfv = NA, ph = NA)
for(i.year in 1:length(years)){
for(i.plot in plots){
# plant-plant interactions
pp.endpoint <- round(sum(pp.interaction.list[[i.year]][[i.plot]]) + .25 * sum(pp.interaction.list[[i.year]][[i.plot]]))
pp.out <- iNEXT(unlist(pp.interaction.list[[i.year]][[i.plot]]), q=c(0), datatype="abundance", endpoint=pp.endpoint)
pp.plot <- ggiNEXT(pp.out, type=2,color.var = "Order.q")+
scale_shape_manual(values = c(19))+
theme_bw(base_size = 12) +
theme(legend.position="none")+
labs(x="",y="") +
ggtitle(paste("plant-plant: ",years[i.year]," plot ",i.plot,sep=""))
pp.plot.list[[length(pp.plot.list)+1]] <- pp.plot
# plant-herb interactions
ph.endpoint <- round(sum(ph.interaction.list[[i.year]][[i.plot]]) + .25 * sum(ph.interaction.list[[i.year]][[i.plot]]))
ph.out <- iNEXT(unlist(ph.interaction.list[[i.year]][[i.plot]]), q=c(0), datatype="abundance", endpoint=ph.endpoint)
ph.plot <- ggiNEXT(ph.out, type=2,color.var = "Order.q")+
scale_shape_manual(values = c(19))+
theme_bw(base_size = 12) +
theme(legend.position="none")+
labs(x="",y="") +
ggtitle(paste("plant-herbivore: ",years[i.year]," plot ",i.plot,sep=""))
ph.plot.list[[length(ph.plot.list)+1]] <- ph.plot
# plant-pol interactions
pfv.endpoint <- round(sum(pfv.interaction.list[[i.year]][[i.plot]]) + .25 * sum(pfv.interaction.list[[i.year]][[i.plot]]))
pfv.out <- iNEXT(unlist(pfv.interaction.list[[i.year]][[i.plot]]), q=c(0), datatype="abundance", endpoint=pfv.endpoint)
pfv.plot <- ggiNEXT(pfv.out, type=2,color.var = "Order.q")+
scale_shape_manual(values = c(19))+
theme_bw(base_size = 12) +
theme(legend.position="none")+
labs(x="",y="") +
ggtitle(paste("plant-pollinator: ",years[i.year]," plot ",i.plot,sep=""))
pfv.plot.list[[length(pfv.plot.list)+1]] <- pfv.plot
pos <- which(community.sc$year == years[i.year] & community.sc$plot == i.plot)
community.sc$pp[pos] <- pp.out$DataInfo$SC
community.sc$ph[pos] <- ph.out$DataInfo$SC
community.sc$pfv[pos] <- pfv.out$DataInfo$SC
}# for i.plot
}# for i.year
mean(community.sc$pp)
mean(community.sc$pfv)
mean(community.sc$ph)
sd(community.sc$pp)
sd(community.sc$pfv)
sd(community.sc$ph)
# all interactions together
mean(as.matrix(community.sc[,3:5]))
# -------------------------------------------------------------------------
pp.plot.full <- wrap_plots(pp.plot.list)
ph.plot.full <- wrap_plots(ph.plot.list)
pfv.plot.full <- wrap_plots(pfv.plot.list)
# -------------------------------------------------------------------------
# ggsave("results/images/Fig_plant_plant_rarefaction.pdf",pp.plot.full,
# device = cairo_pdf,
# width = 16,height = 12,dpi = 300)
#
# ggsave("results/images/Fig_plant_herb_rarefaction.pdf",ph.plot.full,
# device = cairo_pdf,
# width = 16,height = 12,dpi = 300)
#
# ggsave("results/images/Fig_plant_pol_rarefaction.pdf",pfv.plot.full,
# device = cairo_pdf,
# width = 16,height = 12,dpi = 300)
|
666ab3a89936c7e521dd8f188ad7da1c79b6fdd0
|
0ce1da8e088edfb8c0f55f0473c2cc1356f590dd
|
/man/logit.Rd
|
250c1004341b250c0e6388f13684ea9e7c50107b
|
[] |
no_license
|
azolling/EBmodules
|
89cf57929870f176e15ce808e19da600206bec08
|
a114f566cc24621bafb3bedb343ba79b77bc80ce
|
refs/heads/master
| 2021-01-19T19:19:02.468274
| 2017-04-23T16:39:59
| 2017-04-23T16:39:59
| 88,411,378
| 1
| 1
| null | null | null | null |
UTF-8
|
R
| false
| true
| 373
|
rd
|
logit.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/bayesian_code_final.R
\name{logit}
\alias{logit}
\title{logit function}
\usage{
logit(p)
}
\arguments{
\item{p}{a vector with values between 0 and 1 which we want to take logit.}
}
\description{
This function computes the logit of a quantity
}
\examples{
logit(runif(100,0,1))
}
\keyword{logit}
|
48c2c0b4e1f91ace8ad59ccac4bd03f6e95fc04b
|
cad6b67bfd5bd73dc217346bb029ddb9fb0ef510
|
/man/bubblesOutput.Rd
|
07cd42b1b207389cfb4267ec8a4a72e8aafd2a2c
|
[
"MIT"
] |
permissive
|
jpmarindiaz/bubbleCloud
|
8e0008766f3ed568a3c4f1f94185edf8d9183e0b
|
9747838ee6e6cced0d8f20f7d1487d6cfe82e96a
|
refs/heads/master
| 2020-03-29T21:24:46.356142
| 2015-05-24T16:38:13
| 2015-05-24T16:38:13
| 31,972,021
| 3
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 313
|
rd
|
bubblesOutput.Rd
|
% Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/bubbles.R
\name{bubblesOutput}
\alias{bubblesOutput}
\title{Widget output function for use in Shiny}
\usage{
bubblesOutput(outputId, width = "100\%", height = "500px")
}
\description{
Widget output function for use in Shiny
}
|
ef90d761310be8deb34e1ac8f6c99fbbdb27faf9
|
5b2f016f1298c790224d83c1e17a425640fc777d
|
/chol/forestPlot.R
|
1cdd68ff55ea69802444d0d85dd5c7eb5a61ac5c
|
[] |
no_license
|
Shicheng-Guo/methylation2020
|
b77017a1fc3629fe126bf4adbb8f21f3cc9738a0
|
90273b1120316864477dfcf71d0a5a273f279ef9
|
refs/heads/master
| 2023-01-15T20:07:53.853771
| 2020-02-28T03:48:13
| 2020-02-28T03:48:13
| 243,668,721
| 3
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,554
|
r
|
forestPlot.R
|
install.packages("metafor")
library("metafor")
rm(list=ls())
load("CD4_RGSS_data_beforecombat.RData")
phen<-read.csv("CD4_RGSS_clinical.csv")
methdata<-CD4_RGSS_data_beforecombat
dim(phen)
dim(methdata)
head(phen)
i=500
Seq<-paste(phen[,3],phen[,2],sep="_")
mean<-tapply(as.numeric(methdata[i,]),Seq,function(x) mean(x,na.rm=T))
sd<-tapply(as.numeric(methdata[i,]),Seq,function(x) sd(x,na.rm=T))
num<-tapply(as.numeric(methdata[i,]),Seq,function(x) length(x))
m1i=mean[seq(1,8,by=2)]
m2i=mean[seq(2,8,by=2)]
sd1i=sd[seq(1,8,by=2)]
sd2i=sd[seq(2,8,by=2)]
n1i=num[seq(1,8,by=2)]
n2i=num[seq(2,8,by=2)]
Source<-unlist(lapply(strsplit(names(m1i),"_"),function(x) x[1]))
data<-data.frame(cbind(n1i,m1i,sd1i,n2i,m2i,sd2i))
data$source=Source
data
es<-escalc(m1i=m1i, sd1i=sd1i, n1i=n1i, m2i=m2i, sd2i=sd2i, n2i=n2i,measure="MD",data=data)
md <- rma(es,slab=source,method = "REML", measure = "MD",data=data)
plot(md)
files=list.files(pattern="*gdc_hg38.txt$",recursive = T)
methdata<-c()
for(i in 1:length(files)){
temp<-read.table(files[i],head=T,sep="\t",row.names = 1)
methdata<-cbind(methdata,temp[,1])
print(i)
}
files=list.files(pattern="*.FPKM-UQ.txt$",recursive = T)
rnaseqdata<-c()
for(i in 1:length(files)){
temp<-read.table(files[i],head=F,sep="\t",row.names = 1)
rnaseqdata<-cbind(rnaseqdata,temp[,1])
print(i)
}
colnames(rnaseqdata)<-files
rownames(rnaseqdata)<-rownames(temp)
save(rnaseqdata,file="rnaseqdata.pancancer.RData")
save.image("rnaseqdata.pancancer.env.RData")
|
07ea18b037f6e6e418a08a4ca9813cc05cf84f4f
|
541a192813be04a1793959edd57dc7abb7834e22
|
/R_code/old/test_multivariate.R
|
a8e6fa3208035e3016043f17aef666d1b1c88bbd
|
[] |
no_license
|
jumping2000/MasterThesis
|
03b2abc057fbfde4914c26dd03643b3424d5268f
|
f0b5dc44ae7e9e87380497d0d03bdfbf86f0e693
|
refs/heads/master
| 2021-10-26T16:00:09.891289
| 2019-04-13T16:56:07
| 2019-04-13T16:56:07
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 4,086
|
r
|
test_multivariate.R
|
###################################################
############### Multivariate Calibration ##########
###################################################
rm(list = ls())
source("MultivariateMertonModel.R")
##### chiamata delle librerie
library(tseries)
library(mvtnorm)
library(pracma)
library(DEoptim)
library(statmod)
library(NMOF)
library(readxl)
library(xlsx)
library(binaryLogic)
##### testing the mvMertonpdf
#* (x, dt, mu, S, theta, delta, lambda, theta_z, delta_z, lambda_z, alpha)
n = 2
m = (c(0,0))
SS = matrix(c(0.8, 0.2,
0.2, 0.1),ncol = 2)
thet = (c(0.5,1.1))
delt = (c(0.1,0.3))
lambd = (c(30,20))
thet_z = 1
delt_z = 0.5
lambd_z = 10
alph = (c(1,2))
xx = rbind(c(0,0))
dt = 1/255
dmvnorm(xx,mean = m, sigma = SS)
res =MultivariateMertonPdf(xx, dt=dt, m,SS,thet,delt,lambd,thet_z,delt_z, lambd_z, alph)
res
N=50
x <- seq(-1,1,length.out=N)
y <- seq(-1,1,length.out=N)
z <- matrix(rep(0,N*N),ncol = N)
X = matrix(rep(0,N*N),ncol = N)
Y = matrix(rep(0,N*N),ncol = N)
z_nojumps= matrix(rep(0,N*N),ncol = N)
for(i in 1:N){
for(j in 1:N){
X[i,j] = x[i]
Y[i,j] = y[j]
z[i,j] = MultivariateMertonPdf(c(x[i],y[j]), dt=dt, m,SS,thet,delt,lambd,thet_z,delt_z, lambd_z, alph)
z_nojumps[i,j] = dmvnorm(c(x[i],y[j]),mean = m*dt, sigma = SS*sqrt(dt))
}
}
library(rgl)
plot3d(X,Y,z, col='green')
points3d(X,Y, z_nojumps, col = 'blue')
legend3d("bottomleft",c("Jumps", "No jumps"))
mean((z-z_nojumps)^2)
################################ negloglikelihood function #############################
## test negloglikelihood function
param=c(m,c(SS[1,1],SS[1,2],SS[2,2]), thet,delt,lambd,thet_z,delt_z,lambd_z,alph)
xx = rmvnorm(1000, mean = m*dt, sigma = SS*dt)
start_time <- Sys.time()
negloglik(param, xx, dt=dt, n = 2)
end_time <- Sys.time()
end_time-start_time
lx =lapply(seq_len(nrow(xx)), function(i) xx[i,])
start_time <- Sys.time()
vnegloglik(param, lx, dt=dt, n = 2)
end_time <- Sys.time()
end_time-start_time
start_time <- Sys.time()
negloglik_2assets(param, xx, dt=dt, n = 2)
end_time <- Sys.time()
end_time-start_time
# no common jump
start_time <- Sys.time()
negloglik_2assets_nocommon(params = c(m,c(SS[1,1],SS[1,2],SS[2,2]), thet,delt,lambd), xx, dt=dt, n = 2)
end_time <- Sys.time()
end_time-start_time
#######################################################
###################### Calibration ####################
control_list = list(itermax = 500, NP = 200, strategy = 6,trace=5)
bounds = BoundsCreator(2, n_common=1)
start_time <- Sys.time()
outDE <- DEoptim(negloglik_2assets,
lower = bounds$lower,
upper = bounds$upper,
control = control_list, dt = dt, x = xx, n=2)
end_time <- Sys.time()
end_time-start_time
### no common jump
bounds_nocommon = BoundsCreator(2, n_common=0)
start_time <- Sys.time()
outDE <- DEoptim(negloglik_2assets_nocommon,
lower = bounds_nocommon$lower,
upper = bounds_nocommon$upper,
control = control_list, dt = dt, x = xx, n=2)
end_time <- Sys.time()
end_time-start_time
ParametersReconstruction(outDE$optim$bestmem,2,common = FALSE)
## ==== DATA ====
## load data set (need a web connection)
x_EuroStoxx<-read.xlsx(file="EuroStoxx.xlsx",sheetName = "sheet1")[,2]
#x <- get.hist.quote(instrument = "AAPL",
# start = "2008-01-01", end = "2009-06-30",
# retclass = "zoo", quote = "AdjClose", compression = "d")
#x_EuroStoxx<-(simulateJump(0.1,0.25,1,-0.2,0.001,5,1/252)[,2])
#x_EuroStoxx<-ProcessoMerton(0.1,0.25,255*10,0.3,0.2,1/255)
## log-returns
dy <- diff(log(as.vector(x_EuroStoxx)))
## assume 255 days in a year (trading days)
dt <- 1 / 255
## ==== DEOPTIM ESTIMATION ====
#set.seed(1234)
outDE <- DEoptim(negloglik,
lower = c( 0.1, -10, 1e-4, -10, 1e-4),
upper = c(100, 10, 10, 10, 10),
control = list(itermax = 500, NP = 100), dt = dt, dy = dy)
#summary(outDE)
parametri=outDE$optim$bestmem
par=parametri[c(2,3,1,4,5)]
plot(x_EuroStoxx, type='l')
TotalTime=length(x_EuroStoxx)*dt
|
97945a1d115e12ab395cc1c2acb1ba0e74d7ff08
|
0b622b091c8d3ccc1fbcb36f35abb8c2b23744e0
|
/M565FinalProject/Code/Chuck/img_proc/createMaskFromEncodedFile.R
|
8b7b8a47da57df4077bdff1e82ee70cd5b83c75d
|
[] |
no_license
|
chuckjia/B565-DataMining
|
12a8cba020a53910e172797ef587affb07d91263
|
42d7dd37470f644abbc30d70c888225f92716ecd
|
refs/heads/master
| 2021-09-27T22:10:47.901946
| 2018-11-12T05:14:57
| 2018-11-12T05:14:57
| 119,938,162
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,793
|
r
|
createMaskFromEncodedFile.R
|
# install.packages("png")
library("png")
# install.packages("rstudioapi")
library(rstudioapi)
createMaskFromEncodedFile <- function(outerFolder, encodedFile, newFolder) {
dset <- read.csv(encodedFile)
ndpt <- nrow(dset)
prevFolder <- ''
imgmat <- matrix(0, nrow = 1, ncol = 1)
maskNo <- 1
height = 0
width = 0
for (row in 1:ndpt) {
if (row %% 100 == 0)
cat("Processing row no. ", row, " out of ", ndpt, " rows\n", sep = "")
dpt <- dset[row,]
currFolder <- as.character(dpt$ImageId)
if (currFolder != prevFolder) {
maskNo <- 1
prevFolder <- currFolder
imgFile <- list.files(file.path(outerFolder, currFolder, 'images'), full.names = T)
img <- readPNG(imgFile)
height = nrow(img)
width = ncol(img)
}
imgmat <- matrix(0, nrow = height, ncol = width)
pixels <- scan(text = as.character(dpt$EncodedPixels), what = 0L, sep = " ", quiet = T)
num_cells <- length(pixels) / 2
for (i in 1:num_cells) {
pixNo = pixels[2 * i - 1]
len = pixels[2 * i]
for (j in 1:len) {
index_col <- ceiling(pixNo / height)
index_row <- pixNo - (index_col - 1) * height
imgmat[index_row, index_col] <- 1
pixNo = pixNo + 1
}
}
folderToWrite <- file.path(outerFolder, currFolder, newFolder)
dir.create(folderToWrite)
outputfilename <- file.path(folderToWrite, paste(maskNo, ".png", sep = ""))
writePNG(imgmat, outputfilename)
maskNo = maskNo + 1
}
}
|
f526dc2dbe04bbcc5a8a5a20d96621fba8916695
|
b61ea73d01d708ad9f337bae20a112ba233f8483
|
/man/validate_region.Rd
|
c6438a68a35982d5e89ef31cc17a2665404219b3
|
[
"MIT"
] |
permissive
|
rnevils/youRtube
|
2466b490f290948c59e22e93356829d73d3c9aeb
|
bf0f3963a364a558405d001c4fc4cbc0deafa1a6
|
refs/heads/master
| 2022-06-12T05:58:55.552068
| 2020-05-05T01:56:51
| 2020-05-05T01:56:51
| 260,322,960
| 0
| 1
|
MIT
| 2020-05-05T01:58:06
| 2020-04-30T21:33:37
|
R
|
UTF-8
|
R
| false
| true
| 441
|
rd
|
validate_region.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/get_top_videos.R
\name{validate_region}
\alias{validate_region}
\title{Validates region inputted by user}
\usage{
validate_region(key, region)
}
\arguments{
\item{key}{Your YouTube API key}
\item{region}{String or numeric value inputted by user}
}
\value{
String indicating if region given is valid or invalid
}
\description{
Validates region inputted by user
}
|
195a54846bf3727a3c4c0bc1d055175e5314bb7a
|
d80b92f205586c7cbae986ba0a41236b682c4db4
|
/R/summary.glmmEP.r
|
189c26b0c10651575550546f408cff986b855c40
|
[] |
no_license
|
cran/glmmEP
|
fdfe931681175cc95ce7df6609ca846e85c490b7
|
31f200bb27d92f2da8b6ebe5c64f461867360659
|
refs/heads/master
| 2020-03-18T21:11:51.476444
| 2019-10-15T07:19:35
| 2019-10-15T07:19:35
| 135,265,706
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 240
|
r
|
summary.glmmEP.r
|
########## R-function: summary.glmmEP ##########
# For summarising the glmmEP() fit object.
# Last changed: 18 JAN 2018
summary.glmmEP <- function(object,...)
return(object$parameters)
########## End of summary.glmmEP ##########
|
2a11d795f40ab166148a6b67ee57b41341c9bf0b
|
f708aec3211a52cad14d9fc5d7cfc1b8253758cf
|
/profiling/1.R-basic/1.C.3_Part3-Visualization.R
|
203a7391c455e4487521e2c411ee6c02d60e4f59
|
[] |
no_license
|
jyanglab/labworkshop
|
93aa1f7f9f0cbb2525d69496ba96fe883e23dba7
|
bd4cbe61ccaa1fa19c7c4784cf91be31972f8d0f
|
refs/heads/master
| 2020-05-24T12:24:59.596074
| 2019-07-19T21:15:31
| 2019-07-19T21:15:31
| 187,267,972
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 12,738
|
r
|
1.C.3_Part3-Visualization.R
|
#
# Let's visualize our data
# ========================
#
# So far we have covered:
#
# - data types in R
# - reading in data
# - subsetting data
# - reading documentation
# - using functions
# - saving data
#
# Of course, we haven't used one of R's most powerful assets: graphics. This
# section is dedicated to creating a plot from the data. While R has very
# powerful default plotting functions, we will be using the "ggplot2" package
# as it relies on a consistent "grammar of graphics" that gives a clear
# relationship between the data and the visualization.
#
# ### What is ggplot2?
#
# The package *ggplot2* is built off of the "grammar of graphics" in which
# plots are built layer by layer, starting with the coordinate plane
# and then adding geometric elements like lines, dots, bars, etc, and assigning
# metadata to values like color or shape.
#
# The advantage of ggplot2 over R's native plotting is that the plots are saved
# as R objects and can be modified by adding layers or even replacing data. This
# tutorial will begin to scratch the surface of how to use ggplot2, but to get a
# better idea of what is possible, you can browse the resources at
# http://ggplot2.tidyverse.org/#learning-ggplot2 or examine the code of
# colleagues (e.g. Alejandro Rojas:
# https://github.com/alejorojas2/Rojas_Survey_Phytopath_2016).
#
#
# After this section, you should have the tools to:
#
# 1. Create a simple plot in ggplot2
# 2. Save plots
# 3. Plot with mean and error bars
#
# Again, since this is a four hour workshop, we do not expect mastery, but this
# at least should give you a starting point. With that in mind, let's get
# started!
#
# ### Getting started
install.packages("ggplot2")
library("ggplot2")
# Data for plotting with ggplot2 must be stored in a data frame
fungicide <- read.csv("data/fungicide_dat.csv") # read.csv automatically outputs a data frame
# Ready to plot? First of all let's think:
#
# 1. What visualization might be appropriate for these data?
# 2. What should be on the axes?
# 3. Should we use lines, points, bars, boxplots, etc?
#
# To help facilitate your thinking, you may refer to the cheatsheet provided in the 'Help' tab
#
#
# Step 1: Creating our plot
# -------------------------
#
# > Note: if you are reading this script after attending the workshop, the plot
# > may look different due to the interactive nature of the workshop. This is
# > intended as an example.
#
# Before we begin, we should become familiar with two functions:
#
# - `ggplot()` initializes a ggplot object from a data set. The data set needs
# to be a data frame.
# - `aes()` is a general way to specify what parts of the ggplot should be
# mapped to variables in your data. e.g. What should be the x and y variables?
#
#
# ### Creating the base of the ggplot
#
# To create our ggplot with nothing on it, we should specify two things:
#
# 1. The data set (fungicide)
# 2. The mapping of the x and y coordinates (from the data set, using aes)
#
# > Note, we can specify the column names without using quotation marks.
yield.plot <- ggplot(data = fungicide,
mapping = aes(x = Treatment,
y = Yield_bu_per_acre))
# If everything worked, you should see nothing. This is because ggplot2 returns
# an R object. This object contains the instructions for creating the
# visualization. When you print this object, the plot is created:
yield.plot
# Now you should see a plot with nothing on it where the x and y axes are
# labeled "Treatment" and "Yield_bu_per_acre", respectively.
#
# To break down what the above function did, it first took in the data set
# `fungicide` and then mapped the x and y aesthetics to the Treatment and
# Yield_bu_per_acre columns. Effectively, this told ggplot how big our canvas
# needs to be in order to display our data, but currently, it doesn't know
# HOW we want to display our data; we need to give it a specific geometry.
#
#
# ### Adding a geometry layer
#
# All functions that add geometries to data start with `geom_`, so if we wanted
# the data to be displayed as a line showing the increase of yield over time,
# we would use `geom_line()`. If we wanted to show the data displayed as points,
# we can use `geom_point()`.
#
# To add a geometry or anything to a ggplot object, we can just use the `+`
# symbol. Here, we will add boxplots.
#
# > Note: From here on out, I will be wrapping all commands with parentheses.
# > This allows the result of the assignment to be displayed automatically.
(yield.plot <- yield.plot +
geom_boxplot())
# If we want to change the color of the boxplots from white (default) to orange,
# we can do this by adding `geom_boxplot(fill = "orange")`.
(yield.plot <- yield.plot +
geom_boxplot(fill = "orange"))
# Instead of all the boxplots having the same color, it will be interesting if we
# could color them according to the Treatment.
(yield.plot <- yield.plot +
geom_boxplot(fill = Treatment))
# Oops! There was an error. It cannot recognize that we are talking about the
# Treatment column from our data set. This is because we have to use the function
# `aes()` whenever we are referring to our data set.
(yield.plot <- yield.plot +
geom_boxplot(aes(fill = Treatment))) # This works!
# To give a title to our plot, we can use `ggtitle()`.
(yield.plot <- yield.plot +
ggtitle("Effect of Fungicides on Yield"))
# We now have a fully functional and informative plot using only few lines of
# code! Producing a visualization of your data can be an extremely useful tool
# for analysis, because it can allow you to see if there are any strange patterns
# or spurious correlations in your variables.
#
# We can click on 'Zoom' to view a bigger version of this plot.
#
# Of course, this plot is not quite publication ready. We need to add some
# customization. Let's manipulate the aesthetics of the plot in how the data and
# labels are displayed. But first, use the cheatsheet or 'Google' to do the
# following exercises:
#
# ### Exercise 1: Create `new_plot` that is similar to `yield.plot`, but the
# ### geometry is a violin plot instead of a box plot.
new_plot <- ggplot(fungicide,
aes(x = Treatment,
y = Yield_bu_per_acre)) +
geom_violin(aes(fill=Treatment)) +
ggtitle("Effect of Fungicides on Yield")
new_plot
# ### Exercise 2: Add another layer to the `new_plot` that flips the
# ### co-ordinate axes (rotates the plot at right angle).
new_plot <- new_plot +
coord_flip()
new_plot
#
# ### Changing axes labels
#
# This is easily done with `xlab()` and `ylab()`:
(yield.plot <- yield.plot + xlab("Treatment Applied"))
(yield.plot <- yield.plot + ylab("Yield (bu/acre)"))
# The labels are now okay, but it's still not publication-ready. The font is too
# small, the background should have no gridlines and the axis text needs to be
# darker.
#
# ### Adjusting Look and Feel (theme)
#
# The first thing we can do is change the default theme from `theme_grey()` to
# `theme_bw()`. We will simultaneously set the base size of the font to be 14pt.
#
(yield.plot <- yield.plot +
theme_bw(base_size = 14))
#
# There are many different default themes available for ggplot2 objects that
# change many aspects of the look and feel. The *ggthemes* contains many popular
# themes such as fivethirtyeight and economist. Of course, we can make it
# prettier before including it in our final manuscript.
#
# To adjust granular aspects of the theme, we can use the `theme()` function,
# which contains a whopping 71 different options all related to the layout of
# the non-data aspects of the plot.
#
#
# ### Exercise 3: Look at `?theme` and figure out the following:
# 1. change the aspect ratio of the panels
# 2. remove the background grid in the panels
?theme
#
# When we inspect the help page of the `theme()` function, we can find out how
# to adjust several parameters to make our plot look acceptable:
#
(yield.plot <- yield.plot +
theme(aspect.ratio = 1)) # This looks the same
(yield.plot <- yield.plot +
theme(aspect.ratio = 2)) # This is too skinny
(yield.plot <- yield.plot +
theme(aspect.ratio = 1.25)) # I think this is perfect!
