blob_id
stringlengths 40
40
| directory_id
stringlengths 40
40
| path
stringlengths 2
327
| content_id
stringlengths 40
40
| detected_licenses
listlengths 0
91
| license_type
stringclasses 2
values | repo_name
stringlengths 5
134
| snapshot_id
stringlengths 40
40
| revision_id
stringlengths 40
40
| branch_name
stringclasses 46
values | visit_date
timestamp[us]date 2016-08-02 22:44:29
2023-09-06 08:39:28
| revision_date
timestamp[us]date 1977-08-08 00:00:00
2023-09-05 12:13:49
| committer_date
timestamp[us]date 1977-08-08 00:00:00
2023-09-05 12:13:49
| github_id
int64 19.4k
671M
⌀ | star_events_count
int64 0
40k
| fork_events_count
int64 0
32.4k
| gha_license_id
stringclasses 14
values | gha_event_created_at
timestamp[us]date 2012-06-21 16:39:19
2023-09-14 21:52:42
⌀ | gha_created_at
timestamp[us]date 2008-05-25 01:21:32
2023-06-28 13:19:12
⌀ | gha_language
stringclasses 60
values | src_encoding
stringclasses 24
values | language
stringclasses 1
value | is_vendor
bool 2
classes | is_generated
bool 2
classes | length_bytes
int64 7
9.18M
| extension
stringclasses 20
values | filename
stringlengths 1
141
| content
stringlengths 7
9.18M
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
daf003d2cb1865698a0251fa0779f0ae8e63f788
|
584025b690582ab9ac588c77ebc5e746e95816f8
|
/man/include.Rd
|
c12f4207091e043bb6a2f8129af3bc9802fa15a4
|
[] |
no_license
|
rstudio/htmltools
|
27b459793e6c7dfb12bcd196e72378072d85e92a
|
251526c9886ca9ab5460c7bd4d73c0d61e2b40b8
|
refs/heads/main
| 2023-08-17T23:16:13.900249
| 2023-08-14T19:49:04
| 2023-08-14T19:49:04
| 18,398,793
| 210
| 82
| null | 2023-09-08T14:34:59
| 2014-04-03T10:11:03
|
R
|
UTF-8
|
R
| false
| true
| 1,326
|
rd
|
include.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/tags.R
\name{include}
\alias{include}
\alias{includeHTML}
\alias{includeText}
\alias{includeMarkdown}
\alias{includeCSS}
\alias{includeScript}
\title{Include Content From a File}
\usage{
includeHTML(path)
includeText(path)
includeMarkdown(path)
includeCSS(path, ...)
includeScript(path, ...)
}
\arguments{
\item{path}{The path of the file to be included. It is highly recommended to
use a relative path (the base path being the Shiny application directory),
not an absolute path.}
\item{...}{Any additional attributes to be applied to the generated tag.}
}
\description{
Load HTML, text, or rendered Markdown from a file and turn into HTML.
}
\details{
These functions provide a convenient way to include an extensive amount of
HTML, textual, Markdown, CSS, or JavaScript content, rather than using a
large literal R string.
}
\note{
\code{includeText} escapes its contents, but does no other processing.
This means that hard breaks and multiple spaces will be rendered as they
usually are in HTML: as a single space character. If you are looking for
preformatted text, wrap the call with \code{\link[=pre]{pre()}}, or consider using
\code{includeMarkdown} instead.
The \code{includeMarkdown} function requires the \code{markdown}
package.
}
|
63a7553f56d85ce836f7943764849a17bcf8e3cc
|
c73553a8936ec72cbbfbcc5e88cabbc946ce32a6
|
/R/generics.R
|
af6f47d82032e5abc5ca4e98d8103d2ba5fce6c0
|
[
"MIT"
] |
permissive
|
stephaneghozzi/trendbreaker
|
b0f9a1475f56bd7f14fb10573c5fe2b807c508e5
|
5002f7f12dc90c079f4391eb88be4166916403c5
|
refs/heads/master
| 2022-10-22T02:47:22.895042
| 2020-06-17T09:17:19
| 2020-06-17T09:17:19
| 275,192,241
| 1
| 0
|
NOASSERTION
| 2020-06-26T15:47:17
| 2020-06-26T15:47:16
| null |
UTF-8
|
R
| false
| false
| 1,561
|
r
|
generics.R
|
#' S3 generics for trendbreaker
#'
#' These are generic functions used by the *trendbreaker* package, mostly used for
#' accessing content of various objects. See `?trendbreaker-accessors` for methods
#' relating to `trendbreaker` objects, and `trendbreaker_model-accessors` for methods
#' relating to `trendbreaker_model` objects.
#'
#' @seealso [trendbreaker-accessors](trendbreaker-accessors),
#' [trendbreaker_model-accessors](trendbreaker_model-accessors)
#'
#' @param x the object to access information from
#'
#' @param ... further arguments used in methods
#'
#' @param data a `data.frame` to be used as training set for the model
#'
#' @rdname trendbreaker-generics
#' @aliases trendbreaker-generics
#' @export
get_model <- function (x, ...) {
UseMethod("get_model", x)
}
#' @export
#' @rdname trendbreaker-generics
get_k <- function (x, ...) {
UseMethod("get_k", x)
}
#' @export
#' @rdname trendbreaker-generics
get_results <- function (x, ...) {
UseMethod("get_results", x)
}
#' @export
#' @rdname trendbreaker-generics
get_outliers <- function (x, ...) {
UseMethod("get_outliers", x)
}
#' @export
#' @rdname trendbreaker-generics
get_formula <- function (x, ...) {
UseMethod("get_formula", x)
}
#' @export
#' @rdname trendbreaker-generics
get_response <- function (x, ...) {
UseMethod("get_response", x)
}
#' @export
#' @rdname trendbreaker-generics
get_family <- function (x, ...) {
UseMethod("get_family", x)
}
#' @export
#' @rdname trendbreaker-generics
train <- function (x, data, ...) {
UseMethod("train", x)
}
|
0d93bf530a8f0f1daeb7190d335bf2a58609a515
|
b4fa675971a21aa98021a1ff2c3f831ad89834e7
|
/RMGWebScraping.R
|
6d6cc528de0e460676e0883d766bfa12b8c00a42
|
[] |
no_license
|
bslakman/representing-chemical-data
|
4b9f59f3fc136e0f6cd152706827acd2076417c5
|
6f015fb5f93724ad767de32bafde93a0ad3cad29
|
refs/heads/master
| 2021-01-24T10:49:29.491673
| 2016-10-05T21:28:48
| 2016-10-05T21:28:48
| 70,100,006
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 11,070
|
r
|
RMGWebScraping.R
|
# Belinda Slakman
# DSCS 6020 Term Project - Web Scraping
# This file will scrape the RMG website at http://rmg.coe.neu.edu for chemical data.
# Load required packages
library('RCurl')
library('XML')
# This function scrapes thermodynamic data from the RMG database according to sourceType (either 'libraries' or 'groups')
# and name (of the library or group).
ScrapeRMGThermo <- function(sourceType, name){
# form the URL by joining the rmg thermo database website with the source type and source name
url <- paste0("http://rmg.coe.neu.edu/database/thermo/", sourceType, "/", name)
webpage <- getURL(url, followlocation=TRUE)
# convert the page into a line-by-line format
tc <- textConnection(webpage)
webpage <- readLines(tc)
close(tc)
# get webpage in tree format
pagetree_parent <- htmlTreeParse(webpage, useInternalNodes = TRUE)
# Figure out how many species there are
lastSpecLabel <- unlist(xpathApply(pagetree_parent,"//*/div[@id='contents']/table[@class='thermoData']/tr[position()=last()]/td[1]/a", xmlValue))
lastSpecSplit <- strsplit(lastSpecLabel, ". ")
numSpecies <- as.numeric(lastSpecSplit[[1]][1])
# create empty vectors
label <- rep(NA, numSpecies)
adj_list <- rep(NA, numSpecies)
Hf <- rep(NA, numSpecies)
Sf <- rep(NA, numSpecies)
Cp_300 <- rep(NA, numSpecies)
Cp_1000 <- rep(NA, numSpecies)
for(index in 1:numSpecies){
# create specific URL for species
url_specific <- paste0(url, "/", index)
# Make sure it exists
if (!url.exists(url_specific)) {next}
webpage <- getURL(url_specific, followlocation=TRUE)
# convert the page into a line-by-line format
tc <- textConnection(webpage)
webpage <- readLines(tc)
close(tc)
# get webpage in tree format
pagetree <- htmlTreeParse(webpage, useInternalNodes = TRUE)
# get the label for the molecule, and add just its name to the list
whole_label <- unlist(xpathApply(pagetree,"//*/h1",xmlValue))
split_label <- strsplit(whole_label, ". ", fixed=TRUE)
label[index] <- split_label[[1]][[2]]
# get the "alt" attribute of the molecule's image, which corresponds to its adjacency list
adj_list_attr <- unlist(xpathApply(pagetree,"//*/div[@id='contents']/p/img/@alt"))
adj_list[index] <- adj_list_attr[[1]]
# Check the thermo format. We'll only process if Group additivity
number_label <- paste0(index, ". ", label[index])
thermo_type <- unlist(xpathApply(pagetree_parent, paste0("//*/table[@class='thermoData']/tr[td[a='", number_label, "']]/td[3]"), xmlValue))
if (!thermo_type=='Group additivity') {next}
# get enthalpy of formation, entropy of formation, and heat capcity at 300K and 1000K
Hf_value <- unlist(xpathApply(pagetree, "//*/table[@class='thermoEntryData']/tr[1]/td[@class='value']/span", xmlValue))
Hf_processing <- strsplit(Hf_value, " ")
Hf[index] <- paste0(Hf_processing[[1]][[1]], " ", Hf_processing[[1]][[4]])
Sf_value <- unlist(xpathApply(pagetree, "//*/table[@class='thermoEntryData']/tr[2]/td[@class='value']/span", xmlValue))
Sf_processing <- strsplit(Sf_value, " ")
Sf[index] <- paste0(Sf_processing[[1]][[1]], " ", Sf_processing[[1]][[4]])
Cp_300_value <- unlist(xpathApply(pagetree, "//*/table[@class='thermoEntryData']/tr[3]/td[@class='value']/span", xmlValue))
Cp_300_processing <- strsplit(Cp_300_value, " ")
Cp_300[index] <- paste0(Cp_300_processing[[1]][[1]], " ", Cp_300_processing[[1]][[4]])
Cp_1000_value <- unlist(xpathApply(pagetree, "//*/table[@class='thermoEntryData']/tr[8]/td[@class='value']/span", xmlValue))
Cp_1000_processing <- strsplit(Cp_1000_value, " ")
Cp_1000[index] <- paste0(Hf_processing[[1]][[1]], " ", Cp_1000_processing[[1]][[4]])
}
# Create data frame of these 10 species
thermo <- data.frame(label, adj_list, Hf, Sf, Cp_300, Cp_1000, stringsAsFactors = FALSE)
thermo <- thermo[complete.cases(thermo[,1]),]
return(thermo)
}
ScrapeRMGKineticsFromLibrary <- function(libraryName){
# form the URL by joining the rmg kinetics library website with the library name
url <- paste0("http://rmg.coe.neu.edu/database/kinetics/libraries/", libraryName)
webpage <- getURL(url, followlocation=TRUE)
# convert the page into a line-by-line format
tc <- textConnection(webpage)
webpage <- readLines(tc)
close(tc)
# get webpage in tree format
pagetree_parent <- htmlTreeParse(webpage, useInternalNodes = TRUE)
# Figure out how many species there are
lastSpecLabel <- unlist(xpathApply(pagetree_parent,"//*/div[@id='contents']/table[@class='kineticsData']/tr[position()=last()]/td[1]/a", xmlValue))
lastSpecSplit <- strsplit(lastSpecLabel, ". ")
numSpecies <- as.numeric(lastSpecSplit[[1]][1])
# create empty vectors for reactants and kinetics data
reactant_1 <- rep(NA, numSpecies)
reactant_2 <- rep(NA, numSpecies)
reactant_3 <- rep(NA, numSpecies)
product_1 <- rep(NA, numSpecies)
product_2 <- rep(NA, numSpecies)
product_3 <- rep(NA, numSpecies)
A <- rep(NA, numSpecies)
n <- rep(NA, numSpecies)
E_A <- rep(NA, numSpecies)
for(index in 1:numSpecies){
# create specific URL for reaction
url_specific <- paste0(url, "/", index)
if (!url.exists(url_specific)) {next}
webpage <- getURL(url_specific, followlocation=TRUE)
# convert the page into a line-by-line format
tc <- textConnection(webpage)
webpage <- readLines(tc)
close(tc)
# get webpage in tree format
pagetree <- htmlTreeParse(webpage, useInternalNodes = TRUE)
reactants <- unlist(xpathApply(pagetree,"//*/div[@id='contents']/table[@class='reaction']/tr/td[1]/a/img/@alt"))
reactant_1[index] <- reactants[[1]]
if(length(reactants) > 1) {reactant_2[index] <- reactants[[2]]}
if(length(reactants) > 2) {reactant_3[index] <- reactants[[3]]}
products <- unlist(xpathApply(pagetree,"//*/div[@id='contents']/table[@class='reaction']/tr/td[3]/a/img/@alt"))
product_1[index] <- products[[1]]
if(length(products) > 1) {product_2[index] <- products[[2]]}
if(length(products) > 2) {product_3[index] <- products[[3]]}
# Check the kinetics format. We'll only process if Arrhenius
number_label <- paste0(index, ". ")
kinetics_type <- unlist(xpathApply(pagetree_parent, paste0("//*/table[@class='kineticsData']/tr[td[a='", number_label, "']]/td[5]"), xmlValue))
if (!kinetics_type=='Arrhenius') {next}
kinetics <- unlist(xpathApply(pagetree,"//*/div[@id='contents']/div[@class='math']", xmlValue))
kinetics_split1 <- strsplit(kinetics, "= ")
if(length(kinetics_split1[[1]]) < 2) {next}
kinetics_split2 <- strsplit(kinetics_split1[[1]][[2]], "T")
A[index] <- kinetics_split2[[1]][[1]]
if(length(kinetics_split2[[1]]) < 2) {next}
kinetics_split3 <- strsplit(kinetics_split2[[1]][[2]], " ")
if(length(kinetics_split3[[1]]) < 9) {next}
n[index] <- kinetics_split3[[1]][[2]]
E_A[index] <- kinetics_split3[[1]][[9]]
}
kinetics <- data.frame(reactant_1, reactant_2, reactant_3, product_1, product_2, product_3, A, n, E_A, stringsAsFactors = FALSE)
kinetics <- kinetics[complete.cases(kinetics[,1]),]
return(kinetics)
}
ScrapeRMGSolvation <- function(){
url <- "http://rmg.mit.edu/database/solvation/libraries/solute"
webpage <- getURL(url, followlocation=TRUE)
# convert the page into a line-by-line format
tc <- textConnection(webpage)
webpage <- readLines(tc)
close(tc)
# get webpage in tree format
pagetree_parent <- htmlTreeParse(webpage, useInternalNodes = TRUE)
# Figure out how many species there are
lastSpecLabel <- unlist(xpathApply(pagetree_parent,"//*/div[@id='contents']/table[@class='solvationData']/tr[position()=last()]/td[1]/a", xmlValue))
lastSpecSplit <- strsplit(lastSpecLabel, ". ")
numSpecies <- as.numeric(lastSpecSplit[[1]][1])
# create empty lists for 10 solutes
label <- rep(NA, numSpecies)
adj_list <- rep(NA, numSpecies)
S <- rep(NA, numSpecies)
B <- rep(NA, numSpecies)
E <- rep(NA, numSpecies)
L <- rep(NA, numSpecies)
A <- rep(NA, numSpecies)
V <- rep(NA, numSpecies)
for(index in 1:numSpecies){
url_solvation <- paste0("http://rmg.mit.edu/database/solvation/libraries/solute/", index)
if (!url.exists(url_solvation)) {next}
webpage <- getURL(url_solvation, followlocation=TRUE)
# convert the page into a line-by-line format
tc <- textConnection(webpage)
webpage <- readLines(tc)
close(tc)
# get webpage in tree format
pagetree <- htmlTreeParse(webpage, useInternalNodes = TRUE)
# get the label for the molecule, and add just its name to the list
whole_label <- unlist(xpathApply(pagetree,"//*/h1",xmlValue))
split_label <- strsplit(whole_label, ". ", fixed=TRUE)
label[index] <- split_label[[1]][[2]]
# get the "alt" attribute of the molecule's image, which corresponds to its adjacency list
adj_list_attr <- unlist(xpathApply(pagetree,"//*/div[@id='contents']/p/a/img/@alt"))
adj_list[index] <- adj_list_attr[[1]]
# Retrieve solvation data
S_full <- unlist(xpathApply(pagetree,"//*/div[@id='contents']/table[@class='solvationEntryData']/table[@class='solvationEntryData']/tr[1]/td[@class='value']/span"), xmlValue)
splitS <- strsplit(xmlValue(S_full[[1]]), " ")
S[index] <- splitS[[1]][1]
B_full <- unlist(xpathApply(pagetree,"//*/div[@id='contents']/table[@class='solvationEntryData']/table[@class='solvationEntryData']/tr[2]/td[@class='value']/span"), xmlValue)
splitB <- strsplit(xmlValue(B_full[[1]]), " ")
B[index] <- splitB[[1]][1]
E_full <- unlist(xpathApply(pagetree,"//*/div[@id='contents']/table[@class='solvationEntryData']/table[@class='solvationEntryData']/tr[3]/td[@class='value']/span"), xmlValue)
splitE <- strsplit(xmlValue(E_full[[1]]), " ")
E[index] <- splitE[[1]][1]
L_full <- unlist(xpathApply(pagetree,"//*/div[@id='contents']/table[@class='solvationEntryData']/table[@class='solvationEntryData']/tr[4]/td[@class='value']/span"), xmlValue)
splitL <- strsplit(xmlValue(L_full[[1]]), " ")
L[index] <- splitL[[1]][1]
A_full <- unlist(xpathApply(pagetree,"//*/div[@id='contents']/table[@class='solvationEntryData']/table[@class='solvationEntryData']/tr[5]/td[@class='value']/span"), xmlValue)
splitA <- strsplit(xmlValue(A_full[[1]]), " ")
A[index] <- splitA[[1]][1]
V_full <- unlist(xpathApply(pagetree,"//*/div[@id='contents']/table[@class='solvationEntryData']/table[@class='solvationEntryData']/tr[6]/td[@class='value']/span"), xmlValue)
splitV <- strsplit(xmlValue(V_full[[1]]), " ")
V[index] <- splitV[[1]][1]
}
solvation <- data.frame(label, adj_list, S, B, E, L, A, V, stringsAsFactors = FALSE)
solvation <- solvation[complete.cases(solvation[,1]),]
return(solvation)
}
#prim_thermo <- ScrapeRMGThermo('libraries', 'primaryThermoLibrary')
#Glarborg_C3_kinetics <- ScrapeRMGKineticsFromLibrary('Glarborg/C3')
#solvation <- ScrapeRMGSolvation()
|
571faec5aa89adbdce04f30bc8d68e2ad1d4924a
|
e94b06a65f3f5ad73bcf869606fd3178f6a18919
|
/Scripts/Trait_differences.R
|
0f2f7d02f735dda8693173dbb54a6f6098683ad8
|
[] |
no_license
|
phil-martin-research/Invas_ES
|
da0a79e9b17498ba26761a393ad177d1399939cd
|
4bf3fc0838428e62ab697aec4480d7b5ec9a8553
|
refs/heads/master
| 2021-10-28T14:40:02.868814
| 2016-08-11T12:59:56
| 2016-08-11T12:59:56
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,707
|
r
|
Trait_differences.R
|
#script to sort out trait data and check for inconsistencies
library(ggplot2)
#load in data
Species_traits<-read.csv("Data/Species_resolved_traits.csv")
Species_traits$Median_height<-ifelse(!is.na(Species_traits$Single_height),Species_traits$Single_height,(Species_traits$Upper_height+Species_traits$Lower_height)/2)
write.csv(Species_traits,"Data/Species_traits_median.csv")
head(Species_traits)
ggplot(Species_traits,aes(y= Trait_db_height,x=Upper_height))+geom_point()+geom_abline()+geom_smooth(method="lm")
ggplot(Species_traits,aes(y= Trait_db_height,x=Lower_height))+geom_point()+geom_abline()+geom_smooth(method="lm")
ggplot(Species_traits,aes(y= Trait_db_height,x=Median_height))+geom_point()+geom_abline()+geom_smooth(method="lm")
#look at how close height values derived from internet are to trait databases
#first calculated as a percentage
sqrt(mean(((Species_traits$Trait_db_height-Species_traits$Upper_height)/Species_traits$Trait_db_height)^2,na.rm = T))-1
sqrt(mean(((Species_traits$Trait_db_height-Species_traits$Lower_height)/Species_traits$Trait_db_height)^2,na.rm = T))-1
sqrt(mean(((Species_traits$Trait_db_height-Species_traits$Median_height)/Species_traits$Trait_db_height)^2,na.rm = T))-1
#then in metres
sqrt(mean(((Species_traits$Trait_db_height-Species_traits$Upper_height))^2,na.rm = T))
sqrt(mean(((Species_traits$Trait_db_height-Species_traits$Lower_height))^2,na.rm = T))
sqrt(mean(((Species_traits$Trait_db_height-Species_traits$Median_height))^2,na.rm = T))
#plot how this varies by height
plot(Species_traits$Trait_db_height,Species_traits$Median_height-Species_traits$Trait_db_height)
M1<-lm(Trait_db_height~Median_height,data=Species_traits)
summary(M1)
|
bb64aad82960ba9f92a9b516160ccca465ccf28c
|
b21d84bf18b039f4832157d6071e07f036c9d6cf
|
/man/GenerateReport.Rd
|
eec98828b11b901ac6975cdb4b8fde76188457f8
|
[] |
no_license
|
apxr/analyzer
|
d0ff7682b1a5fc9ad5ae1bf8b0546d052c2bcba0
|
6dd8de796323ed9b40288c50f7080a9044301f59
|
refs/heads/master
| 2023-01-30T09:57:47.253439
| 2020-12-14T23:16:48
| 2020-12-14T23:16:48
| 260,771,475
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 2,102
|
rd
|
GenerateReport.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/GenerateReport.R
\name{GenerateReport}
\alias{GenerateReport}
\title{Generate the report}
\usage{
GenerateReport(
dtpath,
catVars,
yvar = NULL,
model = "linReg",
title = "Report",
output_format = "html_document",
output_dir = NULL,
normality_test_method = "ks",
interactive.plots = FALSE,
include.vars = NULL
)
}
\arguments{
\item{dtpath}{dataset path}
\item{catVars}{vector of categorical variables names}
\item{yvar}{y variable name if present else \code{NULL}}
\item{model}{type of model - \code{linReg} for linear regression
\code{binClass} for binary classification and \code{multiClass} for
multiclass classification}
\item{title}{Title of the generated report}
\item{output_format}{output report format. \code{'html_documennt'} for
html file or \code{pdf_document} for pdf file output. OR
\code{c("html_document", "pdf_document")} for both.}
\item{output_dir}{Directory where the output files needs to be stored.}
\item{normality_test_method}{method for normality test for a variable.
Values can be \code{shapiro}
for Shapiro-Wilk test or
\code{'anderson'} for 'Anderson-Darling' test of normality or \code{ks} for
'Kolmogorov-Smirnov'}
\item{interactive.plots}{for interactive variable exploration}
\item{include.vars}{include only these variables from the full data}
}
\value{
creates a rmarkdown and html/pdf file. Invisibly returns \code{TRUE}
on successful run and \code{FALSE} in case of error
}
\description{
\code{GenerateReport} generates the markdown report in one command
}
\details{
This function creates a rmarkdown report which can be converted to
html or pdf format file.