(yield.plot <- yield.plot +
theme(panel.grid = element_blank()))
#
#
# Since the information in the legend is repetitive, we can remove it. If you
# 'Google' how to remove the legend in ggplot2, you will find that you can use
# `guides(fill=FALSE)`.
#
(yield.plot <- yield.plot +
guides(fill = FALSE))
#
#
#
#
# ### Putting it all together
#
# Because we can add information to a plot with the `+` symbol, we can add all
# of the elements in one go. Let's combine what we have above.
#
yield.plot <- ggplot(fungicide,
aes(x = Treatment,
y = Yield_bu_per_acre)) +
geom_boxplot(aes(fill = Treatment)) +
ggtitle("Effect of Fungicides on Yield") +
xlab("Treatment Applied") +
ylab("Yield (bu/acre)") +
theme_bw(base_size = 14) +
theme(aspect.ratio = 1.25, # We can provide multiple arguments
panel.grid = element_blank()) +
guides(fill=FALSE)
yield.plot
# How can we show that the boxplot of Fungicide_B is significantly different?
# Hint:`annotate` it.
?annotate
# ### Exercise 4: Unravel the working of `annotate` by pasting examples in the console.
(yield.plot <- yield.plot +
annotate(geom = "text",
x = 3,
y = 176.5,
label = "P < 0.05",
color = "red",
size = 5))
# Congratulations! Your plot is ready for publishing!
#
# We can now create a similar plot for Severity Data.
severity.plot <- ggplot(fungicide,
aes(x = Treatment, y = Severity)) +
geom_boxplot() +
ggtitle("Effect of Fungicides on Disease Severity") +
theme_bw(base_size = 14) +
theme(aspect.ratio = 1.5,
panel.grid = element_blank()) +
xlab("Treatment Applied") +
ylab("Disease Severity")
severity.plot
#
# The text of the title is not in the center. To format text elements of the
# plot, we can use the function `element_text()` inside `theme()`. Since we
# need to edit the text of the plot title, we need to specify
# `plot.title = element_text()`.
(severity.plot <- severity.plot +
theme(plot.title = element_text(hjust = 0.5)))
#
# Step 2: Saving our plot
# -----------------------
#
# Now that we have our plot finished, we can save it with the `ggsave()`
# function, which allows us to save it as a pdf, png, svg, eps, etc. file.
# Or, we can click on 'Export' (button just above the plot) and save it.
ggsave(filename = "results/figure1.pdf", width = 88, units = "mm")
#
# Step 3: Plotting with mean and error bars
# -----------------------------------------
#
# One another type of plot that is very common in applied agricultural data
# sets is that has mean and standard errors for each treatment. Mean can be
# depicted in terms of bars or points on the plot. Let's practice this on
# fungicide data.
#
# Before we can plot mean and standard errors, we have to calculate them first,
# by using techniques we learned in Part 2 of the workshop. We will need to load
# `dplyr` and `plotrix`. Base 'R' does not contain a function to calculate standard
# error, but the package `plotrix` does. Moreover, this package can has functions
# for creating specialized plots and other plotting accessories.
library("dplyr")
install.packages("plotrix")
library("plotrix")
fungicide_m_se <- fungicide %>%
select(Treatment, Severity) %>%
group_by(Treatment) %>%
summarise(mean_sev = mean(Severity),
se_sev = std.error(Severity))
# Now, we can create a plot with mean and standard error
m_se_plot <- ggplot(data = fungicide_m_se,
aes(x = Treatment,
y = mean_sev))
m_se_plot
# ### Bar graph with standard errors
(m_se_plot_bar <- m_se_plot +
geom_col(aes(fill = Treatment),
width = 0.5))
(m_se_plot_bar <- m_se_plot_bar +
geom_errorbar(aes(ymin = mean_sev - se_sev,
ymax = mean_sev + se_sev),
width = 0.2))
# ### Point plot with standard errors
(m_se_plot_point <- m_se_plot +
geom_point(aes(color = Treatment),
size = 3))
(m_se_plot_point <- m_se_plot_point +
geom_errorbar(aes(ymin = mean_sev - se_sev,
ymax = mean_sev + se_sev,
color = Treatment),
width = 0.1))
# You can follow the same steps that we followed for `yield.plot` to transform these
# plots to publication quality.
|
759bed9279aac4bf56c6ec956de3b4a55d89814f
|
ffdea92d4315e4363dd4ae673a1a6adf82a761b5
|
/data/genthat_extracted_code/rwt/examples/denoise.Rd.R
|
88840158ef08f1d297d7a9ca47f5c0b3205d2ecd
|
[] |
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
| 544
|
r
|
denoise.Rd.R
|
library(rwt)
### Name: denoise
### Title: Wavelet-based Denoising
### Aliases: denoise denoise.dwt denoise.udwt DWT.TRANSFORM.TYPE
### UDWT.TRANSFORM.TYPE MAD.VARIANCE.ESTIMATOR STD.VARIANCE.ESTIMATOR
### SOFT.THRESHOLD.TYPE HARD.THRESHOLD.TYPE MAX.DECOMPOSITION
### CALC.THRESHOLD.TO.USE DEFAULT.DWT.THRESHOLD.MULTIPLIER
### DEFAULT.UDWT.THRESHOLD.MULTIPLIER default.dwt.option
### default.udwt.option
### Keywords: interface
### ** Examples
sig <- makesig(SIGNAL.DOPPLER)
h <- daubcqf(6)
ret.dwt <- denoise.dwt(sig$x, h$h.0)
|
ffdbc57185f3a51120d2e8e7461fe20ba3a920f9
|
865e787ca5d51f3d4b3c5b8af1af3976baa2bc95
|
/man/eval_M_Z.Rd
|
5fd896c245f8669107ff5c3918ca13639ce0937c
|
[] |
no_license
|
wgmueller1/mmppr
|
f43066e12caf90664f8c34f2a8035b34a9a3f082
|
63f971e550a4a232638b9b1470bf3ebc6e78a266
|
refs/heads/master
| 2021-01-15T17:20:28.159453
| 2014-06-27T12:39:54
| 2014-06-27T12:39:54
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 261
|
rd
|
eval_M_Z.Rd
|
% Generated by roxygen2 (4.0.1.99): do not edit by hand
\name{eval_M_Z}
\alias{eval_M_Z}
\title{eval_M_Z}
\usage{
eval_M_Z(M, Z, prior)
}
\arguments{
\item{M}{}
\item{Z}{}
\item{prior}{}
}
\description{
This function evaluates p(M|Z)
}
\examples{
eval_M_Z
}
|
d45f20e3d7c5a088dee4576d9511332a6068c954
|
cea373fba99a36d39f64b9de0a748e0b8bda8a37
|
/05_function/venn_intersects_upgrade.R
|
ee17baff925d49f676764e6dec5aabe514473914
|
[] |
no_license
|
TheJacksonLaboratory/wild_AD_mic_scRNA
|
6f949456cf42e297be9970bc03e6f169ba562be5
|
fb9cb603ba1e05af4f518945ca714508dc8181cb
|
refs/heads/master
| 2023-03-15T13:59:23.071302
| 2021-03-03T02:14:48
| 2021-03-03T02:14:48
| 314,021,339
| 1
| 2
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,158
|
r
|
venn_intersects_upgrade.R
|
## generate intersection table compatible with Vennerable library
library(Vennerable)
library(tidyverse)
venn_intersects_upgrade <- function(x_list){
tmp <- Venn(x_list)
intersect_name <- tmp@IndicatorWeight %>% rownames()
Weight <- tmp@IndicatorWeight %>% as_tibble()
names(Weight) <- str_remove(names(Weight), "\\.")
Weight$intersect_name <- intersect_name
Set_name_weight <- Weight %>% filter(Weight!=0) %>% select(Weight, intersect_name) # extract the the intersection terms with "non-zero" elements
Sets <- tmp@IntersectionSets
Gene_ID <- character()
for (i in seq_along(Sets)){
for(j in seq_along(Sets[[i]])){
Gene_ID <- c(Gene_ID, Sets[[i]][j])
}
}
intersect_label <- map2(Set_name_weight$intersect_name, Set_name_weight$Weight, rep)
intersect_label_vecter <- character()
for (i in seq_along(intersect_label)){
for(j in seq_along(intersect_label[[i]])){
intersect_label_vecter <- c(intersect_label_vecter, intersect_label[[i]][j])
}
}
gene_intersect <- data.frame(Gene_ID, intersect_label_vecter)
colnames(gene_intersect) <- c("Orig_Symbol", "Intersections")
return(gene_intersect)
}
|
ea5da434b25d6375e2ed0214167cefd95dc98d04
|
84af2a5d4cc82c218c6ea63ef6a3f8fdc299c1ac
|
/R/SETAR_model.R
|
15e9c1f61cff2ac0c034504d525410a09a0e47c6
|
[] |
no_license
|
cran/NonlinearTSA
|
c173ef17fad7165d3f13ad3034ae3b6de4520f4b
|
8fa55067e97fa0d4ceafe62999b03169c6dca109
|
refs/heads/master
| 2023-02-25T02:38:07.707991
| 2021-01-23T15:30:02
| 2021-01-23T15:30:02
| 270,956,992
| 1
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,870
|
r
|
SETAR_model.R
|
#' SETAR model estimation
#'
#' This function allows you to estimate SETAR model
#'
#' @param y series name,
#' @param delay_order Delay order,
#' @param lag_length lag length
#' @param trim_value trimmed value, .15, .10, .5
#' @return "Model" Estimated model
#' @return "threshold" the value of threshold
#' @keywords nonlinear model estimation
#' @references
#'
#' Burak Guris, R Uygulamalı Dogrusal Olmayan Zaman Serileri Analizi, DER Yayinevi, 2020.
#' @export
#' @importFrom stats embed lm resid
#'
#' @examples
#'
#'\donttest{
#'x <- rnorm(100)
#'SETAR_model(x, 1, 12, .15)
#'
#'
#'data(IBM)
#'SETAR_model(IBM, 1, 12, .05)
#'}
#'
#'
SETAR_model <- function(y, delay_order, lag_length, trim_value){
mat = embed(y,(lag_length+1))
thres = sort(y)
gy = mat[,2]
sayi = round(length(thres)*trim_value)
trim_thres = thres[(sayi+1):(length(thres)-sayi)]
sabit = rep(1,dim(mat)[1])
den = matrix(0 ,nrow = dim(mat)[1], ncol = length(trim_thres))
for(i in 1:length(trim_thres)){
for(ii in 1:dim(mat)[1]){
if(gy[ii] < trim_thres[i]){
den[ii,i] = 1
} else {
den[ii,i] = 0
}
}
}
var_SSR = NULL
for(iii in 1:ncol(den)){
model <- lm(mat[,1]~I(sabit*den[,iii])+I(mat[,2:ncol(mat)]*den[,iii])+I(sabit*(1-den[,iii]))+I(mat[,2:ncol(mat)]*(1-den[,iii]))-1)
SSR = sum((resid(model))^2)
var_SSR[iii] = SSR/length(gy)
}
son = which.min(var_SSR)
threshold = trim_thres[son]
Constant1 = (sabit*den[,son])
Regime1_ = (mat[,2:ncol(mat)]*den[,son])
Constant2 = (sabit*(1-den[,son]))
Regime2_ = (mat[,2:ncol(mat)]*(1-den[,son]))
model_son = lm(mat[,1]~Constant1+Regime1_+Constant2+Regime2_-1)
kontrol = sum((resid(model_son))^2)/length(gy)
my_list <- list("model"=summary(model_son),"threshold"=threshold)
return(my_list)
}
|
e0d65c7d7c45679eed779fb0ddd58503fd972a87
|
bad08314942d890670cb8186827e93387f8242cb
|
/R/oneWayAnova.R
|
72b55f3e8a8e081d1f9d8ee2b04e685c9c3c4d9b
|
[] |
no_license
|
stamats/MKmisc
|
faaa5a4bc04d015143fcd2d468bc11aa12ef5633
|
e738e1f1b18899af42c1149335c6ee063e9de80c
|
refs/heads/master
| 2022-11-25T06:06:56.692986
| 2022-11-19T15:35:13
| 2022-11-19T15:35:13
| 33,780,395
| 10
| 2
| null | 2015-06-29T18:02:53
| 2015-04-11T15:13:48
|
R
|
UTF-8
|
R
| false
| false
| 321
|
r
|
oneWayAnova.R
|
## Modification of function Anova in package genefilter
oneWayAnova <- function(cov, na.rm = TRUE, var.equal = FALSE){
function(x) {
if (na.rm) {
drop <- is.na(x)
x <- x[!drop]
cov <- cov[!drop]
}
oneway.test(x ~ cov, var.equal = var.equal)$p.value
}
}
|
1cbbd4a1513bd7180a49830e2d7d9d5433e8a19e
|
c1f1d1615f7f3eb62382fcdf38c6eb96c6ec0040
|
/LuasStrike/LuasStrike.R
|
3854720a10d9f4f71bea0e0e99871f19383be077
|
[] |
no_license
|
mryap/SentimentAnalysis
|
fc9c333ad9401fca5017211d86b3f16ffd998c09
|
b0cb3cbd39dcbc9b71cd286ec00b175059ca0237
|
refs/heads/main
| 2023-01-23T15:56:38.557807
| 2020-12-07T14:35:23
| 2020-12-07T14:35:23
| 319,342,929
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 893
|
r
|
LuasStrike.R
|
install.packages("base64enc")
library(httr)
library(twitteR)
library(base64enc)
consumer_key <- 'kDbigJNR6f9BMu6o2Sy95IJp2'
consumer_secret <- 'oXB0795wCX0KwaMC8kT3DgUk45Zv94Bu2DMFVqak0dYUTordEk'
access_token <- '9465632-warwshtU6ax4XsrHYPc5llNWMe17xqAczznh6AH0NN'
access_secret <- 'XULeCzfftRUqUUrzj3Hla2Uv29YU7eojRBE27akvLUnGZ'
setup_twitter_oauth(consumer_key, consumer_secret, access_token, access_secret)
save(setup_twitter_oauth, file='twitter_authentication.Rdata')
load('twitter_authentication.Rdata')
#Once you launched the code first time, you can start from this line in the future (libraries should be connected)
tweets<-searchTwitter("#LuasStrike", n=10000,lang='en')
dataTweet<-plyr::ldply(tweets,as.data.frame)
#To count how many missing values are in the replyToSN column of this data frame (i.e. dataTweet)
sum(is.na(dataTweet$replyToSN))
|
75ed534f5eda2a2411fe48ab1f4d44c8908f6a67
|
1888f6a3e9892150524e392e9288456c4564ef62
|
/cachematrix.R
|
33a35d813f4e2dd9ec736d92847800fd692061a6
|
[] |
no_license
|
PKathib/ProgrammingAssignment2
|
1e07012bcf0628746309835a1d59f7d4df9ce3cb
|
717b70cf749a1682c52814259109a6d6847d985a
|
refs/heads/master
| 2021-01-18T10:15:01.807760
| 2016-02-20T04:22:27
| 2016-02-20T04:22:27
| 52,133,547
| 0
| 0
| null | 2016-02-20T03:02:41
| 2016-02-20T03:02:39
| null |
UTF-8
|
R
| false
| false
| 1,813
|
r
|
cachematrix.R
|
## We are creating two functions in R.
## 1. makeCacheMatrix, 2. cacheSolve
## Both these functions are used to get the inverse of the matrix and then to cache them for future use
## as matrix inversion is an expensive process.
## # 1. Set the value of the passed matrix
## # 2. Get the value of the passed matrix
## # 3. Set the inverse of the passed matrix
## # 4. Get the inverse of the passed matrix
makeCacheMatrix <- function(x = matrix()) {
inv <- NULL
set <- function(y) {
x <<- y
inv <<- NULL
}
get <- function() x
setinverse <- function(inverse) inv <<- inverse
getinverse <- function() inv
list(set=set, get=get, setinverse=setinverse, getinverse=getinverse)
}
## Write a short comment describing this function
cacheSolve <- function(x, ...) {
## Return a matrix that is the inverse of 'x'
inv <- x$getinverse()
if(!is.null(inv)) {
message("reading from cache...")
return(inv)
}
data <- x$get()
inv <- solve(data)
x$setinverse(inv)
inv
}
# inverse matrix results verified from http://www.purplemath.com/modules/mtrxinvr.htm
# s <- matrix(c(1,3,3,1,4,3,1,3,4), nrow=3, ncol=3,byrow = TRUE)
# s
# m = makeCacheMatrix(s)
# m$get()
#
# cacheSolve(m)
## Test Data
# > s <- matrix(c(1,3,3,1,4,3,1,3,4), nrow=3, ncol=3,byrow = TRUE)
# > s
# [,1] [,2] [,3]
# [1,] 1 3 3
# [2,] 1 4 3
# [3,] 1 3 4
# > m = makeCacheMatrix(s)
# > m$get()
# [,1] [,2] [,3]
# [1,] 1 3 3
# [2,] 1 4 3
# [3,] 1 3 4
# > cacheSolve(m)
# [,1] [,2] [,3]
# [1,] 7 -3 -3
# [2,] -1 1 0
# [3,] -1 0 1
# >
## Test Data
|
f86bfd05d7c53d64912a0eec5c121e8623f11dc4
|
860efbde82499c1cc307e36b57f6af41fe37225e
|
/man/analyze.gain.Rd
|
9c3f0ac1bfe5604822bb4bdf71c71aeff6ac85d3
|
[] |
no_license
|
cran/gainML
|
92a2ffb79ca5026e9e509edcdc1cc43b151ddb92
|
f85e402726004d6f9a31f812cc0a66bf83eabffc
|
refs/heads/master
| 2020-12-21T23:34:19.542072
| 2019-06-28T12:40:07
| 2019-06-28T12:40:07
| 236,601,381
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 7,466
|
rd
|
analyze.gain.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/analysis_total.R
\name{analyze.gain}
\alias{analyze.gain}
\title{Analyze Potential Gain from Passive Device Installation on WTGs by Using a
Machine Learning-Based Tool}
\usage{
analyze.gain(df1, df2, df3, p1.beg, p1.end, p2.beg, p2.end, ratedPW, AEP,
pw.freq, freq.id = 3, time.format = "\%Y-\%m-\%d \%H:\%M:\%S",
k.fold = 5, col.time = 1, col.turb = 2, bootstrap = NULL,
free.sec = NULL, neg.power = FALSE)
}
\arguments{
\item{df1}{A dataframe for reference turbine data. This dataframe must
include five columns: timestamp, turbine id, wind direction, power output,
and air density.}
\item{df2}{A dataframe for baseline control turbine data. This dataframe must
include four columns: timestamp, turbine id, wind speed, and power output.}
\item{df3}{A dataframe for neutral control turbine data. This dataframe must
include four columns and have the same structure with \code{df2}.}
\item{p1.beg}{A string specifying the beginning date of period 1. By default,
the value needs to be specified in \samp{\%Y-\%m-\%d} format, for example,
\code{'2014-10-24'}. A user can use a different format as long as it is
consistent with the format defined in \code{time.format} below.}
\item{p1.end}{A string specifying the end date of period 1. For example, if
the value is \code{'2015-10-24'}, data observed until
\code{'2015-10-23 23:50:00'} would be considered for period 1.}
\item{p2.beg}{A string specifying the beginning date of period 2.}
\item{p2.end}{A string specifying the end date of period 2. Defined similarly
as \code{p1.end}.}
\item{ratedPW}{A kW value that describes the (common) rated power of the
selected turbines (REF and CTR-b).}
\item{AEP}{A kWh value describing the annual energy production from a single
turbine.}
\item{pw.freq}{A matrix or a dataframe that includes power output bins and
corresponding frequency in terms of the accumulated hours during an annual
period.}
\item{freq.id}{An integer indicating the column number of \code{pw.freq} that
describes the frequency of power bins in terms of the accumulated hours
during an annual period. By default, this parameter is set to 3.}
\item{time.format}{A string describing the format of time stamps used in the
data to be analyzed. The default value is \code{'\%Y-\%m-\%d \%H:\%M:\%S'}.}
\item{k.fold}{An integer defining the number of data folds for the period 1
analysis and prediction. In the period 1 analysis, \eqn{k}-fold cross
validation (CV) will be applied to choose the optimal set of covariates
that results in the least prediction error. The value of \code{k.fold}
corresponds to the \eqn{k} of the \eqn{k}-fold CV. The default value is 5.}
\item{col.time}{An integer specifying the column number of time stamps in
wind turbine datasets. The default value is 1.}
\item{col.turb}{An integer specifying the column number of turbines' id in
wind turbine datasets. The default value is 2.}
\item{bootstrap}{An integer indicating the current replication (run) number
of bootstrap. If set to \code{NULL}, bootstrap is not applied. The default
is \code{NULL}. A user is not recommended to set this value and directly
run bootstrap; instead, use \code{\link{bootstrap.gain}} to run bootstrap.}
\item{free.sec}{A list of vectors defining free sectors. Each vector in the
list has two scalars: one for starting direction and another for ending
direction, ordered clockwise. For example, a vector of \code{c(310 , 50)}
is a valid component of the list. By default, this is set to \code{NULL}.}
\item{neg.power}{Either \code{TRUE} or \code{FALSE}, indicating whether or
not to use data points with a negative power output, respectively, in the
analysis. The default value is \code{FALSE}, i.e., negative power output
data will be eliminated.}
}
\value{
The function returns a list of several objects (lists) that includes
all the analysis results from all steps. \describe{ \item{\code{data}}{A
list of arranged datasets including period 1 and period 2 data as well as
\eqn{k}-folded training and test datasets generated from the period 1 data.
See also \code{\link{arrange.data}}.} \item{\code{p1.res}}{A list
containing period 1 analysis results. This includes the optimal set of
predictor variables, period 1 prediction for the REF turbine and CTR-b
turbine, the corresponding error measures such as RMSE and BIAS, and BIAS
curves for both REF and CTR-b turbine models; see \code{\link{analyze.p1}}
for the details.} \item{\code{p2.res}}{A list containing period 2 analysis
results. This includes period 2 prediction for the REF turbine and CTR-b
turbine. See also \code{\link{analyze.p2}}.} \item{\code{gain.res}}{A list
containing gain quantification results. This includes effect curve, offset
curve, and gain curve as well as the measures of effect (gain without
offset), offset, and (the final) gain; see \code{\link{quantify.gain}} for
the details.} }
}
\description{
Implements the gain analysis as a whole; this includes data arrangement,
period 1 analysis, period 2 analysis, and gain quantification.
}
\details{
Builds a machine learning model for a REF turbine (device installed)
and a baseline CTR turbine (CTR-b; without device installation and
preferably closest to the REF turbine) by using data measurements from a
neutral CTR turbine (CTR-n; without device installation). Gain is
quantified by evaluating predictions from the machine learning models and
their differences during two different time periods, namely, period 1
(without device installation on the REF turbine) and period 2 (device
installed on the REF turbine).
}
\note{
\itemize{ \item This function will execute four other functions in
sequence, namely, \code{\link{arrange.data}}, \code{\link{analyze.p1}},
\code{\link{analyze.p2}}, \code{\link{quantify.gain}}. \item A user can
alternatively run the four funtions by calling them individually in
sequence.}
}
\examples{
df.ref <- with(wtg, data.frame(time = time, turb.id = 1, wind.dir = D,
power = y, air.dens = rho))
df.ctrb <- with(wtg, data.frame(time = time, turb.id = 2, wind.spd = V,
power = y))
df.ctrn <- df.ctrb
df.ctrn$turb.id <- 3
# For Full Sector Analysis
res <- analyze.gain(df.ref, df.ctrb, df.ctrn, p1.beg = '2014-10-24',
p1.end = '2014-10-25', p2.beg = '2014-10-25', p2.end = '2014-10-26',
ratedPW = 1000, AEP = 300000, pw.freq = pw.freq, k.fold = 2)
# In practice, one may use annual data for each of period 1 and period 2 analysis.
# One may typically use k.fold = 5 or 10.
# For Free Sector Analysis
free.sec <- list(c(310, 50), c(150, 260))
res <- analyze.gain(df.ref, df.ctrb, df.ctrn, p1.beg = '2014-10-24',
p1.end = '2014-10-25', p2.beg = '2014-10-25', p2.end = '2014-10-26',
ratedPW = 1000, AEP = 300000, pw.freq = pw.freq, k.fold = 2,
free.sec = free.sec)
gain.res <- res$gain.res
gain.res$gain #This will provide the final gain value.
}
\references{
H. Hwangbo, Y. Ding, and D. Cabezon, 'Machine Learning Based
Analysis and Quantification of Potential Power Gain from Passive Device
Installation,' arXiv:1906.05776 [stat.AP], Jun. 2019.
\url{https://arxiv.org/abs/1906.05776}.
}
\seealso{
\code{\link{arrange.data}}, \code{\link{analyze.p1}},
\code{\link{analyze.p2}}, \code{\link{quantify.gain}}
}
|
f62778905382e394986d0aa870672235479ce5ca
|
36bf489230433e0e8ae7b88cf6a9f9b0cf299f15
|
/plot3.R
|
abe8a8c2470c3b7a8a58314a54142b4e60cca3e4
|
[] |
no_license
|
samermounir/ExData_Plotting1
|
27fcc7c3ee881dbebad56e5483f9f4de207c27d3
|
fad4e75e97e34b4df78dfdcd7f980524c1adede8
|
refs/heads/master
| 2021-01-18T00:13:59.859161
| 2014-12-05T15:23:05
| 2014-12-05T15:23:05
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,549
|
r
|
plot3.R
|
############################################################
##You have to extract the data into your working directory##
############################################################
## Step 0 using the lubridate library
library(lubridate)
## Step 1 read the data into the elecdata including doing all the adjustments
## like reading the header , seperator ";", removing the rows containing n"?"
elecdata<-read.table("household_power_consumption.txt",sep=";",header=TRUE,na.strings="?",colClasses = c(rep("character", 2), rep("numeric", 7)))
## Step 2 creating a new data set for first & Second of Feb. 2007 only
mydata <- elecdata[elecdata$Date %in% c("1/2/2007","2/2/2007"),]
## Step 3 Creating the plot and putting the submetering data in different colos using the points command
par(bg=NA) ## set the background to transparent
plot(dmy(mydata$Date)+ hms(mydata$Time),mydata$Sub_metering_1 ,type="n",xlab="",ylab ="Energy sub metering",cex.lab=1)
points (dmy(mydata$Date)+ hms(mydata$Time),mydata$Sub_metering_1,type="l")
points(dmy(mydata$Date)+ hms(mydata$Time),mydata$Sub_metering_2,col="red",type="l")
points(dmy(mydata$Date)+ hms(mydata$Time),mydata$Sub_metering_3,col="blue",type="l")
## Step 4 Creating the legend
legend("topright",c("Sub_metering_1","Sub_metering_2","Sub_metering_3"),lty =c(1,1),col=c("black","red","blue"),text.font=2,cex=0.8,pt.cex=1)
## Step 5 Copy my plot to a PNG file 480 * 480
dev.copy(png, file = "plot3.png",width=480,height=480) ## Copy my plot to a PNG file
dev.off() ## Don't forget to close the PNG device!
|
26f6d8cc7130dd909e5fb4ccf6489c38b76bd59e
|
d8e6354d5fcc6f3f1202fccccaf816f11d6a0518
|
/R Files/ps2.r
|
a08ca5c4d635a1bb22403c123ad9f9b9b550c2b2
|
[] |
no_license
|
nishidhvlad/Repository
|
4f7ff19c7c4cb92f53fc6d3f4df79dc2e82a7a04
|
b10e664b6d9912bb49f5d20d0ab16562f76a90de
|
refs/heads/master
| 2020-03-26T04:29:44.084478
| 2018-08-12T22:36:21
| 2018-08-12T22:36:21
| 144,506,506
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 7,510
|
r
|
ps2.r
|
################################
# QUESTION 1
################################
rm(list=ls(all=TRUE)) #remove all variables
library(data.table)
context1 <- fread("attend.csv") #read data file
attendrt <- context1$attend/32
hwrt <- context1$hw/8
summary(context1)
model1 <- lm(termGPA~priGPA+ACT+attendrt+hwrt, data=context1)
summary(model1)
# Coefficients:
# Estimate Std. Error t value Pr(>|t|)
# (Intercept) -1.286983 0.164169 -7.839 1.77e-14 ***
# priGPA 0.548962 0.042418 12.942 < 2e-16 ***
# ACT 0.036099 0.006051 5.966 3.92e-09 ***
# attendrt 0.155436 6.770 2.81e-11 ***
# hwrt 0.913031 0.116932 7.808 2.22e-14 ***
#termGPA = -1.287 + 0.549*priGPA + 0.036*ACT + 1.052*attendrt + 0.913*hwrt
#Interpretations:-
#1. Estimated Coeffiecient for attendrt in model 1 is 1.052
#2. Estimated Coeffiecent for hwrt in model 1 is 0.913
#3. The termGPA of 2.906 for a student with a 32 ACT and a 2.2 priGPA who attended 28 lectures and turned-in 8 homework assignments
#4. The termGPA of 3.407 for a student with a 20 ACT and 3.9 priGPA who attended 28 lectures and turned in 8 homework assignments.