}
\examples{
# Assigning the temporary folder in Documnets/temp fodler
GenerateReport(dtpath = "mtcars.csv",
catVars = c("cyl", "vs", "am", "gear"),
yvar = "vs", model = "binClass",
output_format = NULL,
title = "Report",
output_dir = NULL, # pass the output directory
interactive.plots = FALSE)
}
|
76e7bea9736caf9f08cabf76d76ff9f374044230
|
d10d46c626637e7f467da67010734f22554c7911
|
/R/01.2-dados_setores_censitarios.R
|
b9edc11d60b3e6780f63c9d6d976bab73a348e71
|
[] |
no_license
|
r-akemii/acesso_oport
|
6e08c945312784c1ed8ba7637e8223a2c2e93817
|
2bb7dd307be0ddb9ac09523b873f01bd3eceed7a
|
refs/heads/master
| 2020-12-13T10:45:26.764169
| 2020-01-16T16:11:03
| 2020-01-16T16:11:03
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,929
|
r
|
01.2-dados_setores_censitarios.R
|
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
###### 0.1.2 Seleciona e agrega microdados dos setores censitarios
# carregar bibliotecas
source('./R/fun/setup.R')
# # Cria data.frame com municipios do projeto
# munis_df <- data.frame( code_muni= c(2304400, 3550308, 3304557, 4106902, 4314902, 3106200, 2211001),
# abrev_muni=c('for', 'sao', 'rio', 'cur', 'por', 'bel', 'ter'),
# name_muni=c('Fortaleza', 'Sao Paulo', 'Rio de Janeiro', 'Curitiba', 'Porto Alegre', 'Belo Horizonte', 'Teresina'))
### 1. Carrega micro dados dos setores censitarios --------------------------------------------------
## Leitura dos dados
setores1 <- data.table::fread("../data-raw/setores_censitarios/dados_censo2010A.csv", select= c('Cod_UF', 'Cod_municipio', 'Cod_setor', 'DomRend_V003', 'Dom2_V002', 'Pess3_V002', 'Pess3_V003', 'Pess3_V004', 'Pess3_V005', 'Pess3_V006'))
names(setores1)
# Raca/cor
# Pess3_V002 # Pessoas Residentes e cor ou raça - branca
# Pess3_V003 # Pessoas Residentes e cor ou raça - preta
# Pess3_V004 # Pessoas Residentes e cor ou raça - amarela
# Pess3_V005 # Pessoas Residentes e cor ou raça - parda
# Pess3_V006 # Pessoas Residentes e cor ou raça - indígena
# filtra apenas municipio do projeto
setores1 <- setores1[Cod_municipio %in% munis_df$code_muni,]
## Renomeia variaveis
# Renda 6.19 - variavel escolhida: V003 = Total do rendimento nominal mensal dos domicílios particulares permanentes
setores_renda <- setores1 %>%
dplyr::select(cod_uf = Cod_UF, cod_muni = Cod_municipio, cod_setor = Cod_setor, renda_total = DomRend_V003, moradores_total = Dom2_V002, cor_branca=Pess3_V002, cor_preta=Pess3_V003, cor_amarela=Pess3_V004, cor_parda=Pess3_V005, cor_indigena=Pess3_V006)
# Criar variavel de renda domicilias per capita de cada setor censitario
setDT(setores_renda)[, renda_per_capta := renda_total / moradores_total]
setores_renda[, cod_setor := as.character(cod_setor)]
### 2. Merge dos dados de renda com shapes dos setores censitarios --------------------------------------------------
# funcao para fazer merge dos dados e salve arquivos na pata 'data'
merge_renda_setores <- function(sigla){
# sigla <- "for"
# status message
message('Woking on city ', sigla, '\n')
# codigo do municipios
code_muni <- subset(munis_df, abrev_muni==sigla )$code_muni
# subset dados dos setores
dados <- subset(setores_renda, cod_muni == code_muni)
# leitura do shape dos setores
sf <- readr::read_rds( paste0("../data-raw/setores_censitarios/", sigla,"/setores_", sigla,".rds") )
# merge
sf2 <- dplyr::left_join(sf, dados, c('code_tract'='cod_setor'))
# salvar
readr::write_rds(sf2, paste0("../data/setores_agregados/setores_agregados_", sigla,".rds"))
}
# aplicar funcao
purrr::walk(munis_df$abrev_muni, merge_renda_setores)
|
64f11195806733984d9bccea8844acafc2e2bc34
|
388e04f6e256c7ad21d5119b7ca56a92b576013c
|
/R_Programming/rankall.R
|
8ecf8198c812368235b0c0ab166deb8f4c653e7c
|
[] |
no_license
|
dinoamaral/DataScienceCousera
|
9f7a9b21683d5b8f69c4575881862c13b92f35c2
|
9acbf12a7be8cc1e8c4915e6548a999b556586f0
|
refs/heads/master
| 2016-09-11T02:28:58.487472
| 2015-09-13T17:05:11
| 2015-09-13T17:05:11
| 38,140,963
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,642
|
r
|
rankall.R
|
setwd("~/GitHub/DataScienceCousera/R_Programming")
hosp_finder <- function(state_sub, col, num) {
outcome_sub <- as.numeric(state_sub[, col])
len <- dim(state_sub[!is.na(outcome_sub), ])[1]
if (num == "best"){
rank <- state_sub[, 2][order(outcome_sub, state_sub[, 2])[1]]
} else if (num == "worst"){
rank <- state_sub[, 2][order(outcome_sub, state_sub[, 2])[len]]
} else if (num > len){
rank <- NA
} else {
rank <- state_sub[, 2][order(outcome_sub, state_sub[, 2])[num]]
}
return(rank)
}
rankall <- function(outcome, num = "best") {
## Read outcome data
measures <- read.csv("./data/outcome-of-care-measures.csv", colClasses="character")
# Create a vector with valid diseases
valid_outcomes <- c("heart attack", "heart failure", "pneumonia")
state_target <- sort(unique(measures$State))
state_len <- length(state_target)
hosp <- rep("", length(state_target))
## Check that state and outcome are valid
valid_outcome <- c("heart attack", "heart failure", "pneumonia")
if (!outcome %in% valid_outcome){
stop("Invalid outcome")
} else {
for (i in 1:state_len){
# serach for each state
state_sub <- measures[measures[,7]==state_target[i], ]
if(outcome == "heart attack") {
hosp[i] <- hosp_finder(state_sub, 11, num)
} else if (outcome == "heart failure") {
hosp[i] <- hosp_finder(state_sub, 17, num)
} else {
hosp[i] <- hosp_finder(state_sub, 23, num)
}
}
}
#create data frame with the hospital names and the abrevisted state name
data_final <- data.frame(hospital=hosp, state=state_target)
return(data_final)
}
|
08bbe9670b5ddb38d99e65badec4c79fa0fd72df
|
9818307f3706a47345e0dec83675adcc681d736c
|
/Rscript/nls.R
|
fdc9441a8cbdaa29dd36a29ac730f9b741aa3d10
|
[
"MIT"
] |
permissive
|
msk940609/rmsp
|
b63b854a12a057e0a0aa93b65d97a29032757676
|
19bd2a16d7b2bbb2b5ddabeece1c84a28b7ec298
|
refs/heads/main
| 2023-07-14T12:31:29.459897
| 2021-08-30T16:15:44
| 2021-08-30T16:15:44
| 396,658,333
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 5,344
|
r
|
nls.R
|
library(devtools)
library(tidyverse)
library(knitr)
library(shiny)
library(data.table)
library(ggplot2)
sp1=fread("Datafile/ex_spectrum1.csv")
sp2=fread("Datafile/ex_spectrum2.csv")
sp1
ggplot()+
geom_point(data=sp1, aes(`Raman shift`, Intensity), col="red")
#geom_line(data=sp1, aes(`Raman shift`, Intensity), col="red")+
#geom_point(data=sp2, aes(`Raman shift`, Intensity), col="blue")
#geom_line(data=sp1, aes(`Raman shift`, Intensity), col="red")
library(broom)
library(plotly)
##final=================
source("myfunc.R") # for the Pruby() function
Lor <- function(x, x0=0, FWHM=1){
2/(pi*FWHM)/( 1 + ((x-x0)/(FWHM/2))^2 )
}
flist <- list.files(path="Data", pattern = "rubis")
# Load all data files and do the fits in a single pipe flow
data <- tibble(name = flist) %>%
mutate(data = map(name,
~read_table2(file = file.path("Data", .),
col_names = c("w", "Int"))),
fit = map(data,
~ nls(data = .,
Int ~ y0 + A1*Lor(w,x1,FWHM1) + A2*Lor(w,x2,FWHM2),
start=list(y0 = 0.01,
x1 = .$w[which.max(.$Int)] - 2,
x2 = .$w[which.max(.$Int)] - 30,
FWHM1 = 10, FWHM2 = 10,
A1 = max(.$Int)*10,
A2 = max(.$Int)*10))),
tidied = map(fit, tidy),
augmented = map(fit, augment),
P = map_dbl(tidied, ~round(Pruby(.$estimate[.$term=='x1']),2))
) %>%
separate(name, c("sample","run", NA), convert=TRUE) %>%
relocate(P, .after = run)
data
###final end=================
### prac https://lmi.cnrs.fr/r/fitting.html#linear-fitting-with-lm=====
x <- seq(-5,7,.1)
y <- dnorm(x, sd = .5) + dnorm(x, mean=2, sd = 1) + runif(length(x))/10 - 0.05
df <- tibble(x=x, y=y)
ggplot(data=df, aes(x,y))+
geom_point()+
ggtitle("Some fake data we want to fit with 2 Gaussians")
myfunc <- function(x, y0, x0, A, B) {
y0 + dnorm(x, sd=A) + dnorm(x, mean=x0, sd=B)
}
##y0: start point
##x:peak position1, considered peoak located at x=0
##A:gaussian standard distribution (sd) at peak 1
##x0:peak poisition2 at xo
##B:gaussian standard distribution (sd) at peak 2
# Fit the data using a user function
fit_nls <- nls(data=df,
y ~ myfunc(x, y0, x0, A, B),
start=list(y0=0, x0=1.5, A=.2, B=.2) # provide starting point
)
summary(fit_nls)
coef(fit_nls)
plot(x, y, pch=16)
lines(x, predict(fit_nls), col="red", lwd=2)
P1 <- ggplot(data=df, aes(x,y))+
ggtitle("Retrieving the fit performed beforehand")+
geom_point(size=2, alpha=.5) +
geom_line(aes(y=predict(fit_nls)), color="red", size=1)
P2 <- ggplot(data=df, aes(x,y))+
ggtitle("Doing the fit directly withing ggplot2")+
geom_point(size=2, alpha=.5) +
geom_smooth(method = "nls",
method.args = list(formula = y ~ myfunc(x, y0, x0, A, B),
start=list(y0=0, x0=1.5, A=.2, B=.2)
),
data = df,
se = FALSE,
color="red")
P1
P2
fit_constr <- nls(data = df,
y ~ myfunc(x, y0, x0, A, B),
start = list(y0=0, x0=5, A=.2, B=.2),
upper = list(y0=Inf, x0=Inf, A=.4, B=1),
lower = list(y0=-Inf, x0=4, A=-Inf, B=-Inf),
algorithm = "port"
)
##forced linear regression peak located at x=0 & x=5
# Plotting the resulting function in blue
ggplot(data=df, aes(x,y))+
ggtitle("Beware of bad constraints!")+
geom_point(size=2, alpha=.5) +
geom_line(aes(y = predict(fit_constr)), color="royalblue", size=1)
##** nls can't affordable with large sd
install.packages("minpack.lm")
library(minpack.lm)
fit_nlsLM <- nlsLM(data = df,
y ~ myfunc(x, y0, x0, A, B),
start = list(y0=0, x0=5, A=1, B=1)
)
summary(fit_nls)
###prac===
library(broom)
library(tidyverse)
library(ggplot2)
theme_set(theme_bw())
# Create fake data
a <- seq(-10,10,.1)
centers <- c(-2*pi,pi,pi/6)
widths <- runif(3, min=0.5, max=1)
amp <- runif(3, min=2, max=10)
noise <- .3*runif(length(a))-.15
d <- tibble(x=rep(a,3),
y=c(amp[1]*dnorm(a,mean=centers[1],sd=widths[1]) + sample(noise),
amp[2]*dnorm(a,mean=centers[2],sd=widths[2]) + sample(noise),
amp[3]*dnorm(a,mean=centers[3],sd=widths[3]) + sample(noise)),
T=rep(1:3, each=length(a))
)
d
# Plot the data
d %>% ggplot(aes(x=x, y=y, color=factor(T))) +
geom_line()
d_fitted <- d %>%
nest(data = -T) %>%
mutate(fit = purrr::map(data, ~ nls(data = .,
y ~ y0 + A*dnorm(x, mean=x0, sd=FW),
start=list(A = max(.$y),
y0 = .01,
x0 = .$x[which.max(.$y)],
FW = .7)
)),
tidied = purrr::map(fit, tidy),
augmented = purrr::map(fit, augment)
)
d_fitted
d_fitted %>%
unnest(augmented)
d_fitted %>%
unnest(augmented) %>%
ggplot(aes(x=x, color=factor(T)))+
geom_point(aes(y=y), alpha=0.5, size=3) +
geom_line(aes(y=.fitted))
|
97ede1d5f28fad143e73e8062bd3b803923a208b
|
44c1963ad10663bc6511c9cd6c6d977cc8bb7656
|
/Plotting Test Heuristics.R
|
88fcb70ec2d73283e28ab914539b984cd5d4e40a
|
[] |
no_license
|
Tomlowbridge07/Local-Observations-Generic-Attack-Time-Distributions
|
912084c726c8b76cece1c013ffbe70ee464961ce
|
774683a7b7c20315ee11f48bb5836a5c79eb62c7
|
refs/heads/master
| 2020-03-22T03:34:19.854980
| 2018-10-08T13:42:40
| 2018-10-08T13:42:40
| 139,439,361
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 5,791
|
r
|
Plotting Test Heuristics.R
|
source("Testing Heuristics.R")
library(ggplot2)
library(reshape)
library(tikzDevice)
#This R file contains functions which take in the Test Matrix and return values
#Test Matrices come in the order MinError,BestHeuristics,AdjMatrix,AttackTimes,Capacity,Lambda,Costs,Whole Errors
#This function returns the matrix of errors for one scenario
DecodeTestMatrixErrors<-function(ScenarioNumber,TestMatrix)
{
print(TestMatrix)
print(TestMatrix[ScenarioNumber,8])
return(TestMatrix[ScenarioNumber,8][[1]])
}
#This function just returns the vector of errors for a scenario
DecodeTestVectorErrors<-function(ScenarioNumber,TestMatrix)
{
Matrix=DecodeTestMatrixErrors(ScenarioNumber,TestMatrix)
return(Matrix[,4])
}
#This function collects the test matrix error data for all heuristics per row
DecodeErrorDataPerHeuristic<-function(TestMatrix)
{
#First we form a matrix of errors rows=heuristicnumber,col=scenarionumber
NumberOfScenarios=nrow(TestMatrix)
NumberOfHeuristics=length(DecodeTestVectorErrors(1,TestMatrix))
MatrixOfErrors=matrix(0,nrow=NumberOfHeuristics,ncol=NumberOfScenarios)
for(ScenarioNumber in 1:NumberOfScenarios)
{
VectorOfErrors=DecodeTestVectorErrors(ScenarioNumber,TestMatrix)
MatrixOfErrors[,ScenarioNumber]=VectorOfErrors
}
return(MatrixOfErrors)
}
#This function takes a heuristic vector and categorizes it
CategorizeHeuristicErrorData<-function(HeuristicErrors,NumberOfCategories,MaxErrorCategory)
{
#Work out categories
#Note we use the number of categories+1 to allow for the maximum category to be picked
CategoryBoundaries=seq(0,MaxErrorCategory,length.out = NumberOfCategories+1)
MidPointOfCategories=((CategoryBoundaries[2]-CategoryBoundaries[1])/2) + CategoryBoundaries
NumberInEachCategory=vector(length=NumberOfCategories+1)
#Now find out how many are in each category
for(CategoryNum in 1:(NumberOfCategories+1))
{
#Set upper and lower bounds
if(CategoryNum==0)
{
LowerBound=-1
}
else
{
LowerBound=CategoryBoundaries[CategoryNum]
}
if(CategoryNum==(NumberOfCategories+1))
{
UpperBound=Inf
}
else
{
UpperBound=CategoryBoundaries[CategoryNum+1]
}
NumberInEachCategory[CategoryNum]=length(HeuristicErrors[HeuristicErrors>LowerBound & HeuristicErrors<=UpperBound])
}
return(list(CategoryMidPoints=MidPointOfCategories,NumberInEachCategory=NumberInEachCategory))
}
#Plot a single heuristic
PlotHeuristicErrorData<-function(HeuristicErrors,NumberOfCategories,MaxErrorCategory)
{
#We run the categorize to get the data to plot
Categorized=CategorizeHeuristicErrorData(HeuristicErrors,NumberOfCategories,MaxErrorCategory)
XCoordinates=Categorized$CategoryMidPoints
YCoordinates=Categorized$NumberInEachCategory
DataFrame=data.frame(XCoordinates,YCoordinates)
Plot<-ggplot(DataFrame,show.legend='True') + geom_point(aes(x = XCoordinates, y = YCoordinates)) +
geom_line(aes(x = XCoordinates, y = YCoordinates))
print(Plot)
return(Plot)
}
#Plot a group of heuristics
#Note the heuristic erros should be provided in a matrix of rows for each heuristic
PlotMultipleHeuristicErrorData<-function(HeuristicErrors,NumberOfCategories,MaxErrorCategory,HeuristicNames=NULL,SaveTexImage=F,FileName=NULL,Size=c(3,2))
{
#First we look at how many graphs we are going to plot
NumHeuristics=nrow(HeuristicErrors)
XCoordinates=matrix(0,ncol=length(CategorizeHeuristicErrorData(HeuristicErrors[1,],NumberOfCategories,MaxErrorCategory)$NumberInEachCategory),nrow=NumHeuristics)
YCoordinates=matrix(0,ncol=length(CategorizeHeuristicErrorData(HeuristicErrors[1,],NumberOfCategories,MaxErrorCategory)$NumberInEachCategory),nrow=NumHeuristics)
#Now categorize the data
for(i in 1:NumHeuristics)
{
if(i==1)
{
#Initialize
Categorized=CategorizeHeuristicErrorData(HeuristicErrors[i,],NumberOfCategories,MaxErrorCategory)
YCoordinates=matrix(0,ncol=length(Categorized$NumberInEachCategory),nrow=NumHeuristics)
XCoordinates=Categorized$CategoryMidPoints
YCoordinates[i,]=Categorized$NumberInEachCategory
YCoordinates[i,]=YCoordinates[i,]/sum(YCoordinates[i,])
Ydataframecol<-data.frame(YCoordinates[i,])
if(is.null(HeuristicNames))
{
names(Ydataframecol)=paste("YCoordinates",toString(i))
}
else
{
names(Ydataframecol)=toString(HeuristicNames[i])
}
XYCoordinates=data.frame(XCoordinates)
XYCoordinates<-cbind(XYCoordinates,Ydataframecol)
}
else
{
Categorized=CategorizeHeuristicErrorData(HeuristicErrors[i,],NumberOfCategories,MaxErrorCategory)
YCoordinates[i,]=Categorized$NumberInEachCategory
YCoordinates[i,]=YCoordinates[i,]/sum(YCoordinates[i,])
Ydataframecol<-data.frame(YCoordinates[i,])
if(is.null(HeuristicNames))
{
names(Ydataframecol)=paste("YCoordinates",toString(i))
}
else
{
names(Ydataframecol)=toString(HeuristicNames[i])
}
XYCoordinates<-cbind(XYCoordinates,Ydataframecol)
}
print(XYCoordinates)
}
#Now we plot the data
DataFrame<-XYCoordinates
print(DataFrame)
MeltedDataFrame<-melt(DataFrame,id="XCoordinates")
print(MeltedDataFrame)
if(SaveTexImage)
{
tikz(file=paste("/local/pmxtol/Dropbox/",toString(FileName),".tex",sep=""),width=Size[1],height=Size[2])
}
Plot<-ggplot(MeltedDataFrame,aes(x=XCoordinates,y=value,color=variable),show.legend='True') + #geom_point()+
geom_line() +xlab("Percentage Error")+ylab("Frequency Density") + labs(color="Heuristic Type")
if(SaveTexImage)
{
print(Plot)
dev.off()
}
print(Plot)
return(Plot)
}
|
0ca158ba017eff5a7426d3a6600d5a3bd85a8203
|
34de2b3ef4a2478fc6a03ea3b5990dd267d20d2d
|
/R/rprograms/file/fileread/fileread1/fileRead.r
|
d05f1383dba73a2fc193173dd6dbf64e149a6b16
|
[
"MIT"
] |
permissive
|
bhishanpdl/Programming
|
d4310f86e1d9ac35483191526710caa25b5f138e
|
9654c253c598405a22cc96dfa1497406c0bd0990
|
refs/heads/master
| 2020-03-26T06:19:01.588451
| 2019-08-21T18:09:59
| 2019-08-21T18:09:59
| 69,140,073
| 0
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 366
|
r
|
fileRead.r
|
#!/usr/bin/env Rscript
# Author : Bhishan Poudel
# Date : Feb 11, 2016
# Program :
# ref: http://www.r-tutor.com/r-introduction/data-frame/data-import
# Setting working directory
this.dir <- dirname(parent.frame(2)$ofile)
setwd(this.dir)
# reading csv file
cat("\n")
fileReadcsv <- "data.csv"
mydata = read.csv(fileReadcsv) # read csv file
print(mydata)
|
9cf46c62edc47a0b3647b16cf8d246f009736bdc
|
ba43d630f29f969fdeb0794019bbdf05fa0e9890
|
/R/gumbel_r.R
|
c66091e0f07e0e3b2e7a6f8e3257f9d69bf2abaf
|
[] |
no_license
|
LuchTiarna/PsyFuns
|
8faaa174c88ee64e631c716dec19c73b298352ac
|
35b5104e82a0293fb214bfee69be336904c13499
|
refs/heads/master
| 2020-06-14T11:17:39.769489
| 2019-07-07T17:04:48
| 2019-07-07T17:04:48
| 194,991,237
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 862
|
r
|
gumbel_r.R
|
#'@name gumbel_r
#'@rdname gumbel_r
#'
#'@title Gumbel_r
#'@description
#'Right gumbel function is a sigmoid type function.
#'\cr
#'It's CDF formula is: exp(- exp(-x))
#'\cr
#'It's PDF formula is: y = exp(- exp(-x) - x)
#'
#'@param x Vector of x parametres
#'@return Vector of result vaues
#'@rdname gumbel_r
gumbel_r <- function(x) {
UseMethod("gumbel_r")
}
#'@rdname gumbel_r
gumbel_r.orig <- function(x) {
UseMethod("gumbel_r.orig")
}
#'@rdname gumbel_r
gumbel_r.inverse <- function(x) {
UseMethod("gumbel_r.inverse")
}
#'@rdname gumbel_r
gumbel_r.orig.cdf <- function(x) {
return(exp(- exp(-x)))
}
#'@rdname gumbel_r
gumbel_r.orig.pdf <- function(x) {
return(exp(- exp(-x) - x))
}
#'@rdname gumbel_r
gumbel_r.inverse.cdf <- function(x) {
return(-log(-log(x)))
}
#'@rdname gumbel_r
gumbel_r.inverse.pdf <- function(x) {
return(-1/(x*log(x)))
}
|
bd5e60029bd8c5da8ed2b5e9f76398c67397417b
|
24bc03ae395f42bd16386d7c0c6dfafb2c360487
|
/R/ji.ds.class.R
|
fae16affd7e8f55711894d532e06f2c4164ac075
|
[] |
no_license
|
datashield/ji.dev.cl
|
25307208dcbfb5213d3ca27aaaa7e3866f94a15d
|
93c378cb4191621897dc509338ca99dd73249e66
|
refs/heads/master
| 2020-05-04T04:25:33.072519
| 2013-12-02T09:44:49
| 2013-12-02T09:44:49
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,361
|
r
|
ji.ds.class.R
|
#'
#' @title Retrieves the class of an object
#' @description this function is similar to R function \code{class}
#' @param datasources a list of opal object(s) obtained after login in to opal servers;
#' these objects hold also the data assign to R, as \code{dataframe}, from opal datasources.
#' @param x an R object
#' @return class of x
#' @author Gaye, A. (amadou.gaye@bristol.ac.uk) and Isaeva, J. (julia.isaeva@fhi.no)
#' @export
#' @examples {
#'
#' # load that contains the login details
#' data(logindata)
#'
#' # login
#' opals <- datashield.login(logins=logindata,assign=TRUE)
#'
#' # Example 1: Get the class of the whole dataset
#' ji.ds.class(datasources=opals, x=quote(D))
#'
#' # Example 2: Get the class of the variable PM_BMI_CONTINUOUS
#' ji.ds.class(datasources=opals, x=quote(D$LAB_TSC))
#' }
#'
ji.ds.class = function(datasources=NULL, x=NULL) {
if(is.null(datasources)){
message("\n\n ALERT!\n")
message(" No valid opal object(s) provided.\n")
message(" Make sure you are logged in to valid opal server(s).\n")
stop(" End of process!\n\n", call.=FALSE)
}
if(is.null(x)){
message("\n\n ALERT!\n")
message(" Please provide a valid object\n")
stop(" End of process!\n\n", call.=FALSE)
}
cally <- call('class', x )
classes <- datashield.aggregate(datasources, cally)
return(classes)
}
|
28bfde191590cf8c2e88e72f6c93641223f16639
|
82a04ac219608d776f5fc6f89325e1d99fe6c04e
|
/5.7 Exercise.R
|
c98d2f575416bb0b830f69bcf8f6436e4e084f2c
|
[
"MIT"
] |
permissive
|
PacktPublishing/Programming-for-Statistics-and-Data-Science
|
ba3ee85580430769b9a81184505a71e50527f162
|
08cb273ca132658a92f3ccd3e66349d495174d97
|
refs/heads/master
| 2023-02-09T01:42:05.078048
| 2023-01-30T09:34:51
| 2023-01-30T09:34:51
| 185,159,458
| 11
| 16
| null | null | null | null |
UTF-8
|
R
| false
| false
| 680
|
r
|
5.7 Exercise.R
|
# Create a 5x5 matrix with the rnorm() function, and a 5x5 matrix with runif(). Create each in a single line of code (Hint: nest the operations)
# For the two matrices, get the following information; for the first four, save the new values as columns in their corresponding matrixes:
#Column averages
#Row averages
#Column sums
#Row sums
#Minimum and maximum value in the matrix
#Minimum and maximum value for the 3rd column in each matrix
#The means and standard deviations for each matrix (compare the two values; if interested in the mathematics side of things, recreate the matrices a couple of times, and compare the results; can you explain what is happening?)
|
05441f3c6de5f6fe2a19592c2c2600dc1fb4812d
|
183caf378df099da122f65ea9b75002b1e12b774
|
/projFocus/ceRNA/DESeq.r
|
17184ddef5401dcd5ede8962c969ffd2ffa1bab4
|
[] |
no_license
|
cwt1/scripts-1
|
f58e476ddb2c83e0480856a95a95a644ad3c001c
|
061d6592aa6ab11c93363fcb40305a57db05e3f2
|
refs/heads/master
| 2021-05-28T01:31:30.896133
| 2014-08-25T19:02:37
| 2014-08-25T19:02:37
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,496
|
r
|
DESeq.r
|
#!/usr/bin/Rscript
#J.HE
#input:
#output:
#TODO:
# args <- commandArgs(TRUE)
# if (is.null(args)){
# print("Please provide parameters")
# exit
# }else{
# print(args)
# }
# library(EBSeq)
library(DESeq)
rootd <- "/ifs/scratch/c2b2/ac_lab/jh3283/projFocus/ceRNA/result/exp"
setwd(rootd)
# load raw expression matrix and prepare a matrix
# infile <- args[1]
infile <- "/ifs/scratch/c2b2/ac_lab/jh3283/projFocus/ceRNA/data/rnaSeq2/brca_rnaSeq2_rsem_raw_1.mat"
designfile <- "/ifs/scratch/c2b2/ac_lab/jh3283/projFocus/ceRNA/data/rnaSeq2/input_EBseqR_ColDesign.txt"
datacolDesign <- read.table(designfile, sep= "\t")
#
table(datacolDesign[,4])
replace = function(df,old,new){
dfnew <- apply(df,c(1,2),function(x){
pattern <- paste("^",old,"$",sep="")
gsub(pattern,new,gsub(" ","",x),perl=T)
})
return(dfnew)
}
# take tumor and normal sample
datacolDesign <- datacolDesign[which(datacolDesign[,4]=="1" | datacolDesign[,4]=="11"),]
datacolDesign <- replace(datacolDesign,1,"tumor")
datacolDesign <- replace(datacolDesign,11,"normal")
datacolDesign[,1] <- sapply(datacolDesign[,1],function(x){gsub("-",".",x)})
dataMat <- read.table(infile,sep="\t",header=T)
row.names(dataMat) <- dataMat[,1]
dataMat <- dataMat[,-1]
designCol <- datacolDesign[,4]
names(designCol) <- datacolDesign[,1]
designCond <- na.omit(designCol[colnames(dataMat)])
dataMat <- dataMat[,names(designCond)]
# prepare the Design
libDesign <- rep("paired-end",ncol(dataMat))
dataMatDesign = data.frame(row.names = colnames(dataMat),
condition = designCond,
libType = libDesign )
condition <- factor( designCond )
# normalization DESeq
dataMat <- apply(dataMat,c(1,2),round)
cds = newCountDataSet( dataMat, condition )
cds = estimateSizeFactors( cds )
sizeFactors( cds )
topleft( counts( cds, normalized=TRUE ) )
cds = estimateDispersions( cds )
str( fitInfo(cds) )
save(cds, file=paste(rootd,"/DESeq_run1_cds.rda",sep=""))
head( fData(cds) )
res = nbinomTest( cds, "tumor", "normal" )
save(res, file=paste(rootd,"/DESeq_run1_res.rda",sep=""))
head(res)
pdf(paste(rootd,"/DESeq_run1.pdf",sep=""))
plotDispEsts( cds )
plotMA(res)
hist(res$pval, breaks=100, col="skyblue", border="slateblue", main="")
dev.off()
# resSig = res[ res$padj < 0.1, ]
# head( resSig[ order(resSig$pval), ] )
# head( resSig[ order( resSig$foldChange, -resSig$baseMean ), ] )
write.csv( res, file=paste(rootd,"/DESeq_run1_DEG_result.csv",sep=""))
# source("http://bioconductor.org/biocLite.R")
# biocLite("genefilter")
|
4fac5ef395e8be29bde39352b8668c121a75fa38
|
eef7754e99418c7c040e2bc18e95b3263d542426
|
/man/sca.Rd
|
c950375b467a04a6a091924d5b23b84d1a009cb6
|
[] |
no_license
|
cran/cabootcrs
|
e63b5ce9d86b5f2b973c19b8a250095f81f81c55
|
a5e3efc3f1e555b547a9b0ffb266052bee11a770
|
refs/heads/master
| 2022-03-06T06:23:19.926820
| 2022-03-02T22:00:02
| 2022-03-02T22:00:02
| 17,694,910
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 1,170
|
rd
|
sca.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sca.R
\name{sca}
\alias{sca}
\title{Performs standard Correspondence Analysis calculations}
\usage{
sca(X, catype = "sca", mcatype = NULL, p = 2, needtrans = FALSE)
}
\arguments{
\item{X}{A data matrix with rows >= cols}
\item{catype}{Can be "sca" for simple CA or "mca" for multiple CA}
\item{mcatype}{If catype="mca" then this can be "Burt", "Indicator"
or "doubled" depending on the analysis required.\cr
This affects the number of meaningful singular values, as does p below}
\item{p}{Number of variables, only needed if catype="mca"}
\item{needtrans}{TRUE if rows < columns so need to transpose in the routine}
}
\value{
An object of class \code{\linkS4class{cabasicresults}}
}
\description{
\code{sca} returns all the basic results from a CA of a matrix with rows >= cols,
in an object of class \code{\linkS4class{cabasicresults}}
}
\details{
This is only intended for internal use by the \code{\link{cabootcrs}} function.
}
\examples{
results <- sca(as.matrix(DreamData))
}
\seealso{
\code{\link{cabootcrs-package}}, \code{\link{cabootcrs}}, \code{\linkS4class{cabasicresults}}
}
|
6667026891e2c8305c1891bd94c8e4063aa8ebfe
|
8f6565e5310613a58bffb390591dbada2d4d00f7
|
/scripts/Second_run.R
|
a41fae3b7d257983400c1c24777fc7161a10e63a
|
[] |
no_license
|
AugustT/whats_flying_tonight
|
2aa0ee6e85ea87449b2347b877bbe2640437c3d7
|
afda2a00ca4df9a4152725812ec65657c1aeac68
|
refs/heads/master
| 2021-01-18T05:11:10.861463
| 2018-11-16T14:38:43
| 2018-11-16T14:38:43
| 45,030,483
| 0
| 1
| null | 2015-10-27T09:33:47
| 2015-10-27T09:33:47
| null |
UTF-8
|
R
| false
| false
| 8,317
|
r
|
Second_run.R
|
## Gather new data, create phenologies and hectad count data ##
rm(list = ls())
# This file should be re-runable. You shouldn't have to change anything in this
# file, just update the sql queries in the NMRS folder so that they include the
# year range of interest. Note the info in the app will also need to be
# updated
##################
# Get Count Data #
##################
# Setup DB connection
library(RODBC)
if(exists("channel") == FALSE) channel = odbcConnect("BRC", uid = "tomaug", pwd = "DoHitDedKu")
# Run an sql query to gather the hectad data files
get_all_query <- paste(readLines('data/NMRS/NMRS summary sp agg 2017.sql'), collapse = ' ')
all_data <- sqlQuery(channel = channel, query = get_all_query)
# save the data
write.table(all_data,
file = 'data/NMRS/NMRS_summary_data.csv',
row.names = FALSE,
sep = ',')
# Write a species summary for MB
species_table <- unique(all_data[, c('CONCEPT', 'NAME', 'RANK')])
# Add to this the species other data
load('data/UKMoths/speciesData_newNames.rdata')
speciesDataRaw <- speciesDataRaw[, c('CONCEPT',
'new_englishname',
'NAME_ENGLISH',
'BINOMIAL')]
names(speciesDataRaw) <- c('CONCEPT', 'english_name', 'old_englishname', 'old_binomial')
species_table_plus <- merge(x = species_table,
y = speciesDataRaw,
by.x = 'CONCEPT',
by.y = 'CONCEPT',
all = T)
write.csv(x = species_table,
file = 'data/NMRS/species_extracted.csv',
row.names = FALSE)
write.csv(x = species_table_plus,
file = 'data/NMRS/species_extracted_translation.csv',
row.names = FALSE)
rm(list = c('all_data'))
#####################
# Cut up count data #
#####################
recData <- read.table('data/NMRS/NMRS_summary_data.csv',
stringsAsFactors = FALSE,
header = TRUE,
sep = ',')
n <- length(sort(unique(recData$SQ_10)))
# parallelise
library(snowfall)
sfInit(cpus = 3, parallel = TRUE)
sfExport('n', 'recData')
# This dataset needs to be cut up into small chunks for loading on the go
sfClusterApplyLB(sort(unique(recData$SQ_10)), fun = function(hec){
cat(grep(paste('^',hec, sep = ''), sort(unique(recData$SQ_10))), 'of', n, hec, '\n')
temp_dat <- recData[recData$SQ_10 == hec, c('CONCEPT','DAYNO','N_RECS')]
save(temp_dat,
file = paste('data/hectad_counts/',
hec,
'.rdata', sep = ''))
rm(list = c('temp_dat'))
})
sfStop()
##################################################
# Create phenology plots and add missing species #
##################################################
# Get the data
rm(list = ls())
library(RODBC)
if(exists("channel") == FALSE) channel = odbcConnect("BRC", uid = "tomaug", pwd = "DoHitDedKu")
get_weeks_query <- paste(readLines('data/NMRS/NMRS sp week counts 2017.sql'), collapse = ' ')
all_weeks_data <- sqlQuery(channel = channel, query = get_weeks_query)
# save the data
write.table(all_weeks_data,
file = 'data/NMRS/NMRS_weeks_counts.csv',
row.names = FALSE,
sep = ',')
rm(list = c('all_weeks_data'))
speciesData <- read.csv('data/NMRS/NMRS_weeks_counts.csv',
stringsAsFactors = FALSE)
library(ggplot2)
library(RColorBrewer)
library(grid)
load(file = 'data/UKMoths/speciesData_newNames2017.rdata')
# reset phenology columns
speciesDataRaw$phenosmall <- NA
speciesDataRaw$phenobig <- NA
for(n in seq_along(unique(speciesData$CONCEPT))){
i <- unique(speciesData$CONCEPT)[n]
cat(i, '... ')
if(!i %in% speciesDataRaw$new_concept){
new_name_data <- sqlQuery(channel = channel,
query = paste0(
"select * from brc.taxa_taxon_register where concept = '",
i,
"' AND valid = 'V'"))
temp <- speciesDataRaw[1,]
temp[,] <- NA
temp[,c('NAME', 'BINOMIAL', 'new_binomial')] <- as.character(new_name_data$BINOMIAL)
temp[,c('NAME_ENGLISH', 'new_englishname')] <- as.character(new_name_data$NAME_ENGLISH)
temp[,c('CONCEPT','new_concept')] <- as.character(i)
temp$URL <- 'http://ukmoths.org.uk'
temp$changed <- 'new'
temp$VALID <- 'V'
cat(' * NEW * ')
speciesDataRaw <- rbind(speciesDataRaw, temp)
}
sp_name <- speciesDataRaw[speciesDataRaw$new_concept == i & !is.na(speciesDataRaw$new_concept), ]
latinName <- as.character(sp_name$new_binomial)
commonName <- as.character(sp_name$new_englishname)
valid <- as.character(sp_name$VALID)
cat(valid, latinName, commonName)
#n_recs is number of distinct site date records
tempDat <- speciesData[speciesData$CONCEPT == i, ]
if(nrow(tempDat) != 53){
tempDat <- rbind(tempDat[,c('CONCEPT', 'WEEKNO', 'N_RECS')],
data.frame(CONCEPT = i,
WEEKNO = c(1:53)[!c(1:53) %in% tempDat$WEEKNO],
N_RECS = 0))
}
# Create a custom colour pallette for the calendar plots
myPalette <- colorRampPalette(brewer.pal(9, 'YlOrBr'))
small_filename <- paste('phenology/',
valid,
gsub(' ', '_', gsub('/', '_', latinName)),
'.png', sep = '')
speciesDataRaw[speciesDataRaw$new_concept == i & !is.na(speciesDataRaw$new_concept),
'phenosmall'] <- small_filename
png(filename = small_filename,
width = 235, height = 60, bg = "transparent")
p <- ggplot(tempDat, aes(x = WEEKNO, y = CONCEPT, fill = N_RECS)) +
geom_tile() +
scale_fill_gradientn(colours = myPalette(50)) +
scale_x_continuous(expand = c(0, 0),
breaks = seq(52/12, 52, 52/12) - (52/12)/2,
labels = c('J','F','M','A','M','J','J','A','S','O','N','D')) +
scale_y_discrete(expand = c(0, 0)) +
# geom_vline(xintercept = c(thisWeek - 0.5, thisWeek + 0.5)) +
ylab('') +
xlab('') +
# geom_hline(yintercept = c(1.5, 2.5), colour = 'white') +
theme_bw() +
theme(text = element_text(size = 12),
legend.position = "none",
plot.background = element_rect(fill = "transparent", colour = NA),
plot.margin = unit(c(0.1,0.1,-0.3,-0.8), "cm"),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
axis.ticks.x = element_blank())
plot(p)
dev.off()
big_filename <- paste('phenology/',
valid,
gsub(' ', '_', gsub('/', '_', latinName)),
'_big.png', sep = '')
speciesDataRaw[speciesDataRaw$new_concept == i & !is.na(speciesDataRaw$new_concept),
'phenobig'] <- big_filename
png(filename = big_filename,
width = 600, height = 120, bg = "transparent")
p <- ggplot(tempDat, aes(x = WEEKNO, y = CONCEPT, fill = N_RECS)) +
geom_tile() +
scale_fill_gradientn(colours = myPalette(50)) +
scale_x_continuous(expand = c(0, 0),
breaks = seq(52/12, 52, 52/12) - (52/12)/2,
labels = c('Jan','Feb','Mar','Apr','May','Jun',
'Jul','Aug','Sep','Oct','Nov','Dec')) +
scale_y_discrete(expand = c(0, 0)) +
# geom_vline(xintercept = c(thisWeek - 0.5, thisWeek + 0.5)) +
# geom_hline(yintercept = c(1.5, 2.5), colour = 'white') +
theme_bw() +
ylab('') +
xlab('') +
theme(text = element_text(size = 12),
legend.position = "none",
axis.text.y = element_blank(),
plot.background = element_rect(fill = "transparent", colour = NA),
axis.ticks.x = element_blank(),
axis.ticks.y = element_blank())
plot(p)
dev.off()
cat(' ... done', '\n')
}
# Save out the new species names table
# unique(speciesData$CONCEPT[!speciesData$CONCEPT %in% speciesDataRaw$new_concept])
# x <- speciesDataRaw[!speciesDataRaw$new_concept %in% unique(speciesData$CONCEPT),]
save(speciesDataRaw, file = 'data/UKMoths/speciesData_newNames2017.rdata')
|
f1bf450ddfb4231064e203d06c44ac404dab4cfb
|
dd4f8da164a0c50e503cad009e2128bf4eb2a7ed
|
/project1.R
|
e706e9f2e2554790486accf057e8ee83cb0581c0
|
[] |
no_license
|
ootmon1/linear-regression
|
5f7574855a3341b6db231f7e21454004931ce843
|
277fc9df1fbbeac72c2bcd174ec1ad9226afe628
|
refs/heads/master
| 2020-11-25T23:53:36.884438
| 2019-12-18T18:13:51
| 2019-12-18T18:13:51
| 228,895,968
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,396
|
r
|
project1.R
|
# Program: Project 1
# Author: John Dixon
# Date: 17 Feb. 2019
# Description: Linear regression using the Boston data set.
# get Boston data frame from MASS library
library(MASS)
df <- Boston[]
# explore the Boston data set
str(df) # show structure of df
summary(df) # show stats for each column
sapply(df, function(x) sum(is.na(x) == TRUE)) # count NAs in each column
head(df, n = 10) # show first 10 rows
# visualize the data set
pairs(df) # show correlation between columns
# plot rm and medv on scatterplot
plot(df$rm, df$medv, main = "Median Home Value vs Avg. Number of Rooms",
xlab = "Avg. Number of Rooms", ylab = "Median Home Value (in $1000s)")
# separate data into train/test
train <- df[c(1:400),]
test <- df[c(401:506),]
# train linear regression model and record start/end time
start_time <- proc.time()
lm1 <- lm(medv ~ rm, data = train)
end_time <- proc.time()
# calculate elapsed time for linear regression algorithm
lr_time <- (end_time[3] - start_time[3])
# print model coefficients
summary(lm1)
# predict test values
pred <- predict(lm1, newdata = test)
# evaluate test predictions
corr <- cor(pred, test$medv)
mse <- mean((pred - test$medv)^2)
rmse <- sqrt(mse)
# print results
print(paste("Elapsed time for linear regression:", lr_time, "seconds"))
print(paste("Correlation:", corr))
print(paste("MSE:", mse))
print(paste("RMSE:", rmse))
|
5ddadcc384f9e9acfe20955ab8277057f73744fc
|
9386a55c2b8b01d0f0f2190221c9ea27521c3c71
|
/wind.event.predictor/clustertest.R
|
1a60dfbbcc302a9bfaf77ae1692e445be9dde78e
|
[] |
no_license
|
lisaoshita/apcd.r
|
0fd50db5a7fbd030eb3e3cc4052bc3ce8bc476e3
|
abae94cf88516e25d66958d2f5d404825d4d9002
|
refs/heads/master
| 2021-07-10T12:30:46.424243
| 2018-12-06T01:19:37
| 2018-12-06T01:19:37
| 129,440,838
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,331
|
r
|
clustertest.R
|
library(dplyr)
library(sp)
# functions to convert polar coordinates to cartesian:
make.x <- function(ws, wd){
ws*cos((90-wd)*pi/180)
}
make.y <- function(ws, wd){
ws*sin((90-wd)*pi/180)
}
s1.training <- training %>%
select(date, year, wd.s1, ws.s1, pm10.cdf)
colnames(s1.training) <- c("date", "year", "wd", "ws", "pm10")
s1.clust <- polarCluster(s1.training,
pollutant = "pm10",
x = "ws",
wd = "wd",
n.clusters = 2)
# get cluster of high PM and create cartersian coordinates:
s1.clust$data %>%
filter(cluster == 2) %>%
mutate(x = make.x(ws, wd)) %>%
mutate(y = make.y(ws, wd)) -> s1.range
# check (looks reasoable):
summary(s1.range)
plot(s1.range$x, s1.range$y, #compare with print(s1.clust)
ylim = c(-15, 15), xlim = c(-15, 15),
asp = 1, pch = 16, cex = 0.5)
# get convex hull
chull.index <- chull(s1.range$x, s1.range$y)
chull.index <- c(chull.index, chull.index[1])
s1.range.chull <- s1.range[chull.index, c("x", "y")]