####### to compare b1 and b2, b1*std(priGPA) > b2*std(ACT)
#5. priGPA has more impact to termGPA since higher priGPA tends to higher termGPA
#6. The termGPA of 2.771 for a student with a 25 ACT and a 3.0 priGPA who attends all the classes, but only finishes half the homework assignments
#7. The termGPA of 2.691 for a similarly qualifieed student who turns in all the homwork assignments, but only attends half the classes
#8. Attendance is of more importance to the change of termGPA as even homework rate increases termGPA do not increases to that extend.
#9. Because attendrt and hwrt gives us the relative value and it is easier to compare both of them as they dont have any units, but in ACT and priGPA , different types of values are present with no common comparable parameter.
###############################################################
# QUESTION 2
###############################################################
rm(list=ls(all=TRUE))
library(data.table)
context2 <- fread("CEOSAL2.csv")
summary(context2)
lsalary <- log(context2$salary)
lmktval <- log(context2$mktval)
lsales <- log(context2$sales)
model2 <- lm(lsalary~lmktval+profits+ceoten, data=context2)
summary(model2)
#Coefficients:
# Estimate Std. Error t value Pr(>|t|)
#(Intercept) 4.7095052 0.3954502 11.909 < 2e-16 ***
# lmktval 0.2386220 0.0559166 4.267 3.25e-05 ***
# profits 0.0000793 0.0001566 0.506 0.6132
#ceoten 0.0114646 0.0055816 2.054 0.0415 *
model3 <- lm(lsalary~lmktval+profits+ceoten+lsales, data=context2)
summary(model3)
#Coefficients:
# Estimate Std. Error t value Pr(>|t|)
#(Intercept) 4.558e+00 3.803e-01 11.986 < 2e-16 ***
# lmktval 1.018e-01 6.303e-02 1.614 0.1083
#profits 2.905e-05 1.503e-04 0.193 0.8470
#ceoten 1.168e-02 5.342e-03 2.187 0.0301 *
# lsales 1.622e-01 3.948e-02 4.109 6.14e-05 ***
#Interpretation:
#1. Since profits consist of negative values, Log transformation can't be performed on data with '-ve' values.
#2. Every 1% increase in the market value is associated with a 0.2386% increase in the salary of chief executive officers for U.S. corporations controlling profits and years as CEO of the company.
#3. Every 1% increase in the market value is associated with a 0.1018% increase in the salary of chief executive officers for U.S. corporations controlling profits, years as CEO of the company and % of sales.
#4. Model2 tells us that mktval is quite significant when it comes to salary whereas Model3 has a different story which says there is Omitted Variable Biasing in Model1 because Sales component is missing. From Model 2, it is clear sales is more significant to Salary as compared to mktval.
#5. Coefficient on profits is particularly not of such significance in model3 that can be clearly inferred from the p-value (>0.05) for profits is very high.
#6. Every 1% increase in the sales is associated with 0.1622% increase in the salary of chief executive officers for U.S. corporations controlling for profits, years as CEO of the company and % of mktval.
#################################################
# Question 3
################################################
rm(list=ls(all=TRUE))
library(data.table)
context3 <- fread("hprice1.csv")
summary(context3)
llotsize <- log(context3$lotsize)
lsqrft <- log(context3$sqrft)
model4 <- lm(price~bdrms+llotsize+lsqrft+colonial, data=context3)
summary(model4)
#Coefficients:
# Estimate Std. Error t value Pr(>|t|)
#(Intercept) -2030.455 210.967 -9.625 3.68e-15 ***
# bdrms 18.572 9.308 1.995 0.0493 *
# llotsize 61.446 12.372 4.966 3.60e-06 ***
# lsqrft 225.508 30.072 7.499 6.41e-11 ***
# colonial 4.134 14.509 0.285 0.7764
lprice <- log(context3$price)
model5 <- lm(lprice~bdrms+llotsize+lsqrft+colonial, data=context3)
summary(model5)
#Coefficients:
#Estimate Std. Error t value Pr(>|t|)
#(Intercept) -1.34959 0.65104 -2.073 0.0413 *
# bdrms 0.02683 0.02872 0.934 0.3530
#llotsize 0.16782 0.03818 4.395 3.25e-05 ***
# lsqrft 0.70719 0.09280 7.620 3.69e-11 ***
# colonial 0.05380 0.04477 1.202 0.2330
#Interpretation
#1. Every 1% increase in the lotsize is associated with a $614.66 increase in the price of the house controlling No. of bedrooms,size of the house and colonial style.
#2. Every 1% increase in the lotsize is associated with a 0.1678% increase in the price of the house controlling for No. of bedrooms,size of the house and colonial style.
#3. The price of the house increases by $4,134 for every colonial house
#4. R-squared value basically suggests that Model4 is a better fit for the given data set.
#5. As per model4 if we increase 1 bedroom master suite then that will eventually increase the price of house by $18572 and increase in 10% of sqft will increase the price of house by $22550. Total price will increase to approximately $342k from $300k. $42k is greater than my valued enjoyment rate of $20k so model 4 is appropriate model for pursuing the expansion
####################################################
# Question 4
###################################################
rm(list=ls(all=TRUE))
context4 <- fread("JTRAIN2.csv")
summary(context4)
model6 <- lm(re78~re75+train+educ+black, data = context4)
summary(model6)
#Coefficients:
# Estimate Std. Error t value Pr(>|t|)
#(Intercept) 1.97686 1.89028 1.046 0.2962
#re75 0.14697 0.09811 1.498 0.1349
#train 1.68422 0.62700 2.686 0.0075 **
# educ 0.41026 0.17267 2.376 0.0179 *
# black -2.11277 0.82941 -2.547 0.0112 *
#Interpretation
#1. Every $1000 increase in re75(Real earnings in 1975) is associated with a $146.97 increase in re78 controlling job training, years of education and black
#2. For every trained low income man, there is a $1684.22 increase in the Real earnings in 1978 controlling re75(Real earnings in 1975), years of education and black. It is significat at 1%
#3. For every black low income man, there is a $2112.28 decrease in the Real earnings in 1978 controlling re75(Real earnings in 1975), years of education, and job training
|
95d53883542d5fda33b19b8597cd71444dcc106c
|
93aed4eda5fe4e23d8f0f042b1180449eac9517c
|
/RakregiszterScraper.R
|
41c8294647faa0a26a8bdd84492496cb51ab9d23
|
[] |
no_license
|
tamas-ferenci/RakregiszterVizualizator
|
543467171eae1456247608a8926f93dfc9304595
|
1e0085f80f56cbb3940d9704c549f5bcedfa704d
|
refs/heads/master
| 2021-10-11T16:05:16.592672
| 2021-09-28T11:50:36
| 2021-09-28T11:50:36
| 146,289,139
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 4,442
|
r
|
RakregiszterScraper.R
|
library(data.table)
ftfy <- reticulate::import("ftfy") # https://github.com/rspeer/python-ftfy
years <- rvest::html_attr(rvest::html_elements(rvest::read_html("http://stat.nrr.hu/"),
xpath = "//select[@id='edit-eve']/option"), "value")[-1]
counties <- rvest::html_text(rvest::html_elements(rvest::read_html("http://stat.nrr.hu/"),
xpath = "//select[@id='edit-megye']/option"), "value")[-1]
counties <- data.frame(do.call(rbind, strsplit(counties, " - ")))
colnames(counties) <- c("CountyNo", "County")
counties$CountyNo <- as.numeric(counties$CountyNo)
counties$County[counties$County=="Györ-Moson-Sopron megye"] <- "Győr-Moson-Sopron megye"
ICDs <- rvest::html_text(rvest::html_elements(rvest::read_html("http://stat.nrr.hu/"),
xpath = "//select[@id='edit-diagkod']/option"), "value")[-1]
ICDs <- data.table(ICDCode = substring(ICDs, 1, 3), ICDName = substring(ICDs, 7, 41))
saveRDS(ICDs, file = "ICDs.rds")
tab <- rbindlist(lapply(years, function(year) {
print(year)
res <- httr::POST("http://stat.nrr.hu/",
body = list("diagkod[]" = "000", "eve[]" = year, "sex[]" = "0",
"megye[]" = "00", "megyechk" = "1", "op" = "Szűrés", "form_id" = "nrr_stat"))
res <- httr::content(res)
ids <- rvest::html_text(rvest::html_nodes(res, xpath = "//div[@id='dialog']/node()[not(self::div)]"))
ids <- ids[grepl("Neme", ids)]
tab <- rvest::html_table(res)
tab <- tab[sapply(tab, ncol)==20]
for(i in 1:length(tab)) {
names(tab[[i]]) <- sapply(names(tab[[i]]), ftfy$fix_text)
tab[[i]]$Diagkód <- sapply(iconv(tab[[i]]$Diagkód, "utf-8", "latin1", sub = ""), ftfy$fix_text)
}
tab <- rbindlist(lapply(1:length(tab), function(i) data.table(tab[[i]], id = ids[i])))
tab <- tab[Diagkód!="Összesen", !"Összesen"]
tab$Sex <- as.numeric(substr(tab$id, 8, 8))
tab$CountyNo <- substr(tab$id, 19, 20)
tab <- tab[!CountyNo%in%c("", "00")]
tab$CountyNo <- as.numeric(tab$CountyNo)
tab$Year <- year
tab$ICDCode <- sapply(strsplit(tab$Diagkód, "-"), `[`, 1)
tab <- merge(CJ(ICDCode = ICDs$ICDCode, Sex = 1:2, CountyNo = 1:20, Year = year), tab, all.x = TRUE)
tab[is.na(Diagkód)][, 6:23] <- 0
tab[, !c("Diagkód", "id")]
}))
colnames(tab)[grepl("-", colnames(tab))] <- paste0("Age", seq(0, 85, 5))
tab$Year <- as.numeric(tab$Year)
tab$Sex <- ifelse(tab$Sex==1, "Férfi", "Nő")
tab <- merge(tab, counties)
tab <- tab[, c("ICDCode", paste0("Age", seq(0, 85, 5)), "Year", "County", "Sex")]
write.csv2(tab, "RawDataWide.csv", row.names = FALSE)
tab <- melt(tab, id.vars = c("ICDCode", "Year", "County", "Sex"), variable.name = "Age", value.name = "N")
tab$Age <- as.numeric(substring(tab$Age, 4))
write.csv2(tab, gzfile("RawDataLong.csv.gz"), row.names = FALSE)
PopPyramid <- rbind(
fread("Nepesseg_OtevesKor_Nemenkent_Megyenkent_1990_2016.csv", dec = ",", skip = 6, header = TRUE,
check.names = TRUE),
fread("Nepesseg_OtevesKor_Nemenkent_Megyenkent_2017_2019.csv", dec = ",", skip = 6, header = TRUE,
check.names = TRUE)
)[, -7]
PopPyramid$Időszak[PopPyramid$Időszak==""] <- NA
PopPyramid$Nem[PopPyramid$Nem==""] <- NA
PopPyramid$Korév[PopPyramid$Korév==""] <- NA
PopPyramid <- tidyr::fill(PopPyramid, Időszak, Nem, Korév, Terület)
PopPyramid$Időszak <- as.numeric(substring(PopPyramid$Időszak, 1, 4))
PopPyramid$Korév <- as.numeric(substring(PopPyramid$Korév, 1, 2))
names(PopPyramid) <- c("Year", "Sex", "Age", "County", "PopJan1", "PopMidYear")
write.csv2(PopPyramid, "PopPyramid.csv", row.names = FALSE)
saveRDS(PopPyramid, "Nepesseg_OtevesKor_Nemenkent_Megyenkent_1990_2019.rds")
tab <- merge(tab, PopPyramid[, .(Year, Sex, Age, County, Population = PopMidYear)],
by = c("County", "Sex", "Age", "Year"))
write.csv2(tab, gzfile("RawDataLongWPop.csv.gz"), row.names = FALSE)
saveRDS(tab, file = "RawDataLongWPop.rds")
StdPops <- fread("StdPops18.csv")
StdPops <- merge(StdPops, tab[, .(sum(Population)), .(Age, Sex)][, .(Age, StdHUN = V1/sum(V1)*1e6), .(Sex)])
write.csv2(StdPops, "StdPops.csv", row.names = FALSE)
saveRDS(StdPops, file = "StdPops.rds")
MapHunNUTS3 <- rgdal::readOGR("OSM_kozighatarok", "admin6", encoding = "UTF8", use_iconv = TRUE,
stringsAsFactors = FALSE)
saveRDS(MapHunNUTS3, file = "MapHunNUTS3.rds")
|
ae0f1cda9c105b53bfed5381bda4bd0a10f99489
|
91ec04b21cd17e36a6784865aee08dc729c0a4ea
|
/cachematrix.R
|
4f6373d7292ea2218d913027d5e08b4669803b9a
|
[] |
no_license
|
praneets/ProgrammingAssignment2
|
a1816cf217581e522439c56cb24682b78a8b3c2c
|
2bf6dd3af99a78887ceddf874720bb148c5cbe90
|
refs/heads/master
| 2021-01-18T06:56:08.406543
| 2014-05-24T10:34:39
| 2014-05-24T10:34:39
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,109
|
r
|
cachematrix.R
|
## Put comments here that give an overall description of what your
## functions do
## This function creates a list of functions that will work on the argument x.
## Argument x will be initiated when this function is called. The four
## functions are set, get, setinv and getinv. The cache for this function is
## the variable inv.
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)
}
## This function actually calculates the inverse of the input matrix. If
## the inverse of the matrix is already available in the cache, it retrieves
## it, or else, it calculates it afresh and then returns it.
cacheSolve <- function(x, ...) {
inv <- x$getinv()
if(!is.null(inv)) {
message("getting cached data")
return(inv)
}
data <- x$get()
inv <- solve(data, ...)
x$setinv(inv)
inv
}
|
e8b8aa76d9ba5ee1e5a0a0ac0de8a8177a071a79
|
7e3f188372012ed9635facb1a2a3b0bab71cef48
|
/man/check.assign.with.multiple.sol.Rd
|
af4ee984a5a8761afb2e72812ef58b574fd3c8aa
|
[] |
no_license
|
skranz/RTutor
|
ae637262b72f48646b013b5c6f89bb414c43b04d
|
f2939b7082cc5639f4695e671d179da0283df89d
|
refs/heads/master
| 2023-07-10T03:44:55.203997
| 2023-06-23T05:33:07
| 2023-06-23T05:33:07
| 11,670,641
| 203
| 61
| null | 2020-06-17T16:11:34
| 2013-07-25T20:47:22
|
R
|
UTF-8
|
R
| false
| true
| 694
|
rd
|
check.assign.with.multiple.sol.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/tests_for_ps.r
\name{check.assign.with.multiple.sol}
\alias{check.assign.with.multiple.sol}
\title{Checks an assignment to a variable with up to 5
possibly correct solutions}
\usage{
check.assign.with.multiple.sol(
sol1,
sol2,
sol3,
sol4,
sol5,
...,
sol.list = list()
)
}
\arguments{
\item{sol1}{An assignment that needs to be checked, e.g. x<-5. Similar for sol2, sol3, sol4, sol5.}
}
\description{
Can be called in a #< test block for a custom test.
}
\examples{
\donttest{
# Assume the task is that x shall be a number
# below 11 and divisible by 5
check.assign.with.multiple.sol(x<-5, x<-10)
}
}
|
a26c00b2b8303340e429d29189fd12a1ab572a8d
|
8c353819bc833ce88ff5c1f2d27f31d40ac2b162
|
/data-raw/ice.R
|
8ad25391d5c60711f944c13ae30087f322b42cbd
|
[] |
no_license
|
AustralianAntarcticDivision/SOmap
|
6e1e91ec59a59be6471ce1b940c9363154f949b7
|
0297ee8aea87015e32a2d3e4f9b009d00a549d29
|
refs/heads/master
| 2023-03-12T07:42:44.477742
| 2023-03-07T02:44:57
| 2023-03-07T02:44:57
| 155,124,496
| 24
| 5
| null | 2023-02-02T22:18:42
| 2018-10-28T23:06:11
|
R
|
UTF-8
|
R
| false
| false
| 295
|
r
|
ice.R
|
ice <- raadtools::readice(latest = TRUE)
date <- getZ(ice)
psproj <- "+proj=stere +lat_0=-90 +lat_ts=-71 +lon_0=0 +k=1 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0"
ice <- setZ(raster::projectRaster(ice, crs = psproj), date)
ice[ice < 1] <- NA
usethis::use_data(ice)
|
694e779bee0d6b4b6aaa894421c0a2c7f09e32f3
|
71aedc7b7f4e70697b09b8786b835df061b2ade9
|
/man/creds_from_file.Rd
|
d333268d3d1e191fdd9c15a737a3916ecfeabb93
|
[] |
no_license
|
jflournoy/scorequaltrics
|
e6027483aedeb16c3ff07956b803511ebed5dc78
|
f3114b315b96ed05d99b7950b538a3c2c11ce0ae
|
refs/heads/master
| 2022-04-28T11:10:43.565376
| 2022-04-21T20:30:07
| 2022-04-21T20:30:07
| 247,803,512
| 1
| 0
| null | 2022-04-21T20:29:57
| 2020-03-16T19:48:30
|
R
|
UTF-8
|
R
| false
| true
| 537
|
rd
|
creds_from_file.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/auth.R
\name{creds_from_file}
\alias{creds_from_file}
\title{creds_from_file}
\usage{
creds_from_file(creds_yaml = "credentials.yaml")
}
\arguments{
\item{creds_yaml}{The .yaml file that contains the user's credentials}
}
\description{
Reads the API key and base URL from a file and sets up the environment
variables necessary for accessing data. Uses
\code{\link[qualtRics]{qualtrics_api_credentials}}, but does not save
credentials to the .Renviron file.
}
|
25e35d17b26ca4f9ae8efef448ebc185567ae0c4
|
2c40d0d8a09d8808acb6187cbc98b091758e3c2e
|
/ClassifiyTracks.R
|
21b037f4e657336ded0bc051acfb8d33b487d58b
|
[] |
no_license
|
thorstenwagner/spie-photonics-europe-2016
|
f98e9b30791fac90b8e5aaa7111611cfbc3bc971
|
9e443d3da321265b83565785cbd1dbc4078679d3
|
refs/heads/master
| 2021-01-10T02:33:47.630061
| 2016-02-17T09:08:33
| 2016-02-17T09:08:33
| 51,763,237
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,760
|
r
|
ClassifiyTracks.R
|
#The MIT License (MIT)
#
#Copyright (c) 2016 Thorsten Wagner (wagner@biomedical-imaging.de)
#
#Permission is hereby granted, free of charge, to any person obtaining a copy
#of this software and associated documentation files (the "Software"), to deal
#in the Software without restriction, including without limitation the rights
#to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
#copies of the Software, and to permit persons to whom the Software is
#furnished to do so, subject to the following conditions
#
#The above copyright notice and this permission notice shall be included in all
#copies or substantial portions of the Software.
#
#THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
#IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
#FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
#AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
#LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
#OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
#SOFTWARE.
library("randomForest");
lvl.to.num <- function(a){
return(as.numeric(as.character(a)));
}
makePrediction <- function(features,model,title){
###Elongation
elong <- lvl.to.num (features$elong);
###Fractal dimension
fd <- lvl.to.num (features$fd);
###Long time / short time diffusion coefficient ratio
LtStRatio <- lvl.to.num (features$LtStRatio);
combinedData <- data.frame(ELONG=elong,FD=fd,LTST.RATIO=LtStRatio);
features.predict <- predict(model,combinedData);
print(title);
print(table(features.predict))
}
load("randomForestModel.RData");
##########################################################
### PREDICTION ###
##########################################################
load("tracks_nta_many_free.RData");
features <- help[[2]];
makePrediction(features,features.model,"### PREDICTION OF FREE DIFFUSION 100nm POLYSTYRENE PARTICLES (NTA) ###");
load("tracks_cyto_confined.RData");
features <- help[[2]];
makePrediction(features,features.model,"### PREDICTION OF CONFINED DIFFUSION 50nm GOLD PARTICLES (DARKFIELD) ###");
load("tracks_cyto_active.RData");
features <- help[[2]];
makePrediction(features,features.model,"### PREDICTION OF ACTIVE DIFFUSION 50nm GOLD PARTICLES (DARKFIELD) ###");
load("tracks_laser_confined.RData");
features <- help[[2]];
makePrediction(features,features.model,"### PREDICTION OF CONFINED DIFFUSION 50nm GOLD PARTICLES (LASER-SCANNING) ###");
load("tracks_laser_active.RData");
features <- help[[2]];
makePrediction(features,features.model,"### PREDICTION OF ACTIVE DIFFUSION 50nm GOLD PARTICLES (LASER-SCANNING) ###");
|
4a2cc4760686d57dc5b0170b2758eec38d200826
|
05a9f722bfd91a75144ebf840f296f932b5baf20
|
/BrestNewlyn/correlationNewlynSubPolarGyreIndex.R
|
1cf96d201f55445c1de1a2fff0c09cfe36cb49ec
|
[] |
no_license
|
simonholgate/R-Scripts
|
05118e4e92118a506eaf29bf6fa9aca4ea3f9477
|
89ab9ee9da1bbce10f4dc9a422259dda64748689
|
refs/heads/master
| 2020-05-17T14:48:02.345740
| 2012-06-21T11:19:58
| 2012-06-21T11:19:58
| 4,738,138
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 3,773
|
r
|
correlationNewlynSubPolarGyreIndex.R
|
library(fields)
library(robust)
source("~/Dropbox/BrestNewlyn/matrixMethods.R")
load("~/Dropbox/brestNewlynData/analysis/paper/correlationACRE/brestNewlyn.tot.ps.RData")
newlyn.full.p.start <- 1916
newlyn.full.p.end <- 2008
newlyn.full.p.yrs <- c(newlyn.full.p.start:newlyn.full.p.end)
hatun.period <- c(which(newlyn.full.p.yrs>=1960):which(newlyn.full.p.yrs==2004))
hatun.yrs <- newlyn.full.p.yrs[hatun.period]
hatun.tot.p <- newlyn.tot.p.full[hatun.period]
filt.hatun.tot.p <- filter(hatun.tot.p, c(0.25, 0.5, 0.25), method="conv", sides=2)
plot(hatun.yrs, -hatun.tot.p, type='l', col='red', lwd=2)
lines(hatun.yrs, -filt.hatun.tot.p, col='blue', lwd=2, lty=2)
grid()
hakkinen.period <- c(which(newlyn.full.p.yrs==1993):which(newlyn.full.p.yrs==2001))
hakkinen.tot.p <- newlyn.tot.p.full[hakkinen.period]
hakkinen <- read.table(file="hakkinenData.txt", sep=",", colClasses=c("numeric", "numeric"),
col.names=c("year", "pc1"))
##x11()
##plot(hakkinen$year, hakkinen$pc1, type='l', col='blue')
intsct <- intersect(which(hakkinen$year>=1993), which(hakkinen$year<=2002))
hakkinen.yrs <- 1993:2001
hakkinen.annual <- hakkinen$pc1[intsct]
dim(hakkinen.annual) <- c(12,length(hakkinen.yrs))
hakkinen.annual <- colMeans(hakkinen.annual)
## Re-scaling Hakkinen data to fit Newlyn
##range(hakkinen.annual) -> -0.1181667 0.1391667
##which(hatun.yrs==1993) -> 34
##which(hatun.yrs==2002) -> 43
##diff(range(-hatun.tot.p[34:43])) -> 1164.665
##mean(-hatun.tot.p[34:43]) -> -102490.1
lines(hakkinen.yrs, hakkinen.annual/0.139/2*1164-102490, col='magenta', lwd=2)
hatun <- read.table(file="hatunData.txt", sep=",", colClasses=c("numeric", "numeric"),
col.names=c("year", "pc1"))
##plot(hatun$year, hatun$pc1, type='l', col='blue', lty=3)
## Re-scaling Hatun data to fit Newlyn
##range(hatun$pc1) -> -8.1 10.3
##diff(range(-hatun.tot.p)) -> 1660.483
##mean(-hatun.tot.p[34:43]) -> -102232.8
lines(hatun$year, hatun$pc1/10.3/2*1660-102232, col='cyan', lwd=2, lty=3)
## Normalised plot
plot(hatun.yrs, (-hatun.tot.p - mean(-hatun.tot.p))/1076.7611, type='l', col='red', lwd=2, ylim=c(-1,1), ann=F)
##lines(hatun.yrs, (-filt.hatun.tot.p - mean(-filt.hatun.tot.p, na.rm=T))/764.2831, col='blue', lwd=2, lty=2)
grid(lwd=2)
title(main="Newlyn sea level and gyre index", ylab="Normalised sea level/gyre index", xlab="Years")
lines(hakkinen.yrs, (hakkinen.annual - mean(hakkinen.annual))/0.1314722, col='magenta', lwd=2)
lines(hatun$year, (hatun$pc1 - mean(hatun$pc1))/9.218182, col='cyan', lwd=2)
intsct <- intersect(which(hatun$year>=1993), which(hatun$year<2002))
hakkinen.hatun <- hatun$pc1[intsct]
## Normalised plot over Hakkinen yrs
plot(hakkinen.yrs, (-hakkinen.tot.p - mean(-hakkinen.tot.p))/254.2495, type='l', col='red', lwd=2, ylim=c(-1,1), ann=F)
##lines(hatun.yrs, (-filt.hatun.tot.p - mean(-filt.hatun.tot.p, na.rm=T))/764.2831, col='blue', lwd=2, lty=2)
grid(lwd=2)
title(main="Newlyn sea level and gyre index", ylab="Normalised sea level/gyre index", xlab="Years")
lines(hakkinen.yrs, (hakkinen.annual - mean(hakkinen.annual))/0.1314722, col='magenta', lwd=2)
lines(hakkinen.yrs, (hakkinen.hatun - mean(hakkinen.hatun))/9.666667, col='cyan', lwd=2)
## Normalised plot over Hakkinen yrs shown over Hatun yrs
plot(hatun.yrs, (-hatun.tot.p - mean(-hakkinen.tot.p))/254.2495, type='l', col='red', lwd=2, ylim=c(-1,1), ann=F)
##lines(hatun.yrs, (-filt.hatun.tot.p - mean(-filt.hatun.tot.p, na.rm=T))/764.2831, col='blue', lwd=2, lty=2)
grid(lwd=2)
title(main="Newlyn sea level and gyre index", ylab="Normalised sea level/gyre index", xlab="Years")
lines(hakkinen.yrs, (hakkinen.annual - mean(hakkinen.annual))/0.1314722, col='magenta', lwd=2)
lines(hatun$year, (hatun$pc1 - mean(hatun$pc1))/9.666667, col='cyan', lwd=2)
|
5bf467df96d204dc4f41df885374134a4639c872
|
36881f038bac0454ac2f1adaabf472074e38fd1d
|
/Code/man/module.input.Rd
|
92f49cee6032e8782b51ad10c35cad96f645da5b
|
[] |
no_license
|
PriSomeda/longleafGY
|
d3a3580de4d86e73ebe3f7e422d81cd906ed0f39
|
bffa7bbfbc5d5d476b6a5a1809df73f35968d147
|
refs/heads/master
| 2020-03-18T20:54:43.555997
| 2018-06-03T21:47:15
| 2018-06-03T21:47:15
| 135,246,715
| 0
| 1
| null | null | null | null |
UTF-8
|
R
| false
| true
| 4,219
|
rd
|
module.input.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/module.input.r
\name{module.input}
\alias{module.input}
\title{Module of input tree- or stand-level data to prepare it for further simulations.}
\usage{
module.input(TYPE = "PLOT", TREEDATA = NA, AREA = NA, SI = NA,
HDOM0 = NA, AGE0 = NA, BA0 = NA, N0 = NA, AGEF = 50,
THINNING = "FALSE", AGET = NA, BAR = NA, t = 5, d = 15,
method = 2)
}
\arguments{
\item{TYPE}{Character for type of input data. PLOT: stand-level data information, TREE: tree-level information. Default is PLOT}
\item{TREEDATA}{Data frame with tree-level information with columns: PLOTID, TREEID, DBH, HT (these should be identical names).}
\item{AREA}{Numeric value of size of the inventory plot (m2). Required for TYPE='TREE'.}
\item{SI}{Numeric value of Site Index (m) (Dominant Height of the plot at age 50 years).}
\item{HDOM0}{Numeric value of Dominant Height (m) at initial age (or age 0).}
\item{AGE0}{Numeric value of initial stand age or age 0 (years).}
\item{BA0}{Numeric value of Basal Area (m2/ha) at age 0 (required for model projection).}
\item{N0}{Numeric value of number of trees per hectare at age 0.}
\item{AGEF}{Numeric value of final stand age (in years) of simulation. Default is 50.}
\item{THINNING}{If TRUE then a thinning is implemented according to AGET and BAR. Default is FALSE.}
\item{AGET}{Numeric value of stand age (in years) where thinning is planned.}
\item{BAR}{Numeric value of Relative Basal Area (\%, 0-1) to be removed when thinning at age AGET.}
\item{t}{Numeric value top stem diameter outside bark for merchantability limit (cm).}
\item{d}{Numeric value of a DBH threshold limit for merchantable trees (cm).}
\item{method}{Numeric value that identifies the method to estimate missing heights from TYPE='TREE'.