# check --> looks good!
lines(s1.range.chull$x, s1.range.chull$y,
col = "red") # add to current plot
# function to see if a point falls in the s1 range:
wind.in.range <- function(ws, wd, range){
# assumes range is a two column df with "x" and "y"
# assumes ws and wd in usual format,
# so must convert to cartesian coords.
# define these functions again, in case they are not
# in environment:
make.x <- function(ws, wd){
ws*cos((90-wd)*pi/180)
}
make.y <- function(ws, wd){
ws*sin((90-wd)*pi/180)
}
xs <- make.x(ws, wd)
ys <- make.y(ws, wd)
# test if in range
res <- point.in.polygon(xs, ys, range$x, range$y)
# return 0 if outside, 1 if inside or on edge, NA if ws or wd is missing
res <- ifelse(res == 0, 0, 1) # see ?point.in.polygon
res[is.na(ws) | is.na(wd)] <- NA # preserve NA's
return(res)
}
## test function: does it 'predict' if ws/wd pair is in high PM10 cluster?
preds <- wind.in.range(s1.clust$data$ws, s1.clust$data$wd, s1.range.chull)
table(s1.clust$data$cluster, preds)
# hmmm... 52 observation from cluster 1 are predicted to be in cluster 2
s1.clust$data[preds == 1 & s1.clust$data$cluster == 1, ]
# these all appear to be on the edge, so OK if predicted to be in cluster 2
|
faf80660eff4c6365bc12089846a7d93e8c85162
|
48dbeff0b9b2fd9336ee33e0a33771bdd4ca5ea2
|
/Script.R
|
25503aa2644fa96079bde735e4cd85f5474915d9
|
[] |
no_license
|
julianarturoc/Reproducible-Research
|
1714cd444015de76d0b7b5d4a6de8ce8e7f5b886
|
7c57f9c99d96522b1b0ed6295c8f5647ab630c77
|
refs/heads/master
| 2022-08-01T08:17:51.387729
| 2020-05-31T04:29:47
| 2020-05-31T04:29:47
| 268,208,223
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,476
|
r
|
Script.R
|
## ------------------------------------------------------------------------
# 0. loading libraries
library(ggplot2)
library(dplyr)
library(lubridate)
## ------------------------------------------------------------------------
# 1. Reading Data
unzip(zipfile="activity.zip")
data<-read.csv(file="activity.csv", header=TRUE, sep=",")
head(data)
# 2. Graph steps per day
t_steps <- tapply(data$steps, data$date, FUN=sum, na.rm=TRUE)
qplot(t_steps, binwidth=1000, xlab="Total number of steps taken per day")
mean(t_steps, na.rm=TRUE)
median(t_steps, na.rm=TRUE)
## ------------------------------------------------------------------------
# 3. Graph average steps taken
averages <- aggregate(x=list(steps=data$steps), by=list(interval=data$interval),
FUN=mean, na.rm=TRUE)
ggplot(data=averages, aes(x=interval, y=steps)) +
geom_line() +
xlab("interval [every 5 minutes]") +
ylab("Number of steps taken [average]")
## ------------------------------------------------------------------------
# Calculating metrics for filling values
averages[which.max(averages$steps),]
missing <- is.na(data$steps)
table(missing)
## ------------------------------------------------------------------------
# Replace each missing value with the mean calculated above
fill <- function(steps, interval) {
filled <- NA
if (!is.na(steps))
filled <- c(steps)
else
filled <- (averages[averages$interval==interval, "steps"])
return(filled)
}
completed.data <- data
completed.data$steps <- mapply(fill, completed.data$steps, completed.data$interval)
t_steps <- tapply(completed.data$steps, completed.data$date, FUN=sum)
qplot(t_steps, binwidth=1000, xlab="total number of steps taken each day")
mean(t_steps)
median(t_steps)
## ------------------------------------------------------------------------
# checking the day
completed.data$date<-as.Date(completed.data$date)
completed.data<-mutate(completed.data, weekdayType=0)
completed.data$weekdayType <- ifelse(weekdays(completed.data$date) %in% c("sabado", "domingo"),
"weekend", "weekday")
## ------------------------------------------------------------------------
day <- weekdays(completed.data$date)
averages <- aggregate(steps ~ interval + day, data=completed.data, mean)
ggplot(averages, aes(interval, steps)) + geom_line() + facet_grid(day ~ .) +
xlab("5-minute interval") + ylab("steps (Q)")
|
0059365c6c6071e96f73d8a0af79d1973540ff4e
|
f681257722365f95a24cfbe26310345b50a4a4f6
|
/R/lcvp_group_search.R
|
f08b7748ccbadad0ee3bc1af57e0428720e45d1c
|
[
"MIT"
] |
permissive
|
idiv-biodiversity/lcvplants
|
ac3008d1a0ec7a19211e670905a7056442082724
|
4521372afff25f0f8e7f43a2f825277dc6a1c5d4
|
refs/heads/master
| 2023-05-23T22:01:33.748106
| 2022-11-07T14:48:18
| 2022-11-07T14:48:18
| 215,979,088
| 14
| 4
|
NOASSERTION
| 2021-09-14T16:56:14
| 2019-10-18T08:37:16
|
R
|
UTF-8
|
R
| false
| false
| 8,323
|
r
|
lcvp_group_search.R
|
#' Search plants by taxa or author names in the Leipzig Catalogue of Plants (LCVP)
#'
#' Search all plant taxa names listed in the "Leipzig
#' Catalogue of Vascular Plants" (LCVP) by order, family,
#' genus or author.
#'
#' @param group_names A character vector specifying the taxa or author names.
#' This includes names of orders, families, genus or authors. Only valid
#' characters are allowed (see \code{\link[base:validEnc]{validEnc}}).
#'
#' @param search_by A character indicating whether to search by "Order",
#' "Family", "Genus" or "Author".
#'
#'@param max_distance It represents the maximum string distance allowed for a
#' match when comparing the submitted name with the closest name matches in the
#' LCVP. The distance used is a generalized Levenshtein distance that indicates
#' the total number of insertions, deletions, and substitutions allowed to
#' match the two names. It can be expressed as an integer or as the fraction of
#' the binomial name. For example, a name with length 10, and a max_distance =
#' 0.1, allow only one change (insertion, deletion, or substitution). A
#' max_distance = 2, allows two changes.
#'
#' @param bind_result If TRUE the function will return one data.frame (default).
#' If False, the function will return a list of separate data.frames for
#' each input group.
#'
#' @param status A character vector indicating what taxa status should be
#' included in the results: "accepted", "synonym", "unresolved", "external".
#'
#' The "unresolved" rank means that the status of the plant name could be
#' either valid or synonym, but the information available does not allow
#' a definitive decision. "external" is an extra rank that lists names
#' outside the scope of this publication but useful to keep on this
#' updated list.
#'
#' @details
#'
#' The algorithm will look for all the plant taxa names listed in the "Leipzig
#' Catalogue of Vascular Plants" (LCVP) based on a user-specified list of
#' orders, families, genus, or authors names. If no match is found, it will try
#' to find the closest name given the maximum distance defined in `max_distance`.
#' If more than one name is fuzzy matched, only the first will be returned.
#'
#' @return
#' A data.frame or a list of data.frames (if \code{bind_result = FALSE})
#' with the following columns for all species of the matched groups:
#'
#' \describe{#' \item{global.Id}{The fixed species id of the input taxon in the
#' Leipzig Catalogue of Vascular Plants (LCVP).}
#' \item{Input.Genus}{A
#' character vector. The input genus of the corresponding vascular plant
#' species name listed in LCVP.}
#' \item{Input.Epitheton}{A character vector.
#' The input epitheton of the corresponding vascular plant species name listed
#' in LCVP.}
#' \item{Rank}{A character vector. The taxonomic rank ("species",
#' subspecies: "subsp.", variety: "var.", subvariety: "subvar.", "forma", or
#' subforma: "subf.") of the corresponding vascular plant species name listed
#' in LCVP.}
#' \item{Input.Subspecies.Epitheton}{A character vector. If the
#' indicated rank is below species, the subspecies epitheton input of the
#' corresponding vascular plant species name listed in LCVP. If the rank is
#' "species", the input is "nil".}
#' \item{Input.Authors}{A character vector.
#' The taxonomic authority input of the corresponding vascular plant species
#' name listed in LCVP.}
#' \item{Status}{A character vector. description if a
#' taxon is classified as ‘valid’, ‘synonym’, ‘unresolved’, ‘external’ or
#' ‘blanks’. The ‘unresolved’ rank means that the status of the plant name
#' could be either valid or synonym, but the information available does not
#' allow a definitive decision. ‘External’ in an extra rank which lists names
#' outside the scope of this publication but useful to keep on this updated
#' list. ‘Blanks’ means that the respective name exists in bibliography but it
#' is neither clear where it came from valid, synonym or unresolved. (see the
#' main text Freiberg et al. for more details)}
#' \item{globalId.of.Output.Taxon}{The fixed species id of the output taxon
#' in LCVP.}
#' \item{Output.Taxon}{A character vector. The list of the accepted
#' plant taxa names according to the LCVP.}
#' \item{Family}{A character vector.
#' The corresponding family name of the Input.Taxon, staying empty if the
#' Status is unresolved.}
#' \item{Order}{A character vector. The corresponding
#' order name of the Input.Taxon, staying empty if the Status is unresolved.}
#' \item{Literature}{A character vector. The bibliography used.}
#' \item{Comments}{A character vector. Further taxonomic comments.}}
#'
#' See \code{\link[LCVP:tab_lcvp]{LCVP:tab_lcvp}} for more details.
#'
#' If no match is found for all searched names the function will
#' return NULL and a warning message.
#'
#' @author
#' Bruno Vilela & Alexander Ziska
#'
#' @seealso
#' \code{\link[lcvplants:lcvp_search]{lcvp_search}},
#' \code{\link[lcvplants:lcvp_fuzzy_search]{lcvp_fuzzy_search}}.
#'
#' @references
#' Freiberg, M., Winter, M., Gentile, A. et al. LCVP, The Leipzig
#' catalogue of vascular plants, a new taxonomic reference list for all known
#' vascular plants. Sci Data 7, 416 (2020).
#' https://doi.org/10.1038/s41597-020-00702-z
#'
#' @keywords R-package nomenclature taxonomy vascular plants
#'
#' @examples
#' # Ensure that LCVP package is available before running the example.
#' # If it is not, see the `lcvplants` package vignette for details
#' # on installing the required data package.
#' if (requireNamespace("LCVP", quietly = TRUE)) { # Do not run this
#'
#' # Search by Genus
#' lcvp_group_search(c("AA", "Adansonia"), search_by = "Genus")
#'
#' # Search by Author and keep only accepted names
#' lcvp_group_search("Schltr.", search_by = "Author", status = "accepted")
#'
#' }
#'@export
lcvp_group_search <- function(group_names,
search_by,
max_distance = 0.2,
bind_result = TRUE,
status = c("accepted",
"synonym",
"unresolved",
"external")) {
hasData() # Check if LCVP is installed
# Check names
if (is.factor(group_names)) {
group_names <- as.character(group_names)
}
.names_check(group_names, "group_names")
.search_by_check(search_by)
.check_status(status)
# Fix entry names
group_names <- .names_standardize(group_names)
# Get position in the table based on group
if (search_by == "Genus") {
ref_names <- LCVP::tab_position$Genus
}
if (search_by == "Family") {
ref_names <- names(LCVP::lcvp_family)
}
if (search_by == "Order") {
ref_names <- names(LCVP::lcvp_order)
}
if (search_by == "Author") {
# not ready yet
ref_names <- names(LCVP::lcvp_authors)
}
# Get the position list
pos_group <- .lcvp_group(group_names,
ref_names,
max_distance)
if (all(is.na(pos_group))) {
return(NULL)
} else {
# Get position list based on the group used
if (search_by == "Genus") {
pos_list <- .genus_search_multiple(pos_group)
}
if (search_by == "Family") {
pos_list <- LCVP::lcvp_family[pos_group]
}
if (search_by == "Order") {
pos_list <- LCVP::lcvp_order[pos_group]
}
if (search_by == "Author") {
# not ready yet
pos_list <- LCVP::lcvp_authors[pos_group]
}
# Return the actual data.frames
result <- list()
for (i in seq_along(pos_list)) {
result[[i]] <- LCVP::tab_lcvp[pos_list[[i]],]
rownames(result[[i]]) <- NULL
if (!all(c("accepted", "synonym", "unresolved", "external") %in% status)) {
result[[i]] <-
result[[i]][result[[i]]$Status %in% status, , drop = FALSE]
}
}
# Bind the results or not
if (bind_result) {
result <- do.call(rbind, result)
if (nrow(result) == 0) {
return(NULL)
}
} else {
names(result) <- ref_names[pos_group]
}
return(result)
}
}
|
f0ff6569afe2760c90d3f932ea8b8d26a9cc9601
|
a189183555031f077a0b52ac55176c808b272e9d
|
/GIS_4.R
|
221a9fd653846aa71fac91d9008f358b071963d6
|
[] |
no_license
|
TomoyaOzawa-DA/GIS
|
030d427c13e1009178d3c92f21273fa78811469a
|
07321e584d43859d9d40a647bcfad5d728bbaa8c
|
refs/heads/main
| 2023-01-06T21:49:55.093138
| 2020-10-30T12:22:52
| 2020-10-30T12:22:52
| 308,618,531
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,762
|
r
|
GIS_4.R
|
###########################################
### 【地理情報講座#3】Rでの距離計算入門 ###
###########################################
### 1. 2つの店舗間の距離計算
# geosphereというライブラリ
install.packages("geosphere")
library("geosphere")
# distGeoというコマンドで距離計算ができる。単位はm。（）内で距離を測りたい2組の緯度経度を指定します。
# 他にもコマンドがありますが、distGEOが一番正確みたいです。
# gas_naha[1, c("longitude", "latitude")]は1行目の"longitude", "latitude"という列に含まれている値を示している。
distGeo(gas_naha[1, c("longitude", "latitude")], gas_naha[2, c("longitude", "latitude")])
# Imapというライブラリでも出来るみたいです。
install.packages("Imap")
library(Imap)
# gdistというコマンドで距離計算ができる。単位は指定可能
gdist(lon.1 = gas_naha[1, "longitude"], lon.2 =gas_naha[2, "longitude"] , lat.1 = gas_naha[1, "latitude"], lat.2 = gas_naha[2, "latitude"], units = "m")
# ちなみに、どっちでも値は等しいです。個人的にはgeosphereの方が書きやすいです。Imapは距離の指定ができるのが嬉しいですね。
### 2. 複数の店舗間の距離を一気に計算する ###
## 2.1. 1行目の店舗との距離を各店舗に対して算出
for (i in 1:48){
gas_naha[i,"1" ] <- distGeo(gas_naha[1, c("longitude", "latitude")], gas_naha[i, c("longitude", "latitude")])
}
## 2.2.全店舗に対して同じことをやってみます。これぞ自動化。
# エラーが出たので、店舗名をFactorから文字列にしています。
gas_naha$store <- as.character(gas_naha$store)
# i行目の店舗との距離を各店舗j（1~48行目）に対して測っている。
for (i in 1:48){
for (j in 1:48){
colname <- gas_naha[i, "store"]
gas_naha[j,colname ] <- distGeo(gas_naha[i, c("longitude", "latitude")], gas_naha[j, c("longitude", "latitude")])
}
}
## 2.3. 競合店舗の数を集計する
for (i in 1:48){
for (j in 1:48){
colname <- gas_naha[i, "store"]
gas_naha[j,colname ] <- distGeo(gas_naha[i, c("longitude", "latitude")], gas_naha[j, c("longitude", "latitude")])
gas_naha[j,colname ] <- ifelse(gas_naha[j,colname ] <= 2000, 1, 0)
}
}
for (i in 1:48){
gas_naha[i, "compe"] <- sum(gas_naha[i, 10:57]) -1
}
### 3. 競合の店舗数と価格の関係を分析する
## 3.1. 価格データを結合してデータを整える
gas_compe <- gas_naha[, c(1, 58)]
price <- read.csv("price.csv")
gas_compe <- merge(gas_compe, price, by = 'store')
gas_compe <- na.omit(gas_compe)
## 3.2. 回帰分析で検証
out <- lm(data = gas_compe, Price ~ compe)
summary(out)
|
ea7e51bded805246f312f3aabd80c8d966172de9
|
d5d9f5fb71e07db529cc6c50d50e18577ed6ab20
|
/scripts/cleaning/wm_math.R
|
09b0d0878fabf3b4aea235a2be9be4ab4046babc
|
[] |
no_license
|
laurafdeza/Dissertation
|
c0ab736cf96227ce49d5833b42587d572deea262
|
6cd94b2e32d0863eb342fe4ce1ddc49e12672beb
|
refs/heads/master
| 2022-04-28T16:47:38.986331
| 2022-03-09T16:39:05
| 2022-03-09T16:39:05
| 251,065,189
| 0
| 4
| null | 2021-03-26T00:08:38
| 2020-03-29T15:27:13
|
HTML
|
UTF-8
|
R
| false
| false
| 3,686
|
r
|
wm_math.R
|
# Working memory
# Scores for working memory are not homogeneous—the highest mean score is for the EN, then MA, and then ES
# (My hypothesis is that the lower scores are due to the need to remember gender and concepts rather than just the word)
# So, since anticipation is closely linked to processing speed, which is another component of working memory,
# we are going to use other OSpan measures to ensure that populations are homogeneous.
# Namely, we are going to use processing speed in match while ignoring storage (which is the score we tested first).
# Load packages
library(data.table)
library(tidyverse)
library(lmerTest)
# Load visuospatial WM data and bind them into one dataframe
csv_files <- list.files (path = "./data/corsi",
pattern = "*.csv",
full.names = T)
corsi_speed <- as_tibble (rbindlist (lapply (csv_files, fread)))
# Select correct trials, rename participants' IDs and participant column
corsi_speed <- corsi_speed %>%
filter(., correct == 1) %>%
rename(., participant = subject_id)
corsi_speed$participant <- str_replace(corsi_speed$participant, "ae", "aes")
corsi_speed$participant <- str_replace(corsi_speed$participant, "ie", "ies")
corsi_speed$participant <- str_replace(corsi_speed$participant, "am", "ams")
corsi_speed$participant <- str_replace(corsi_speed$participant, "im", "ims")
corsi_speed$participant <- str_replace(corsi_speed$participant, "mo", "mon")
# Find random effects for each participant
corsi_glm <- lmer(time ~ level + (1 | participant),
data = corsi_speed)
corsi_ranef <- ranef(corsi_glm) %>% as_tibble()
corsi_sel <- corsi_ranef %>%
select(., grp, condval, condsd) %>%
rename(., participant = grp,
corsi_rt = condval,
corsi_sd = condsd)
### Repeat process for verbal WM
# Load data
csv_files <- list.files (path = "./data/ospan",
pattern = "*.csv",
full.names = T)
ospan_speed <- as_tibble (rbindlist (lapply (csv_files, fread)))
# Select correct trials, rename participants' IDs
ospan_speed <- ospan_speed %>%
filter(., correct_resp == 1)
ospan_speed$subject_id <- str_replace(ospan_speed$subject_id, "ae", "aes")
ospan_speed$subject_id <- str_replace(ospan_speed$subject_id, "ie", "ies")
ospan_speed$subject_id <- str_replace(ospan_speed$subject_id, "am", "ams")
ospan_speed$subject_id <- str_replace(ospan_speed$subject_id, "im", "ims")
ospan_speed$subject_id <- str_replace(ospan_speed$subject_id, "mo", "mon")
# Calculate random effects for each participant
ospan_glm <- lmer(rt_formula ~ seq_length + (1 | subject_id),
data = ospan_speed)
ospan_ranef <- ranef(ospan_glm) %>% as_tibble()
ospan_sel <- ospan_ranef %>%
select(., grp, condval, condsd) %>%
rename(., participant = grp,
ospan_rt = condval,
ospan_sd = condsd)
### merge dataframes and save resulting one as a .csv for analysis
wm_speed <- merge(corsi_sel, ospan_sel, by="participant")
write.csv(wm_speed,'./data/clean/wm_processing_speed.csv', row.names = F)
# # Load all data and bind them into one dataframe (model: https://iamkbpark.com)
# csv_files <- list.files (path = "./data/wm",
# pattern = "*.csv",
# full.names = T)
#
# wm_ENES <- as_tibble (rbindlist (lapply (csv_files, fread)))
#
# # Mean of RT for each participant
# wm_RT <- wm_ENES %>%
# filter(., subj_form_resp == correct_resp) %>%
# group_by(., subject_id) %>%
# summarize(., mean_rt = mean(rt_formula)) %>%
# write.csv(., "./data/clean/wm_EN_ES.csv")
|
467575501c988c1a86c177d01bf9c8c1ba63d509
|
c15b64c651fac5e9e6cd41044d8c9766620e55ce
|
/Statistical Summaries.R
|
b5b046221cc8ae933c823120a50f6a454dfec140
|
[] |
no_license
|
kunal09/R_programming
|
ac846c36e98445b045d8eef2d292ed4ee2433cb2
|
5a6d9f0e104bd3370657951f8508f7570a71f1ab
|
refs/heads/master
| 2021-08-23T23:05:03.119763
| 2017-12-07T00:58:39
| 2017-12-07T00:58:39
| 113,111,109
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 417
|
r
|
Statistical Summaries.R
|
#Statistical Summaries
mydata <- read.table("http://www.sthda.com/upload/boxplot_format.txt",stringsAsFactors = FALSE)
mydata
#provides a statstical summary of the data
summary(mydata$V2)
#how many times some value has occured
table(mydata$V3)
#returns the min value of a function
min(mydata$V2)
range(mydata$V2)
median(mydata$V3)
sd(mydata$V2)
#will return unique values
unique(mydata$V3)
length(unique(mydata$V3))
|
2fec8f48c0e6416725f022ec71550091cfc78c65
|
7b0c33ec54d5b05d81b10de246a8fb8c23fc0c1d
|
/dectree.R
|
c9844e68a3e84341e7eb111046bcc7a7e217ee62
|
[] |
no_license
|
nirave/nfl-red-wine-supervised-learning
|
1f9855119f9e422cfe7f8b5cc374a70b9d4458ed
|
db6c35e0f149b33af3863192118f9ab1314bfc4f
|
refs/heads/master
| 2021-01-25T01:03:13.239211
| 2017-06-18T23:22:13
| 2017-06-18T23:22:13
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 650
|
r
|
dectree.R
|
library(RWeka)
modeldecisiontree_cv<-function(training, cat, dataType, learnType) {
train_control <- trainControl(method="repeatedcv", number=10, repeats=3)
param_grid <- expand.grid(C = c(0.01, 0.05, .1,.2,.3,.4))
trained<-train(as.formula(paste(cat, " ~.")), data=training, trControl=train_control, tuneGrid = param_grid, method="J48")
plot_cross(trained$results[1], trained$results[2], dataType, learnType, "c")
return (trained)
}
modeldecisiontree<-function(training, cat, dataType, learnType) {
return(J48(as.formula(paste(cat, " ~.")), data = training, control = Weka_control(M=2,U=FALSE)))
}
|
7527845e5f0efa963409242b9168bbfe85bd4419
|
29666f9152da5d06e208a248add2393d2130180c
|
/man/safe_month_min.Rd
|
986fc4b3f147a5b5215e8949dac5aa47fe6fd36e
|
[] |
no_license
|
Nicktz/fmxdat
|
147433ce18ec493fdf3431e4fe40e05fc8e4a253
|
ec52cdcc65d3431edce67a0ff65b5258a5184bd7
|
refs/heads/master
| 2023-08-18T14:03:45.452330
| 2023-08-08T07:27:38
| 2023-08-08T07:27:38
| 205,252,472
| 0
| 1
| null | null | null | null |
UTF-8
|
R
| false
| true
| 1,022
|
rd
|
safe_month_min.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/safe_month_min.R
\name{safe_month_min}
\alias{safe_month_min}
\title{safe_month_min}
\usage{
safe_month_min(datesel, N = 6)
}
\arguments{
\item{Ra}{N months back}
}
\value{
N months back
}
\description{
Safely goes back N months for monthly data.
This is an improved version of using filter(date >= last(date) %-% months(6)) - c.f. below:
library(tidyverse);library(lubridate)
df <- data.frame(date = rmsfuns::dateconverter(StartDate = lubridate::ymd(20200831), EndDate = lubridate::ymd(20210228), Transform = 'calendarEOM'))
df %>% filter(date > last(date) %m-% months(6))
Note that this gives 7 months, not 6.
Instead, use:
df %>% filter(date >= safe_month_min(last(date), N = 6))
}
\examples{
library(tidyverse);library(lubridate)
df <- data.frame(date = rmsfuns::dateconverter(StartDate = lubridate::ymd(20200131), EndDate = lubridate::ymd(20210228), Transform = 'calendarEOM'))
df \%>\% filter(date >= safe_month_min(last(date), N = 6))
}
|
8d5bb3849756d6fd7dd986f6cad4f94e6e452588
|
8c84ea2e7e9c74c085ac1c04d3af2f2818264e97
|
/scripts/ch11.R
|
a52f685b6a2a043f3d96a18146218cd98bc8a8c1
|
[] |
no_license
|
liao961120/rethinking_code
|
250351fc54e61dd81e8e23bacf9c1e4a4a35b346
|
5b4d07eb7a71c5735825066c564dec11b399f6aa
|
refs/heads/main
| 2023-06-01T13:03:19.506478
| 2021-06-20T05:16:14
| 2021-06-20T05:16:14
| 335,544,180
| 0
| 2
| null | null | null | null |
UTF-8
|
R
| false
| false
| 16,157
|
r
|
ch11.R
|
#+ Setup
remotes::install_github('rmcelreath/rethinking', upgrade=F)
library(rethinking)
#' ## R code 11.1
#+ R code 11.1
library(rethinking)
data(chimpanzees)
d <- chimpanzees
#' ## R code 11.2
#+ R code 11.2
d$treatment <- 1 + d$prosoc_left + 2*d$condition
#' ## R code 11.3
#+ R code 11.3
xtabs( ~ treatment + prosoc_left + condition , d )
#' ## R code 11.4
#+ R code 11.4
m11.1 <- quap(
alist(
pulled_left ~ dbinom( 1 , p ) ,
logit(p) <- a ,
a ~ dnorm( 0 , 10 )
) , data=d )
#' ## R code 11.5
#+ R code 11.5
set.seed(1999)
prior <- extract.prior( m11.1 , n=1e4 )
#' ## R code 11.6
#+ R code 11.6
p <- inv_logit( prior$a )
dens( p , adj=0.1 )
#' ## R code 11.7
#+ R code 11.7
m11.2 <- quap(
alist(
pulled_left ~ dbinom( 1 , p ) ,
logit(p) <- a + b[treatment] ,
a ~ dnorm( 0 , 1.5 ),
b[treatment] ~ dnorm( 0 , 10 )
) , data=d )
set.seed(1999)
prior <- extract.prior( m11.2 , n=1e4 )
p <- sapply( 1:4 , function(k) inv_logit( prior$a + prior$b[,k] ) )
#' ## R code 11.8
#+ R code 11.8
dens( abs( p[,1] - p[,2] ) , adj=0.1 )
#' ## R code 11.9
#+ R code 11.9
m11.3 <- quap(
alist(
pulled_left ~ dbinom( 1 , p ) ,
logit(p) <- a + b[treatment] ,
a ~ dnorm( 0 , 1.5 ),
b[treatment] ~ dnorm( 0 , 0.5 )
) , data=d )
set.seed(1999)
prior <- extract.prior( m11.3 , n=1e4 )
p <- sapply( 1:4 , function(k) inv_logit( prior$a + prior$b[,k] ) )
mean( abs( p[,1] - p[,2] ) )
#' ## R code 11.10
#+ R code 11.10
# trimmed data list
dat_list <- list(
pulled_left = d$pulled_left,
actor = d$actor,
treatment = as.integer(d$treatment) )
#' ## R code 11.11
#+ R code 11.11
m11.4 <- ulam(
alist(
pulled_left ~ dbinom( 1 , p ) ,
logit(p) <- a[actor] + b[treatment] ,
a[actor] ~ dnorm( 0 , 1.5 ),
b[treatment] ~ dnorm( 0 , 0.5 )
) , data=dat_list , chains=4 , log_lik=TRUE )
precis( m11.4 , depth=2 )
#' ## R code 11.12
#+ R code 11.12
post <- extract.samples(m11.4)
p_left <- inv_logit( post$a )
plot( precis( as.data.frame(p_left) ) , xlim=c(0,1) )
#' ## R code 11.13
#+ R code 11.13
labs <- c("R/N","L/N","R/P","L/P")
plot( precis( m11.4 , depth=2 , pars="b" ) , labels=labs )
#' ## R code 11.14
#+ R code 11.14
diffs <- list(
db13 = post$b[,1] - post$b[,3],
db24 = post$b[,2] - post$b[,4] )
plot( precis(diffs) )
#' ## R code 11.15
#+ R code 11.15
pl <- by( d$pulled_left , list( d$actor , d$treatment ) , mean )
pl[1,]
#' ## R code 11.16
#+ R code 11.16
plot( NULL , xlim=c(1,28) , ylim=c(0,1) , xlab="" ,
ylab="proportion left lever" , xaxt="n" , yaxt="n" )
axis( 2 , at=c(0,0.5,1) , labels=c(0,0.5,1) )
abline( h=0.5 , lty=2 )
for ( j in 1:7 ) abline( v=(j-1)*4+4.5 , lwd=0.5 )
for ( j in 1:7 ) text( (j-1)*4+2.5 , 1.1 , concat("actor ",j) , xpd=TRUE )
for ( j in (1:7)[-2] ) {
lines( (j-1)*4+c(1,3) , pl[j,c(1,3)] , lwd=2 , col=rangi2 )
lines( (j-1)*4+c(2,4) , pl[j,c(2,4)] , lwd=2 , col=rangi2 )
}
points( 1:28 , t(pl) , pch=16 , col="white" , cex=1.7 )
points( 1:28 , t(pl) , pch=c(1,1,16,16) , col=rangi2 , lwd=2 )
yoff <- 0.01
text( 1 , pl[1,1]-yoff , "R/N" , pos=1 , cex=0.8 )
text( 2 , pl[1,2]+yoff , "L/N" , pos=3 , cex=0.8 )
text( 3 , pl[1,3]-yoff , "R/P" , pos=1 , cex=0.8 )
text( 4 , pl[1,4]+yoff , "L/P" , pos=3 , cex=0.8 )
mtext( "observed proportions\n" )
#' ## R code 11.17
#+ R code 11.17
dat <- list( actor=rep(1:7,each=4) , treatment=rep(1:4,times=7) )
p_post <- link( m11.4 , data=dat )
p_mu <- apply( p_post , 2 , mean )
p_ci <- apply( p_post , 2 , PI )
#' ## R code 11.18
#+ R code 11.18
d$side <- d$prosoc_left + 1 # right 1, left 2
d$cond <- d$condition + 1 # no partner 1, partner 2
#' ## R code 11.19
#+ R code 11.19
dat_list2 <- list(
pulled_left = d$pulled_left,
actor = d$actor,
side = d$side,
cond = d$cond )
m11.5 <- ulam(
alist(
pulled_left ~ dbinom( 1 , p ) ,
logit(p) <- a[actor] + bs[side] + bc[cond] ,
a[actor] ~ dnorm( 0 , 1.5 ),
bs[side] ~ dnorm( 0 , 0.5 ),
bc[cond] ~ dnorm( 0 , 0.5 )
) , data=dat_list2 , chains=4 , log_lik=TRUE )
#' ## R code 11.20
#+ R code 11.20
compare( m11.5 , m11.4 , func=PSIS )
#' ## R code 11.21
#+ R code 11.21
post <- extract.samples( m11.4 , clean=FALSE )
str(post)
#' ## R code 11.22
#+ R code 11.22
m11.4_stan_code <- stancode(m11.4)
m11.4_stan <- stan( model_code=m11.4_stan_code , data=dat_list , chains=4 )
compare( m11.4_stan , m11.4 )
#' ## R code 11.23
#+ R code 11.23
post <- extract.samples(m11.4)
mean( exp(post$b[,4]-post$b[,2]) )
#' ## R code 11.24
#+ R code 11.24
data(chimpanzees)
d <- chimpanzees
d$treatment <- 1 + d$prosoc_left + 2*d$condition
d$side <- d$prosoc_left + 1 # right 1, left 2
d$cond <- d$condition + 1 # no partner 1, partner 2
d_aggregated <- aggregate(
d$pulled_left ,
list( treatment=d$treatment , actor=d$actor ,
side=d$side , cond=d$cond ) ,
sum )
colnames(d_aggregated)[5] <- "left_pulls"
#' ## R code 11.25
#+ R code 11.25
dat <- with( d_aggregated , list(
left_pulls = left_pulls,
treatment = treatment,
actor = actor,
side = side,
cond = cond ) )
m11.6 <- ulam(
alist(
left_pulls ~ dbinom( 18 , p ) ,
logit(p) <- a[actor] + b[treatment] ,
a[actor] ~ dnorm( 0 , 1.5 ) ,
b[treatment] ~ dnorm( 0 , 0.5 )
) , data=dat , chains=4 , log_lik=TRUE )
#' ## R code 11.26
#+ R code 11.26
compare( m11.6 , m11.4 , func=PSIS )
#' ## R code 11.27
#+ R code 11.27
# deviance of aggregated 6-in-9
-2*dbinom(6,9,0.2,log=TRUE)
# deviance of dis-aggregated
-2*sum(dbern(c(1,1,1,1,1,1,0,0,0),0.2,log=TRUE))
#' ## R code 11.28
#+ R code 11.28
library(rethinking)
data(UCBadmit)
d <- UCBadmit
#' ## R code 11.29
#+ R code 11.29
dat_list <- list(
admit = d$admit,
applications = d$applications,
gid = ifelse( d$applicant.gender=="male" , 1 , 2 )
)
m11.7 <- ulam(
alist(
admit ~ dbinom( applications , p ) ,
logit(p) <- a[gid] ,
a[gid] ~ dnorm( 0 , 1.5 )
) , data=dat_list , chains=4 )
precis( m11.7 , depth=2 )
#' ## R code 11.30
#+ R code 11.30
post <- extract.samples(m11.7)
diff_a <- post$a[,1] - post$a[,2]
diff_p <- inv_logit(post$a[,1]) - inv_logit(post$a[,2])
precis( list( diff_a=diff_a , diff_p=diff_p ) )
#' ## R code 11.31
#+ R code 11.31
postcheck( m11.7 )
# draw lines connecting points from same dept
for ( i in 1:6 ) {
x <- 1 + 2*(i-1)
y1 <- d$admit[x]/d$applications[x]
y2 <- d$admit[x+1]/d$applications[x+1]
lines( c(x,x+1) , c(y1,y2) , col=rangi2 , lwd=2 )
text( x+0.5 , (y1+y2)/2 + 0.05 , d$dept[x] , cex=0.8 , col=rangi2 )
}
#' ## R code 11.32
#+ R code 11.32
dat_list$dept_id <- rep(1:6,each=2)
m11.8 <- ulam(
alist(
admit ~ dbinom( applications , p ) ,
logit(p) <- a[gid] + delta[dept_id] ,
a[gid] ~ dnorm( 0 , 1.5 ) ,
delta[dept_id] ~ dnorm( 0 , 1.5 )
) , data=dat_list , chains=4 , iter=4000 )
precis( m11.8 , depth=2 )
#' ## R code 11.33
#+ R code 11.33
post <- extract.samples(m11.8)
diff_a <- post$a[,1] - post$a[,2]
diff_p <- inv_logit(post$a[,1]) - inv_logit(post$a[,2])
precis( list( diff_a=diff_a , diff_p=diff_p ) )
#' ## R code 11.34
#+ R code 11.34
pg <- with( dat_list , sapply( 1:6 , function(k)
applications[dept_id==k]/sum(applications[dept_id==k]) ) )
rownames(pg) <- c("male","female")
colnames(pg) <- unique(d$dept)
round( pg , 2 )
#' ## R code 11.35
#+ R code 11.35
y <- rbinom(1e5,1000,1/1000)
c( mean(y) , var(y) )
#' ## R code 11.36
#+ R code 11.36
library(rethinking)
data(Kline)
d <- Kline
d
#' ## R code 11.37
#+ R code 11.37
d$P <- scale( log(d$population) )
d$contact_id <- ifelse( d$contact=="high" , 2 , 1 )
#' ## R code 11.38
#+ R code 11.38
curve( dlnorm( x , 0 , 10 ) , from=0 , to=100 , n=200 )
#' ## R code 11.39
#+ R code 11.39
a <- rnorm(1e4,0,10)
lambda <- exp(a)
mean( lambda )
#' ## R code 11.40
#+ R code 11.40
curve( dlnorm( x , 3 , 0.5 ) , from=0 , to=100 , n=200 )
#' ## R code 11.41
#+ R code 11.41
N <- 100
a <- rnorm( N , 3 , 0.5 )
b <- rnorm( N , 0 , 10 )
plot( NULL , xlim=c(-2,2) , ylim=c(0,100) )
for ( i in 1:N ) curve( exp( a[i] + b[i]*x ) , add=TRUE , col=grau() )
#' ## R code 11.42
#+ R code 11.42
set.seed(10)
N <- 100
a <- rnorm( N , 3 , 0.5 )
b <- rnorm( N , 0 , 0.2 )
plot( NULL , xlim=c(-2,2) , ylim=c(0,100) )
for ( i in 1:N ) curve( exp( a[i] + b[i]*x ) , add=TRUE , col=grau() )
#' ## R code 11.43
#+ R code 11.43
x_seq <- seq( from=log(100) , to=log(200000) , length.out=100 )
lambda <- sapply( x_seq , function(x) exp( a + b*x ) )
plot( NULL , xlim=range(x_seq) , ylim=c(0,500) , xlab="log population" ,
ylab="total tools" )
for ( i in 1:N ) lines( x_seq , lambda[i,] , col=grau() , lwd=1.5 )
#' ## R code 11.44
#+ R code 11.44
plot( NULL , xlim=range(exp(x_seq)) , ylim=c(0,500) , xlab="population" ,
ylab="total tools" )
for ( i in 1:N ) lines( exp(x_seq) , lambda[i,] , col=grau() , lwd=1.5 )
#' ## R code 11.45
#+ R code 11.45
dat <- list(
T = d$total_tools ,
P = d$P ,
cid = d$contact_id )
# intercept only
m11.9 <- ulam(
alist(
T ~ dpois( lambda ),
log(lambda) <- a,
a ~ dnorm( 3 , 0.5 )
), data=dat , chains=4 , log_lik=TRUE )
# interaction model
m11.10 <- ulam(
alist(
T ~ dpois( lambda ),
log(lambda) <- a[cid] + b[cid]*P,
a[cid] ~ dnorm( 3 , 0.5 ),
b[cid] ~ dnorm( 0 , 0.2 )
), data=dat , chains=4 , log_lik=TRUE )
#' ## R code 11.46
#+ R code 11.46
compare( m11.9 , m11.10 , func=PSIS )
#' ## R code 11.47
#+ R code 11.47
k <- PSIS( m11.10 , pointwise=TRUE )$k
plot( dat$P , dat$T , xlab="log population (std)" , ylab="total tools" ,
col=rangi2 , pch=ifelse( dat$cid==1 , 1 , 16 ) , lwd=2 ,
ylim=c(0,75) , cex=1+normalize(k) )
# set up the horizontal axis values to compute predictions at
ns <- 100
P_seq <- seq( from=-1.4 , to=3 , length.out=ns )
# predictions for cid=1 (low contact)
lambda <- link( m11.10 , data=data.frame( P=P_seq , cid=1 ) )
lmu <- apply( lambda , 2 , mean )
lci <- apply( lambda , 2 , PI )
lines( P_seq , lmu , lty=2 , lwd=1.5 )
shade( lci , P_seq , xpd=TRUE )
# predictions for cid=2 (high contact)
lambda <- link( m11.10 , data=data.frame( P=P_seq , cid=2 ) )
lmu <- apply( lambda , 2 , mean )
lci <- apply( lambda , 2 , PI )
lines( P_seq , lmu , lty=1 , lwd=1.5 )
shade( lci , P_seq , xpd=TRUE )
#' ## R code 11.48
#+ R code 11.48
plot( d$population , d$total_tools , xlab="population" , ylab="total tools" ,
col=rangi2 , pch=ifelse( dat$cid==1 , 1 , 16 ) , lwd=2 ,
ylim=c(0,75) , cex=1+normalize(k) )
ns <- 100
P_seq <- seq( from=-5 , to=3 , length.out=ns )
# 1.53 is sd of log(population)
# 9 is mean of log(population)
pop_seq <- exp( P_seq*1.53 + 9 )
lambda <- link( m11.10 , data=data.frame( P=P_seq , cid=1 ) )
lmu <- apply( lambda , 2 , mean )
lci <- apply( lambda , 2 , PI )
lines( pop_seq , lmu , lty=2 , lwd=1.5 )
shade( lci , pop_seq , xpd=TRUE )
lambda <- link( m11.10 , data=data.frame( P=P_seq , cid=2 ) )
lmu <- apply( lambda , 2 , mean )
lci <- apply( lambda , 2 , PI )
lines( pop_seq , lmu , lty=1 , lwd=1.5 )
shade( lci , pop_seq , xpd=TRUE )
#' ## R code 11.49
#+ R code 11.49
dat2 <- list( T=d$total_tools, P=d$population, cid=d$contact_id )
m11.11 <- ulam(
alist(
T ~ dpois( lambda ),
lambda <- exp(a[cid])*P^b[cid]/g,
a[cid] ~ dnorm(1,1),
b[cid] ~ dexp(1),
g ~ dexp(1)
), data=dat2 , chains=4 , log_lik=TRUE )
#' ## R code 11.50
#+ R code 11.50
num_days <- 30
y <- rpois( num_days , 1.5 )
#' ## R code 11.51
#+ R code 11.51
num_weeks <- 4
y_new <- rpois( num_weeks , 0.5*7 )
#' ## R code 11.52
#+ R code 11.52
y_all <- c( y , y_new )
exposure <- c( rep(1,30) , rep(7,4) )
monastery <- c( rep(0,30) , rep(1,4) )
d <- data.frame( y=y_all , days=exposure , monastery=monastery )
#' ## R code 11.53
#+ R code 11.53
# compute the offset
d$log_days <- log( d$days )
# fit the model
m11.12 <- quap(
alist(
y ~ dpois( lambda ),
log(lambda) <- log_days + a + b*monastery,
a ~ dnorm( 0 , 1 ),
b ~ dnorm( 0 , 1 )
), data=d )
#' ## R code 11.54
#+ R code 11.54
post <- extract.samples( m11.12 )
lambda_old <- exp( post$a )
lambda_new <- exp( post$a + post$b )
precis( data.frame( lambda_old , lambda_new ) )
#' ## R code 11.55
#+ R code 11.55
# simulate career choices among 500 individuals
N <- 500 # number of individuals
income <- c(1,2,5) # expected income of each career
score <- 0.5*income # scores for each career, based on income
# next line converts scores to probabilities
p <- softmax(score[1],score[2],score[3])
# now simulate choice
# outcome career holds event type values, not counts
career <- rep(NA,N) # empty vector of choices for each individual
# sample chosen career for each individual
set.seed(34302)
for ( i in 1:N ) career[i] <- sample( 1:3 , size=1 , prob=p )
#' ## R code 11.56
#+ R code 11.56
code_m11.13 <- "
data{
int N; // number of individuals
int K; // number of possible careers
int career[N]; // outcome
vector[K] career_income;
}
parameters{
vector[K-1] a; // intercepts
real<lower=0> b; // association of income with choice
}
model{
vector[K] p;
vector[K] s;
a ~ normal( 0 , 1 );
b ~ normal( 0 , 0.5 );
s[1] = a[1] + b*career_income[1];
s[2] = a[2] + b*career_income[2];
s[3] = 0; // pivot
p = softmax( s );
career ~ categorical( p );
}
"
#' ## R code 11.57
#+ R code 11.57
dat_list <- list( N=N , K=3 , career=career , career_income=income )
m11.13 <- stan( model_code=code_m11.13 , data=dat_list , chains=4 )
precis( m11.13 , 2 )
#' ## R code 11.58
#+ R code 11.58
post <- extract.samples( m11.13 )
# set up logit scores
s1 <- with( post , a[,1] + b*income[1] )
s2_orig <- with( post , a[,2] + b*income[2] )
s2_new <- with( post , a[,2] + b*income[2]*2 )
# compute probabilities for original and counterfactual
p_orig <- sapply( 1:length(post$b) , function(i)
softmax( c(s1[i],s2_orig[i],0) ) )
p_new <- sapply( 1:length(post$b) , function(i)
softmax( c(s1[i],s2_new[i],0) ) )
# summarize
p_diff <- p_new[2,] - p_orig[2,]
precis( p_diff )
#' ## R code 11.59
#+ R code 11.59
N <- 500
# simulate family incomes for each individual
family_income <- runif(N)
# assign a unique coefficient for each type of event
b <- c(-2,0,2)
career <- rep(NA,N) # empty vector of choices for each individual
for ( i in 1:N ) {
score <- 0.5*(1:3) + b*family_income[i]
p <- softmax(score[1],score[2],score[3])
career[i] <- sample( 1:3 , size=1 , prob=p )
}
code_m11.14 <- "
data{
int N; // number of observations
int K; // number of outcome values
int career[N]; // outcome
real family_income[N];
}
parameters{
vector[K-1] a; // intercepts
vector[K-1] b; // coefficients on family income
}
model{
vector[K] p;
vector[K] s;
a ~ normal(0,1.5);
b ~ normal(0,1);
for ( i in 1:N ) {
for ( j in 1:(K-1) ) s[j] = a[j] + b[j]*family_income[i];
s[K] = 0; // the pivot
p = softmax( s );
career[i] ~ categorical( p );
}
}
"
dat_list <- list( N=N , K=3 , career=career , family_income=family_income )
m11.14 <- stan( model_code=code_m11.14 , data=dat_list , chains=4 )
precis( m11.14 , 2 )
#' ## R code 11.60
#+ R code 11.60
library(rethinking)
data(UCBadmit)
d <- UCBadmit
#' ## R code 11.61
#+ R code 11.61
# binomial model of overall admission probability
m_binom <- quap(
alist(
admit ~ dbinom(applications,p),
logit(p) <- a,
a ~ dnorm( 0 , 1.5 )
), data=d )
# Poisson model of overall admission rate and rejection rate
# 'reject' is a reserved word in Stan, cannot use as variable name
dat <- list( admit=d$admit , rej=d$reject )
m_pois <- ulam(
alist(
admit ~ dpois(lambda1),
rej ~ dpois(lambda2),
log(lambda1) <- a1,
log(lambda2) <- a2,
c(a1,a2) ~ dnorm(0,1.5)
), data=dat , chains=3 , cores=3 )
#' ## R code 11.62
#+ R code 11.62
inv_logit(coef(m_binom))
#' ## R code 11.63
#+ R code 11.63
k <- coef(m_pois)
a1 <- k['a1']; a2 <- k['a2']
exp(a1)/(exp(a1)+exp(a2))
|
1b0abf0959dc72cdd79906d448a9bd637dbe456e
|
e63df753a1819766d0b40dbf035ebaaa2c0d1526
|
/man/sort.predictions.frame.Rd
|
1e6a8840440e0547df8c24c99627fb40371fd830
|
[] |
no_license
|
cran/asremlPlus
|
cf74d404854a1f2b37e5acefe7fae86132b01a3e
|
7a5b7b2124a5b736fc49b99f91eba4b6a41b5fb3
|
refs/heads/master
| 2023-08-31T16:36:03.614939
| 2023-08-24T07:30:12
| 2023-08-24T09:31:08
| 36,970,354
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 9,930
|
rd
|
sort.predictions.frame.Rd
|
\name{sort.predictions.frame}
\alias{sort.predictions.frame}
\title{Sorts a \code{\link{predictions.frame}} according to the predicted values
associated with a factor.}
\description{Sorts the rows of a \code{\link{predictions.frame}} according to the predicted values
in the \code{predictions.frame}. These predicted values are generally obtained using
\code{predict.asreml} by specifying a \code{classify} term comprised of one or
more variables. Generally, the values associated with one variable are sorted in
parallel within each combination of values of the other variables. When there is more
than one variable in the \code{classify} term, the sorting is controlled using
one or more of \code{sortFactor}, \code{sortParallelToCombo} and \code{sortOrder}.