1: parametrized DBH-height model that requires DBH, BA and AGE, 2: fits a simple DBH-height model from
available measurements using the equation: ln(Ht) = b0 + b1/DBH. Default method=2.}
}
\value{
A list containing the following:
\code{- SI} Site Index (m).
\code{- AGE0} Initial stand age or age 0 (years).
\code{- HDOM0} Dominant Height (m) at initial age (or age 0).
\code{- BA0} Basal Area (m2/ha) at age 0.
\code{- N0} Number of trees per hectare at age 0.
\code{- QD} Mean quadratic diameter (cm) at age 0.
\code{- SDIR0} Relative stand density index (\%) at age 0.
\code{- VOL_OB0} Total stand-level volume outside bark (m3/ha) at age 0.
\code{- VOL_IB0} Total stand-level volume inside bark (m3/ha) at age 0.
\code{- VOLm_OB0} Merchantable stand-level volume outside bark (m3/ha) at age 0.
\code{- VOLm_IB0} Merchantable stand-level volume inside bark (m3/ha) at age 0.
\code{- AGEF} Final stand age (in years) of simulation.
\code{- THINNING} Logical that indicates if thinning is implemented according to
AGET and BAR.
\code{- AGET} Stand age (in years) where thinning is planned.
\code{- BAR} Relative Basal Area (\%, 0-1) to be removed when thinning at age
AGET.
\code{- t} Top stem diameter outside bark for merchantability limit (cm).
\code{- d} DBH threshold limit for merchantable trees (cm).
\code{- method} Selection of the method to estimate missing heights from TYPE='TREE'.
}
\description{
\code{module.input} Prepares tree- or stand-level data from a single plot, checks and completes missing values, and
calculates several stand-level parameters including total volume. It also reads required information for further
simulations including simulation age and details of future thinning. Some information is only traspassed to other modules.
Note that form tree-level data individual tree (complete or incomplete) information is required.
}
\examples{
# Example 1 - Input stand-level data
module.input(TYPE='PLOT', BA0=17.3, SI=30, AGE0=17, N0=1200, AGEF=18)
module.input(TYPE='PLOT', BA0=17.3, HDOM0=16, AGE0=17, N0=1200, AGEF=18)
module.input(TYPE='PLOT', HDOM0=16, AGE0=17, N0=1200, AGEF=18) # BA obtained by prediction
# Example 2 - Input with individual tree data
module.input(TYPE='TREE', TREEDATA=treedata, AREA=500, AGE0=23, AGEF=32)
}
\author{
Priscila Someda-Dias, Salvador A. Gezan
}
|
32fe4b0ca157ec62e7d2ef35dc729f27370af628
|
47f1ebbeb9e1c2f639926da5fc42cd7ae508f350
|
/man/makeACSdf.Rd
|
cdc05e1171ed01c0fcde74d2351e014bd0d6a1cc
|
[] |
no_license
|
maxsibilla/spew
|
ee72183a2aa43a31025247134bafdc1d4a6fb6e6
|
375526cef25f7029e12d4148d37ac11a3560a6f7
|
refs/heads/master
| 2021-01-20T17:46:47.928742
| 2017-05-10T14:23:38
| 2017-05-10T14:23:38
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 438
|
rd
|
makeACSdf.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/acs-tables.R
\name{makeACSdf}
\alias{makeACSdf}
\title{make a more usable df from acs objects}
\usage{
makeACSdf(acs_obj)
}
\arguments{
\item{acs_obj}{class "acs" from acs package}
}
\value{
a formatted df where the rows are the geographies and the columns are summary table values
}
\description{
make a more usable df from acs objects
}
\details{
to come
}
|
1df62e558423ddffbd4cefbc028566006a147e4c
|
c73f0d6bf7ba22627fdbf8b2fca89ba4c8058c9a
|
/plot1.R
|
d33e2c919976256df4ab67fe80ade70e8950e040
|
[] |
no_license
|
wills8/ExData_Plotting1
|
d457495b6e5f05d988553d518e9ea477457633f9
|
3c9c1baaa97ad0ffdb785e92912dfdfbfbc2e1aa
|
refs/heads/master
| 2020-06-11T10:27:19.429294
| 2019-06-26T15:47:32
| 2019-06-26T15:47:32
| 193,931,514
| 0
| 0
| null | 2019-06-26T15:28:03
| 2019-06-26T15:28:02
| null |
UTF-8
|
R
| false
| false
| 616
|
r
|
plot1.R
|
# John Hopkins Exploratory Data Analysis Project 1
# Data Source: UC Irvine Machine Learning Repository
# Data Title: Electric Power Consumption
# PLOT 1
# Read data in and subset data
data <- read.table("household_power_consumption.txt", sep = ";", header = TRUE, na.strings = "?")
subData <- data[data$Date %in% c("1/2/2007", "2/2/2007"), ]
# Create histogram and png file of Global Active Power
attach(subData)
png("plot1.png", height = 480, width = 480)
hist(Global_active_power, xlab = "Global Active Power (kilowatts)",
main = "Global Active Power",
col = "Red")
dev.off()
|
1490c84dc423983e6e8c6e4bd4c2bde9b86a8bbe
|
506e917f1a30059c0d61d897e099f178c85098b8
|
/R/perf.metric.R
|
3b90a0cf3026a2b7491faa5c90b8f4067a46d640
|
[] |
no_license
|
pierrecattin/thesis-resources
|
03262e980857493a208fd11198503c6af0170091
|
6a321aa377a00f82a86e5f61d6170ab0ba849f52
|
refs/heads/master
| 2021-06-22T08:00:42.441620
| 2018-08-24T15:23:18
| 2018-08-24T15:23:18
| 136,451,744
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 907
|
r
|
perf.metric.R
|
#' Performance Metric
#'
#' @param freq.acc data frame containing at least columns Freqency, Accuracy and Sr if metric is "mean.sr"
#' @param metric c("mean.accuracy", "mean.sr")
#'
#' @return Performance metric computed as a mean accros frequency levels
#' @export
#'
perf.metric <- function(freq.acc, metric){
freq.grid <- seq(min(round(freq.acc$Frequency, 2)),
max(round(freq.acc$Frequency, 2)), by=0.01)
# find lines in freq.acc that have the closest freqency to each element of freq.grid
find.closest <- function(freq){
return(which.min(abs(freq.acc$Frequency - freq)))
}
indices <- sapply(X=freq.grid, FUN=find.closest)
# compute metric
freq.acc.filtered <- freq.acc[indices,]
value <- switch(metric,
mean.accuracy = mean(freq.acc.filtered$Accuracy),
mean.sr = mean(freq.acc.filtered$Sr),
stop("unknown metric"))
return(value)
}
|
21278bd12ef8df0699e678ad4c3d27435c659e99
|
ffdea92d4315e4363dd4ae673a1a6adf82a761b5
|
/data/genthat_extracted_code/event/examples/hboxcox.Rd.R
|
b51d4897789bc4a8d534d0da4926997cbe62416b
|
[] |
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
| 177
|
r
|
hboxcox.Rd.R
|
library(event)
### Name: hboxcox
### Title: Log Hazard Function for a Box-Cox Process
### Aliases: hboxcox
### Keywords: distribution
### ** Examples
hboxcox(2, 5, 5, 2)
|
49da1f7aad633a382beca45ef50434fdad798ef0
|
452042d9a5cb876a90a9cb1f4c802d0f4b1453c7
|
/R/espn_draftpicks.R
|
960d25cf2de4af8e7be24d927068311c13f4b90c
|
[
"MIT"
] |
permissive
|
jpiburn/ffscrapr
|
dc420370f6940275aaa8cb040c5ec001a25268b8
|
4e7bda862500c47d1452c84a83adce7ee1987088
|
refs/heads/main
| 2023-06-02T00:09:09.670168
| 2021-06-12T15:52:23
| 2021-06-12T15:52:23
| 377,976,824
| 1
| 0
|
NOASSERTION
| 2021-06-17T22:42:26
| 2021-06-17T22:42:26
| null |
UTF-8
|
R
| false
| false
| 677
|
r
|
espn_draftpicks.R
|
#### ff_draftpicks - ESPN ####
#' ESPN Draft Picks
#'
#' @param conn the list object created by `ff_connect()`
#' @param ... other arguments (currently unused)
#'
#' @describeIn ff_draftpicks ESPN: does not support future/draft pick trades - for draft results, please use ff_draft.
#'
#' @examples
#' \donttest{
#' conn <- espn_connect(
#' season = 2018,
#' league_id = 1178049,
#' espn_s2 = Sys.getenv("TAN_ESPN_S2"),
#' swid = Sys.getenv("TAN_SWID")
#' )
#'
#' ff_draftpicks(conn)
#' }
#'
#' @export
ff_draftpicks.espn_conn <- function(conn, ...) {
rlang::warn("ESPN does not support draft pick trades. For draft results, please use ff_draft()")
return(NULL)
}
|
93dee8c29ef9feca00f6d2ebac62209cbdfe320b
|
fc70b4b8f15ec7062ad57714ad81441015b559b8
|
/inst/app/server/projection/reactive.R
|
f1e0211f94a765b97451476ebff7c15d08b27376
|
[] |
no_license
|
jackolney/CascadeDashboard
|
02aa85dc78e6ab916ba6e01328b45f483d81b0c0
|
25e29abd233ba365501900c800f81ae0beadc0c6
|
refs/heads/master
| 2020-07-07T05:58:55.039907
| 2017-04-04T07:58:23
| 2017-04-04T07:58:26
| 66,279,770
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 264
|
r
|
reactive.R
|
plotOptimCostImpact.ranges <- reactiveValues(x = NULL, y = NULL)
plotOpt_909090.ranges <- reactiveValues(x = NULL, y = NULL)
plotOpt_DALYs.ranges <- reactiveValues(x = NULL, y = NULL)
plotOpt_DALYs_909090.ranges <- reactiveValues(x = NULL, y = NULL)
|
9999cb982cd61e7077d24bd65b833c1a2cfe5041
|
fc96da2f9cac0702e0caa7b40ca9a732ad798bcb
|
/scripts/archive/sfa_gwis_gwastools/sfa_gwis_gwastools_fhs.R
|
61f1e9e96448d3906a4dc5fc305be56b06c452b6
|
[] |
no_license
|
echoheqian/whi-diet-response
|
d452df48fd1c04b9c5eb81be8255727edeeb35ec
|
0372f9c7fc5e48ae4b7121a504b6979ed175fa38
|
refs/heads/master
| 2023-04-24T14:25:19.611522
| 2021-05-05T15:54:31
| 2021-05-05T15:54:31
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 5,544
|
r
|
sfa_gwis_gwastools_fhs.R
|
silent <- lapply(
c("knitr", "tidyverse", "cowplot", "doParallel", "kableExtra",
"glmnet", "broom", "GWASTools", "SNPRelate"), library, character.only=T)
args <- commandArgs(trailingOnly=T)
dv_withEx <- args[1]
rf <- args[2]
main_effect_threshold <- args[3]
INT <- function(x) qnorm((rank(x, na.last="keep") - 0.5) / sum(!is.na(x)))
winsorize <- function(x, num_SDs=5) {
bounds <- mean(x, na.rm=T) + num_SDs * c(-1, 1) * sd(x, na.rm=T)
case_when(x < bounds[1] ~ bounds[1],
x > bounds[2] ~ bounds[2],
TRUE ~ x)
}
make_qqplot <- function(p_vec, plotTitle="Title") {
p_vec <- p_vec[!is.na(p_vec)]
qqplot(-log10(1:length(p_vec) / length(p_vec)), -log10(p_vec), pch=".",
main=plotTitle, xlab="Expected (-logP)", ylab="Observed (-logP)")
abline(0, 1, col="red")
}
gControl <- function(pVals) {
# See van Iterson 2017 methods and/or Lehne 2015 code for details on genomic control for EWAS
# Below is modeled after Lehne 2015
lambda <- median(qchisq(pVals, df=1, lower.tail=F), na.rm=T) / qchisq(0.5, df=1)
round(lambda, 2)
}
bim_to_SnADF <- function(bfile) {
bim_df <- read_tsv(paste0(bfile, ".bim"),
col_names=c("chromosome", "rsID", "cm",
"position", "A1", "A2")) %>%
mutate(snpID=1:nrow(.)) %>%
mutate_at(vars(chromosome, position), as.integer)
SnpAnnotationDataFrame(data.frame(bim_df, stringsAsFactors=F))
}
fam_to_ScADF <- function(bfile) {
phenos <- read_delim("../data/processed/gen4/fhs_gwas_phenos.txt",
delim=" ")
fam_df <- read_delim(paste0(bfile, ".fam"),
delim="\t", col_names=c("FID", "IID", "father", "mother",
"sex", "pheno")) %>%
left_join(phenos, by=c("IID")) %>%
mutate(scanID=IID)
ScanAnnotationDataFrame(data.frame(fam_df, stringsAsFactors=F))
}
make_gds <- function(bfile, gds_name, summary=F) {
snpgdsBED2GDS(paste0(bfile, ".bed"),
paste0(bfile, ".fam"),
paste0(bfile, ".bim"),
gds_name,
cvt.snpid="int")
if (summary) snpgdsSummary(gds_name)
}
run_gwis_chunk <- function(genoData, outcome, covars, ivar, start, stop, robust=F) {
assocRegression(
genoData,
outcome=outcome,
model.type="linear",
covar=covars,
ivar=ivar,
robust=robust,
snpStart=start,
snpEnd=stop)
}
covar_sets <- list(
fat_carbEx=c("age", "fat", "pro", "alc", "tot_cal"),
sfa_carbEx=c("age", "sfa", "mufa", "pufa", "pro", "alc", "tot_cal"),
myhei=c("age", "myhei"),
fat_carbEx_Bin=c("age", "fatBin", "pro", "alc", "tot_cal"),
sfa_carbEx_Bin=c("age", "sfaBin", "mufa", "pufa", "pro", "alc", "tot_cal"),
myhei_Bin=c("age", "myheiBin")
)
interaction_vars <- list(
fat_carbEx="fat",
sfa_carbEx="sfa",
myhei="myhei",
fat_carbEx_Bin="fatBin",
sfa_carbEx_Bin="sfaBin",
myhei_Bin="myheiBin"
)
outcome_transforms <- list(
bmi="logBMI",
# hsCRP="logHSCRP",
tg="logTG",
glu="logGLU",
ldl="ldl",
sbp="logSBP",
delta_bmi="delta_bmi",
delta_tg="delta_tg",
delta_glu="delta_glu",
delta_ldl="delta_ldl",
delta_sbp="delta_sbp"
)
outcome_basenames <- list(
bmi="bmi",
# hsCRP="logHSCRP",
tg="tg",
glu="glu",
ldl="ldl",
sbp="sbp",
delta_bmi="bmi",
delta_tg="tg",
delta_glu="glu",
delta_ldl="ldl",
delta_sbp="sbp"
)
run_gwis <- function(outcome, dv, genoset, robust=T) {
covars <- covar_sets[[dv]]
ivar <- interaction_vars[[dv]]
outcome_transform <- outcome_transforms[[outcome]]
outcome_basename <- outcome_basenames[[outcome]]
bfile <- paste0("../data/processed/fhs_subsets/fhs_",
outcome_basename, "_", genoset)
snpAnnot <- bim_to_SnADF(bfile)
scanAnnot <- fam_to_ScADF(bfile)
gds_name <- paste0(bfile, ".gds")
# make_gds(bfile, gds_name)
gds <- GdsGenotypeReader(openfn.gds(gds_name, allow.fork=T))
genoData <- GenotypeData(gds, snpAnnot=snpAnnot, scanAnnot=scanAnnot)
num_cores <- detectCores()
chunks <- as.integer(cut(1:nsnp(genoData), num_cores))
cl <- makeForkCluster(num_cores)
registerDoParallel(cl)
res <- foreach(idx=unique(chunks), .combine=rbind,
.packages="GWASTools") %dopar%
run_gwis_chunk(genoData, outcome_transform, covars, ivar,
min(which(chunks == idx)), max(which(chunks == idx)),
robust=robust)
stopCluster(cl)
close(gds)
res
}
####### RUN GWIS #######
gwis_res <- run_gwis(rf, dv_withEx, main_effect_threshold)
annotate_sumstats <- function(ss, bfile, maf_filter=0.01) {
anno <- bim_to_SnADF(bfile) %>%
getAnnotation() %>%
select(snpID, rsID, chromosome, position, A1, A2)
ss %>%
filter(MAF > maf_filter) %>%
select(snpID, MAF, n, GxE.Est, GxE.SE, GxE.pval) %>%
inner_join(anno, by="snpID") %>%
select(SNP=rsID, CHR=chromosome, BP=position, A1, A2, MAF, N=n,
BETA=GxE.Est, SE=GxE.SE, P=GxE.pval)
}
dv <- interaction_vars[[dv_withEx]]
write_tsv(gwis_res,
paste0("../data/processed/gen4/fhs_res/fhs_", dv, "_", rf, "_",
main_effect_threshold, ".res_raw"))
res_anno <- annotate_sumstats(gwis_res,
paste0("../data/processed/fhs_subsets/fhs_",
rf, "_", main_effect_threshold))
write_tsv(res_anno,
paste0("../data/processed/gen4/fhs_res/fhs_", dv, "_", rf, "_",
main_effect_threshold, ".res"))
|
a4d53fa74f9bee0d737b0768e888af7e71b70c80
|
47f4ff8f58149e3b7301b455f47754aa041dc8f0
|
/server.r
|
fe844b38aa4c4010141b9584b5d7315d428963ed
|
[] |
no_license
|
ashish9308/SentimentAnalysis
|
fa6fa7fb4fdab3cfa53e54fc5fec94ca0b311477
|
43ef62aea356ddaa3e4474933b48b9a5fe9175ce
|
refs/heads/master
| 2021-05-04T22:17:10.619013
| 2020-01-30T13:18:04
| 2020-01-30T13:18:04
| 120,026,675
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 11,132
|
r
|
server.r
|
#We need to have an app created at https://dev.twitter.com/apps before making any API requests to Twitter.
#It's a standard method for developers to gain API access, and, more importantly,
#it helps Twitter to observe and restricts developer from making high load API requests.
shinyServer(function(input, output) {
library("shiny")
library("tm")
library("wordcloud")
library("twitteR")
library("plyr")
library("stringr")
library("caret")
library("RColorBrewer")
library("sentiment")
library("shinydashboard")
#The first step toward getting any kind of token access from Twitter is to create an app on it.
#You have to go to https://dev.twitter.com/ and log in with your Twitter credentials.
api_key <- "O2VNH2udBjqp1tUkIAfMTmqVs"
api_secret <- "DEN4MpFhgSTgzCtIag94uf4vGts6hZLzpkOtJxG823JKvVikMS"
access_token <- "174395040-5ISwJ3MpJ77pkZEj4B0nHowljekXOwrqjwGIRuoJ"
access_token_secret <- "R2NJHAGxuLCPlkzCzU3S0lQmng9RO8aj6Sj1sv4DfyvBm"
setup_twitter_oauth(api_key,api_secret,access_token,access_token_secret)
observeEvent(input$executetweet,{
#We'll start with the searchTwitter()function (discussed previously) on the TwitteR package to gather the tweets
tweets= searchTwitter(input$text, n = input$n, lang= "en", since= format(input$dateRange[1]), until=format(input$dateRange[2]))
#Before applying any intelligent algorithms to gather more insights from the tweets collected so far, let's first clean the corpus.
tweetstext <- sapply(tweets, function(x) x$getText())
catch.error = function(x)
{
# let us create a missing value for test purpose
y = NA
# Try to catch that error (NA) we just created
catch_error = tryCatch(tolower(x), error=function(e) e)
# if not an error
if (!inherits(catch_error, "error"))
y = tolower(x)
# check result if error exists, otherwise the function works fine.
return(y)
}
cleanTweets<- function(tweet){
# Clean the tweet for sentiment analysis
# remove html links, which are not required for sentiment analysis
tweet = gsub("(f|ht)(tp)(s?)(://)(.*)[.|/](.*)", " ", tweet)
# First we will remove retweet entities from the stored tweets (text)
tweet = gsub("(RT|via)((?:\\b\\W*@\\w+)+)", " ", tweet)
# Then remove all "#Hashtag"
tweet = gsub("#\\w+", " ", tweet)
# Then remove all "@people"
tweet = gsub("@\\w+", " ", tweet)
# Then remove all the punctuation
tweet = gsub("[[:punct:]]", " ", tweet)
# Then remove numbers, we need only text for analytics
tweet = gsub("[[:digit:]]", " ", tweet)
# finally, we remove unnecessary spaces (white spaces, tabs etc)
tweet = gsub("[ \t]{2,}", " ", tweet)
tweet = gsub("^\\s+|\\s+$", "", tweet)
# if anything else, you feel, should be removed, you can. For example "slang words" etc using the above function and methods.
# Next we'll convert all the word in lower case. This makes uniform pattern.
tweet = catch.error(tweet)
tweet
}
cleanTweetsAndRemoveNAs<- function(Tweets) {
TweetsCleaned = sapply(Tweets, cleanTweets)
# Remove the "NA" tweets from this tweet list
TweetsCleaned = TweetsCleaned[!is.na(TweetsCleaned)]
names(TweetsCleaned) = NULL
# Remove the repetitive tweets from this tweet list
TweetsCleaned = unique(TweetsCleaned)
TweetsCleaned
}
TweetsCleaned = cleanTweetsAndRemoveNAs( tweetstext)
# After getting the cleaned Twitter data, we are going to use few such R packages to assess the sentiments in the tweets.
#It's worth mentioning here that not all the tweets represent sentiments. A few tweets can be just information/facts, while others
#can be customer care responses.
# Ideally, they should not be used to assess the customer sentiment about a particular organization.
# As a first step, we'll use a Naive algorithm, which gives a score based on the number of times a positive or a negative word
#occurred in the given sentence (and, in our case, in a tweet).
# Here we are scanning positive-words.txt and negative-words.txt file for comparing it with our tweets in order to get sentiments.
#Here are a few examples of existing positive and negative sentiment words:
# Positive: Love, best, cool, great, good, and amazing
# Negative: Hate, worst, sucks, awful, and nightmare
pos = scan('positive-words.txt', what = 'character', comment.char = ';')
neg = scan('negative-words.txt', what = 'character', comment.char = ';')
pos.words = pos
neg.words = neg
#Now, we create a function, score.sentiment(), which computes the raw sentiment based on the simple matching algorithm:
getSentimentScore = function(sentences, words.positive, words.negative, .progress='none')
{
require(plyr)
require(stringr)
scores = laply(sentences, function(sentence, words.positive, words.negative) {
# Let first remove the Digit, Punctuation character and Control characters:
sentence = gsub('[[:cntrl:]]', '', gsub('[[:punct:]]', '', gsub('\\d+', '', sentence)))
# Then lets convert all to lower sentence case:
sentence = tolower(sentence)
# Now lets split each sentence by the space delimiter
words = unlist(str_split(sentence, '\\s+'))
# Get the boolean match of each words with the positive & negative opinion-lexicon
pos.matches = !is.na(match(words, words.positive))
neg.matches = !is.na(match(words, words.negative))
# Now get the score as total positive sentiment minus the total negatives
score = sum(pos.matches) - sum(neg.matches)
return(score)
}, words.positive, words.negative, .progress=.progress )
# Return a data frame with respective sentence and the score
return(data.frame(text=sentences, score=scores))
}
#Let's now move one step further. Instead of using simple matching of opinion lexicon, we'll use something called Naive Bayes
#to decide on the emotion present in any tweet. We will require packages called Rstem and sentiment to assist with this.
#It's important to mention here that both these packages are no longer available in CRAN, so we have to provide the repository location
#as a parameter install.package() function.