If there is only one variable in the \code{classify} then the \code{\link{predictions.frame}}
is sorted according to the order of the complete set of predictions.}
\usage{\method{sort}{predictions.frame}(x, decreasing = FALSE, classify, sortFactor = NULL,
sortParallelToCombo = NULL, sortNestingFactor = NULL,
sortOrder = NULL, ...)}
\arguments{
\item{x}{A \code{\link{predictions.frame}}.}
\item{decreasing}{A \code{\link{logical}} passed to \code{order} that detemines whether
the order is for increasing or decreasing magnitude of the predicted
values.}
\item{classify}{A \code{\link{character}} string giving the variables that
define the margins of the multiway table that was predicted.
Multiway tables are specified by forming an interaction type
term from the classifying variables, that is, separating the
variable names with the \code{:} operator.}
\item{sortFactor}{A \code{\link{character}} containing the name of the
\code{factor} that indexes the set of predicted values that determines
the sorting of the components. If there is only one variable in the
\code{classify} term then \code{sortFactor} can be \code{NULL} and
the order is defined by the complete set of predicted values.
If there is more than one variable in the \code{classify} term
then \code{sortFactor} must be set. In this case the \code{sortFactor}
is sorted in the same order within each combination of the values of
the \code{sortParallelToCombo} variables: the \code{classify} variables, excluding the
\code{sortFactor}. There should be only one predicted value for
each unique value of \code{sortFactor} within each set defined by a
combination of the values of the \code{classify} variables, excluding the
\code{sortFactor} \code{factor}.
The order to use is determined by either \code{sortParallelToCombo} or
\code{sortOrder}.}
\item{sortParallelToCombo}{A \code{\link{list}} that specifies a combination of the values
of the \code{factor}s and \code{numeric}s, excluding \code{sortFactor}, that
are in \code{classify}. Each of the components of the supplied \code{\link{list}}
is named for a \code{classify} variable and specifies a single value for it. The
combination of this set of values will be used to define a subset of the predicted
values whose order will define the order of \code{sortFactor}. Each of the other
combinations of the values of the \code{factor}s and \code{numeric}s will be sorted
in parallel. If \code{sortParallelToCombo} is \code{NULL} then the first value of
each \code{classify} variable, except for the \code{sortFactor} \code{factor},
in the \code{predictions} component is used to define \code{sortParallelToCombo}.
If there is only one variable in the \code{classify} then
\code{sortParallelToCombo} is ignored.}
\item{sortNestingFactor}{A \code{\link{character}} containing the name of the
\code{factor} that defines groups of the \code{sortFactor} within which the predicted
values are to be ordered.
If there is only one variable in the \code{classify} then
\code{sortNestingFactor} is ignored.}
\item{sortOrder}{A \code{\link{character}} vector whose length is the same as the number
of levels for \code{sortFactor} in the \code{predictions.frame}.
It specifies the desired order of the levels in the reordered
the \code{\link{predictions.frame}}.
The argument \code{sortParallelToCombo} is ignored.
The following creates a \code{sortOrder} vector \code{levs} for factor
\code{f} based on the values in \code{x}:
\code{levs <- levels(f)[order(x)]}.}
\item{\dots}{further arguments passed to or from other methods. Not used at present.}
}
\value{The sorted \code{\link{predictions.frame}}. Also, the \code{sortFactor} and
\code{sortOrder} attributes are set.}
\details{The basic technique is to change the order of the levels of the \code{sortFactor}
within the \code{predictions.frame} so
that they are ordered for a subset of predicted values, one for each levels of the
\code{sortFactor}. When the \code{classify} term consists of more than one
variable then a subset of one combination of the values of variables other than
the \code{sortFactor}, the \code{sortParallelToCombo} combination, must be chosen for determining the
order of the \code{sortFactor} levels. Then the sorting of the rows (and columns)
will be in parallel within each combination of the values of \code{sortParallelToCombo} variables:
the \code{classify} term, excluding the \code{sortFactor}.}
\author{Chris Brien}
\seealso{\code{\link{as.predictions.frame}}, \code{\link{print.predictions.frame}},
\code{\link{sort.alldiffs}}, \cr
\code{\link{predictPlus.asreml}}, \code{\link{predictPresent.asreml}}}
\examples{
##Halve WaterRunoff data to reduce time to execute
data(WaterRunoff.dat)
tmp <- subset(WaterRunoff.dat, Date == "05-18")
##Use asreml to get predictions and associated statistics
\dontrun{
#Analyse pH
m1.asr <- asreml(fixed = pH ~ Benches + (Sources * (Type + Species)),
random = ~ Benches:MainPlots,
keep.order=TRUE, data= tmp)
current.asrt <- as.asrtests(m1.asr, NULL, NULL)
current.asrt <- as.asrtests(m1.asr)
current.asrt <- rmboundary(current.asrt)
m1.asr <- current.asrt$asreml.obj
#Get predictions and associated statistics
TS.diffs <- predictPlus.asreml(classify = "Sources:Type",
asreml.obj = m1.asr, tables = "none",
wald.tab = current.asrt$wald.tab,
present = c("Type","Species","Sources"))
#Use sort.predictions.frame and save order for use with other response variables
TS.preds <- TS.diffs$predictions
TS.preds.sort <- sort(TS.preds, sortFactor = "Sources",
sortParallelToCombo = list(Type = "Control"))
sort.order <- attr(TS.preds.sort, which = "sortOrder")
#Analyse Turbidity
m2.asr <- asreml(fixed = Turbidity ~ Benches + (Sources * (Type + Species)),
random = ~ Benches:MainPlots,
keep.order=TRUE, data= tmp)
current.asrt <- as.asrtests(m2.asr)
#Use pH sort.order to sort Turbidity alldiffs object
TS.diffs2 <- predictPlus(m2.asr, classify = "Sources:Type",
pairwise = FALSE, error.intervals = "Stand",
tables = "none", present = c("Type","Species","Sources"))
TS.preds2 <- TS.diffs2$predictions
TS.preds2.sort <- sort(TS.preds, sortFactor = "Sources", sortOder = sort.order)
}
## Use lmeTest and emmmeans to get predictions and associated statistics
if (requireNamespace("lmerTest", quietly = TRUE) &
requireNamespace("emmeans", quietly = TRUE))
{
#Analyse pH
m1.lmer <- lmerTest::lmer(pH ~ Benches + (Sources * (Type + Species)) +
(1|Benches:MainPlots),
data=na.omit(tmp))
TS.emm <- emmeans::emmeans(m1.lmer, specs = ~ Sources:Type)
TS.preds <- summary(TS.emm)
den.df <- min(TS.preds$df, na.rm = TRUE)
## Modify TS.preds to be compatible with a predictions.frame
TS.preds <- as.predictions.frame(TS.preds, predictions = "emmean",
se = "SE", interval.type = "CI",
interval.names = c("lower.CL", "upper.CL"))
#Use sort.predictions.frame and save order for use with other response variables
TS.preds.sort <- sort(TS.preds, classify = "Sources:Type", sortFactor = "Sources",
sortParallelToCombo = list(Type = "Control"))
sort.order <- attr(TS.preds.sort, which = "sortOrder")
#Analyse Turbidity
m2.lmer <- lmerTest::lmer(Turbidity ~ Benches + (Sources * (Type + Species)) +
(1|Benches:MainPlots),
data=na.omit(tmp))
TS.emm <- emmeans::emmeans(m2.lmer, specs = ~ Sources:Type)
TS.preds <- summary(TS.emm)
den.df <- min(TS.preds$df, na.rm = TRUE)
## Modify TS.preds to be compatible with a predictions.frame
TS.preds <- as.predictions.frame(TS.preds, predictions = "emmean",
se = "SE", interval.type = "CI",
interval.names = c("lower.CL", "upper.CL"))
}
}
\keyword{asreml}
|
68f5d6a14bdf1707f4458025b1f41906ddb1c6fd
|
4843d2c1c8bc436d4ddc660cb4f9a90d33713382
|
/ARD/Castle_Farm/Rhizosphere/full_analysis.R
|
a268f4b03c71f0e0cc471923abe13d132b98e422
|
[] |
no_license
|
harrisonlab/Metabarcoding_projects
|
530ec0fed4d4ad45cbb47d600f2a8be1182da1fd
|
ac0590843eda264502802ec7c614a1c7d75728f9
|
refs/heads/master
| 2022-03-19T15:11:29.620885
| 2022-02-24T10:14:17
| 2022-02-24T10:14:17
| 41,956,604
| 1
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 61,247
|
r
|
full_analysis.R
|
#===============================================================================
# Load libraries
#===============================================================================
library(DESeq2)
library(BiocParallel)
library(data.table)
library(plyr)
library(dplyr)
library(ggplot2)
library(devtools)
library(Biostrings)
library(vegan)
library(lmPerm)
library(phyloseq)
library(ape)
register(MulticoreParam(12))
load_all("~/pipelines/metabarcoding/scripts/myfunctions")
environment(plot_ordination) <- environment(ordinate) <- environment(plot_richness) <- environment(phyloseq::ordinate)
#assignInNamespace("plot_ordination",value=plot_ordination,ns="phyloseq")
#===============================================================================
# Load data
#===============================================================================
# load otu count table
countData <- read.table("BAC.otus_table.txt",header=T,sep="\t",row.names=1, comment.char = "")
# load sample metadata
colData <- read.table("colData",header=T,sep="\t",row.names=1)
# load taxonomy data
taxData <- read.table("BAC.taxa",header=F,sep=",",row.names=1)
# reorder columns
taxData<-taxData[,c(1,3,5,7,9,11,13,2,4,6,8,10,12,14)]
# add best "rank" at 0.65 confidence and tidy-up the table
taxData<-phyloTaxaTidy(taxData,0.65)
# get unifrac dist
phylipData <- fread.phylip("BAC.phy")
njtree <- nj(as.dist(phylipData))
# save data into a list
ubiom_BAC <- list(
countData=countData,
colData=colData,
taxData=taxData,
phylipData=phylipData,
njtree=njtree,
RHB="BAC"
)
# Fungi all in one call
ubiom_FUN <- list(
countData=read.table("FUN.otus_table.txt",header=T,sep="\t",row.names=1,comment.char = ""),
colData=read.table("colData",header=T,sep="\t",row.names=1),
taxData=phyloTaxaTidy(read.table("FUN.taxa",header=F,sep=",",row.names=1)[,c(1,3,5,7,9,11,13,2,4,6,8,10,12,14)],0.65),
phylipData=fread.phylip("FUN.phy"),
RHB="FUN"
)
ubiom_FUN$njtree <- nj(as.dist(ubiom_FUN$phylipData))
# Oomycetes
ubiom_OO <- list(
countData=read.table("OO.otus_table.txt",header=T,sep="\t",row.names=1,comment.char = ""),
colData=read.table("colData2",header=T,sep="\t",row.names=1),
taxData=phyloTaxaTidy(read.table("OO.taxa",header=F,sep=",",row.names=1)[,c(1,3,5,7,9,11,13,2,4,6,8,10,12,14)],0.65),
phylipData=fread.phylip("OO.phy"),
RHB="OO"
)
ubiom_OO$njtree <- nj(as.dist(ubiom_OO$phylipData))
rownames(ubiom_OO$colData) <- paste0("X",gsub("_","\\.",ubiom_OO$colData$name),"_",sub("D.*","",rownames(ubiom_OO$colData)))
rownames(ubiom_OO$colData) <- sub("XG","G",rownames(ubiom_OO$colData))
# Nematodes
ubiom_NEM <- list(
countData=read.table("NEM.otus_table.txt",header=T,sep="\t",row.names=1,comment.char = ""),
colData=read.table("colData2",header=T,sep="\t",row.names=1),
taxData=phyloTaxaTidy(read.table("NEM.taxa",header=F,sep=",",row.names=1)[,c(1,3,5,7,9,11,13,2,4,6,8,10,12,14)],0.65),
phylipData=fread.phylip("NEM.phy"),
RHB="NEM"
)
ubiom_NEM$njtree <- nj(as.dist(ubiom_NEM$phylipData))
rownames(ubiom_NEM$colData) <- paste0("X",gsub("_","\\.",ubiom_NEM$colData$name),"_",sub("D.*","",rownames(ubiom_NEM$colData)))
rownames(ubiom_NEM$colData) <- sub("XG","G",rownames(ubiom_NEM$colData))
#===============================================================================
# Combine species
#===============================================================================
#### combine species at 0.95 (default) confidence (if they are species)
# Fungi
invisible(mapply(assign, names(ubiom_FUN), ubiom_FUN, MoreArgs=list(envir = globalenv())))
combinedTaxa <- combineTaxa("FUN.taxa")
countData <- combCounts(combinedTaxa,countData)
taxData <- combTaxa(combinedTaxa,taxData)
ubiom_FUN$countData <- countData
ubiom_FUN$taxData <- taxData
# Nematodes
# oomycetes
#===============================================================================
# ****FUNGI****
#===============================================================================
invisible(mapply(assign, names(ubiom_FUN), ubiom_FUN, MoreArgs=list(envir = globalenv())))
#===============================================================================
# Create DEseq objects
#===============================================================================
# ensure colData rows and countData columns have the same order
colData <- colData[names(countData),]
# remove low count and control samples
myfilter <- (colSums(countData)>=1000) & colData$Condition!="C"
# remove Pair of any sample with a low count
exclude<-which(!myfilter)
myfilter <- myfilter&sapply(colData$Pair,function(x) length(which(x==colData$Pair[-exclude]))>1)
# apply filter
colData <- droplevels(colData[myfilter,])
countData <- countData[,myfilter]
# simple Deseq design
design<-~1
#create DES object
# colnames(countData) <- row.names(colData)
dds<-DESeqDataSetFromMatrix(countData,colData,design)
#===============================================================================
# **Normalised**
#===============================================================================
sizeFactors(dds) <-sizeFactors(estimateSizeFactors(dds))
#===============================================================================
# Alpha diversity analysis
#===============================================================================
# Add spatial information as a numeric and plot
colData$Location<-as.number(colData$Pair)
ggsave(paste(RHB,"Alpha.pdf",sep="_"),plot_alpha(counts(dds,normalize=T),colData,design="Condition",colour="Condition",cbPalette=T,legend="hidden",measures=c("Chao1", "Shannon", "Simpson","Observed"),limits=c(0,1000,"Chao1")))
### permutation based anova on diversity index ranks ###
# get the diversity index data
all_alpha_ord <- plot_alpha(counts(dds,normalize=T),colData,design="Condition",returnData=T)
# add column names as row to metadata (or use tribble)
colData$samples <- rownames(colData)
# join diversity indices and metadata
all_alpha_ord <- as.data.table(inner_join(all_alpha_ord,colData,by=c("Samples"="samples")))
# perform anova for each index
colData$Pair<-as.factor(colData$Pair)
sink(paste(RHB,"ALPHA_stats.txt",sep="_"))
setkey(all_alpha_ord,S.chao1)
print("Chao1")
summary(aovp(as.numeric(as.factor(all_alpha_ord$S.chao1))~Pair+Condition,all_alpha_ord))
setkey(all_alpha_ord,shannon)
print("Shannon")
summary(aovp(as.numeric(as.factor(all_alpha_ord$shannon))~Pair+Condition,all_alpha_ord))
setkey(all_alpha_ord,simpson)
print("simpson")
summary(aovp(as.numeric(as.factor(all_alpha_ord$simpson))~Pair+Condition,all_alpha_ord))
setkey(all_alpha_ord,S.ACE)
print("ACE")
summary(aovp(as.numeric(as.factor(all_alpha_ord$S.ACE))~Pair+Condition,all_alpha_ord))
sink()
#===============================================================================
# Filter data
#============================================================================
# plot cummulative reads (will also produce a data table "dtt" in the global environment)
ggsave(paste(RHB,"OTU_counts.pdf",sep="_"),plotCummulativeReads(counts(dds,normalize=T)))
dds <- dds[rowSums(counts(dds, normalize=T))>4,]
#===============================================================================
# Beta diversity PCA/NMDS
#===============================================================================
### PCA ###
# perform PC decomposition of DES object
mypca <- des_to_pca(dds)
# to get pca plot axis into the same scale create a dataframe of PC scores multiplied by their variance
d <-t(data.frame(t(mypca$x)*mypca$percentVar))
# Add spatial information as a numeric and plot
colData$Location<-as.number(colData$Pair)
# plot the PCA
pdf(paste(RHB,"PCA.pdf",sep="_"))
plotOrd(d,colData,design="Condition",xlabel="PC1",ylabel="PC2")
plotOrd(d,colData,shape="Condition",design="Location",continuous=T,xlabel="PC1",ylabel="PC2")
dev.off()
ggsave(paste(RHB,"PCA_loc.pdf",sep="_"),plotOrd(d,colData,shape="Condition",design="Location",continuous=T,xlabel="PC1",ylabel="PC2",alpha=0.75,pointSize=2))
ggsave(paste(RHB,"PCA_Original.pdf",sep="_"),plotOrd(d,colData,design="Condition",continuous=F,xlabel="PC1",ylabel="PC2",alpha=0.75,pointSize=2))
g <- plotOrd(d,colData,design="Condition",continuous=F,axes=c(1,3),plot="Label",labelSize=2.5,cbPalette=T,label="Pair",legend="bottom")
g$layers[[1]] <- NULL
g <- g + geom_point(size = 0, stroke = 0) # OR geom_point(shape = "") +
g <- g + geom_label(show.legend = FALSE,size=2.5)
g <- g + guides(colour = guide_legend(override.aes = list(size = 5, shape = c(utf8ToInt("H"), utf8ToInt("S")))))
g <- g + scale_colour_manual(name = "Condition", breaks = c("H","S"), labels = c("",""),values=c("#000000", "#E69F00"))
ggsave(paste(RHB,"PCA_NEW_1vs3.pdf",sep="_"),g)
g_fun_fig4 <- plotOrd(d,colData,design="Condition",facet="Pair",axes=c(1,2),alpha=0.75,pointSize=2,cbPalette=T,legend="bottom") +
geom_line(aes(group=facet),alpha=0.1,linetype=3,colour="#000000")
g2 <- g_fun_fig4 + ggtitle("B")+ theme_classic_thin(base_size=12)%+replace% theme(plot.title = element_text(hjust = -0.1,size=14),legend.position="bottom")
### remove spatial information (this uses the factor "Pair" not the numeric "Location") and plot
pc.res <- resid(aov(mypca$x~colData$Pair,colData))
d <- t(data.frame(t(pc.res*mypca$percentVar)))
ggsave(paste(RHB,"PCA_without_paired_var.pdf",sep="_"),plotOrd(d,colData,design="Condition",continuous=F,xlabel="PC1",ylabel="PC2"))
# ANOVA
sink(paste(RHB,"PCA_ANOVA.txt",sep="_"))
print("ANOVA")
lapply(seq(1:3),function(x) summary(aov(mypca$x[,x]~Pair+Condition,colData(dds))))
print("PERMANOVA")
lapply(seq(1:3),function(x) summary(aovp(mypca$x[,x]~Pair+Condition,colData(dds))))
sink()
# Sum of variances in PC scores
sum_squares <- t(apply(mypca$x,2,function(x)
t(summary(aov(x~Pair+Condition,colData(dds)))[[1]][2]))
)
colnames(sum_squares) <- c("Pair","Condition","residual")
x<-t(apply(sum_squares,1,prop.table))
perVar <- x * mypca$percentVar
colSums(perVar)
colSums(perVar)/sum(colSums(perVar))*100
### NMDS ###
# phyloseq has functions (using Vegan) for making NMDS plots
myphylo <- ubiom_to_phylo(list(counts(dds,normalize=T),taxData,colData))
# add tree to phyloseq object
phy_tree(myphylo) <- njtree
# calculate NMDS ordination using weighted unifrac scores
ordu = ordinate(myphylo, "NMDS", "unifrac", weighted=TRUE)
# plot with plotOrd (or use plot_ordination)
ggsave(paste(RHB,"Unifrac_NMDS.pdf",sep="_"),plotOrd(ordu$points,colData,shape="Condition",design="Location",continuous=T,xlabel="NMDS1",ylabel="NMDS2",alpha=0.75,pointSize=2))
# permanova of unifrac distance
sink(paste(RHB,"PERMANOVA_unifrac.txt",sep="_"))
adonis(distance(myphylo,"unifrac",weighted=T)~Pair+Condition,colData(dds),parallel=12,permutations=9999)
sink()
# calculate NMDS ordination with bray-curtis distance matrix
ordu = ordinate(myphylo, "NMDS", "bray",stratmax=50)
# plot with plotOrd (or use plot_ordination)
ggsave(paste(RHB,"BRAY_NMDS.pdf",sep="_"),plotOrd(ordu$points,colData,shape="Condition",design="Location",continuous=T,xlabel="NMDS1",ylabel="NMDS2",alpha=0.75,pointSize=2))
# phyla plots - keep top 7 phyla only
phylum.sum = tapply(taxa_sums(myphylo), tax_table(myphylo)[, "phylum"], sum, na.rm=TRUE)
top7phyla = names(sort(phylum.sum, TRUE))[1:7]
myphylo_slim = prune_taxa((tax_table(myphylo)[, "phylum"] %in% top7phyla), myphylo)
# split phylo into H and S
H <- prune_samples(colData$Condition=="H",myphylo_slim)
S <- prune_samples(colData$Condition=="S",myphylo_slim)
# calculate ordination
Hord <- ordinate(H, "NMDS", "bray")
Sord <- ordinate(S, "NMDS", "bray")
# plot with plot_ordination
theme_set(theme_facet_blank(angle=0,vjust=0,hjust=0.5))
pdf(paste(RHB,"NMDS_taxa_by_Condition.pdf",sep="_"))
plot_ordination(H, Hord, type="taxa", color="phylum")+ facet_wrap(~phylum, 3)
plot_ordination(S, Sord, type="taxa", color="phylum")+ facet_wrap(~phylum, 3)
dev.off()
# theme_set(theme_classic_thin())
# plot_ordination(myphylo, ordu, type="samples", shape="Condition", color="Location",continuous=T)
# PCoA
# ordu = ordinate(myphylo, "PCoA", "unifrac", weighted=TRUE)
# d <-t(data.frame(t(ordu$vectors)*ordu$values$Relative_eig))
# plotOrd(d,colData,shape="Condition",design="Location",continuous=T,xlabel="PCoA1",ylabel="PCoA2")
#===============================================================================
# differential analysis
#===============================================================================
# filter for low counts - this can affect the FD probability and DESeq2 does apply its own filtering for genes/otus with no power
# but, no point keeping OTUs with 0 count
dds<-dds[ rowSums(counts(dds,normalize=T))>0,]
# p value for FDR cutoff
alpha <- 0.1
# the full model
full_design <- ~Pair + Condition
# add full model to dds object
design(dds) <- full_design
# calculate fit
dds <- DESeq(dds,parallel=T)
# calculate results for default contrast (S vs H)
res <- results(dds,alpha=alpha,parallel=T)
# merge results with taxonomy data
res.merge <- data.table(inner_join(data.table(OTU=rownames(res),as.data.frame(res)),data.table(OTU=rownames(taxData),taxData)))
write.table(res.merge, paste(RHB,"diff.txt",sep="_"),quote=F,sep="\t",na="",row.names=F)
# output sig fasta
writeXStringSet(readDNAStringSet(paste0(RHB,".otus.fa"))[ res.merge[padj<=0.05]$OTU],paste0(RHB,".sig.fa"))
#==============================================================================