TweetsClassEmo = classify_emotion(TweetsCleaned, algorithm="bayes", prior=1.0)
#Let's substitute NA values with the word unknown to make further analysis easier:
Emotion = TweetsClassEmo[,7]
Emotion[is.na(Emotion)] = "unknown"
#Further, we'll use another function, classify_polarity(), provided by the sentiment package, to classify the tweets into two classes,
#pos (positive sentiment) and neg (negative sentiment). The idea is to compute the log likelihood of a tweet, assuming it belongs to
#either of the two classes. Once these likelihoods are calculated, a ratio of the pos-likelihood to neg-likelihood is calculated, and,
#based on this ratio, the tweets are classified as belonging to a particular class. It's important to note that if this ratio turns out
#to be 1, then the overall sentiment of the tweet is assumed to be "neutral". The code is as follows:
TweetsClassPol = classify_polarity(TweetsCleaned, algorithm="bayes")
# we will fetch polarity category best_fit for our analysis purposes,
Pol = TweetsClassPol[,4]
# Let us now create a data frame with the above results
SentimentDataFrame = data.frame(text=TweetsCleaned, emotion=Emotion, polarity=Pol, stringsAsFactors=FALSE)
# rearrange data inside the frame by sorting it
SentimentDataFrame = within(SentimentDataFrame, emotion <- factor(emotion, levels=names(sort(table(emotion), decreasing=TRUE))))
#Function to plot sentiments in tweets
plotSentiments1<- function (sentiment_dataframe,title) {
library(ggplot2)
ggplot(sentiment_dataframe, aes(x=emotion)) + geom_bar(aes(y=..count.., fill=emotion)) +
scale_fill_brewer(palette="Dark2") +
ggtitle(title) +
theme(legend.position='right') + ylab('Number of Tweets') + xlab('Emotion Categories')
}
## Similarly we will plot distribution of polarity in the tweets
plotSentiments2 <- function (sentiment_dataframe,title) {
library(ggplot2)
ggplot(sentiment_dataframe, aes(x=polarity)) +
geom_bar(aes(y=..count.., fill=polarity)) +
scale_fill_brewer(palette="RdGy") +
ggtitle(title) +
theme(legend.position='right') + ylab('Number of Tweets') + xlab('Polarity Categories')
}
#Now lets make word cloud of tweets
removeCustomeWords <- function (TweetsCleaned) {
for(i in 1:length(TweetsCleaned)){
TweetsCleaned[i] <- tryCatch({
TweetsCleaned[i] = removeWords(TweetsCleaned[i], c(stopwords("english"), "care", "guys", "can", "dis", "didn", "guy" ,"booked", "plz"))
TweetsCleaned[i]
}, error=function(cond) {
TweetsCleaned[i]
}, warning=function(cond) {
TweetsCleaned[i]
})
}
return(TweetsCleaned)
}
getWordCloud <- function (sentiment_dataframe, TweetsCleaned, Emotion) {
emos = levels(factor(sentiment_dataframe$emotion))
n_emos = length(emos)
emo.docs = rep("", n_emos)
TweetsCleaned = removeCustomeWords(TweetsCleaned)
for (i in 1:n_emos){
emo.docs[i] = paste(TweetsCleaned[Emotion == emos[i]], collapse=" ")
}
corpus = Corpus(VectorSource(emo.docs))
tdm = TermDocumentMatrix(corpus)
tdm = as.matrix(tdm)
colnames(tdm) = emos
require(wordcloud)
suppressWarnings(comparison.cloud(tdm, colors = brewer.pal(n_emos, "Dark2"), scale = c(3,.5), random.order = FALSE, title.size = 1.5))
}
########## Plotting actual plots
output$plot1 <- renderPlot({
Result = getSentimentScore(TweetsCleaned, pos.words , neg.words)
hist(Result$score, main =paste("Sentiments of tweets Vs Frequency of sentiments" ), col = "orange")
output$text <- renderText({
paste("Mean Sentiment score", mean(Result$score))
})
})
output$plot2 <- renderPlot({
plotSentiments1(SentimentDataFrame, 'Sentiment Analysis of Tweets on Twitter')
})
output$plot3 <- renderPlot({
plotSentiments2(SentimentDataFrame, 'Polarity Analysis of Tweets on Twitter')
})
output$plot4 <- renderPlot({
getWordCloud(SentimentDataFrame, TweetsCleaned, Emotion)
})
})
})
|
c9d6f73cafa41b2d19f208c9076e338465389131
|
e2ad154f7a7001a2a393141993432bd04a5889b2
|
/man/getQuizTemplate.Rd
|
7e20beb055396b4c8bedc363a816c36ae6af2e2f
|
[
"Apache-2.0"
] |
permissive
|
takewiki/learnr
|
0dab59ce219986e97727cc03042f6f66af3e497c
|
0a9b4bc729d30e7a546bf840e7711c18a2930d36
|
refs/heads/master
| 2020-03-16T23:01:31.349360
| 2019-06-21T08:25:05
| 2019-06-21T08:25:05
| 133,061,854
| 1
| 0
|
Apache-2.0
| 2018-05-11T16:13:46
| 2018-05-11T16:13:46
| null |
UTF-8
|
R
| false
| true
| 586
|
rd
|
getQuizTemplate.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/getQuizTemplate.R
\name{getQuizTemplate}
\alias{getQuizTemplate}
\title{获取测试的模板数据，提供中英文2种格式}
\usage{
getQuizTemplate(path = "./", lang = "en")
}
\arguments{
\item{path}{文件的路径，默认为当前项目目录}
\item{lang}{语言的内容，默认为英文，也可以为中文，指定为cn即可}
}
\value{
实际没有返回值,后续考虑完善
}
\description{
获取测试的模板数据，提供中英文2种格式
}
\examples{
#getQuizTemplate(lang='cn');
}
|
6254a6d45a9a01b7e604f3c8d9372260f12d555a
|
fa60f8262586afbf25096cfb954e5a9d391addf7
|
/R_Machine_Learning/r_14_2(Diabetes_Random_Forest).R
|
cdc504b04b3676eb70bf5b5bcdeb52d913ab49e1
|
[] |
no_license
|
pprasad14/Data_Science_with_R
|
ce5a2526dad5f6fa2c1fdff9f97c71d2655b2570
|
3b61e54b7b4b0c6a6ed0a5cc8243519481bb11b9
|
refs/heads/master
| 2020-05-05T08:56:39.708519
| 2019-04-06T20:42:11
| 2019-04-06T20:42:11
| 179,884,402
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,759
|
r
|
r_14_2(Diabetes_Random_Forest).R
|
#load data
dataset = read.csv("Diabetes.csv")
# another way to make train and test set without caTools
set.seed(123)
id = sample(2, nrow(dataset), prob = c(0.7,0.3),
replace = T)
table(id)
training_set = dataset[id==1,]
test_set = dataset[id ==2,]
#building descision tree
library(rpart)
classifier = rpart(is_diabetic ~ ., data = training_set)
#plot
plot(classifier, margin = 0.1)
text(classifier, cex = 0.7) # cex is font
#####
library(rpart.plot)
rpart.plot(classifier, type = 3, digits = 3, fallen.leaves = T, cex = 0.6)
# Predictions
y_pred1 = predict(classifier, newdata = test_set, type = "class") # we dont want probs
#Confusion Matrix
library(caret)
confusionMatrix(test_set$is_diabetic, y_pred1)
#Random Forest
library(randomForest)
set.seed(123)
rf = randomForest(is_diabetic ~ ., data = training_set)
print(rf)
# plot classifier2
plot(rf) # 3 lines, for different no of 'mtry'
#pred
library(caret)
y_pred2 = predict(rf, newdata = test_set)
confusionMatrix(test_set$is_diabetic, y_pred2)
rf3 = randomForest(is_diabetic ~ ., data = training_set,
importance = T, mtry = 5, ntree = 500)
rf3
#best no of trees
best = tuneRF(training_set[-9], training_set$is_diabetic,
stepFactor = 1, improve = 0.05, trace = T, plot = T)
#Importance
importance(classifier2) # check the importance of variables
varImpPlot(classifier2) #to visualize the important variables
# Tree package
#install.packages("tree")
library(tree)
classifier4 = tree(is_diabetic ~ ., data = training_set)
plot (classifier4, margin = 0.1)
text(classifier4, cex = 0.7)
y_pred4 = predict(classifier4, newdata = test_set, type = "class")
#CM
confusionMatrix(test_set$is_diabetic, y_pred4)
|
68bf46f9dc4ca5efe521539d493391236f6c30af
|
af901bc01d668ecd411549625208b07024df3ffd
|
/man/string.Rd
|
af2f09ee1ac52f16fe237465c602ac8dc02c566d
|
[
"MIT",
"BSD-2-Clause"
] |
permissive
|
r-lib/rlang
|
2784186a4dafb2fde7357c79514b3761803d0e66
|
c55f6027928d3104ed449e591e8a225fcaf55e13
|
refs/heads/main
| 2023-09-06T03:23:47.522921
| 2023-06-07T17:01:51
| 2023-06-07T17:01:51
| 73,098,312
| 355
| 128
|
NOASSERTION
| 2023-08-31T13:11:13
| 2016-11-07T16:28:57
|
R
|
UTF-8
|
R
| false
| true
| 1,522
|
rd
|
string.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utils-encoding.R
\name{string}
\alias{string}
\title{Create a string}
\usage{
string(x, encoding = NULL)
}
\arguments{
\item{x}{A character vector or a vector or list of string-like
objects.}
\item{encoding}{If non-null, set an encoding mark. This is only
declarative, no encoding conversion is performed.}
}
\description{
\ifelse{html}{\href{https://lifecycle.r-lib.org/articles/stages.html#experimental}{\figure{lifecycle-experimental.svg}{options: alt='[Experimental]'}}}{\strong{[Experimental]}}
These base-type constructors allow more control over the creation
of strings in R. They take character vectors or string-like objects
(integerish or raw vectors), and optionally set the encoding. The
string version checks that the input contains a scalar string.
}
\examples{
# As everywhere in R, you can specify a string with Unicode
# escapes. The characters corresponding to Unicode codepoints will
# be encoded in UTF-8, and the string will be marked as UTF-8
# automatically:
cafe <- string("caf\uE9")
Encoding(cafe)
charToRaw(cafe)
# In addition, string() provides useful conversions to let
# programmers control how the string is represented in memory. For
# encodings other than UTF-8, you'll need to supply the bytes in
# hexadecimal form. If it is a latin1 encoding, you can mark the
# string explicitly:
cafe_latin1 <- string(c(0x63, 0x61, 0x66, 0xE9), "latin1")
Encoding(cafe_latin1)
charToRaw(cafe_latin1)
}
\keyword{internal}
|
3e213b4053723f1798a3b80f6be684766efc5618
|
ffdea92d4315e4363dd4ae673a1a6adf82a761b5
|
/data/genthat_extracted_code/hutils/examples/ahull.Rd.R
|
6e5347a52456c24062a17a338815729e85c2c5e9
|
[] |
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
| 169
|
r
|
ahull.Rd.R
|
library(hutils)
### Name: ahull
### Title: Maximum area given x and y coordinates
### Aliases: ahull
### ** Examples
ahull(, c(0, 1, 2, 3, 4), c(0, 1, 2, 0, 0))
|
60ce03c6825db3146b2016dfb8a1fea2ca85d0b5
|
b352edcb8ffea55c1b1bf4fae9be834213acf550
|
/plot3.R
|
a1dd2a3a27a8dc6fddd1a9ec2b9a62074df25197
|
[] |
no_license
|
connorburleigh/ExData_Plotting1
|
b4db52f2ce2fda22f94cbcc093e79ac2672d957b
|
cda28544f8fe8f2d952c4dd76b97a75fff8ad5c9
|
refs/heads/master
| 2021-01-24T23:35:09.938602
| 2015-08-07T16:28:03
| 2015-08-07T16:28:03
| 38,980,661
| 0
| 0
| null | 2015-07-12T22:37:24
| 2015-07-12T22:37:23
| null |
UTF-8
|
R
| false
| false
| 1,268
|
r
|
plot3.R
|
## downlaod.file("https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip","/Users/connorburleigh/Coursera/power_consumption.zip",method="curl")
library(lubridate)
wd<-getwd()
temp <- tempfile()
download.file("https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip",temp,method="curl")
data <- read.table(unz(temp, "household_power_consumption.txt"),na.strings="?", sep=";",)
unlink(temp)
## for local testing ## data<-read.csv("/Users/connorburleigh/Coursera/household_power_consumption.txt", sep=";",na.strings="?",stringsAsFactors=FALSE)
Date_and_Time<-paste(data$Date,data$Time)
Date_and_Time<-dmy_hms(Date_and_Time)
clean_data<-cbind(Date_and_Time, data)
relevant_data<-clean_data[clean_data$Date_and_Time >= ymd_hms("2007-2-1 00:00:00")& clean_data$Date_and_Time < ymd_hms("2007-2-3 00:00:00"),]
png("plot3.png", height=480, width = 480)
plot(relevant_data$Date_and_Time,relevant_data$Sub_metering_1, type="l",ylab="", xlab="")
lines(relevant_data$Date_and_Time,relevant_data$Sub_metering_2,col="red")
lines(relevant_data$Date_and_Time,relevant_data$Sub_metering_3,col="blue")
title(ylab= "Energy sub metering")
legend('topright',legend=legend_headings, lwd = 1, col=c("black","red","blue"))
dev.off()
|
112befc89ac2ba26e62ecdc9bbfad9dc1c8337d0
|
dabe6fa5a3caf17d8b0c3dab939c614e3143775a
|
/2_model/src/evaluate.R
|
5794101f0602c6858876abae1475f76a8e59e35f
|
[] |
no_license
|
wdwatkins/lake-temperature-neural-networks
|
d0c166183adaf82ef6562312a6fcd49a864ff255
|
626e569cb397f4d67299ebf3d80e1e8fa5dc4057
|
refs/heads/master
| 2020-03-25T05:36:10.759004
| 2019-11-16T01:29:16
| 2019-11-16T01:29:16
| 143,456,068
| 1
| 0
| null | 2018-08-03T17:37:01
| 2018-08-03T17:37:01
| null |
UTF-8
|
R
| false
| false
| 505
|
r
|
evaluate.R
|
evaluate_model <- function(model_list_ind, dat_ind, rmd_file, site_id, output_html, priority_lakes) {
model_list <- readRDS(as_data_file(model_list_ind))
dat <- readRDS(as_data_file(dat_ind))
lake_name <- lookup_lake_name(site_id, priority_lakes)
meets_data_criteria <- priority_lakes %>% filter(site_id == !!site_id) %>%
pull(meets_data_criteria)
rmarkdown::render(
input = rmd_file,
output_format = "html_document",
output_file = output_html,
output_dir = "2_model/doc")
}
|
3e5989032e601345b8f6b7339a53589c6d2ce34b
|
44143d0c480e1cabf87f2c44909afe2aa85bd67c
|
/man/summary.QTE.Rd
|
20bd6aba13590fb3629f767ab70d0eb8fa88c3bc
|
[] |
no_license
|
bcallaway11/qte
|
c383e991a3969e9e50e30477e701ee11c500d574
|
09830e766b7f9e28643928e9a170f73d9c4c0bcf
|
refs/heads/master
| 2023-08-30T21:43:34.342973
| 2023-08-15T21:37:43
| 2023-08-15T21:37:43
| 19,584,525
| 8
| 5
| null | null | null | null |
UTF-8
|
R
| false
| true
| 352
|
rd
|
summary.QTE.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/qte.R
\name{summary.QTE}
\alias{summary.QTE}
\title{Summary}
\usage{
\method{summary}{QTE}(object, ...)
}
\arguments{
\item{object}{A QTE Object}
\item{...}{Other params (to work as generic method, but not used)}
}
\description{
\code{summary.QTE} summarizes QTE objects
}
|
4d6c59c59edba85969b7d9d698d3e7fe2413af0c
|
ffdea92d4315e4363dd4ae673a1a6adf82a761b5
|
/data/genthat_extracted_code/rmutil/examples/Levy.Rd.R
|
e353872507527841c666297726f470c46f1de7e9
|
[] |
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
| 210
|
r
|
Levy.Rd.R
|
library(rmutil)
### Name: Levy
### Title: Levy Distribution
### Aliases: dlevy plevy qlevy rlevy
### Keywords: distribution
### ** Examples
dlevy(5, 2, 1)
plevy(5, 2, 1)
qlevy(0.6, 2, 1)
rlevy(10, 2, 1)
|
9e9d7379d34f764b0a04d122e853e89f168da823
|
d125c6d235454381dfc70bf01056563af9ae071c
|
/R/children.R
|
f12451bc5a4ad34508f30e7935b7de0faf117413
|
[
"MIT"
] |
permissive
|
dmkaplan2000/taxize
|
02e68eef6e62fcb5233c11928b5bd47b0cf339e3
|
5b6c4479eb7188d0289d7df6aff3b1a9d61422df
|
refs/heads/master
| 2021-01-22T10:51:43.095152
| 2015-06-18T15:44:07
| 2015-06-18T15:44:07
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 5,015
|
r
|
children.R
|
#' Retrieve immediate children taxa for a given taxon name or ID.
#'
#' This function is different from \code{\link{downstream}} in that it only collects immediate
#' taxonomic children, while \code{\link{downstream}} collects taxonomic names down to a specified
#' taxonomic rank, e.g., getting all species in a family.
#'
#' @export
#'
#' @param x character; taxons to query.
#' @param db character; database to query. One or more of \code{itis}, \code{col}, or \code{ncbi}.
#' @param rows (numeric) Any number from 1 to inifity. If the default NA, all rows are
#' considered. Note that this parameter is ignored if you pass in a taxonomic id of any of the
#' acceptable classes: tsn, colid. NCBI has a method for this function but rows doesn't work.
#' @param ... Further args passed on to \code{\link{col_children}},
#' \code{\link{gethierarchydownfromtsn}}, or \code{\link{ncbi_children}}.
#' See those functions for what parameters can be passed on.
#'
#' @return A named list of data.frames with the children names of every supplied taxa.
#' You get an NA if there was no match in the database.
#'
#' @examples \dontrun{
#' # Plug in taxon names
#' children("Salmo", db = 'col')
#' children("Salmo", db = 'itis')
#' children("Salmo", db = 'ncbi')
#'
#' # Plug in IDs
#' (id <- get_colid("Apis"))
#' children(id)
#'
#' ## Equivalently, plug in the call to get the id via e.g., get_colid into children
#' identical(children(id), children(get_colid("Apis")))
#'
#' (id <- get_colid("Apis"))
#' children(id)
#' children(get_colid("Apis"))
#'
#' # Many taxa
#' (sp <- names_list("genus", 3))
#' children(sp, db = 'col')
#' children(sp, db = 'itis')
#'
#' # Two data sources
#' (ids <- get_ids("Apis", db = c('col','itis')))
#' children(ids)
#' ## same result
#' children(get_ids("Apis", db = c('col','itis')))
#'
#' # Use the rows parameter
#' children("Poa", db = 'col')
#' children("Poa", db = 'col', rows=1)
#'
#' # use curl options
#' library("httr")
#' res <- children("Poa", db = 'col', rows=1, config=verbose())
#' res <- children("Salmo", db = 'itis', config=verbose())
#' res <- children("Salmo", db = 'ncbi', config=verbose())
#' }
children <- function(...){
UseMethod("children")
}
#' @method children default
#' @export
#' @rdname children
children.default <- function(x, db = NULL, rows = NA, ...)
{
nstop(db)
switch(db,
itis = {
id <- get_tsn(x, rows = rows, ...)
setNames(children(id, ...), x)
},
col = {
id <- get_colid(x, rows = rows, ...)
setNames(children(id, ...), x)
},
ncbi = {
if (all(grepl("^[[:digit:]]*$", x))) {
id <- x
class(id) <- "uid"
setNames(children(id, ...), x)
} else {
out <- ncbi_children(name = x, ...)
structure(out, class='children', db='ncbi', .Names=x)
}
},
# ubio = {
# id <- get_ubioid(x, ...)
# out <- children(id, ...)
# names(out) <- x
# },
stop("the provided db value was not recognised", call. = FALSE)
)
}
#' @method children tsn
#' @export
#' @rdname children
children.tsn <- function(x, db = NULL, ...)
{
fun <- function(y){
# return NA if NA is supplied
if (is.na(y)) {
out <- NA
} else {
out <- gethierarchydownfromtsn(tsn = y, ...)
}
}
out <- lapply(x, fun)
names(out) <- x
class(out) <- 'children'
attr(out, 'db') <- 'itis'
return(out)
}
#' @method children colid
#' @export
#' @rdname children
children.colid <- function(x, db = NULL, ...) {
fun <- function(y){
# return NA if NA is supplied
if(is.na(y)){
out <- NA
} else {
out <- col_children(id = y, ...)
}
return(out)
}
out <- lapply(x, fun)
if(length(out)==1){ out=out[[1]] } else { out=out }
class(out) <- 'children'
attr(out, 'db') <- 'col'
return(out)
}
# children.ubioid <- function(x, db = NULL, ...) {
# fun <- function(y){
# # return NA if NA is supplied
# if(is.na(y)){
# out <- NA
# } else {
# hierid <- ubio_classification_search(namebankID = y)
# hierid <- hierid[ grep(104, hierid$classificationtitleid), 'hierarchiesid' ]
# out <- ubio_classification(hierarchiesID = hierid, childrenFlag = 1, ...)[['children']]
# }
# return(out)
# }
# out <- lapply(x, fun)
# class(out) <- 'children'
# attr(out, 'db') <- 'ubio'
# return(out)
# }
#' @method children ids
#' @export
#' @rdname children
children.ids <- function(x, db = NULL, ...)
{
fun <- function(y, ...){
# return NA if NA is supplied
if (is.na(y)) {
out <- NA
} else {
out <- children(y, ...)
}
return(out)
}
out <- lapply(x, fun)
class(out) <- 'children_ids'
return(out)
}
#' @method children uid
#' @export
#' @rdname children
children.uid <- function(x, db = NULL, ...)
{
out <- ncbi_children(id = x, ...)
class(out) <- 'children'
attr(out, 'db') <- 'ncbi'
return(out)
}
|
8aa0bc862701f663f54287dbb3cd3e47dd8bceee
|
40eccb8e26853d23ea3d35ac15225a420333ad55
|
/spinGameSamples.R
|
9fef52cafa95987e02290a5bde72590a68a27416
|
[] |
no_license
|
dinhtuanphan/CoolProjects
|
72df0fe4f1dd56f85bb883a1eb3821a9c4a4159b
|
2eaffa8c3d278d1c4cbc52bb2fb3c1e6403c937a
|
refs/heads/main
| 2023-08-28T12:27:39.514791
| 2021-10-01T19:20:03
| 2021-10-01T19:20:03
| 370,442,930
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 243
|
r
|
spinGameSamples.R
|
runs <- 100000
spin <- function() {
expectation <-
sum(sample(c(1/2, -1/4, 1/2), size = 10, replace = TRUE))
if (expectation < 0) {
return(1)
} else {
return(0)
}
}
prob <- sum(replicate(runs, spin())) / runs
print(prob)
|
a50094e817c264cd3018be07080152adfcc67fcb
|
747803a7abca38892a07ae46239ef58300d7bc0c
|
/exercises/exercise1_vectors.R
|
73b8dca0acc66ef3218206db775577dc03197306
|
[] |
no_license
|
m-nabais/neurasmus_rmarkdown_workshop
|
0b07d7a84acd49c9c7d00a86f5e9c35c0f283cc6
|
29c986910572a8638f6542ac504ff87ebed5ea34
|
refs/heads/master
| 2022-11-06T13:03:08.217465
| 2020-06-13T13:49:55
| 2020-06-13T13:49:55
| 268,495,378
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,625
|
r
|
exercise1_vectors.R
|
#########################
## Example 1 - vectors ##
#########################
a <- c(1,2,5.3,6,-2,4) # numeric vector
b <- c("one","two","three") # character vector
c <- c(TRUE,TRUE,TRUE,FALSE,TRUE,FALSE) #logical vector
d <- c("1", 1, "TRUE", TRUE, "Hello")
##########################
## Exercise 1 - vectors ##
##########################
# 1. Create a variable called "vec1" and assign it the vector
# c("Hello", ",", "", "world", "!")
# 2. Find the number of elements of vec1.
# 3. Assign the 1st, 3rd and last elements of the vector to the variables
# x, y and z, respectively.
# 4. Run the following code:
print(paste("The 1st element of vec1 is:", x))
print(paste("The 3rd element of vec1 is:", y))
print(paste("The last element of vec1 is:", z))
# 4.1 Why is the 3rd element of vec1 not printing anything? Is there a mistake in your code?
# 4.2 Could you access the last element of vec1? Did you use a built-in R function? If not, can you think of any?
# 4.3. What is the paste() function doing? How is it different from the c()? Hint: use the function length().
# 5. What happens if you print vector *d* in the example above? What type has vector *d*?
# 6. Can you replace the 3rd element of vec1 y "pretty"? Hint: vectos are mutable and you can replace an element with [].
#########################
# Optional exercises: ##
#########################
# 1. Spend some time adding, multiplying, subtracting and dividing the vectors a, b, c and d. Are all operations allowed with every vector? Pay attention to the errors displayed! Do they make sense?
# 2. How would you modify the first element of a?
|
be00aaf3a7a91dbe70b0b45a1b7b3639b17ce785
|
f634745ad3168a636a5d6a1de2b339aab108b9f5
|
/digit.R
|
83f7d3d69b39fdc755e8999e6ebeb91655082a87
|
[] |
no_license
|
anupamsingh81/grades
|
33c49826ea436b02d005419344118ccdc2d9c2e2
|
2058a2ed5631e81981cc09420dccd6bb8ec2af59
|
refs/heads/master
| 2021-09-09T01:44:45.980347
| 2018-03-13T07:08:55
| 2018-03-13T07:08:55
| 125,007,751
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 541
|
r
|
digit.R
|
library(digitizeR)
app <- wpd.launch()
plot(res_2005$V1,res_2005$V2)
library(tidyverse)
res_2005$V1
res_2005 =
res_2005 %>% rename(marks=V1,percent=V2) %>%
ggplot(aes(marks,percent))+ #stat_smooth()
geom_line()
res_2005 %>% filter(V1<81)
pnorm(60,mean(res_2005$V1),sd(res_2005$V1))
mean(res_2005$V1)
r1=res_2005 %>% rename(marks=V1,percent=V2) %>% mutate(percent=abs(as.integer(100*percent)),marks=floor(marks))
jj=map2(.x=r1$marks,.y=r1$percent,.f=rep)
kk=jj %>% unlist()
median(kk)
|
1d87a0af5ba21b0bc716568435b597cff6471373
|
1a1a686b70a443f0d61b0e6903323a396c18c212
|
/R/ct_update_databases.R
|
8351c9201f7ccf8c237c4765e3648fad48d77dac
|
[] |
no_license
|
cran/comtradr
|
8d3e6d4073a49b215bce43d181f1f7daab61a2bf
|
aa054cbec4893182cae0b78dee951c0c21b46477
|
refs/heads/master
| 2022-04-29T19:10:49.795077
| 2022-04-20T05:32:29
| 2022-04-20T05:32:29
| 87,315,342
| 2
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 12,128
|
r
|
ct_update_databases.R
|
#' Check for updates to country/commodity databases
#'
#' Use of the Comtrade API requires access to the Comtrade countries database
#' and commodities database. The \code{comtradr} package keeps each DB saved
#' as a data frame in the package directory, as Comtrade makes updates to
#' these DB's infrequently (roughly once per year).
#'
#' This function will check to see if Comtrade has made any updates to either
#' database. If an update is found, it will download the updated DB and save
#' it to the \code{comtradr} package directory, and update the DB for use
#' within the current R session.
#'
#' @param force logical, if TRUE, both the country and commodity databases
#' will be downloaded, regardless of the status of the DB's on file. Default
#' value is FALSE.
#' @param verbose logical, if TRUE, an update status message will be printed
#' to console. Default value is TRUE.
#' @param commodity_type Trade data classification scheme to use, see
#' "details" for a list of the valid inputs. Default value is "HS", which is
#' the default "type" of the commodity database on file upon install of
#' \code{comtradr}. Please note that if the value passed to this arg doesn't
#' match the values in variable "type" of the current commodity DB, then
#' this function will replace the current commodity DB with that of the
#' type specified by this arg. If you don't intend to change the type of the
#' current commodity DB, then no input for this arg is required. To see
#' the "type" of the current commodity DB, use
#' \code{\link{ct_commodity_db_type}}.
#' @param commodity_url Default value NULL, otherwise this should be the base
#' url of the Comtrade json data directory. Only necessary if the Comtrade
#' site changes from "https://comtrade.un.org/data/cache/". This partial
#' url string will have a commodity extension appended to it to create a
#' valid url. The commodity extension will be chosen based on the input to
#' arg \code{commodity_type}.
#' @param reporter_url Default value NULL, otherwise this should be a url as a
#' char string that points to the reporter areas JSON dataset on the Comtrade
#' website. Only necessary if the Comtrade site changes from
#' \url{https://comtrade.un.org/data/cache/reporterAreas.json}
#' @param partner_url Default value NULL, otherwise this should be a url as a
#' char string that points to the reporter areas JSON dataset on the Comtrade
#' website. Only necessary if the Comtrade site changes from
#' \url{https://comtrade.un.org/data/cache/partnerAreas.json}
#'
#' @details The default for arg \code{commodity_type} is \code{HS}. Below is a
#' list of all valid inputs with a very brief description for each, for more
#' information on each of these types, see
#' \url{https://comtrade.un.org/data/doc/api/#DataAvailabilityRequests}
#' \itemize{
#' \item \code{HS}: Harmonized System (HS), as reported
#' \item \code{HS1992}: HS 1992
#' \item \code{HS1996}: HS 1996
#' \item \code{HS2002}: HS 2002
#' \item \code{HS2007}: HS 2007
#' \item \code{HS2012}: HS 2012
#' \item \code{HS2017}: HS 2017
#' \item \code{SITC}: Standard International Trade Classification (SITC), as
#' reported
#' \item \code{SITCrev1}: SITC Revision 1
#' \item \code{SITCrev2}: SITC Revision 2
#' \item \code{SITCrev3}: SITC Revision 3
#' \item \code{SITCrev4}: SITC Revision 4
#' \item \code{BEC}: Broad Economic Categories
#' \item \code{EB02}: Extended Balance of Payments Services Classification
#' }
#'
#' @return Updated database of commodities and countries.
#'
#' @export
#'
#' @examples \dontrun{
#' ct_update_databases()
#' }
ct_update_databases <- function(force = FALSE, verbose = TRUE,
commodity_type = c("HS", "HS1992", "HS1996",
"HS2002", "HS2007",
"HS2012", "HS2017", "SITC",
"SITCrev1", "SITCrev2",
"SITCrev3", "SITCrev4",
"BEC", "EB02"),
commodity_url = NULL,
reporter_url = NULL,
partner_url = NULL) {
# Input validation.
stopifnot(is.logical(force))
stopifnot(is.logical(verbose))
if (!is.null(commodity_url)) {
stopifnot(is.character(commodity_url))
commodity_url <- commodity_url
} else {
commodity_url <- "https://comtrade.un.org/data/cache/"
}
if (!is.null(reporter_url)) {
stopifnot(is.character(reporter_url))
reporter_url <- reporter_url
} else {
reporter_url <- "https://comtrade.un.org/data/cache/reporterAreas.json"
}
if (!is.null(partner_url)) {
stopifnot(is.character(partner_url))
partner_url <- partner_url
} else {
partner_url <- "https://comtrade.un.org/data/cache/partnerAreas.json"
}
# Append the correct url str to the end of commodity_url, based on arg
# "commodity_type".
commodity_type <- match.arg(commodity_type)
if (commodity_type == "HS") {
commodity_url <- paste0(commodity_url, "classificationHS.json")
} else if (commodity_type == "HS1992") {
commodity_url <- paste0(commodity_url, "classificationH0.json")
} else if (commodity_type == "HS1996") {
commodity_url <- paste0(commodity_url, "classificationH1.json")
} else if (commodity_type == "HS2002") {
commodity_url <- paste0(commodity_url, "classificationH2.json")
} else if (commodity_type == "HS2007") {
commodity_url <- paste0(commodity_url, "classificationH3.json")
} else if (commodity_type == "HS2012") {
commodity_url <- paste0(commodity_url, "classificationH4.json")
} else if (commodity_type == "HS2017") {
commodity_url <- paste0(commodity_url, "classificationH5.json")
} else if (commodity_type == "SITC") {
commodity_url <- paste0(commodity_url, "classificationST.json")
} else if (commodity_type == "SITCrev1") {
commodity_url <- paste0(commodity_url, "classificationS1.json")
} else if (commodity_type == "SITCrev2") {
commodity_url <- paste0(commodity_url, "classificationS2.json")
} else if (commodity_type == "SITCrev3") {
commodity_url <- paste0(commodity_url, "classificationS3.json")
} else if (commodity_type == "SITCrev4") {
commodity_url <- paste0(commodity_url, "classificationS4.json")
} else if (commodity_type == "BEC") {
commodity_url <- paste0(commodity_url, "classificationBEC.json")
} else if (commodity_type == "EB02") {
commodity_url <- paste0(commodity_url, "classificationEB02.json")
}
# Create output message.
if (verbose) {
msg <- "All DB's are up to date, no action required"
}
# Get the current date/time.
curr_date <- format(Sys.time(), "%a, %d %b %Y %X %Z")
# If "force" is FALSE, get the current databases as data frames.
if (!force) {
country_df <- get_country_db()
commodity_df <- get_commodity_db()
}
# Get the commodity database from the Comtrade website. Compare the
# "last-modified" date value within the header to the "date" attribute of
# the current commodity database on file. If the "last-modified" date is
# newer than the date of the current database, or the value passed to arg
# "commodty_type" doesn't match the "type" attribute of the current database,
# or arg "force" is TRUE, the old DB will be replaced by the newer DB.
# Replacement will be for both the current session and within the data dir
# of the comtradr package.
res <- httr::GET(commodity_url, httr::user_agent(get("ua", envir = ct_env)))
if (force ||
commodity_type != attributes(commodity_df)$type ||
httr::headers(res)$`last-modified` > attributes(commodity_df)$date) {
# Extract data frame.
commodity_df <- res %>%
httr::content("text", encoding = "UTF-8") %>%
jsonlite::fromJSON(simplifyDataFrame = TRUE) %>%
magrittr::extract2("results") %>%
`colnames<-`(c("code", "commodity", "parent"))
# Assign attributes to the data frame (current date/time, and type).
attributes(commodity_df)$date <- curr_date
attributes(commodity_df)$type <- commodity_type
# Save df to data dir of the comtradr package.
save(
commodity_df,
file = paste0(system.file("extdata", package = "comtradr"),
"/commodity_table.rda"),
compress = "bzip2"
)
# Save df to ct_env.
assign("commodity_df", commodity_df, envir = ct_env)
# Update the output message.
if (verbose) {
msg <- paste0("Updates found. The following datasets have been ",
"downloaded: commodities DB")
}
}
# Get the reporter country database and the partner country database from
# the Comtrade website. Compare the "last-modified" date value within the
# header of each to the "date" attribute of the current country_table
# database on file. If either of the "last-modified" dates is newer than the
# date of the database on file, or if arg "force" is TRUE, replace the
# database on file with the data pulled from the Comtrade website.
country_update <- FALSE
res_rep <- httr::GET(reporter_url,
httr::user_agent(get("ua", envir = ct_env)))
res_par <- httr::GET(partner_url,
httr::user_agent(get("ua", envir = ct_env)))
if (force ||
httr::headers(res_rep)$`last-modified` > attributes(country_df)$date ||
httr::headers(res_rep)$`last-modified` > attributes(country_df)$date) {
country_update <- TRUE
# Get reporters dataset as data frame.
reporters <- res_rep %>%
httr::content("text", encoding = "UTF-8") %>%
jsonlite::fromJSON(simplifyDataFrame = TRUE) %>%
magrittr::extract2("results") %>%
`colnames<-`(c("code", "country_name"))
# Get partners dataset as data frame.
partners <- res_par %>%
httr::content("text", encoding = "UTF-8") %>%
jsonlite::fromJSON(simplifyDataFrame = TRUE) %>%
magrittr::extract2("results") %>%
`colnames<-`(c("code", "country_name"))
# Get all countries and codes as vectors.
countries <- c(reporters$country_name, partners$country_name)
codes <- c(reporters$code, partners$code)
# Initialize output data frame.
country_df <- data.frame("country_name" = unique(countries),
stringsAsFactors = FALSE)
# Add country codes to country_df.
country_df$code <- vapply(unique(countries), function(x) {
codes[match(x, countries)]
}, character(1), USE.NAMES = FALSE)
# Add logical vector indicating, for each obs of country_df, whether the
# country appears as a reporter.
country_df$reporter <- vapply(unique(countries), function(x) {
any(reporters$country_name == x)
}, logical(1), USE.NAMES = FALSE)
# Add logical vector indicating, for each obs of country_df, whether the
# country appears as a partner.
country_df$partner <- vapply(unique(countries), function(x) {
any(partners$country_name == x)
}, logical(1), USE.NAMES = FALSE)
}
# Assign attributes to the data frame (current date/time).
attributes(country_df)$date <- curr_date
# If updates were found for the country reference dataset, then save the
# updated country DB to the data dir of the comtradr package, and update
# "country_df" within ct_env.
if (country_update) {
save(
country_df,
file = paste0(system.file("extdata", package = "comtradr"),
"/country_table.rda"),
compress = "bzip2"
)
# Save country_df to ct_env.
assign("country_df", country_df, envir = ct_env)
# Update the output message.
if (verbose) {
if (grepl("Updates found", msg, fixed = TRUE)) {
msg <- paste0(msg, ", countries DB")
} else {
msg <- paste0("Updates found. The following datasets have been ",
"downloaded: countries DB")
}
}
}
# Finally, print to console the results of the update function (msg).
if (verbose) {
message(msg)
}
return(invisible())
}
|
ceebb0d882ce5f257cadee2776171d2ea64df92c
|
f351a9b16a1fd6da52b2c2fa00bda484e7ef6f1c
|
/SNP_analysis/snp_PCA2.R
|
de9196602b67688db7e1eb22fb0197b7f3a0cdfa
|
[] |
no_license
|
tania-k/Friedmanniomyces_popgen
|
19f7b105ba28d78367752b73ab416b3477787a6e
|
816e8e98fe6ac693729c0a58ef8dbde3484a8d8a
|
refs/heads/master
| 2023-04-12T14:30:20.450499
| 2022-11-20T11:35:33
| 2022-11-20T11:35:33
| 568,384,429
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 7,832
|
r
|
snp_PCA2.R
|
library(gdsfmt)
library(SNPRelate)
library(dplyr)
library(wordcloud)
library(tm)
library(ggfortify)
library(ggplot2)
library(plotly)
library(phyloseq)
library(ggrepel)
library(ggbiplot)
library(RColorBrewer)
gdsfile = "plots/snps_selected.gds"
vcf.fn <- "vcf/CCFEE_5001_v1.All.SNP.combined_selected.vcf.gz"
if(!file.exists(gdsfile)){
snpgdsVCF2GDS_R(vcf.fn, gdsfile,method="biallelic.only")
#option=snpgdsOption(CM002236=1,CM002237=2,CM002238=3,CM002239=4,CM002240=5,CM002241=6,CM002242=7))
}
snpgdsSummary(gdsfile)
genofile <- snpgdsOpen(gdsfile)
chroms <- read.gdsn(index.gdsn(genofile,"snp.chromosome"))
#get.attr.gdsn(index.gdsn(genofile, "snp.chromosome"))
chr <- strtoi(sub("SCAF_([0-9]+)","\\1",chroms,perl=TRUE))
pca <- snpgdsPCA(genofile,num.thread=2,autosome.only=FALSE)
pc.percent <- pca$varprop*100
id <- pca$sample.id
#id <- c("5001", "5193", "5195", "5199", "5200", "5208", "524", "5273", "5275", "5277", "5281", "5283", "5307", "5311", "5311_v1", "5486", "6074", "6081", "6082", "6096", "6249", "6250", "6464", "670", "690")
#pca$sample.id = id
head(round(pc.percent, 2))
pdf("plots/PCA_snp_plots5.pdf")
tab <- data.frame(sample.id = pca$sample.id,
# pop = pheno$MinimalMediaGrowth,
EV1=pca$eigenvect[,1], # PCA vector 1
EV2=pca$eigenvect[,2], # PCA vector 2
stringsAsFactors=FALSE)
#p1<-plot(tab$EV2, tab$EV1,
#, col=as.integer(tab$pop),
#xlab="eigenvector 2", ylab="eigenvector 1", main="PCA SNP plot")
#text(x = pca$eigenvect[,2], y = pca$eigenvect[,1], labels = tab$sample.id, pos = 1 ,cex =0.8, offset = 0.5)
#mx <- apply(tab$EV2,5,max)
#mn <- apply(tab$EV1,5,min)
#https://blog.fellstat.com/?cat=11
p2 <-textplot(tab$EV2, tab$EV1, id, cex=1, new=TRUE, show.lines=TRUE, xlim=c(-0.15,0.65),ylim=c(-0.30,0.40))
set.seed(100)
# recode the snp.gds to support chromosomes?
ibs.hc <- snpgdsHCluster(snpgdsIBS(genofile, num.thread=2,autosome.only=FALSE))
rv <- snpgdsCutTree(ibs.hc)
plot(rv$dendrogram, leaflab="none", main="Friedmanniomyces endolithicus Strains")
snpgdsDrawTree(rv, type="z-score", main="Friedmanniomyces endolithicus Strains")
snpgdsDrawTree(rv, main="Friedmanniomyces endolithicus Strains",
edgePar=list(col=rgb(0.5,0.5,0.5, 0.75), t.col="black"))
table(rv$samp.group)
df = data.frame(group = rv$samp.group)
rownames(df) = pca$sample.id
write.csv(df,"plots/popset_inferred.csv")
tab <- data.frame(sample.id = pca$sample.id,
pop = rv$samp.group,
EV1=pca$eigenvect[,1], # PCA vector 1
EV2=pca$eigenvect[,2], # PCA vector 2
stringsAsFactors=FALSE)
#plot(tab$EV2, tab$EV1,
# col=as.integer(tab$pop),
# xlab="eigenvector 2", ylab="eigenvector 1", main="PCA SNP plot")
#CORRSNP <- snpgdsPCACorr(pca, genofile, eig.which=1:4,num.thread=2)
#savepar <- par(mfrow=c(3,1), mai=c(0.3, 0.55, 0.1, 0.25))
#for (i in 1:3)
#{
# plot(abs(CORRSNP$snpcorr[i,]), ylim=c(0,1), xlab="", ylab=paste("PC", i),
# col=factor(chr), pch="+")
#}
options(ggrepel.max.overlaps = Inf)
#http://rstudio-pubs-static.s3.amazonaws.com/53162_cd16ee63c24747459ccd180f69f07810.html
#https://ggrepel.slowkow.com/articles/examples.html
metadata <- read.csv("metadata.tsv", sep = "\t", row.names = 1, header = TRUE)
metadata2 <- read.csv("metadata.tsv", sep = "\t")
dataset_pcr <- metadata2[2:3]
pca_res <- prcomp(dataset_pcr, scale. = TRUE)
biplot = ggbiplot(pcobj = pca_res,
choices = c(1,2),
#label.position = "identity",
#label.repel = TRUE,
obs.scale = 1,
var.scale = 1, # Scaling of axis
#labels = row.names(metadata), # Add labels as rownames
#labels.size = 3.5,
varname.size = 3.5,
varname.abbrev = FALSE, # Abbreviate variable names (TRUE)
var.axes = FALSE, # Remove variable vectors (TRUE)
circle = FALSE, # Add unit variance circle (TRUE)
ellipse = TRUE,
groups = metadata$Ploidy) # Adding ellipses
#print(biplot)
#repel that shit https://stackoverflow.com/questions/68738673/how-to-repel-labels-in-ggplot
#turn off the legend to get the secret a's to disappear from legend https://stackoverflow.com/questions/18337653/remove-a-from-legend-when-using-aesthetics-and-geom-text
biplot2 = biplot + labs(title = "PCA of SNPs generated with 25 strains of \nFriedmanniomyces endolithicus",
colour = "Ploidy") + theme_bw() + expand_limits(x = c(-1, 3.5)) +
geom_label_repel(mapping = aes(label = row.names(metadata), color = factor(metadata$Ploidy)), show.legend = FALSE) +
scale_color_manual(values = c("Diploid" = "purple", "Haploid"="orange","Triploid"="steelblue"))
#+ xlim = c(-2.0,2.65) + ylim = c(-1.5,2.50)
print(biplot2)
biplot3 = ggbiplot(pcobj = pca_res,
choices = c(1,2),
#label.position = "identity",
#label.repel = TRUE,
obs.scale = 1,
var.scale = 1, # Scaling of axis
#labels = row.names(metadata), # Add labels as rownames
#labels.size = 3.5,
varname.size = 3.5,
varname.abbrev = FALSE, # Abbreviate variable names (TRUE)
var.axes = FALSE, # Remove variable vectors (TRUE)
circle = FALSE, # Add unit variance circle (TRUE)
ellipse = TRUE,
groups = factor(metadata2$Year)) # Adding ellipses
#print(biplot3)
#repel that shit https://stackoverflow.com/questions/68738673/how-to-repel-labels-in-ggplot
biplot4 = biplot + labs(title = "PCA of SNPs generated with 25 strains of \nFriedmanniomyces endolithicus",
colour = "Year") + theme_bw() + expand_limits(x = c(-1, 3.5)) +
geom_label_repel(mapping = aes(label = row.names(metadata), color = factor(metadata$Year)), show.legend = FALSE) +
scale_color_manual(values = c("Diploid" = "purple", "Haploid"="orange","Triploid"="steelblue", "1981" = "#FF5733", "1997" = "#7AFF33", "2004" = "#33FCFF", "2010" = "#FCFF33", "2011" = "#336BFF", "2016" = "#FF33AC"))
print(biplot4)
biplot5 = ggbiplot(pcobj = pca_res,
choices = c(1,2),
#label.position = "identity",
#label.repel = TRUE,
obs.scale = 1,
var.scale = 1, # Scaling of axis
#labels = row.names(metadata), # Add labels as rownames
#labels.size = 3.5,
varname.size = 3.5,
varname.abbrev = FALSE, # Abbreviate variable names (TRUE)
var.axes = FALSE, # Remove variable vectors (TRUE)
circle = FALSE, # Add unit variance circle (TRUE)
ellipse = TRUE,
groups = factor(metadata$Elevation)) # Adding ellipses
#print(biplot)
#repel that shit https://stackoverflow.com/questions/68738673/how-to-repel-labels-in-ggplot
biplot6 = biplot + labs(title = "PCA of SNPs generated with 25 strains of \nFriedmanniomyces endolithicus",
colour = "Elevation") + theme_bw() + expand_limits(x = c(-1, 3.5)) +
geom_label_repel(mapping = aes(label = row.names(metadata), color = factor(metadata$Elevation)), show.legend = FALSE) +
scale_color_manual(values = c("Diploid" = "purple", "Haploid"="orange","Triploid"="steelblue", "200" = "#FF5733" , "1000" = "#7AFF33", "2000" = "#33FCFF", "3000" = "#FCFF33"))
print(biplot6)
|
b2fd28ac5b667477314e144d2b8cb30adcd22e69
|
5d8eb44c6dccda49d67ccd48052909a12cc7da30
|
/GSE22544_Bootstrap.R
|
0ed3138717f140b9e1e1a6bb92835054a15fc0f6
|
[] |
no_license
|
justjooz/research-experience
|
c797b75a439ca9003b52ada3ed372ddf84f6aedc
|
a467ce10f4d5fe2722f18f066100913e84465e82
|
refs/heads/master
| 2020-06-25T00:01:16.290799
| 2019-08-03T04:23:30
| 2019-08-03T04:23:30
| 199,132,600
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 9,575
|
r
|
GSE22544_Bootstrap.R
|
# this R script shows the process to obtain significant genes, binarize them, and do a 1000x bootstrap
# followed by using a distance matrix/heat map
# and confusion matrix to obtain the F-score, recall, precision
# Set working directory
setwd("D:/Code/RE/My R scripts")
# ==========================================================================================================
# Reading GSE22544 dataframe into script
new_df_GSE22544 <- read.csv("new_df_GSE22544.csv", header = T, sep = ",", stringsAsFactors = F, row.names = 1) # read exprs dataframe
new_df_GSE22544 <- cbind(new_df_GSE22544[, c(4,8,9,13)], new_df_GSE22544[,-c(4,8,9,13)]) # rearranges the normal classes to the first 4 columns
new_df_GSE22544 <- na.omit(new_df_GSE22544) # removes NAs which causes probs with for loop
# (2) Method 1: using for loops to produce binary matrix (but leads to error that says "data are essentially constant")
# ===========================================================================================================
significantgenes <- c() # to initialise the vector
boot_list <- list()
library(progress)
pb <- progress_bar$new(total = 1000)
for(j in 1:1000){ # 1000 is the number of bootstraps ## cannot be run if done on bigger datasets
significantgenes <- c()
boot_normal <- sample(new_df_GSE22544[, 1:4], size=4, replace=TRUE)
boot_IDC <- sample(new_df_GSE22544[,5:20], size=4, replace=TRUE)
for(i in 1:2000){ # 9994 is the number of genes # if set to 9995, "data are essentially constant" error
ttest_1 <- try(t.test(boot_normal[i,], boot_IDC[i,]))
if (ttest_1$p.value <= 0.05) # if p value is <= 0.05(less than or equals),
{
significantgenes <- append(significantgenes, 1) # assign the genes (rows) as 1 (significant)
}
else # if p value is > 0.05,
{
significantgenes <- append(significantgenes, 0) # assign the genes (rows) as 0 (not significant)
}
}
boot_list <- append(boot_list, list(significantgenes))
pb$tick()
Sys.sleep(1 / 1000)
}
# (2) Method 2: Using "genefilter" package's "rowttests" function (runs faster than nested loop & w/o error)
# =================================================================================================
# -------- Prep ----------------
#BiocManager::install("genefilter")
library(genefilter)
class_factor_2 <- as.factor(c(rep("normal", 4), rep("IDC", 4))) # creating a class factor (normal & IDC)
# ------ rowttests function to generate binary matrix -----------
significantgenes_2 <- c()
boot_list_2 <- list()
library(progress)
pb <- progress_bar$new(total = 1000)
for (i in 1:1000){
m_normal <- as.matrix(sample(new_df_GSE22544[,1:4], size = 4, replace = T)) # samples 4 normal patients into a matrix
m_IDC <- as.matrix(sample(new_df_GSE22544[,5:20], size = 4, replace = T)) # samples 4 normal patients into a matrix
m_bind <- cbind(m_normal, m_IDC) # has 8 columns, first 4 columns are sampled normal, last 4 columns are sample IDC
ttest_2 <- rowttests(m_bind, class_factor_2) # rowttests tests all 9994 rows(genes) w/o error
significantgenes_2 <- as.numeric(ttest_2$p.value < 0.05) # <0.05 forms a boolean output, and it is changed to numeric by as.numeric.
boot_list_2 <- append(boot_list_2, list(significantgenes_2)) # append each sampling as a list to a list
pb$tick() # for progress bar
Sys.sleep(1 / 1000) # for progress bar
}
# -------rowttests function to generate binary matrix -----------
# (3) Convert list into matrix
# ==================================================================================================
boot_mat_2 <- matrix(unlist(boot_list_2), ncol = 1000, byrow = FALSE) # converting list into matrix
# dim(boot_mat_2)
# (4) Computing rowsums of each gene to see significance out of 1000
# ===========================================================================
sum_vect <- rowSums(boot_mat_2)
tail(sort(sum_vect), 5)
hist(sum_vect, freq=FALSE, breaks = 200,
main = "Distribution of Significant Samplings in Breast Cancer Genes",
xlab = "Significant Samplings",
col = "orange",
xlim=c(0,1000),
ylim = c(0, 0.020)) # to visually inspect to determine point threshold of significance
# `sum_sig` is assigned the numeric output of the boolean of `i>180`
sum_sig <- as.numeric(sum_vect>180)
# (5) Creating confusion matrix
# =======================================================
first_sample <- as.factor(boot_mat_2[,1]) # actual
sum_sig <- as.factor(sum_sig) # observation
# install.packages("caret")
library(caret) # for confusion matrix function
confusionMatrix(sum_sig, first_sample)
# Confusion Matrix and Statistics
# Reference
# Prediction 0 1
# 0 9025 (TP) 324 (FP)
# 1 546 (FN) 99 (TN)
#
# Accuracy : 0.9129
# 95% CI : (0.9072, 0.9184)
# No Information Rate : 0.9577
# P-Value [Acc > NIR] : 1
#
# Kappa : 0.1415
#
# Mcnemar's Test P-Value : 6.752e-14
#
# Sensitivity : 0.9430
# Specificity : 0.2340
# Pos Pred Value : 0.9653
# Neg Pred Value : 0.1535
# Prevalence : 0.9577
# Detection Rate : 0.9030
# Detection Prevalence : 0.9355
# Balanced Accuracy : 0.5885
#
# 'Positive' Class : 0
conf_mat <- (confusionMatrix(sum_sig, first_sample))$table
# (6) Calculating metrics: Precision, Recall, F-Score
# ========================================================================
# Precision: TP/(TP+FP):
precision <- conf_mat[1,1]/sum(conf_mat[1,1:2])
precision # [1] 0.9653439
# Recall: TP/(TP+FN):
recall <- conf_mat[1,1]/sum(conf_mat[1:2,1])
recall # [1] 0.9429527
# F-Score: 2 * precision * recall /(precision + recall):
f_score <- 2 * precision * recall / (precision + recall)
# [1] 0.9540169
# Metrics
# Precision: 0.9653439
# Recall: 0.9429527
# F-score: 0.9540169
# Visualising the Confusion Matrix
set.seed(1234)
cm <-confusionMatrix(sum_sig, first_sample)
# ?confusionMatrix
draw_confusion_matrix <- function(cm) {
layout(matrix(c(1,1,2)))
par(mar=c(2,2,2,2))
plot(c(100, 345), c(300, 450), type = "n", xlab="", ylab="", xaxt='n', yaxt='n')
title('CONFUSION MATRIX', cex.main=2.5)
# create the matrix
rect(150, 430, 240, 370, col='#3F97D0')
text(195, 438, 'Positive', cex=2)
rect(250, 430, 340, 370, col='#F7AD50')
text(295, 438, 'Negative', cex=2)
text(125, 370, 'Predicted', cex=2, srt=90, font=2)
text(245, 450, 'Actual', cex=2, font=2)
rect(150, 305, 240, 365, col='#F7AD50')
rect(250, 305, 340, 365, col='#3F97D0')
text(143, 400, 'Positive', cex=2, srt=90)
text(143, 335, 'Negative', cex=2, srt=90)
# add in the cm results
res <- as.numeric(cm$table)
text(195, 400, res[1], cex=4, font=2, col='black')
text(195, 335, res[2], cex=4, font=2, col='black')
text(295, 400, res[3], cex=4, font=2, col='black')
text(295, 335, res[4], cex=4, font=2, col='black')
# add in the specifics
plot(c(100, 0), c(100, 0), type = "n", xlab="", ylab="", main = "DETAILS", xaxt='n', yaxt='n')
text(10, 85, names(cm$byClass[1]), cex=1.2, font=2)
text(10, 70, round(as.numeric(cm$byClass[1]), 3), cex=1.2)
text(30, 85, names(cm$byClass[2]), cex=1.2, font=2)
text(30, 70, round(as.numeric(cm$byClass[2]), 3), cex=1.2)
text(50, 85, names(cm$byClass[5]), cex=1.2, font=2)
text(50, 70, round(as.numeric(cm$byClass[5]), 3), cex=1.2)
text(70, 85, names(cm$byClass[6]), cex=1.2, font=2)
text(70, 70, round(as.numeric(cm$byClass[6]), 3), cex=1.2)
text(90, 85, names(cm$byClass[7]), cex=1.2, font=2)
text(90, 70, round(as.numeric(cm$byClass[7]), 3), cex=1.2)
# add in the accuracy information
text(30, 35, names(cm$overall[1]), cex=1.5, font=2)
text(30, 20, round(as.numeric(cm$overall[1]), 3), cex=1.4)
text(70, 35, names(cm$overall[2]), cex=1.5, font=2)
text(70, 20, round(as.numeric(cm$overall[2]), 3), cex=1.4)
}
draw_confusion_matrix(cm)
# (7) Create user-defined function that calculates jaccard coefficient (intersection over union)
# =========================================================================
jaccard_fun <- function (x,y) {
M.11 = sum(x == 1 & y == 1)
M.10 = sum(x == 1 & y == 0)
M.01 = sum(x == 0 & y == 1)
return (M.11 / (M.11 + M.10 + M.01))
}
# Initialising dataframe for imputation
jaccard_df <- as.data.frame(matrix(NA,nrow = 1000, ncol = 1000))
names(jaccard_df) <- paste0('S', 1:1000)
rownames(jaccard_df) <- paste0('S', 1:1000)
library(progress)
pb <- progress_bar$new(total = 1000)
# for loop that will produce the distance heatmap/
for (r in 1:1000) {
for (c in 1:1000) {
if (c == r) { # if rows iteration is the same as column iteration,
jaccard_df[r,c] = 1 # assign as 1
} else if (c > r) { # if not then when columns is more than rows, add the variables of rows and
jaccard_df[r,c] <- jaccard_fun(boot_mat_2[,r], boot_mat_2[,c]) # replace with list from above
}
}
pb$tick() # for progress bar
Sys.sleep(1 / 1000) # for progress bar
}
write.csv(boot_mat_2, file = "D:/Code/RE/My R scripts/boot_mat_2.csv", row.names = T)
write.csv(jaccard_df, file = "D:/Code/RE/My R scripts/jaccard_df.csv", row.names = T)
|
04a4d26728b3ad230536941168e6750d5f018ec8
|
cb4b8d511a14f1655120bb8737266296c5e46059
|
/R/birds/Density_stuff/species_density.R
|
2db88f5f2b324b5609ea3b48b77826a9a62997a4
|
[] |
no_license
|
Josh-Lee1/JL_honours
|
40361e2f8b78fac9676ff32a8e0ce7a0603f6152
|
db6792a039d824fdb518f9e06c3cc27ecca6da8a
|
refs/heads/master
| 2023-03-29T22:28:19.500012
| 2021-04-15T04:40:20
| 2021-04-15T04:40:20
| 295,877,409
| 0
| 0
| null | 2021-03-16T06:17:06
| 2020-09-16T00:02:18
|
HTML
|
UTF-8
|
R
| false
| false
| 2,535
|
r
|
species_density.R
|
library(tidyverse)
library(Distance)
library(mrds)
library(lme4)
library(sjPlot)
library(sjmisc)
birds <- read.csv("Data/Raw/Birds.csv")
birds$Treatment<- with(birds, paste0(Formation, Fire))
birds <- birds %>%
group_by(Site) %>%
mutate(species_richness = n_distinct(Species)) %>%
rename(distance = Distance) %>%
rename(Region.Label = Site) %>%
as.data.frame()
birds$Area <- '628'
birds$Effort <- '1'
birds$Sample.Label <- '1'
birds$Area <- as.numeric(birds$Area)
birds$Effort <- as.numeric(birds$Effort)
#Fitting detection function
birds_hr_loc <- ds(birds, truncation = 400, key = "hr", formula = ~ Location)
#getting species lists for habitats
birds2 <- birds %>%
mutate(habitat = ifelse(Formation == "Rainforest", 1, 2)) %>%
select(Species, habitat) %>%
distinct() %>%
group_by(Species) %>%
summarise_all(sum) %>%
mutate(rfonly = ifelse(habitat == 1, 1, 0),
dsonly = ifelse(habitat == 2, 1, 0),
bothhab = ifelse(habitat == 3, 1, 0))
#Stratafying results by species
species_level_dht2 <- dht2(birds_hr_loc, flatfile = birds, stratification = "object", strat_formula = ~Species)
species_densities <- species_level_dht2 %>%
left_join(birds2, by = "Species") %>%
select(Species, Abundance, Abundance_se, habitat:bothhab) %>%
filter(Species != "Total") %>%
arrange(Abundance) %>%
mutate(Species=factor(Species, levels=Species))
species_densities$habitat <- as.character(species_densities$habitat)
#make a first trial plot
ggplot(data = species_densities,
aes(Species,
Abundance,
ymax = Abundance+Abundance_se,
ymin = Abundance-Abundance_se,
colour = habitat)) +
geom_point() +
geom_pointrange() +
coord_flip()
#try splitting by habitat
rfonly<- filter(species_densities, habitat == "1")
dsonly<- filter(species_densities, habitat == "2")
both<- filter(species_densities, habitat == "3")
ggplot(data = rfonly,
aes(Species,
Abundance,
ymax = Abundance+Abundance_se,
ymin = Abundance-Abundance_se)) +
geom_point() +
geom_pointrange() +
coord_flip()
ggplot(data = dsonly,
aes(Species,
Abundance,
ymax = Abundance+Abundance_se,
ymin = Abundance-Abundance_se)) +
geom_point() +
geom_pointrange() +
coord_flip()
ggplot(data = both,
aes(Species,
Abundance,
ymax = Abundance+Abundance_se,
ymin = Abundance-Abundance_se)) +
geom_point() +
geom_pointrange() +
coord_flip()
|
bbb4666d4f79f89a569434d303a011e9115e34d3
|
c1b66b5db23476c16ddd62b1afbcda48d4bde719
|
/cluster_analysis/opt_clust.R
|
0012fe32eb0bd6e0511b89a6e58e28fee4066b79
|
[] |
no_license
|
venice-juanillas/eib-tools
|
38e19a67864fc70b8db29199db173f082c15cca1
|
4a946819ca1676c8947c3ac4ee92e71e0259fbaa
|
refs/heads/master
| 2020-03-18T06:17:22.384260
| 2018-05-22T09:08:26
| 2018-05-22T09:08:26
| 134,386,147
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 4,045
|
r
|
opt_clust.R
|
#######################################################################
# Umesh Rosyara, April 10, 2018
# CIMMYT
# optimum QTL: This function is used to do find optimum number of QTLs
######################################################################
rm(list = objects()); ls() # CLEAR 'WorkSpace' (R environment)
library(optparse)
option_list = list(
make_option(c("-f", "--file"), type="character", default=NULL,
help="dataset file name", metavar="input_file"),
make_option(c("-m", "--method"), type="character", default="silhouette",
help="the method to be used for estimating the optimal number of clusters", metavar="method "),
make_option(c("-k", "--maxclust"), type="integer", default=2,
help=" the maximum number of clusters to consider, must be at least 2", metavar="num of clusters "),
make_option(c("-b", "--nboot"), type="integer", default=200,
help="number of Monte Carlo (bootstrap) samples", metavar="num of groups "),
make_option(c("-c", "--clusterfile"), type="character", default="cluster_file.txt",
help="Cluster membership file[default= %default]", metavar="membership_file"),
make_option(c("-g", "--graph"), type="character", default="graph.html",
help="Graph File [default= %default]", metavar="graph_file")
);
opt_parser = OptionParser(option_list=option_list);
opt = parse_args(opt_parser);
if (is.null(opt$file)){
print_help(opt_parser)
stop("At least one argument must be supplied (input file).\n", call.=FALSE)
}
data1 <- read.csv(file = opt$file, header = TRUE, stringsAsFactors = FALSE)
#data1 <- t(data)
data1c <- data1[,-1]
# PCA /cluster analysis works on numeric variables, so we need to code the AA=1, AB=0, BB=-1 or similar numerical encoding
# imputation of missing values
# this example is just imputing with mean, but the final should have better imputing algorithm
# I think that was already in place at IRRI hackathan
#impute with population mean
data1i <- apply(data1c[,-1],1,function(x){ix <- which(is.na(x)); x[ix] <- mean(x,na.rm=T); return(x)})
# data with no missing value
data2 <- t(data1i)
rownames(data2) <- data1[,1]
##################################################################
# Finding optimum number of clusters
#Determining Optimal Clusters
library(tidyverse) # data manipulation
library(cluster) # clustering algorithms
library(factoextra) # clustering algorithms & visualization
set.seed(1234)
# we need to wrap the function fviz_nbclust
#fviz_nbclust(): Dertemines and visualize the optimal number of clusters using different methods: within cluster sums of squares, average silhouette and gap statistics.