# **qPCR**
#===============================================================================
dds<-DESeqDataSetFromMatrix(countData,colData,design)
# Correction from aboslute quantification
sizeFactors(dds) <- colData$funq
# Correction from aboslute quantification v2
# sizeFactors(dds) <- sizeFactors(dds)/colData$funq
#===============================================================================
# Alpha diversity analysis
#===============================================================================
# Add spatial information as a numeric and plot
colData$Location<-as.number(colData$Pair)
ggsave(paste(RHB,"qPCR_Alpha.pdf",sep="_"),plot_alpha(counts(dds,normalize=T),colData,design="Condition",colour="Condition",cbPalette=T,legend="hidden",measures=c("Chao1", "Shannon", "Simpson","Observed")))
### permutation based anova on diversity index ranks ###
# get the diversity index data
all_alpha_ord <- plot_alpha(counts(dds,normalize=T),colData,design="Condition",returnData=T)
# add column names as row to metadata (or use tribble)
colData$samples <- rownames(colData)
# join diversity indices and metadata
all_alpha_ord <- as.data.table(inner_join(all_alpha_ord,colData,by=c("Samples"="samples")))
# perform anova for each index
colData$Pair<-as.factor(colData$Pair)
sink(paste(RHB,"qPCR_ALPHA_stats.txt",sep="_"))
setkey(all_alpha_ord,S.chao1)
print("Chao1")
summary(aovp(as.numeric(as.factor(all_alpha_ord$S.chao1))~Pair+Condition,all_alpha_ord))
setkey(all_alpha_ord,shannon)
print("Shannon")
summary(aovp(as.numeric(as.factor(all_alpha_ord$shannon))~Pair+Condition,all_alpha_ord))
setkey(all_alpha_ord,simpson)
print("simpson")
summary(aovp(as.numeric(as.factor(all_alpha_ord$simpson))~Pair+Condition,all_alpha_ord))
setkey(all_alpha_ord,S.ACE)
print("ACE")
summary(aovp(as.numeric(as.factor(all_alpha_ord$S.ACE))~Pair+Condition,all_alpha_ord))
sink()
#===============================================================================
# Filter data
#============================================================================
# plot cummulative reads (will also produce a data table "dtt" in the global environment)
ggsave(paste(RHB,"qPCR_OTU_counts.pdf",sep="_"),plotCummulativeReads(counts(dds,normalize=T)))
dds <- dds[rowSums(counts(dds, normalize=T))>4,]
#===============================================================================
# Beta diversity PCA/NMDS
#===============================================================================
### PCA ###
# perform PC decomposition of DES object
mypca <- des_to_pca(dds)
# to get pca plot axis into the same scale create a dataframe of PC scores multiplied by their variance
d <-t(data.frame(t(mypca$x)*mypca$percentVar))
# Add spatial information as a numeric and plot
colData$Location<-as.number(colData$Pair)
# plot the PCA
pdf(paste(RHB,"qPCR_PCA.pdf",sep="_"))
plotOrd(d,colData,design="Condition",xlabel="PC1",ylabel="PC2")
plotOrd(d,colData,shape="Condition",design="Location",continuous=T,xlabel="PC1",ylabel="PC2")
dev.off()
ggsave(paste(RHB,"qPCR_PCA_loc.pdf",sep="_"),plotOrd(d,colData,shape="Condition",design="Location",continuous=T,xlabel="PC1",ylabel="PC2",alpha=0.75,pointSize=2,ylims=c(-2,4)))
### remove spatial information (this uses the factor "Pair" not the numeric "Location") and plot
pc.res <- resid(aov(mypca$x~colData$Pair,colData))
d <- t(data.frame(t(pc.res*mypca$percentVar)))
ggsave(paste(RHB,"qPCR_PCA_deloc.pdf",sep="_"),plotOrd(d,colData,shape="Condition",design="Location",continuous=T,xlabel="PC1",ylabel="PC2"))
# ANOVA
sink(paste(RHB,"qPCR_PCA_ANOVA.txt",sep="_"))
print("ANOVA")
lapply(seq(1:3),function(x) summary(aov(mypca$x[,x]~Pair+Condition,colData(dds))))
print("PERMANOVA")
lapply(seq(1:3),function(x) summary(aovp(mypca$x[,x]~Pair+Condition,colData(dds))))
sink()
# Sum of variances in PC scores
sum_squares <- t(apply(mypca$x,2,function(x)
t(summary(aov(x~Pair+Condition,colData(dds)))[[1]][2]))
)
colnames(sum_squares) <- c("Pair","Condition","residual")
x<-t(apply(sum_squares,1,prop.table))
perVar <- x * mypca$percentVar
colSums(perVar)
colSums(perVar)/sum(colSums(perVar))*100
### NMDS ###
# phyloseq has functions (using Vegan) for making NMDS plots
myphylo <- ubiom_to_phylo(list(counts(dds,normalize=T),taxData,colData))
# add tree to phyloseq object
phy_tree(myphylo) <- njtree
# calculate NMDS ordination using weighted unifrac scores
ordu = ordinate(myphylo, "NMDS", "unifrac", weighted=TRUE)
# plot with plotOrd (or use plot_ordination)
ggsave(paste(RHB,"qPCR_Unifrac_NMDS.pdf",sep="_"),plotOrd(ordu$points,colData,shape="Condition",design="Location",continuous=T,xlabel="NMDS1",ylabel="NMDS2",alpha=0.75,pointSize=2))
# permanova of unifrac distance
sink(paste(RHB,"qPCR_PERMANOVA_unifrac.txt",sep="_"))
adonis(distance(myphylo,"unifrac",weighted=T)~Pair+Condition,colData(dds),parallel=12,permutations=9999)
sink()
# calculate NMDS ordination with bray-curtis distance matrix
ordu = ordinate(myphylo, "NMDS", "bray",stratmax=50)
# plot with plotOrd (or use plot_ordination)
ggsave(paste(RHB,"qPC_BRAY_NMDS.pdf",sep="_"),plotOrd(ordu$points,colData,shape="Condition",design="Location",continuous=T,xlabel="NMDS1",ylabel="NMDS2",alpha=0.75,pointSize=2))
# phyla plots - keep top 7 phyla only
phylum.sum = tapply(taxa_sums(myphylo), tax_table(myphylo)[, "phylum"], sum, na.rm=TRUE)
top7phyla = names(sort(phylum.sum, TRUE))[1:7]
myphylo_slim = prune_taxa((tax_table(myphylo)[, "phylum"] %in% top7phyla), myphylo)
# split phylo into H and S
H <- prune_samples(colData$Condition=="H",myphylo_slim)
S <- prune_samples(colData$Condition=="S",myphylo_slim)
Hord <- ordinate(H, "NMDS", "bray")
Sord <- ordinate(S, "NMDS", "bray")
theme_set(theme_facet_blank(angle=0,vjust=0,hjust=0.5))
pdf(paste(RHB,"qPCR_NMDS_taxa_by_Condition.pdf",sep="_"))
plot_ordination(H, Hord, type="taxa", color="phylum",ylims=c(-0.8,1.2),xlims=c(-0.8,1.2))+ facet_wrap(~phylum, 3)
plot_ordination(S, Sord, type="taxa", color="phylum",ylims=c(-0.8,1.2),xlims=c(-0.8,1.2))+ facet_wrap(~phylum, 3)
dev.off()
#===============================================================================
# differential analysis
#===============================================================================
# filter for low counts - this can affect the FD probability and DESeq2 does apply its own filtering for genes/otus with no power
# but, no point keeping OTUs with 0 count
dds<-dds[ rowSums(counts(dds,normalize=T))>0,]
# p value for FDR cutoff
alpha <- 0.1
# the full model
full_design <- ~Pair + Condition
# add full model to dds object
design(dds) <- full_design
# calculate fit
dds <- DESeq(dds,parallel=T)
# calculate results for default contrast (S vs H)
res <- results(dds,alpha=alpha,parallel=T)
# merge results with taxonomy data
res.merge <- data.table(inner_join(data.table(OTU=rownames(res),as.data.frame(res)),data.table(OTU=rownames(taxData),taxData)))
write.table(res.merge, paste(RHB,"qPCR_diff.txt",sep="_"),quote=F,sep="\t",na="",row.names=F)
# output sig fasta
writeXStringSet(readDNAStringSet(paste0(RHB,".otus.fa"))[ res.merge[ padj<=0.05]$OTU],paste0(RHB,".qPCR_sig.fa"))
#===============================================================================
# ****BACTERIA****
#===============================================================================
invisible(mapply(assign, names(ubiom_BAC), ubiom_BAC, MoreArgs=list(envir = globalenv())))
#===============================================================================
# Create DEseq objects
#===============================================================================
# ensure colData rows and countData columns have the same order
colData <- colData[names(countData),]
# remove low count and control samples
myfilter <- (colSums(countData)>=1000) & colData$Condition!="C"
# remove Pair of any sample with a low count
exclude<-which(!myfilter)
myfilter <- myfilter&sapply(colData$Pair,function(x) length(which(x==colData$Pair[-exclude]))>1)
# apply filter
colData <- droplevels(colData[myfilter,])
countData <- countData[,myfilter]
# simple Deseq design
design<-~1
#create DES object
# colnames(countData) <- row.names(colData)
dds<-DESeqDataSetFromMatrix(countData,colData,design)
#===============================================================================
# ****Normalised****
#===============================================================================
sizeFactors(dds) <-sizeFactors(estimateSizeFactors(dds))
#===============================================================================
# Alpha diversity analysis
#===============================================================================
# plot alpha diversity - plot_alpha will convert normalised abundances to integer values
# Add spatial information as a numeric and plot
colData$Location<-as.number(colData$Pair)
ggsave(paste(RHB,"Alpha.pdf",sep="_"),plot_alpha(counts(dds,normalize=T),colData,design="Condition",colour="Condition",cbPalette=T,legend="hidden",measures=c("Chao1", "Shannon", "Simpson","Observed"),limits=c(0,2000,"Chao1")))
### permutation based anova on diversity index ranks ###
# get the diversity index data
all_alpha_ord <- plot_alpha(counts(dds,normalize=T),colData,design="Condition",returnData=T)
# add column names as row to metadata (or use tribble)
colData$samples <- rownames(colData)
# join diversity indices and metadata
all_alpha_ord <- as.data.table(inner_join(all_alpha_ord,colData,by=c("Samples"="samples")))
# perform anova for each index
colData$Pair<-as.factor(colData$Pair)
sink(paste(RHB,"ALPHA_stats.txt",sep="_"))
setkey(all_alpha_ord,S.chao1)
print("Chao1")
summary(aovp(as.numeric(as.factor(all_alpha_ord$S.chao1))~Pair+Condition,all_alpha_ord))
setkey(all_alpha_ord,shannon)
print("Shannon")
summary(aovp(as.numeric(as.factor(all_alpha_ord$shannon))~Pair+Condition,all_alpha_ord))
setkey(all_alpha_ord,simpson)
print("simpson")
summary(aovp(as.numeric(as.factor(all_alpha_ord$simpson))~Pair+Condition,all_alpha_ord))
setkey(all_alpha_ord,S.ACE)
print("ACE")
summary(aovp(as.numeric(as.factor(all_alpha_ord$S.ACE))~Pair+Condition,all_alpha_ord))
sink()
#===============================================================================
# Filter data
#============================================================================
### read accumulation filter
# plot cummulative reads (will also produce a data table "dtt" in the global environment)
ggsave(paste(RHB,"OTU_counts.pdf",sep="_"),plotCummulativeReads(counts(dds,normalize=T)))
dds <- dds[rowSums(counts(dds, normalize=T))>4,]
#===============================================================================
# Beta diversity PCA/NMDS
#===============================================================================
### PCA ###
# perform PC decomposition of DES object
mypca <- des_to_pca(dds)
# to get pca plot axis into the same scale create a dataframe of PC scores multiplied by their variance
d <-t(data.frame(t(mypca$x)*mypca$percentVar))
# Add spatial information as a numeric and plot
colData$Location<-as.number(colData$Pair)
# plot the PCA
pdf(paste(RHB,"PCA.pdf",sep="_"))
plotOrd(d,colData,design="Condition",xlabel="PC1",ylabel="PC2")
plotOrd(d,colData,shape="Condition",design="Location",continuous=T,xlabel="PC1",ylabel="PC2")
dev.off()
ggsave(paste(RHB,"PCA_loc.pdf",sep="_"),plotOrd(d,colData,shape="Condition",design="Location",continuous=T,xlabel="PC1",ylabel="PC2",alpha=0.75,pointSize=2))
g_bac_fig4 <- plotOrd(d,colData,design="Condition",facet="Pair",axes=c(1,3),ylims=c(-2,4),alpha=0.75,pointSize=2,cbPalette=T,legend="none") +
geom_line(aes(group=facet),alpha=0.1,linetype=3,colour="#000000")
g1 <- g_bac_fig4 + ggtitle("A")+ theme_classic_thin(base_size=12)%+replace% theme(plot.title = element_text(hjust = -0.07,size=14),legend.position="none")
g <- plotOrd(d,colData,shape="Condition",design="Pair",continuous=T,axes=c(1,3),ylims=c(-2,4),alpha=0.75,pointSize=2) +
+ scale_colour_gradient(guide=F,low="red",high="yellow")
ggsave("NEW_Figure_4.pdf",grid.arrange(g1,g2,nrow=2),width=7,height=8)
### remove spatial information (this uses the factor "Pair" not the numeric "Location") and plot
pc.res <- resid(aov(mypca$x~colData$Pair,colData))
d <- t(data.frame(t(pc.res*mypca$percentVar)))
ggsave(paste(RHB,"PCA_deloc.pdf",sep="_"),plotOrd(d,colData,shape="Condition",design="Location",continuous=T,xlabel="PC1",ylabel="PC2"))
# ANOVA
sink(paste(RHB,"PCA_ANOVA.txt",sep="_"))
print("ANOVA")
lapply(seq(1:3),function(x) summary(aov(mypca$x[,x]~Pair+Condition,colData(dds))))
print("PERMANOVA")
lapply(seq(1:3),function(x) summary(aovp(mypca$x[,x]~Pair+Condition,colData(dds))))
sink()
# Sum of variances in PC scores
sum_squares <- t(apply(mypca$x,2,function(x)
t(summary(aov(x~Pair+Condition,colData(dds)))[[1]][2]))
)
colnames(sum_squares) <- c("Pair","Condition","residual")
x<-t(apply(sum_squares,1,prop.table))
perVar <- x * mypca$percentVar
colSums(perVar)
colSums(perVar)/sum(colSums(perVar))*100
### NMDS ###
# phyloseq has functions (using Vegan) for making NMDS plots
myphylo <- ubiom_to_phylo(list(counts(dds,normalize=T),taxData,colData))
# add tree to phyloseq object
phy_tree(myphylo) <- njtree
# calculate NMDS ordination using weighted unifrac scores
ordu = ordinate(myphylo, "NMDS", "unifrac", weighted=TRUE)
# plot with plotOrd (or use plot_ordination)
ggsave(paste(RHB,"Unifrac_NMDS.pdf",sep="_"),plotOrd(ordu$points,colData,shape="Condition",design="Location",continuous=T,xlabel="NMDS1",ylabel="NMDS2",alpha=0.75,pointSize=2))
# permanova of unifrac distance
sink(paste(RHB,"PERMANOVA_unifrac.txt",sep="_"))
adonis(distance(myphylo,"unifrac",weighted=T)~Pair+Condition,colData(dds),parallel=12,permutations=9999)
sink()
# calculate NMDS ordination with bray-curtis distance matrix
ordu = ordinate(myphylo, "NMDS", "bray",stratmax=50)
# plot with plotOrd (or use plot_ordination)
ggsave(paste(RHB,"BRAY_NMDS.pdf",sep="_"),plotOrd(ordu$points,colData,shape="Condition",design="Location",continuous=T,xlabel="NMDS1",ylabel="NMDS2",alpha=0.75,pointSize=2))
# phyla plots - keep top 7 phyla only
phylum.sum = tapply(taxa_sums(myphylo), tax_table(myphylo)[, "phylum"], sum, na.rm=TRUE)
top12phyla = names(sort(phylum.sum, TRUE))[1:12]
myphylo_slim = prune_taxa((tax_table(myphylo)[, "phylum"] %in% top12phyla), myphylo)
# split phylo into H and S
H <- prune_samples(colData$Condition=="H",myphylo_slim)
S <- prune_samples(colData$Condition=="S",myphylo_slim)
# calculate ordination
Hord <- ordinate(H, "NMDS", "bray")
Sord <- ordinate(S, "NMDS", "bray")
# plot with plot_ordination
theme_set(theme_facet_blank(angle=0,vjust=0,hjust=0.5))
pdf(paste(RHB,"NMDS_taxa_by_Condition.pdf",sep="_"))
plot_ordination(H, Hord, type="taxa", color="phylum")+ facet_wrap(~phylum, 3)
plot_ordination(S, Sord, type="taxa", color="phylum")+ facet_wrap(~phylum, 3)
dev.off()
#===============================================================================
# differential analysis
#===============================================================================
# filter for low counts - this can affect the FD probability and DESeq2 does apply its own filtering for genes/otus with no power
# but, no point keeping OTUs with 0 count
dds<-dds[rowSums(counts(dds,normalize=T))>0,]
# p value for FDR cutoff
alpha <- 0.1
# the full model
full_design <- ~Pair + Condition
# add full model to dds object
design(dds) <- full_design
# calculate fit
dds <- DESeq(dds,parallel=T)
# calculate results for default contrast (S vs H)
res <- results(dds,alpha=alpha,parallel=T)
# merge results with taxonomy data
res.merge <- data.table(inner_join(data.table(OTU=rownames(res),as.data.frame(res)),data.table(OTU=rownames(taxData),taxData)))
write.table(res.merge, paste(RHB,"diff.txt",sep="_"),quote=F,sep="\t",na="",row.names=F)
# output sig fasta
writeXStringSet(readDNAStringSet(paste0(RHB,".otus.fa"))[res.merge[padj<=0.05]$OTU],paste0(RHB,".sig.fa"))
#==============================================================================
# ****qPCR****
#===============================================================================
dds<-DESeqDataSetFromMatrix(countData,colData,design)
# Correction from aboslute quantification
sizeFactors(dds) <- colData$bacq
# Correction from aboslute quantification v2
# sizeFactors(dds) <- sizeFactors(dds)/colData$bacq
#===============================================================================
# Alpha diversity analysis
#===============================================================================
# plot alpha diversity - plot_alpha will convert normalised abundances to integer values
# Add spatial information as a numeric and plot
colData$Location<-as.number(colData$Pair)
ggsave(paste(RHB,"qPCR_Alpha.pdf",sep="_"),plot_alpha(counts(dds,normalize=T),colData,design="Condition",colour="Condition",cbPalette=T,legend="hidden",measures=c("Chao1", "Shannon", "Simpson","Observed"),limits=c(0,2000,"Chao1")))
### permutation based anova on diversity index ranks ###
# get the diversity index data
all_alpha_ord <- plot_alpha(counts(dds,normalize=T),colData,design="Condition",returnData=T)
# add column names as row to metadata (or use tribble)
colData$samples <- rownames(colData)
# join diversity indices and metadata
all_alpha_ord <- as.data.table(inner_join(all_alpha_ord,colData,by=c("Samples"="samples")))
# perform anova for each index
colData$Pair<-as.factor(colData$Pair)
sink(paste(RHB,"qPCR_ALPHA_stats.txt",sep="_"))
setkey(all_alpha_ord,S.chao1)
print("Chao1")
summary(aovp(as.numeric(as.factor(all_alpha_ord$S.chao1))~Pair+Condition,all_alpha_ord))
setkey(all_alpha_ord,shannon)
print("Shannon")
summary(aovp(as.numeric(as.factor(all_alpha_ord$shannon))~Pair+Condition,all_alpha_ord))
setkey(all_alpha_ord,simpson)
print("simpson")
summary(aovp(as.numeric(as.factor(all_alpha_ord$simpson))~Pair+Condition,all_alpha_ord))
setkey(all_alpha_ord,S.ACE)
print("ACE")
summary(aovp(as.numeric(as.factor(all_alpha_ord$S.ACE))~Pair+Condition,all_alpha_ord))
sink()
#===============================================================================
# Filter data
#============================================================================
# plot cummulative reads (will also produce a data table "dtt" in the global environment)
ggsave(paste(RHB,"qPCR_OTU_counts.pdf",sep="_"),plotCummulativeReads(counts(dds,normalize=T)))
dds <- dds[rowSums(counts(dds, normalize=T))>4,]
#===============================================================================
# Beta diversity PCA/NMDS
#===============================================================================
### PCA ###
# perform PC decomposition of DES object
mypca <- des_to_pca(dds)
# to get pca plot axis into the same scale create a dataframe of PC scores multiplied by their variance
d <-t(data.frame(t(mypca$x)*mypca$percentVar))
# Add spatial information as a numeric and plot
colData$Location<-as.number(colData$Pair)
# plot the PCA
pdf(paste(RHB,"qPCR_PCA.pdf",sep="_"))
plotOrd(d,colData,design="Condition",xlabel="PC1",ylabel="PC2")
plotOrd(d,colData,shape="Condition",design="Location",continuous=T,xlabel="PC1",ylabel="PC2")
dev.off()
ggsave(paste(RHB,"qPCR_PCA_loc.pdf",sep="_"),plotOrd(d,colData,shape="Condition",design="Location",continuous=T,xlabel="PC1",ylabel="PC2",alpha=0.75,pointSize=2))
### remove spatial information (this uses the factor "Pair" not the numeric "Location") and plot
pc.res <- resid(aov(mypca$x~colData$Pair,colData))
d <- t(data.frame(t(pc.res*mypca$percentVar)))
ggsave(paste(RHB,"qPCR_PCA_deloc.pdf",sep="_"),plotOrd(d,colData,shape="Condition",design="Location",continuous=T,xlabel="PC1",ylabel="PC2"))
# ANOVA
sink(paste(RHB,"qPCR_PCA_ANOVA.txt",sep="_"))
print("ANOVA")
lapply(seq(1:3),function(x) summary(aov(mypca$x[,x]~Pair+Condition,colData(dds))))
print("PERMANOVA")
lapply(seq(1:3),function(x) summary(aovp(mypca$x[,x]~Pair+Condition,colData(dds))))
sink()
# Sum of variances in PC scores
sum_squares <- t(apply(mypca$x,2,function(x)
t(summary(aov(x~Pair+Condition,colData(dds)))[[1]][2]))
)
colnames(sum_squares) <- c("Pair","Condition","residual")
x<-t(apply(sum_squares,1,prop.table))
perVar <- x * mypca$percentVar
colSums(perVar)
colSums(perVar)/sum(colSums(perVar))*100
### NMDS ###
# phyloseq has functions (using Vegan) for making NMDS plots
myphylo <- ubiom_to_phylo(list(counts(dds,normalize=T),taxData,colData))
# add tree to phyloseq object
phy_tree(myphylo) <- njtree
# calculate NMDS ordination using weighted unifrac scores
ordu = ordinate(myphylo, "NMDS", "unifrac", weighted=TRUE)
# plot with plotOrd (or use plot_ordination)
ggsave(paste(RHB,"qPCR_Unifrac_NMDS.pdf",sep="_"),plotOrd(ordu$points,colData,shape="Condition",design="Location",continuous=T,xlabel="NMDS1",ylabel="NMDS2",alpha=0.75,pointSize=2))
# calculate NMDS ordination with bray-curtis distance matrix
ordu = ordinate(myphylo, "NMDS", "bray",stratmax=50)
# plot with plotOrd (or use plot_ordination)
ggsave(paste(RHB,"qPCR_BRAY_NMDS.pdf",sep="_"),plotOrd(ordu$points,colData,shape="Condition",design="Location",continuous=T,xlabel="NMDS1",ylabel="NMDS2",alpha=0.75,pointSize=2))
# permanova of unifrac distance
sink(paste(RHB,"qPCR_PERMANOVA_unifrac.txt",sep="_"))
adonis(distance(myphylo,"unifrac",weighted=T)~Pair+Condition,colData(dds),parallel=12,permutations=9999)
sink()
# phyla plots - keep top 7 phyla only
phylum.sum = tapply(taxa_sums(myphylo), tax_table(myphylo)[, "phylum"], sum, na.rm=TRUE)