#fviz_nbclust(x, FUNcluster = NULL, method = c("silhouette", "wss","gap_stat"), diss = NULL, k.max = 10, nboot = 100,
# verbose = interactive(), barfill = "steelblue", barcolor = "steelblue",
# linecolor = "steelblue", print.summary = TRUE, ...)
#User needs to choose the following options
# x - data set
# FUNcluster - allowed values: kmeans, cluster::pam, cluster::clara, cluster::fanny, hcut
# method - the method to be used for estimating the optimal number of clusters. Possible values are "silhouette" (for average silhouette width), "wss" (for total within sum of square) and "gap_stat" (for gap statistics
# k.max - the maximum number of clusters to consider, must be at least 2
# nboot - integer, number of Monte Carlo ("bootstrap") samples. Used only for determining the number of clusters using gap statistic.
# Usage three different methods
pdf(opt$graph)
if(opt$method == "silhouette"){
output1 <- fviz_nbclust(data2, FUNcluster=kmeans, method = "silhouette", k.max = opt$maxclust)
}else if(opt$method == "gap_stat"){
output1 <- fviz_nbclust(data2, FUNcluster=kmeans, method = "gap_stat",k.max = opt$maxclust, nboot = opt$nboot)
}else if(opt$method == "wss"){
output1 <- fviz_nbclust(data2, FUNcluster=kmeans, method ="wss", k.max = opt$maxclust)
}
print(output1)
dev.off()
|
1b525f2b1e8ee1b277560c8243d3e03d2ca9e29c
|
ffdea92d4315e4363dd4ae673a1a6adf82a761b5
|
/data/genthat_extracted_code/NISTunits/examples/NISTsqrFtTOsqrMeter.Rd.R
|
aa6f91ddb02690cd2f0f2657af0cb1be8592f16a
|
[] |
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
| 202
|
r
|
NISTsqrFtTOsqrMeter.Rd.R
|
library(NISTunits)
### Name: NISTsqrFtTOsqrMeter
### Title: Convert square foot to square meter
### Aliases: NISTsqrFtTOsqrMeter
### Keywords: programming
### ** Examples
NISTsqrFtTOsqrMeter(10)
|
2be2268a6b800ca061515b7b087704183f09dd22
|
949041354ceeea0eaf534ef0f108c43382279a23
|
/landscape.R
|
318198236ec0b5e8055ca6e92619b9deb546d4ed
|
[] |
no_license
|
cbig/dsexplained
|
9b87a9647494b702771044be764e6555520a8f56
|
6e55e7862e351e746dac7aa7f04227ab2882079b
|
refs/heads/master
| 2021-01-10T13:33:47.587174
| 2015-10-12T12:45:59
| 2015-10-12T12:45:59
| 44,098,321
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,368
|
r
|
landscape.R
|
# Name: simulate_landscape.R
#
# Author: jlehtoma
###############################################################################
## Function create.landscape can be used to create landscapes of varying
## complexity.
## Params:
## x - matrix of coordinates, or vector of x coordinates
## y - vector of y coordinates
## z - vector of z coordinates
## model - string; describes the landscape model in
## c("simple", "random", "GaussRF")
## patches - logical; indicates whether distinct patches are created
## [NOT IMPLEMENTED]
batch.create.landscape <- function(n, ...) {
landscapes <- list()
for (i in 1:n) {
landscapes[[paste("feat", i, sep="")]] = create.landscape(...)
}
return(landscapes)
}
# Generic wrapper for calling different landscape models
create.landscape <- function(x, y, z, ftype, patches=FALSE, ...) {
ftypes <- c("simple", "random", "GaussRF")
# Check the input data
if (!is.vector(x) | !is.vector(y)) {
msg = paste("Both x (", typeof(x), ") and y (", typeof(y),
") need to be vectors.", sep="")
stop(msg)
}
# Check the supported types
if (!ftype %in% ftypes){
msg <- paste("Type ", ftype, " not suitable. Use one of: ", ftypes)
stop(msg)
}
switch(ftype,
simple = create.simple.landscape(x, y, z, patches),
random = create.random.landscape(x, y, z, patches),
GaussRF = create.GaussRF.landscape(x, y, z, patches, ...))
}
create.simple.landscape <- function(x, y, z, patches) {
return("Simple")
}
create.random.landscape <- function(x, y, z, patches) {
return("Random")
}
## Create a landscape using Gaussian random field'
## Params:
## x - vector of x coordinates
## y - vector of y coordinates
## z - matrix of z values ...
create.GaussRF.landscape <- function(x, y, z=NULL, patches, seed=0,
model="stable", mean=0, variance=10, nugget=1, scale=10, alpha=1.0,
positive=TRUE) {
if (!require(RandomFields)) {
stop("Package RandomFields must be installed in order to proceed.")
}
# If a seed is provided, use that to always provide similar landscape
if (seed) {
set.seed(seed)
}
#browser()
# Parameters for GaussRF, see ?GaussRF for more details
f <- GaussRF(x=x, y=y, model=model, grid=TRUE,
param=c(mean, variance, nugget, scale, alpha))
# Get only positive values by adding minimun value to all elements
if (positive) {
f <- f + abs(min(f))
}
return(f)
}
|
fb2d27438f912c91e1b59f9b8ebad22b10144050
|
19e11f1eee51bf74e3c903490c1f8f962387e765
|
/tests/viterbi_test.R
|
dafb4c65c784ff764fa48ef49a5754de18f8fb60
|
[] |
no_license
|
Una95Singo/MarketStates
|
8630040e7cfed3cc9b5e28d9f2ae235c849e0229
|
d7e4ba80f0f42a797e0b9fc8387ea1e735d068e9
|
refs/heads/master
| 2022-02-18T08:31:55.124928
| 2022-02-06T17:00:48
| 2022-02-06T17:00:48
| 235,183,871
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 4,442
|
r
|
viterbi_test.R
|
# Test script for the viterbi algorithm
# una singo, SNGUNA003
# 9 January 2020
# import R functions ------------
source("R/viterbi.R")
# libraries ---------------------
library('tidyverse')
library(readxl)
# Test Case 1 -------------------
data = read_excel("data/Test/viterbiDataset.xlsx")
vit = viterbi(D=data[, 2:5], K=4, gamma=400)
vit
# Test Case 2 -------------------
# Viterbi Algorithm using true estimates -----------------------------
data = read_excel("~/Desktop/viterbiDataset.xlsx",sheet = 'Sheet2')
state1.distance = data$`True Distances`[1:10]
state2.distance = data$`True Distances`[11:20]
# Input
D = cbind(state1.distance, state2.distance)
gamma = 1
Time = nrow(D)
K = 2
# initialize
PrevCost = rep(0,K)
CurrentCost = rep(0, K)
FinalMinVal = NA
FinalPath = NA
PrevPath = list ()
PrevPath[[1]] = NA
PrevPath[[2]] = NA
CurrentPath = list()
CurrentPath[[1]] = NA
CurrentPath[[2]] = NA
#PrevCost = D[1,]
#PrevPath[[1]] = 1
#PrevPath[[2]] = 2
# looper
for ( t in 1:Time){
for (k in 1:K){
MinVal = which(PrevCost == min(PrevCost))[1]
if((PrevCost[MinVal]+gamma) > PrevCost[k]){
CurrentCost[k] = PrevCost[k] + D[t,k]
CurrentCost = unlist(CurrentCost)
CurrentPath[[k]] = c(PrevPath[[k]], k)
}
else{
CurrentCost[k] = PrevCost[MinVal] + gamma + D[t,k]
CurrentCost = unlist(CurrentCost)
CurrentPath[[k]] = c(PrevPath[[MinVal]], k)
}
}
#update after iterating throuh the states. the paper updates before the state ends.
PrevCost = CurrentCost
PrevPath = CurrentPath
}
CurrentCost
FinalMinVal = which(CurrentCost == min(CurrentCost))[1]
FinalPath = CurrentPath[[FinalMinVal]]
FinalPath = na.omit(FinalPath)
FinalPath
# Viterbi Algorithm using true estimates -----------------------------
data = read_excel("~/Desktop/viterbiDataset.xlsx",sheet = 'Sheet2')
K = 2 # number of states
Time = length(data$Observation)
States = sample(c(1,2), size =Time, replace = T)
ExpectationMaximisation = 1
State.history = matrix(nrow = ExpectationMaximisation, ncol = Time)
Cost.history = c()
for (EM in 1:ExpectationMaximisation) {
States = sample(c(1,2), size =Time, replace = T)
# state index
state1.index = which(States==1)
state2.index = which(States==2)
# state returns
state1.return = data$Observation[state1.index]
state2.return = data$Observation[state2.index]
# State sample statistics
state1.mu = mean(state1.return)
state2.mu = mean(state2.return)
state1.sd = sd(state1.return)
state2.sd = sd(state2.return)
# Mahalanobis distance ---------------------------------
state1.distance = ((data$Observation - state1.mu)^2/state1.sd^2)
state2.distance = ((data$Observation - state2.mu)^2/state2.sd^2)
# Input
D = cbind(state1.distance, state2.distance)
gamma = 1
Time = nrow(D)
K = 2
# initialize
PrevCost = rep(0,K)
CurrentCost = rep(0, K)
FinalMinVal = NA
FinalPath = NA
PrevPath = list ()
PrevPath[[1]] = NA
PrevPath[[2]] = NA
CurrentPath = list()
CurrentPath[[1]] = NA
CurrentPath[[2]] = NA
#PrevCost = D[1,]
#PrevPath[[1]] = 1
#PrevPath[[2]] = 2
# looper
for ( t in 1:Time){
for (k in 1:K){
MinVal = which(PrevCost == min(PrevCost))[1]
if((PrevCost[MinVal]+gamma) > PrevCost[k]){
CurrentCost[k] = PrevCost[k] + D[t,k]
CurrentCost = unlist(CurrentCost)
CurrentPath[[k]] = c(PrevPath[[k]], k)
}
else{
CurrentCost[k] = PrevCost[MinVal] + gamma + D[t,k]
CurrentCost = unlist(CurrentCost)
CurrentPath[[k]] = c(PrevPath[[MinVal]], k)
}
}
#update after iterating throuh the states. the paper updates before the state ends.
PrevCost = CurrentCost
PrevPath = CurrentPath
}
CurrentCost
FinalMinVal = which(CurrentCost == min(CurrentCost))[1]
FinalPath = CurrentPath[[FinalMinVal]]
FinalPath = na.omit(FinalPath)
Cost.history = c(Cost.history, min(CurrentCost))
State.history[EM,] = unlist(FinalPath)
}
(state1.mu - state2.mu)^2
FinalPath
state1.mu
state2.mu
# get mode
getmode <- function(v) {
uniqv <- unique(v)
uniqv[which.max(tabulate(match(v, uniqv)))]
}
hist(Cost.history)
Final = which(Cost.history ==getmode(Cost.history))
State.history[Final,]
apply(State.history,2, getmode)
State.history[1:10,]
# Test Case 3 -------------------
|
1c9eb611fab72ff2af9433c921ec39be52f131de
|
e6f1fbb059464a6e6580c13bfb12daddc6b45374
|
/MakeZipGraphOnNPI.R
|
a7175691520aa3579f3d5e5235a79895eb854bbc
|
[] |
no_license
|
thuhale/AHClustering
|
f98e6ea47dab761dc5ea036ae6cfe8c7c9863e8e
|
3501799b71ad1d4e5cdd5e63ad8b000447ece007
|
refs/heads/master
| 2021-01-18T22:16:24.077827
| 2017-02-07T05:52:57
| 2017-02-07T05:52:57
| 72,385,602
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,853
|
r
|
MakeZipGraphOnNPI.R
|
rm(list = ls())
library(readr)
library(Matrix)
library(sparseAHC)
library(zipcode)
data(zipcode)
library(geosphere)
library(igraph)
##CUSTOM FUNCTION
# reorganize the clus
reorganize = function(df, clus){
col = which(colnames(df) == clus)
unique_label = sort(unique(df[,col]))
ind = c(1:length(unique(df[,col])))
mapping = data.frame(unique_label,ind)
for(i in 1:length(mapping$unique_label)){
df[,col][df[,col] == mapping$unique_label[i]] = mapping$ind[i]
}
return(df)
}
makeMemMatrix = function(clus){
m = matrix(0, nrow = length(clus), ncol = length(unique(clus)))
for (i in 1:length(unique(clus))){
m[,i][clus==i] = 1
}
return(m)
}
makeMemList = function(m){
clus = apply(m, 1, function(x) which(x>0))
return(clus)
}
agg = read.csv("Data/CoreZip_clus.csv", colClasses = "character")
hrr = read.csv("Data/ZipHsaHrr14.csv")
hrr = hrr[, c("zipcode14", "hrrnum")]
colnames(hrr) = c("zip", "hrr")
hrr$zip = clean.zipcodes(hrr$zip)
agg = merge(agg, hrr, by = "zip", all.x = T)
#agg = agg[!is.na(agg$hrr),]
agg = reorganize(agg, "hrr") ## label of agg needs from 1:k
##MAKE ZIP GRAPH
short = read.csv("Data/NPI_zip.csv")
ref = read_csv(file = "Data/physician-shared-patient-patterns-2014-days180.txt", col_names = F,col_types = "cciii")
colnames(ref) = c("NPI", "NPI2", "Ties", "Unique", "Sameday")
ref = ref[ref$NPI %in% short$NPI & ref$NPI2 %in% short$NPI,]
ref = merge(ref, short, by = "NPI", all.x = T)
ref = merge(ref, short, by.x = "NPI2", by.y = "NPI", all.x = T)
colnames(ref)[6:7] = c("zip", "zip2")
ref = ref[, c(2,1,3,4,5,6,7)]
ref$zip = clean.zipcodes(ref$zip)
ref$zip2 = clean.zipcodes(ref$zip2)
tmp = ref[ref$zip %in% agg$zip & ref$zip2 %in% agg$zip, ]
ref = tmp
#make the graph
g = graph.edgelist(as.matrix(ref[,6:7]), directed = F)
E(g)$weight = ref$Unique
g = simplify(g)
save(g, file = "Data/Zip_Cluster.RData")
|
5a8f86881bdaa88e56d9d954040fbc8377933ada
|
55fe7eeb9397100fcd544c2b8bcecfcbcabdbb06
|
/REPSI_Tool_02.00_Mesaurement_Data/Query_99999_YYYY-MM-DD_HH-MI-SS.HS.R/Query_81002_2007-01-21_11-22-19.96.R
|
e0ccc80f76a90b047d608086f83ae78e43286b63
|
[
"Apache-2.0"
] |
permissive
|
walter-weinmann/repsi-tool
|
d5e7b71838dc92d61c1a06a2c7f2541a0c807b32
|
5677cdf1db38672eff7f1abcf6dca677eb93a89c
|
refs/heads/master
| 2021-01-10T01:34:55.746282
| 2016-01-26T05:31:17
| 2016-01-26T05:31:17
| 49,252,156
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,458
|
r
|
Query_81002_2007-01-21_11-22-19.96.R
|
if (exists("A_A")) remove("A_A")
if (exists("A_U")) remove("A_U")
if (exists("C_A")) remove("C_A")
if (exists("C_U")) remove("C_U")
A_U<-c(3878102,3863925,3953180,3779476,4356739,3983607,3931598,3958639,3835372,4538178,4005537,3990393,3967956,3912965,3948423,4173723,4004452,4097618,5126453,3787584,3937871,3864664,3789573,4127359,3919709,3824347,3778638,3723743,3710992,3792414,3733278,3867896,3695111,3795197,3730392,3674430,3832837,3796981,4857877,3671992,3803738,3714972,3673536,3686582,3675477,3678518,4638083,3632263,4156708,3743290)
A_A<-c(4243610,4485317,5651563,4010158,4075925,4270333,4262124,4180031,4765163,4250753,4237512,4883776,4409779,4211027,4131655,4146415,4395734,4102529,4565201,4391890,4065061,3976891,4016386,4877000,4056289,4117407,4040150,4252690,3989492,4056056,3961274,4343522,3937625,3936817,4125323,4154398,4000738,3963088,4062487,4508145,3911219,3920344,3908535,4163975,3876892,3912886,3973520,4202091,4097141,4244374)
if (exists("A_U")) boxplot.stats(A_U)
if (exists("A_A")) boxplot.stats(A_A)
if (exists("C_U")) boxplot.stats(C_U)
if (exists("C_A")) boxplot.stats(C_A)
if (exists("A_U")) summary(A_U)
if (exists("A_A")) summary(A_A)
if (exists("C_U")) summary(C_U)
if (exists("C_A")) summary(C_A)
if (exists("A_U")) boxplot(A_A,A_U,col="lightblue",horizontal=TRUE,match=TRUE,names=c("(A_A)","(A_U)"),notch=TRUE)
if (exists("C_U")) boxplot(C_A,C_U,col="lightblue",horizontal=TRUE,match=TRUE,names=c("(C_A)","(C_U)"),notch=TRUE)
|
7419854581469ee2d763e740d7760c3ee95111be
|
497153f9a15f53b5b2b4ce0d375ea7f9848d75bb
|
/src/06-CORUM_Shuffle_Results.R
|
fe92c61b5ab19e988d6a99710962b26c71f58189
|
[] |
no_license
|
joshbiology/pan-meyers-et-al
|
b43b4299e56ff979fe4bce751574e7bbca8244d8
|
2d72ea626e2c8f4422cc5413bbd835f462a20a62
|
refs/heads/master
| 2022-01-07T00:16:14.916776
| 2018-05-18T19:26:57
| 2018-05-18T19:26:57
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 6,895
|
r
|
06-CORUM_Shuffle_Results.R
|
library(igraph)
library(ggridges)
library(ProjectTemplate)
load.project(override.config = list(munging=F, cache_loading=F))
if (config$threads > 1) {
library(doMC)
registerDoMC(cores=config$threads)
do_parallel <- T
} else {
do_parallel <- F
}
out_dir <- file.path("./output/empirical_shuffle", Sys.Date())
dir.create(out_dir, recursive=T, showWarnings=F)
load("./cache/corum_list.RData")
load("./cache/avana_dep_corr.RData")
load("./cache/avana_2017_dep_corr.RData")
load("./cache/gecko_dep_corr.RData")
load("./cache/coxpres_db.RData")
load("./cache/rnai_dep_corr.RData")
load("./cache/wang_dep_corr.RData")
corum_avana_shuffle <- readRDS("./data/interim/shuffle_corum_avana_10k.rds")
corum_avana_2017_shuffle <- readRDS("./data/interim/shuffle_corum_avana_2017_10k.rds")
corum_rnai_shuffle <- readRDS("./data/interim/shuffle_corum_rnai_10k.rds")
corum_gecko_shuffle <- readRDS("./data/interim/shuffle_corum_gecko_10k.rds")
corum_wang_shuffle <- readRDS("./data/interim/shuffle_corum_wang_10k.rds")
corum_coxpr_shuffle <- readRDS("./data/interim/shuffle_corum_coxpr_10k.rds")
corum_avana_dat <- prepare_dataset(avana_dep_corr, corum_list)
corum_avana_2017_dat <- prepare_dataset(avana_2017_dep_corr, corum_list)
corum_rnai_dat <- prepare_dataset(rnai_dep_corr, corum_list)
corum_gecko_dat <- prepare_dataset(gecko_dep_corr, corum_list)
corum_wang_dat <- prepare_dataset(wang_dep_corr, corum_list)
corum_coxpr_dat <- prepare_dataset(coxpres_db, corum_list)
rank_thresholds <- 2^(0:13)
corum_avana_true <-
run_int_ext_true(corum_avana_dat$edgelist,
corum_avana_dat$genesets,
rank_thresholds)
corum_avana_true %>%
edgedens_true_gg(title="CORUM mapped onto Avana Similarity Network") +
ggsave(file.path(out_dir, "edgedens_true_corum_avana.pdf"),
width=6, height=4)
corum_avana_2017_true <-
run_int_ext_true(corum_avana_2017_dat$edgelist,
corum_avana_2017_dat$genesets,
rank_thresholds)
corum_avana_2017_true %>%
edgedens_true_gg(title="CORUM mapped onto Avana2017 Similarity Network") +
ggsave(file.path(out_dir, "edgedens_true_corum_avana_2017.pdf"),
width=6, height=4)
corum_rnai_true <-
run_int_ext_true(corum_rnai_dat$edgelist,
corum_rnai_dat$genesets,
rank_thresholds)
corum_rnai_true %>%
edgedens_true_gg(title="CORUM mapped onto RNAi Similarity Network") +
ggsave(file.path(out_dir, "edgedens_true_corum_rnai.pdf"),
width=6, height=4)
corum_coxpr_true <-
run_int_ext_true(corum_coxpr_dat$edgelist,
corum_coxpr_dat$genesets,
rank_thresholds)
corum_coxpr_true %>%
edgedens_true_gg(title="CORUM mapped onto COXPRESdb Similarity Network") +
ggsave(file.path(out_dir, "edgedens_true_corum_coexpressdb.pdf"),
width=6, height=4)
corum_gecko_true <-
run_int_ext_true(corum_gecko_dat$edgelist,
corum_gecko_dat$genesets,
rank_thresholds)
corum_gecko_true %>%
edgedens_true_gg(title="CORUM mapped onto GeCKOv2 Similarity Network") +
ggsave(file.path(out_dir, "edgedens_true_corum_gecko.pdf"),
width=6, height=4)
corum_wang_true <-
run_int_ext_true(corum_wang_dat$edgelist,
corum_wang_dat$genesets,
rank_thresholds)
corum_wang_true %>%
edgedens_true_gg(title="CORUM mapped onto Wang2017 Similarity Network") +
ggsave(file.path(out_dir, "edgedens_true_corum_wang.pdf"),
width=6, height=4)
corum_true <- bind_rows(
corum_avana_true %>% mutate(Network="Avana"),
corum_avana_2017_true %>% mutate(Network="Avana 2017"),
corum_rnai_true %>% mutate(Network="RNAi"),
corum_coxpr_true %>% mutate(Network="COXPRESdb"),
corum_gecko_true %>% mutate(Network="GeCKOv2"),
corum_wang_true %>% mutate(Network="Wang 2017")
)
corum_shuffle <- bind_rows(
corum_avana_shuffle %>% mutate(Network="Avana"),
corum_avana_2017_shuffle %>% mutate(Network="Avana 2017"),
corum_rnai_shuffle %>% mutate(Network="RNAi"),
corum_coxpr_shuffle %>% mutate(Network="COXPRESdb"),
corum_gecko_shuffle %>% mutate(Network="GeCKOv2"),
corum_wang_shuffle %>% mutate(Network="Wang 2017")
)
empirical_shuffle_results <-
inner_join(corum_true, corum_shuffle) %>%
group_by(Network, Rank, Geneset) %>%
mutate(IntExtTrue = ifelse(is.nan(IntExtTrue), 0, IntExtTrue)) %>%
summarise(N_Above = sum(ifelse(is.nan(unlist(IntExtShuffle)),
0 >= IntExtTrue,
unlist(IntExtShuffle) >= IntExtTrue)),
Total = length(unlist(IntExtShuffle))) %>%
mutate(EmpiricalP = (N_Above+1)/(Total+1)) %>%
group_by(Network) %>%
mutate(FDR = p.adjust(EmpiricalP, method='fdr'))
rank_fdrs <- empirical_shuffle_results %>%
group_by(Network, Geneset) %>%
summarise(BestFDR = min(FDR, na.rm=T),
BestRank = Rank[FDR == BestFDR][1],
FirstRank = min(Rank[FDR < 0.05], na.rm=T)) %>%
group_by(Network) %>%
arrange(FirstRank) %>%
mutate(Recall = cumsum(BestFDR < 0.05)/length(corum_list))
write_tsv(rank_fdrs, ".data/interim/corum_shuffle_10k_results.tsv")
rank_fdrs %>%
filter(BestFDR < 0.05) %>%
select(Network, Geneset) %>%
write_tsv(".data/interim/corum_shuffle_10k_sig_genesets.tsv")
ggplot(rank_fdrs, aes(FirstRank, Recall, color=Network)) +
geom_hline(aes(yintercept=RecallFinal, color=Network), linetype=2,
data=rank_fdrs %>% group_by(Network) %>%
summarize(RecallFinal = sum(BestFDR<0.05) /
length(corum_list))) +
geom_line() +
scale_y_continuous(limits=c(0, 0.5), expand=c(0, 0)) +
scale_x_continuous(trans="log2", breaks=rank_thresholds) +
labs(x="Rank Threshold", y="Recall of Complexes",
title="CORUM mapped onto Similarity Networks") +
ggsave(file.path(out_dir, "rank_recall_corum.pdf"),
width=6, height=4)
rank_fdrs %>%
group_by(Network) %>%
arrange(BestFDR) %>%
mutate(Recall = cumsum(BestFDR < 1)/length(corum_list)) %>%
ggplot(aes(Recall, 1-BestFDR, color=Network)) +
geom_hline(yintercept=0.95, linetype=2) +
geom_vline(aes(xintercept=RecallFinal, color=Network), linetype=2,
data=rank_fdrs %>% group_by(Network) %>%
summarize(RecallFinal = sum(BestFDR<0.05) /
length(corum_list))) +
geom_line() +
scale_y_continuous(limits=c(0.5, 1.02), expand=c(0, 0)) +
scale_x_continuous(expand=c(0, 0)) +
labs(y="Precision", x="Recall",
title="CORUM Precision-Recall in Similarity Networks") +
ggsave(file.path(out_dir, "precision_recall_corum.pdf"),
width=6, height=4)
|
4bbcc027882a9dc7f77b73de3a6cecac607871bd
|
eff63f358252d5fe474e215fd11c17bdf5e5f716
|
/man/tune_and_update.Rd
|
9449468d73c3e35cac9f7d37aad9b0683cd32845
|
[] |
no_license
|
gabrielcrepeault/xgbmr
|
0fc50af27f93a2e469f4d50e770bb285641f9df9
|
50701662bf9900b6d1fc6fb631a9f887f92958fd
|
refs/heads/master
| 2020-09-13T13:45:34.814325
| 2019-12-21T11:50:08
| 2019-12-21T11:50:36
| 222,803,677
| 4
| 1
| null | null | null | null |
UTF-8
|
R
| false
| true
| 1,104
|
rd
|
tune_and_update.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/tune_and_update.R
\name{tune_and_update}
\alias{tune_and_update}
\title{tune_and_update}
\usage{
tune_and_update(learner, task, param_set, plot = T, show.info = T, root)
}
\arguments{
\item{learner}{Un objet R de classe "Learner"}
\item{task}{Un objet R de class "Task"}
\item{param_set}{Un objet R de classe "ParamSet"}
\item{plot}{indicateur pour indiquer si on veut produire un graphique résumé
de la procédure CV5}
\item{export}{indicateur pour indiquer si on veut sauvegarder dans un fichier .rda
l'objet TuneResult (lorsque c'est long à rouler, c'est intéressant de sauvegarder des
résultats intermédiaires.}
}
\value{
Une liste contenant un graphique, l'objet TuneResult, le data.frame ayant
permi de créer le graphique et le nouvel objet Learner mis à jour avec la valeur optimale
du paramètre.
}
\description{
Fonction pour wrapper la fonction tuneParams de mlr avec les différents
paramètres à fixer. Ça réduit le code dans le fichier principal de tuning.
}
\author{
Gabriel Crépeault-Cauchon
}
|
0bca72f3bbd87fcebf9f6eb00cbbaed89a726b80
|
c89182ec5149c2959bd2ec41e4bcdf754c10e3fb
|
/cashematrixexample.R
|
a60f9c8320bb2793002976bacc725d24f10b6542
|
[] |
no_license
|
DmitryFesenko/ProgrammingAssignment2
|
b576222bb3b8fa6c04f599d79558074f04d853a0
|
7f944a2392368a71b8afcf339b3d7e7fa4bc538b
|
refs/heads/master
| 2021-01-12T06:38:48.246118
| 2017-01-01T20:18:08
| 2017-01-01T20:18:08
| 77,404,305
| 0
| 0
| null | 2016-12-26T20:01:31
| 2016-12-26T20:01:31
| null |
UTF-8
|
R
| false
| false
| 934
|
r
|
cashematrixexample.R
|
makeVector <- function(x = numeric()) {
m <- NULL #begins by setting the mean to NULL as a placeholder for a future value
set <- function(y) {
x <<- y #defines a function to set the vector, x, to a new vector, y,
m <<- NULL #and resets the mean, m, to NULL
}
get <- function() x #returns the vector, x
setmean <- function(mean) m <<- mean #sets the mean, m, to mean
getmean <- function() m #returns the mean, m
list(set = set, get = get, #returns the 'special vector' containing all of the functions just defined
setmean = setmean,
getmean = getmean)
}
cachemean <- function(x, ...) {
m <- x$getmean()
if(!is.null(m)) {
message("getting cached data")
return(m)
}
data <- x$get()
m <- mean(data, ...)
x$setmean(m)
m
}
|
96c34bf6648fe5652ce1bf025bea1f2dfae892b2
|
1fa56b40529a4b720d6e3a5b4720d3e2e47e5c8d
|
/test.R
|
f02f5306bfbb944334009499a0a366134ce54c3f
|
[
"MIT"
] |
permissive
|
elserch/findYourWine
|
f0364aa945efa73f06773fc1e50da50f52674d0f
|
0a551bd64aea4b1747421b61495d3dfa3b6ce2d2
|
refs/heads/master
| 2020-03-22T15:19:20.005615
| 2018-09-16T20:22:19
| 2018-09-16T20:22:19
| 140,243,832
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 3,153
|
r
|
test.R
|
install.packages("dplyr", dependencies = TRUE)
install.packages("ggplot2")
install.packages("forcats")
install.packages("DT")
install.packages("ggformula")
#install.packages("ddply")
library(ggplot2)
library(dplyr)
library(ggformula)
# load file
wine_reviews <- read.csv("/Users/christianelser/github/findYourWine/winemag-data_first150k.csv")
# filter all out without price value
all_with_price <- select(filter(wine_reviews, trimws(price) !=""), c(country, points, price, variety))
# add column ratio
all_with_price <- transform(all_with_price, price_points_ratio = price / points)
View(all_with_price)
# get average points per country
average_points_country <- all_with_price %>% group_by(country) %>% summarise(mean_points = mean(points))
View(average_points_country)
average_points_country_variety <- all_with_price %>% group_by(.dots=c("country", "variety")) %>% summarize(mean_points = mean(points))
View(average_points_country_variety)
average_price_points_ratio_country_variety <- all_with_price %>% group_by(.dots=c("country", "variety")) %>% summarize(mean_price_points_ratio = mean(price_points_ratio ))
View(average_points_country_variety)
all_countries <- unique(all_with_price$country)
View(all_countries)
all_countries_count <- table(all_with_price$country)
View(all_countries_count)
all_countries_list <- as.list(as.data.frame(t(all_countries)))
View(all_countries_list)
all_with_price_sorted <- all_with_price[order(price_points_ratio),]
all_with_price_output <- select(filter(all_with_price, trimws(points) < 91), c(country, points, price, price_points_ratio, variety))
View(all_with_price_output)
SnZ <- aggregate(crime, by = list(Straftat = crime$Straftat, Zeit = crime$time.tag), FUN = length)
names(SnZ) <- c("Straftat", "Zeit", "Count")
ggplot(SnZ, aes(x= Straftat , y= factor(Zeit))) +
geom_tile(aes(fill= Count)) +
scale_x_discrete("Straftat", expand = c(0,0)) +
scale_y_discrete("Zeitraum", expand = c(0,-2)) +
scale_fill_gradient("Anzahl Straftaten", low = "gray", high = "red") +
theme_bw() +
ggtitle("Straftaten nach Tageszeit") +
theme(panel.grid.major = element_line(colour = NA), panel.grid.minor = element_line
(colour = NA))
library(ggplot2)
#------------------
# CREATE DATA FRAME
#------------------
df.team_data <- expand.grid(teams = c("Team A", "Team B", "Team C", "Team D")
,metrics = c("Metric 1", "Metric 2", "Metric 3", "Metric 4", "Metric 5")
)
# add variable: performance
set.seed(41)
df.team_data$performance <- rnorm(nrow(df.team_data))
#inspect
head(df.team_data)
#---------------------------
# PLOT: heatmap
# - here, we use geom_tile()
#---------------------------
ggplot(data = df.team_data, aes(x = metrics, y = teams)) +
geom_tile(aes(fill = performance))
p + theme_grey(base_size = base_size)
+ labs(x = "", y = "") + scale_x_discrete(expand = c(0, 0))
+ scale_y_discrete(expand = c(0, 0))
+ opts(legend.position = "none", axis.ticks = theme_blank(),
axis.text.x = theme_text(size = base_size * 0.8, angle = 330, hjust = 0, colour = "grey50"))
plot <- gf_point(price ~ points, data = all_with_price)
View(plot)
|
d36cc90bf9ed7ed95fb9291f2b655b148ab868a5
|
be66c01c8da7d84562f54195ffedc26488362db0
|
/TwitterAnalysis.r
|
017fa71c5d2d951ada1e67682f5571a12fadc037
|
[] |
no_license
|
ashwin-srinivas7/Twitter-Flu-Trends
|
2035d2c2d410a543959e4864f21d01abd6c30d1a
|
b75740e6551b94bbf4f1392151879ecccf954d9b
|
refs/heads/master
| 2020-12-09T14:40:08.855719
| 2020-01-14T06:39:29
| 2020-01-14T06:39:29
| 233,336,731
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 6,401
|
r
|
TwitterAnalysis.r
|
setwd("D:/Study Material/Projects/Twitter Flu Analysis/Outputs")
# ------------ Install and load all libraries
if (!requireNamespace("devtools", quietly = TRUE)) {
install.packages("devtools")
}
devtools::install_github("mkearney/rtweet") #install dev version of rtweet
install.packages("httpuv")
install.packages("stringi") # for using stri_enc_toutf8() to encode the string
library(stringi)
library(rtweet)
library(ggmap)
library(RJSONIO)
library(ggplot2)
library(maps)
# --------- Create the twitter token using create_token() for accessing twitter data
## Before doing that, set the callback URL for the application in dev.twitter.com as http://127.0.0.1:1410
appname <- "Ash_Lab1"
key <- "XGrXBWBAL5HnqdlDm9LNTiN6C"
secret <- "BQaArLP6Lsc7RGDNQa7QhTWx8tN0c7xGxhz5lDppfX9ZVmqHrU"
twitter_token <- create_token(
app = appname,
consumer_key = key,
consumer_secret = secret)
## ---------- Test code to extract tweets with #seattle
#rt <- data.frame(search_tweets(
# "#seattle", n = 180, include_rts = FALSE
#))
#rt <- apply(rt,2,as.character) #include this line to avoid error while exporting to csv
#write.csv(rt,file="rt.csv")
#head(rt)
#class(rt)
#---------------------------------------------------------
#come up with keywords for twitter searches about flu
t_keys <- cbind("flu sick",
"flu vaccine",
"flu virus",
"flu ill",
"flu doctor",
"flu symptom",
"fly doctor",
"flu season",
"flu outbreak",
"flu cases",
"flu areas",
"flu foods",
"flu effects",
"flu fever",
"flu cough",
"flu catch",
"flu feeling",
"flu hospital",
"flu type",
"flu influenza",
"flu contagious",
"flu sucks")
for(i in 1:22)
{
tweet_info <- search_tweets(t_keys[,i], geocode = lookup_coords("usa"), retryonratelimit = TRUE,include_rts = FALSE, n=10000)
user_info <- users_data(tweet_info)
ai <- paste("A", i, sep = "")
bi <- paste("B", i, sep = "")
assign(ai, tweet_info)
assign(bi, user_info)
}
twitter_data <- rbind(A1,A2,A3,A4,A5,A6,A7,A8,A9,A10,A11,A12,A13,A14,A15,A16,A17,A18,A19,A20,A21,A22)
twitter_data<- apply(twitter_data,2,as.character)
twitter_data <- data.frame(twitter_data)
save_as_csv(twitter_data,"twitter_data.csv")
user_data <- rbind(B1,B2,B3,B4,B5,B6,B7,B8,B9,B10,B11,B12,B13,B14,B15,B16,B17,B18,B19,B20,B21,B22)
user_data<- apply(user_data,2,as.character)
user_data <- data.frame(user_data)
user_data$location = tolower(user_data$location) #convert locations to lowercase
save_as_csv(user_data,"finalUser_data.csv")
userData_locations <- user_data$location ##store the locations in a different vector
userData_locations <- userData_locations[userData_locations!=""] #remove blank values from this vector
head(userData_locations)
write.csv(userData_locations,"userlocations.csv")
#cleaned data - removed special characters manually on Excel
register_google(key="AIzaSyAreX0LejrXWQqZqYQbFMfCmxFLVI6zFPE") #register with google before using geocode to get latitude and longitude
latlon_locations <- c()
head(latlon_locations)
userData_locations <- read.csv(file.choose()) # userlocations.csv - manually clean this file. remove special ASCII characers from location
head(userData_locations)
userData_locations <- apply(userData_locations,2,as.character)
userData_locations <- userData_locations[userData_locations!=""]
latlon_locations <- geocode(userData_locations) ##get latitude and longitude using google API
head(latlon_locations)
latlon_locations <- apply(latlon_locations,2,as.character)
write.csv(latlon_locations,"coordinatesOfTweets.csv")
## ----------------------- REVERSE GEOCODING (convert lat lon coordinates into states)
latlon_locations <- read.csv(file.choose()) #choose "coordinatesOfTweets.csv. Did this as my global environment was cleared.
latlon_locations <- latlon_locations[complete.cases(latlon_locations), ] #keep only complete cases i.e. remove NA rows
# latlon_locations[1,]
## drop the first column
drops <- c("X")
latlon_locations <- latlon_locations[ , !(names(latlon_locations) %in% drops)]
## swap the latitude and longitutes as the API takes latitude and longitude
temp <- latlon_locations$lon
latlon_locations$lon <- latlon_locations$lat
latlon_locations$lat <- temp
## Note that the column labels will not change. only the values will be swapped.
nrow(latlon_locations)
data.json <- c()
i<-0
for(i in 1:23503){
#replace the spaces with %20 and append the lat lon string. This is going to be appended to the URL below to obtain the political area data
latlngStr <- gsub(' ','%20', paste(latlon_locations[i,], collapse=",")) #collapse the commas as it is a csv
#when the lat lon string is appended to this URL, the API will obtain data in the JSON format
connectStr <- paste("http://www.datasciencetoolkit.org/coordinates2politics/",latlngStr, sep="")
con <- url(connectStr)
data.json[i] <- fromJSON(paste(readLines(con), collapse=""))
close(con)
}
data.json1 <- data.json
d <- unlist(data.json1)
i <- 0
j <- c()
for(i in 1:length(d)){
if(d[i] == "state"){
j <- rbind(j,d[i+1]) ## Bind all the states together into vector j from the JSON object
}
}
write.table(j,"FinalLocationsList.csv")
j1 <- read.csv(file.choose()) ##Closed workspace so choose the file "FinalLocationsList.csv
head(j1)
j1$count <- rep(1,nrow(j1)) # make new column called "count" and give value 1 to each row to count
agg_j1 <- aggregate(newcol ~ politics.name, data = j1, FUN = function(x)sum(x, na.rm = TRUE))
head(agg_j1)
nrow(agg_j1)
head(j1)
write.csv(agg_j1,"FinalCountOfStates.csv")
states <- map_data("state")
agg_j1$region <- tolower(agg_j1$State)
map.df <- merge(states,agg_j1, by="region", all.x=T) ## Merge the states obtaines from JSON to the "state" object by region
head(map.df)
map.df <- map.df[order(map.df$order),]
# ----------- Plot the heatmap
map1 <- ggplot(map.df, aes(x = long, y = lat, group = group))+
geom_polygon(aes(fill=count))+
coord_map()+ggtitle("2018-19 Influenza Season Twitter Data")+
theme(plot.title = element_text(hjust = 0.5))+
guides(fill=guide_legend(title="Activity Level"))
map1
|
363b9c28b8610515135c921c3734b3bac79a9db6
|
df28d71337c2d4551fdb0e1b02c4d7e26ad5c58e
|
/ARIMA_Aviation.R
|
ad8a5851dc93a9f718511cb3859c20acea1d040c
|
[] |
no_license
|
AkshayChopade07/R-Codes
|
891242066d95cbfd7724975b3ea3344a8bff8f72
|
6d8a2737ce6a6ae139846458677453ee390f6a15
|
refs/heads/master
| 2023-04-29T12:41:19.215814
| 2021-05-20T06:47:49
| 2021-05-20T06:47:49
| 263,832,190
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 894
|
r
|
ARIMA_Aviation.R
|
install.packages(c("forecast","fpp","smooth","tseries"))
library(forecast)
library(fpp)
library(smooth)
library(tseries)
# Converting data into time series object
# Loading Aviation Data
aviation<-read.csv("C:/Users/Immortal/Documents/R/Aviation.CSV") # Aviation.csv
View(aviation)
amts<-ts(aviation$Sales,frequency = 4,start=c(86))
View(amts)
plot(amts)
#dividing entire data into training and testing data
train<-amts[1:38]
test<-amts[39:42] # Considering only 4 Quarters of data for testing because data itself is Quarterly
# seasonal data
# converting time series object
train<-ts(train,frequency = 4)
test<-ts(test,frequency = 4)
plot(train)
acf(train)
pacf(train)
# Auto.Arima model on the price agg data
library(forecast)
model_AA <- auto.arima(train)
model_AA
acf(model_AA$residuals)
windows()
plot(forecast(model_AA,h=4),xaxt="n") #xaxt- x axis text
|
2de6c17f68cecd0527a79290a4899dc642c30665
|
dbe8b293b4654cac8ab2f1be4febd54eef4a45a9
|
/R/spCdfplot.R
|
f2aa4291acb5f6c52ddc78aa5145462fbbfb78f8
|
[] |
no_license
|
cran/simPopulation
|
2555bda823b1e2190a7a76881fa3254d350a8ea2
|
fcd172cf8c91d1b23ee29fa4e52a111ed08fb72b
|
refs/heads/master
| 2021-01-19T14:58:56.505267
| 2013-12-10T00:00:00
| 2013-12-10T00:00:00
| 17,699,673
| 0
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 7,126
|
r
|
spCdfplot.R
|
# ---------------------------------------
# Author: Andreas Alfons
# Vienna University of Technology
# ---------------------------------------
spCdfplot <- function(x, ...) UseMethod("spCdfplot")
spCdfplot.default <- function(x, weights = NULL, cond = NULL, dataS,
dataP = NULL, approx = NULL, n = 10000, bounds = TRUE, ...) {
## initializations
if(!is.character(x) || length(x) == 0) {
stop("'x' must be a character vector of positive length")
}
if(!is.null(weights) && (!is.character(weights) || length(weights) > 1)) {
stop("'weights' must be a single character string or NULL")
}
if(!is.null(cond) && !is.character(cond)) {
stop("'cond' must be a character vector or NULL")
}
if(!inherits(dataS, "data.frame")) stop("'dataS' must be a data.frame")
# check if 'dataP' is valid
if(!is.null(dataP) && !inherits(dataP, "data.frame")) {
if(ok <- inherits(dataP, "list")) {
if(length(dataP)) ok <- all(sapply(dataP, inherits, "data.frame"))
else dataP <- NULL
}
if(!ok) stop("'data' must be a data.frame or a list of data.frames")
}
nP <- if(inherits(dataP, "data.frame")) 2 else 1+length(dataP)
# check 'approx'
if(is.null(approx)) approx <- !is.null(dataP)
else if(!is.logical(approx) || length(approx) == 0) approx <- FALSE
if(is.null(dataP)) {
if(length(approx) > 1) approx <- isTRUE(approx[1])
} else {
if(length(approx) == 1) {
approx <- c(FALSE, rep.int(isTRUE(approx), nP-1))
} else approx <- c(isTRUE(approx[1]), rep.int(isTRUE(approx[2]), nP-1))
}
# check 'n'
if(any(approx) && (!is.numeric(n) || length(n) == 0)) {
stop("'n' is not numeric or does not have positive length")
}
if(is.null(dataP)) {
if(length(n) > 1) n <- if(approx) n[1] else NA
} else {
if(length(n) == 1) n <- ifelse(approx, n, NA)
else n <- c(if(approx[1]) n[1] else NA, ifelse(approx[-1], n[2], NA))
}
# check 'bounds'
bounds <- isTRUE(bounds)
# define labels for grouping variable
if(is.null(dataP)) lab <- ""
else {
pop <- "Population"
if(!inherits(dataP, "data.frame")) {
nam <- names(dataP)
if(is.null(nam)) pop <- paste(pop, 1:(nP-1))
else {
replace <- which(nchar(nam) == 0)
nam[replace] <- paste(pop, replace)
pop <- nam
}
}
lab <- c("Sample", pop)
}
## construct objects for 'xyplot'
# from sample
tmp <- getCdf(x, weights, cond, dataS,
approx=approx[1], n=n[1], name=lab[1])
values <- tmp$values
app <- t(tmp$approx)
# from population(s)
if(!is.null(dataP)) {
if(inherits(dataP, "data.frame")) {
tmp <- getCdf(x, NULL, cond, dataP,
approx=approx[2], n=n[2], name=lab[2])
values <- rbind(values, tmp$values)
app <- rbind(app, tmp$approx)
} else {
tmp <- mapply(function(dP, approx, n, l) {
getCdf(x, NULL, cond, dP,
approx=approx, n=n, name=l)
}, dataP, approx[-1], n[-1], lab[-1],
SIMPLIFY=FALSE, USE.NAMES=FALSE)
values <- rbind(values,
do.call(rbind, lapply(tmp, function(x) x$values)))
app <- rbind(app, do.call(rbind, lapply(tmp, function(x) x$approx)))
}
}
## construct formula for 'xyplot'
form <- ".y~.x" # basic formula
if(length(x) > 1) cond <- c(".var", cond)
if(!is.null(cond)) {
cond <- paste(cond, collapse = " + ") # conditioning variabels
form <- paste(form, cond, sep=" | ") # add conditioning to formula
}
## define local version of 'xyplot'
localXyplot <- function(form, values, xlab = NULL, ylab = NULL,
auto.key = TRUE, ...,
# these arguments are defined so that they aren't supplied twice:
x, data, allow.multiple, outer, panel, prepanel, groups) {
# prepare legend
if(isTRUE(auto.key)) auto.key <- list(points=FALSE, lines=TRUE)
else if(is.list(auto.key)) {
if(is.null(auto.key$points)) auto.key$points <- FALSE
if(is.null(auto.key$lines)) auto.key$lines <- TRUE
}
# this produces a 'NOTE' during 'R CMD check':
# xyplot(form, data=values, groups=if(nP == 1) NULL else .name,
# panel=panelSpCdfplot, prepanel=prepanelSpCdfplot,
# xlab=xlab, ylab=ylab, auto.key=auto.key, ...)
command <- paste("xyplot(form, data=values,",
"groups=if(nP == 1) NULL else .name,",
"panel=panelSpCdfplot, prepanel=prepanelSpCdfplot,",
"xlab=xlab, ylab=ylab, auto.key=auto.key, ...)")
eval(parse(text=command))
}
## call 'xyplot'
localXyplot(as.formula(form), values, approx=app, bounds=bounds, ...)
}
## panel function
panelSpCdfplot <- function(x, y, approx, bounds = TRUE, ...) {
if(isTRUE(bounds)) {
panel.refline(h=0, ...)
panel.refline(h=1, ...)
}
localPanelXyplot <- function(..., approx, type, distribute.type) {
i <- packet.number()
type <- ifelse(approx[,i], "l", "s")
panel.xyplot(..., type=type, distribute.type=TRUE)
}
localPanelXyplot(x, y, approx=approx, ...)
}
## prepanel function
prepanelSpCdfplot <- function(x, y, ...) list(ylim=c(0,1))
## internal utility functions
# get data.frame and logical indicating approximation
getCdf <- function(x, weights = NULL,
cond = NULL, data, ..., name = "") {
if(is.null(cond)) {
x <- data[, x]
w <- if(length(weights) == 0) NULL else data[, weights]
prepCdf(x, w, ..., name=name)
} else {
tmp <- tapply(1:nrow(data), data[, cond, drop=FALSE],
function(i) {
x <- data[i, x]
w <- if(length(weights) == 0) NULL else data[i, weights]
g <- unique(data[i, cond, drop=FALSE])
res <- prepCdf(x, w, ..., name=name)
res$values <- cbind(res$values,
g[rep.int(1, nrow(res$values)), , drop=FALSE])
res
})
values <- do.call(rbind, lapply(tmp, function(x) x$values))
approx <- as.vector(sapply(tmp, function(x) x$approx))
list(values=values, approx=approx)
}
}
# prepare one or more variables
prepCdf <- function(x, w, ..., name = "") UseMethod("prepCdf")
prepCdf.data.frame <- function(x, w, ..., name = "") {
tmp <- lapply(x, prepCdf, w, ..., name=name)
values <- mapply(function(x, v) cbind(x$values, .var=v),
tmp, names(x), SIMPLIFY=FALSE, USE.NAMES=FALSE)
values <- do.call(rbind, values)
approx <- sapply(tmp, function(x) x$approx)
list(values=values, approx=approx)
}
prepCdf.default <- function(x, w, ..., name = "") {
tmp <- spCdf(x, w, ...)
values <- data.frame(.x=c(tmp$x[1], tmp$x), .y=c(0, tmp$y), .name=name)
list(values=values, approx=tmp$approx)
}
|
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