top12phyla = names(sort(phylum.sum, TRUE))[1:12]
myphylo_slim = prune_taxa((tax_table(myphylo)[, "phylum"] %in% top12phyla), myphylo)
# split phylo into H and S
H <- prune_samples(colData$Condition=="H",myphylo_slim)
S <- prune_samples(colData$Condition=="S",myphylo_slim)
Hord <- ordinate(H, "NMDS", "bray")
Sord <- ordinate(S, "NMDS", "bray")
theme_set(theme_facet_blank(angle=0,vjust=0,hjust=0.5))
pdf(paste(RHB,"qPCR_NMDS_taxa_by_Condition.pdf",sep="_"))
plot_ordination(H, Hord, type="taxa", color="phylum",ylims=c(-0.8,1.2),xlims=c(-0.8,1.2))+ facet_wrap(~phylum, 3)
plot_ordination(S, Sord, type="taxa", color="phylum",ylims=c(-0.8,1.2),xlims=c(-0.8,1.2))+ facet_wrap(~phylum, 3)
dev.off()
#===============================================================================
# differential analysis
#===============================================================================
# filter for low counts - this can affect the FD probability and DESeq2 does apply its own filtering for genes/otus with no power
# but, no point keeping OTUs with 0 count
dds<-dds[rowSums(counts(dds,normalize=T))>0,]
# p value for FDR cutoff
alpha <- 0.1
# the full model
full_design <- ~Pair + Condition
# add full model to dds object
design(dds) <- full_design
# calculate fit
dds <- DESeq(dds,parallel=T)
# calculate results for default contrast (S vs H)
res <- results(dds,alpha=alpha,parallel=T)
# merge results with taxonomy data
res.merge <- data.table(inner_join(data.table(OTU=rownames(res),as.data.frame(res)),data.table(OTU=rownames(taxData),taxData)))
write.table(res.merge, paste(RHB,"qPCR_diff.txt",sep="_"),quote=F,sep="\t",na="",row.names=F)
# output sig fasta
writeXStringSet(readDNAStringSet(paste0(RHB,".otus.fa"))[res.merge[padj<=0.05]$OTU],paste0(RHB,".qPCR_sig.fa"))
#===============================================================================
# ****OOMYCETES****
#===============================================================================
invisible(mapply(assign, names(ubiom_OO), ubiom_OO, MoreArgs=list(envir = globalenv())))
#===============================================================================
# Create DEseq objects
#===============================================================================
# ensure colData rows and countData columns have the same order
colData <- colData[names(countData),]
# remove low count and control samples
myfilter <- (colSums(countData)>=1000) & colData$Condition!="C"
# remove Pair of any sample with a low count
exclude<-which(!myfilter)
myfilter <- myfilter&sapply(colData$Pair,function(x) length(which(x==colData$Pair[-exclude]))>1)
# apply filter
colData <- droplevels(colData[myfilter,])
countData <- countData[,myfilter]
# simple Deseq design
design<-~1
#create DES object
# colnames(countData) <- row.names(colData)
dds<-DESeqDataSetFromMatrix(countData,colData,design)
# calculate size factors
sizeFactors(dds) <-sizeFactors(estimateSizeFactors(dds))
### filter to remove OTUs which are unlikely part of the correct kingdom (best to do this before Alpha diversity analysis)
myfilter <- row.names(countData[row.names(countData) %in% row.names(taxData[(taxData$kingdom=="SAR"|as.numeric(taxData$k_conf)<=0.5),]),])
dds <- dds[myfilter,]
#===============================================================================
# Alpha diversity analysis
#===============================================================================
# plot alpha diversity - plot_alpha will convert normalised abundances to integer values
# Add spatial information as a numeric and plot
colData$Location<-as.number(colData$Pair)
ggsave(paste(RHB,"Alpha.pdf",sep="_"),plot_alpha(counts(dds,normalize=T),colData,design="Condition",colour="Condition",cbPalette=T,legend="hidden",measures=c("Chao1", "Shannon", "Simpson","Observed")))
### permutation based anova on diversity index ranks ###
# get the diversity index data
all_alpha_ord <- plot_alpha(counts(dds,normalize=T),colData,design="Condition",returnData=T)
# add column names as row to metadata (or use tribble)
colData$samples <- rownames(colData)
# join diversity indices and metadata
all_alpha_ord <- as.data.table(inner_join(all_alpha_ord,colData,by=c("Samples"="samples")))
# perform anova for each index
colData$Pair<-as.factor(colData$Pair)
sink(paste(RHB,"ALPHA_stats.txt",sep="_"))
setkey(all_alpha_ord,S.chao1)
print("Chao1")
summary(aovp(as.numeric(as.factor(all_alpha_ord$S.chao1))~Pair+Condition,all_alpha_ord))
setkey(all_alpha_ord,shannon)
print("Shannon")
summary(aovp(as.numeric(as.factor(all_alpha_ord$shannon))~Pair+Condition,all_alpha_ord))
setkey(all_alpha_ord,simpson)
print("simpson")
summary(aovp(as.numeric(as.factor(all_alpha_ord$simpson))~Pair+Condition,all_alpha_ord))
setkey(all_alpha_ord,S.ACE)
print("ACE")
summary(aovp(as.numeric(as.factor(all_alpha_ord$S.ACE))~Pair+Condition,all_alpha_ord))
sink()
#===============================================================================
# Filter data
#============================================================================
# plot cummulative reads (will also produce a data table "dtt" in the global environment)
ggsave(paste(RHB,"OTU_counts.pdf",sep="_"),plotCummulativeReads(counts(dds,normalize=T)))
dds <- dds[rowSums(counts(dds, normalize=T))>4,]
#===============================================================================
# Beta diversity PCA/NMDS
#===============================================================================
### PCA ###
# perform PC decomposition of DES object
mypca <- des_to_pca(dds)
# to get pca plot axis into the same scale create a dataframe of PC scores multiplied by their variance
d <-t(data.frame(t(mypca$x)*mypca$percentVar))
# Add spatial information as a numeric and plot
colData$Location<-as.number(colData$Pair)
# plot the PCA
pdf(paste(RHB,"PCA.pdf",sep="_"))
plotOrd(d,colData,design="Condition",xlabel="PC1",ylabel="PC2")
plotOrd(d,colData,shape="Condition",design="Location",continuous=T,xlabel="PC1",ylabel="PC2")
dev.off()
ggsave(paste(RHB,"PCA_loc.pdf",sep="_"),plotOrd(d,colData,shape="Condition",design="Location",continuous=T,xlabel="PC1",ylabel="PC2",alpha=0.75,pointSize=2))
### remove spatial information (this uses the factor "Pair" not the numeric "Location") and plot
pc.res <- resid(aov(mypca$x~colData$Pair,colData))
d <- t(data.frame(t(pc.res*mypca$percentVar)))
ggsave(paste(RHB,"PCA_deloc.pdf",sep="_"),plotOrd(d,colData,shape="Condition",design="Location",continuous=T,xlabel="PC1",ylabel="PC2"))
# ANOVA
sink(paste(RHB,"PCA_ANOVA.txt",sep="_"))
print("ANOVA")
lapply(seq(1:3),function(x) summary(aov(mypca$x[,x]~Pair+Condition,colData(dds))))
print("PERMANOVA")
lapply(seq(1:3),function(x) summary(aovp(mypca$x[,x]~Pair+Condition,colData(dds))))
sink()
# Sum of variances in PC scores
sum_squares <- t(apply(mypca$x,2,function(x)
t(summary(aov(x~Pair+Condition,colData(dds)))[[1]][2]))
)
colnames(sum_squares) <- c("Pair","Condition","residual")
x<-t(apply(sum_squares,1,prop.table))
perVar <- x * mypca$percentVar
colSums(perVar)
colSums(perVar)/sum(colSums(perVar))*100
### NMDS ###
# phyloseq has functions (using Vegan) for making NMDS plots
myphylo <- ubiom_to_phylo(list(counts(dds,normalize=T),taxData,colData))
# add tree to phyloseq object
phy_tree(myphylo) <- njtree
# calculate NMDS ordination using weighted unifrac scores
ordu = ordinate(myphylo, "NMDS", "unifrac", weighted=TRUE)
# plot with plotOrd (or use plot_ordination)
ggsave(paste(RHB,"Unifrac_NMDS.pdf",sep="_"),plotOrd(ordu$points,colData,shape="Condition",design="Location",continuous=T,xlabel="NMDS1",ylabel="NMDS2",alpha=0.75,pointSize=2))
# permanova of unifrac distance
sink(paste(RHB,"PERMANOVA_unifrac.txt",sep="_"))
adonis(distance(myphylo,"unifrac",weighted=T)~Pair+Condition,colData(dds),parallel=12,permutations=9999)
sink()
# calculate NMDS ordination with bray-curtis distance matrix
ordu = ordinate(myphylo, "NMDS", "bray",stratmax=50)
# plot with plotOrd (or use plot_ordination)
ggsave(paste(RHB,"BRAY_NMDS.pdf",sep="_"),plotOrd(ordu$points,colData,shape="Condition",design="Location",continuous=T,xlabel="NMDS1",ylabel="NMDS2",alpha=0.75,pointSize=2))
# phyla plots - keep top 7 phyla only
phylum.sum = tapply(taxa_sums(myphylo), tax_table(myphylo)[, "phylum"], sum, na.rm=TRUE)
top7phyla = names(sort(phylum.sum, TRUE))[1:7]
myphylo_slim = prune_taxa((tax_table(myphylo)[, "phylum"] %in% top7phyla), myphylo)
# split phylo into H and S
H <- prune_samples(colData$Condition=="H",myphylo_slim)
S <- prune_samples(colData$Condition=="S",myphylo_slim)
# calculate ordination
Hord <- ordinate(H, "NMDS", "bray")
Sord <- ordinate(S, "NMDS", "bray")
# plot with plot_ordination
theme_set(theme_facet_blank(angle=0,vjust=0,hjust=0.5))
pdf(paste(RHB,"NMDS_taxa_by_Condition.pdf",sep="_"))
plot_ordination(H, Hord, type="taxa", color="phylum")+ facet_wrap(~phylum, 3)
plot_ordination(S, Sord, type="taxa", color="phylum")+ facet_wrap(~phylum, 3)
dev.off()
#===============================================================================
# differential analysis
#===============================================================================
# filter for low counts - this can affect the FD probability and DESeq2 does apply its own filtering for genes/otus with no power
# but, no point keeping OTUs with 0 count
dds<-dds[rowSums(counts(dds,normalize=T))>0,]
# p value for FDR cutoff
alpha <- 0.1
# the full model
full_design <- ~Pair + Condition
# add full model to dds object
design(dds) <- full_design
# calculate fit
dds <- DESeq(dds,parallel=T)
# calculate results for default contrast (S vs H)
res <- results(dds,alpha=alpha,parallel=T)
# merge results with taxonomy data
res.merge <- data.table(inner_join(data.table(OTU=rownames(res),as.data.frame(res)),data.table(OTU=rownames(taxData),taxData)))
write.table(res.merge, paste(RHB,"diff.txt",sep="_"),quote=F,sep="\t",na="",row.names=F)
# output sig fasta
writeXStringSet(readDNAStringSet(paste0(RHB,".otus.fa"))[res.merge[padj<=0.05]$OTU],paste0(RHB,".sig.fa"))
#==============================================================================
# **qPCR**
#===============================================================================
dds<-DESeqDataSetFromMatrix(countData,colData,design)
# Correction from aboslute quantification of fungal ITS
sizeFactors(dds) <- sizeFactors(estimateSizeFactors(dds))/left_join(colData,ubiom_FUN$colData)$funq
sizeFactors(dds) <- left_join(colData,ubiom_FUN$colData)$funq
### filter to remove OTUs which are unlikely part of the correct kingdom (best to do this before Alpha diversity analysis)
myfilter <- row.names(countData[row.names(countData) %in% row.names(taxData[(taxData$kingdom=="SAR"|as.numeric(taxData$k_conf)<=0.5),]),])
dds <- dds[myfilter,]
#===============================================================================
# Alpha diversity analysis
#===============================================================================
# plot alpha diversity - plot_alpha will convert normalised abundances to integer values
# Add spatial information as a numeric and plot
colData$Location<-as.number(colData$Pair)
ggsave(paste(RHB,"Alpha_qPCR.pdf",sep="_"),plot_alpha(counts(dds,normalize=T),colData,design="Condition",colour="Condition",cbPalette=T,legend="hidden",measures=c("Chao1", "Shannon", "Simpson","Observed")))
### permutation based anova on diversity index ranks ###
# get the diversity index data
all_alpha_ord <- plot_alpha(counts(dds,normalize=T),colData,design="Condition",returnData=T)
# add column names as row to metadata (or use tribble)
colData$samples <- rownames(colData)
# join diversity indices and metadata
all_alpha_ord <- as.data.table(inner_join(all_alpha_ord,colData,by=c("Samples"="samples")))
# perform anova for each index
colData$Pair<-as.factor(colData$Pair)
sink(paste(RHB,"ALPHA_stats_qPCR.txt",sep="_"))
setkey(all_alpha_ord,S.chao1)
print("Chao1")
summary(aovp(as.numeric(as.factor(all_alpha_ord$S.chao1))~Pair+Condition,all_alpha_ord))
setkey(all_alpha_ord,shannon)
print("Shannon")
summary(aovp(as.numeric(as.factor(all_alpha_ord$shannon))~Pair+Condition,all_alpha_ord))
setkey(all_alpha_ord,simpson)
print("simpson")
summary(aovp(as.numeric(as.factor(all_alpha_ord$simpson))~Pair+Condition,all_alpha_ord))
setkey(all_alpha_ord,S.ACE)
print("ACE")
summary(aovp(as.numeric(as.factor(all_alpha_ord$S.ACE))~Pair+Condition,all_alpha_ord))
sink()
#===============================================================================
# Filter data
#============================================================================
# plot cummulative reads (will also produce a data table "dtt" in the global environment)
ggsave(paste(RHB,"OTU_counts_qPCR.pdf",sep="_"),plotCummulativeReads(counts(dds,normalize=T)))
dds <- dds[rowSums(counts(dds, normalize=T))>4,]
#===============================================================================
# Beta diversity PCA/NMDS
#===============================================================================
### PCA ###
# perform PC decomposition of DES object
mypca <- des_to_pca(dds)
# to get pca plot axis into the same scale create a dataframe of PC scores multiplied by their variance
d <-t(data.frame(t(mypca$x)*mypca$percentVar))
# Add spatial information as a numeric and plot
colData$Location<-as.number(colData$Pair)
# plot the PCA
pdf(paste(RHB,"PCA_qPCR.pdf",sep="_"))
plotOrd(d,colData,design="Condition",xlabel="PC1",ylabel="PC2")
plotOrd(d,colData,shape="Condition",design="Location",continuous=T,xlabel="PC1",ylabel="PC2")
dev.off()
ggsave(paste(RHB,"PCA_loc_qPCR.pdf",sep="_"),plotOrd(d,colData,shape="Condition",design="Location",continuous=T,xlabel="PC1",ylabel="PC2",alpha=0.75,pointSize=2))
### remove spatial information (this uses the factor "Pair" not the numeric "Location") and plot
pc.res <- resid(aov(mypca$x~colData$Pair,colData))
d <- t(data.frame(t(pc.res*mypca$percentVar)))
ggsave(paste(RHB,"PCA_deloc_qPCR.pdf",sep="_"),plotOrd(d,colData,shape="Condition",design="Location",continuous=T,xlabel="PC1",ylabel="PC2"))
# ANOVA
sink(paste(RHB,"PCA_ANOVA_qPCR.txt",sep="_"))
print("ANOVA")
lapply(seq(1:3),function(x) summary(aov(mypca$x[,x]~Pair+Condition,colData(dds))))
print("PERMANOVA")
lapply(seq(1:3),function(x) summary(aovp(mypca$x[,x]~Pair+Condition,colData(dds))))
sink()
# Sum of variances in PC scores
sum_squares <- t(apply(mypca$x,2,function(x)
t(summary(aov(x~Pair+Condition,colData(dds)))[[1]][2]))
)
colnames(sum_squares) <- c("Pair","Condition","residual")
x<-t(apply(sum_squares,1,prop.table))
perVar <- x * mypca$percentVar
colSums(perVar)
colSums(perVar)/sum(colSums(perVar))*100
### NMDS ###
# phyloseq has functions (using Vegan) for making NMDS plots
myphylo <- ubiom_to_phylo(list(counts(dds,normalize=T),taxData,colData))
# add tree to phyloseq object
phy_tree(myphylo) <- njtree
# calculate NMDS ordination using weighted unifrac scores
ordu = ordinate(myphylo, "NMDS", "unifrac", weighted=TRUE)
# plot with plotOrd (or use plot_ordination)
ggsave(paste(RHB,"Unifrac_NMDS_qPCR.pdf",sep="_"),plotOrd(ordu$points,colData,shape="Condition",design="Location",continuous=T,xlabel="NMDS1",ylabel="NMDS2",alpha=0.75,pointSize=2))
# permanova of unifrac distance
sink(paste(RHB,"PERMANOVA_unifrac_qPCR.txt",sep="_"))
adonis(distance(myphylo,"unifrac",weighted=T)~Pair+Condition,colData(dds),parallel=12,permutations=9999)
sink()
# calculate NMDS ordination with bray-curtis distance matrix
ordu = ordinate(myphylo, "NMDS", "bray",stratmax=50)
# plot with plotOrd (or use plot_ordination)
ggsave(paste(RHB,"BRAY_NMDS_qPCR.pdf",sep="_"),plotOrd(ordu$points,colData,shape="Condition",design="Location",continuous=T,xlabel="NMDS1",ylabel="NMDS2",alpha=0.75,pointSize=2))
# phyla plots - keep top 7 phyla only
phylum.sum = tapply(taxa_sums(myphylo), tax_table(myphylo)[, "phylum"], sum, na.rm=TRUE)
top7phyla = names(sort(phylum.sum, TRUE))[1:7]
myphylo_slim = prune_taxa((tax_table(myphylo)[, "phylum"] %in% top7phyla), myphylo)
# split phylo into H and S
H <- prune_samples(colData$Condition=="H",myphylo_slim)
S <- prune_samples(colData$Condition=="S",myphylo_slim)
# calculate ordination
Hord <- ordinate(H, "NMDS", "bray")
Sord <- ordinate(S, "NMDS", "bray")
# plot with plot_ordination
theme_set(theme_facet_blank(angle=0,vjust=0,hjust=0.5))
pdf(paste(RHB,"NMDS_taxa_by_Condition_qPCR.pdf",sep="_"))
plot_ordination(H, Hord, type="taxa", color="phylum")+ facet_wrap(~phylum, 3)
plot_ordination(S, Sord, type="taxa", color="phylum")+ facet_wrap(~phylum, 3)
dev.off()
#===============================================================================
# differential analysis
#===============================================================================
# filter for low counts - this can affect the FD probability and DESeq2 does apply its own filtering for genes/otus with no power
# but, no point keeping OTUs with 0 count
dds<-dds[rowSums(counts(dds,normalize=T))>0,]
# p value for FDR cutoff
alpha <- 0.1
# the full model
full_design <- ~Pair + Condition
# add full model to dds object
design(dds) <- full_design
# calculate fit
dds <- DESeq(dds,parallel=T)
# calculate results for default contrast (S vs H)
res <- results(dds,alpha=alpha,parallel=T)
# merge results with taxonomy data
res.merge <- data.table(inner_join(data.table(OTU=rownames(res),as.data.frame(res)),data.table(OTU=rownames(taxData),taxData)))
write.table(res.merge, paste(RHB,"diff_qPCR.txt",sep="_"),quote=F,sep="\t",na="",row.names=F)
# output sig fasta
writeXStringSet(readDNAStringSet(paste0(RHB,".otus.fa"))[res.merge[padj<=0.05]$OTU],paste0(RHB,".sig_qPCR.fa"))
#===============================================================================
# ****NEMATODE****
#===============================================================================
invisible(mapply(assign, names(ubiom_NEM), ubiom_NEM, MoreArgs=list(envir = globalenv())))
#===============================================================================
# Create DEseq objects
#===============================================================================
# ensure colData rows and countData columns have the same order
colData <- colData[names(countData),]
# remove low count and control samples
myfilter <- (colSums(countData)>=1000) & colData$Condition!="C"
# remove Pair of any sample with a low count
exclude<-which(!myfilter)
myfilter <- myfilter&sapply(colData$Pair,function(x) length(which(x==colData$Pair[-exclude]))>1)
# apply filter
colData <- droplevels(colData[myfilter,])
countData <- countData[,myfilter]
# simple Deseq design
design<-~1
#create DES object
# colnames(countData) <- row.names(colData)
dds<-DESeqDataSetFromMatrix(countData,colData,design)
# calculate size factors
sizeFactors(dds) <-sizeFactors(estimateSizeFactors(dds))
### filter to remove OTUs which are unlikely part of the correct kingdom (best to do this before Alpha diversity analysis)
myfilter <- row.names(taxData[as.number(taxData$c_conf)>0.9 & as.number(taxData$o_conf)>0.9,])
dds <- dds[rownames(dds)%in%myfilter,]
#===============================================================================
# Alpha diversity analysis
#===============================================================================
# plot alpha diversity - plot_alpha will convert normalised abundances to integer values
# Add spatial information as a numeric and plot
colData$Location<-as.number(colData$Pair)
ggsave(paste(RHB,"Alpha.pdf",sep="_"),plot_alpha(counts(dds,normalize=T),colData,design="Condition",colour="Condition",cbPalette=T,legend="hidden",measures=c("Chao1", "Shannon", "Simpson","Observed")))
### permutation based anova on diversity index ranks ###
# get the diversity index data
all_alpha_ord <- plot_alpha(counts(dds,normalize=T),colData,design="Condition",returnData=T)
# add column names as row to metadata (or use tribble)
colData$samples <- rownames(colData)
# join diversity indices and metadata
all_alpha_ord <- as.data.table(inner_join(all_alpha_ord,colData,by=c("Samples"="samples")))
# perform anova for each index
colData$Pair<-as.factor(colData$Pair)
sink(paste(RHB,"ALPHA_stats.txt",sep="_"))
setkey(all_alpha_ord,S.chao1)
print("Chao1")
summary(aovp(as.numeric(as.factor(all_alpha_ord$S.chao1))~Pair+Condition,all_alpha_ord))
setkey(all_alpha_ord,shannon)
print("Shannon")
summary(aovp(as.numeric(as.factor(all_alpha_ord$shannon))~Pair+Condition,all_alpha_ord))
setkey(all_alpha_ord,simpson)
print("simpson")
summary(aovp(as.numeric(as.factor(all_alpha_ord$simpson))~Pair+Condition,all_alpha_ord))
setkey(all_alpha_ord,S.ACE)
print("ACE")
summary(aovp(as.numeric(as.factor(all_alpha_ord$S.ACE))~Pair+Condition,all_alpha_ord))
sink()
#===============================================================================
# Filter data
#============================================================================
### read accumulation filter
# plot cummulative reads (will also produce a data table "dtt" in the global environment)
ggsave(paste(RHB,"OTU_counts.pdf",sep="_"),plotCummulativeReads(counts(dds,normalize=T)))
dds <- dds[rowSums(counts(dds, normalize=T))>4,]
#===============================================================================
# Beta diversity PCA/NMDS
#===============================================================================
### PCA ###
# perform PC decomposition of DES object
mypca <- des_to_pca(dds)
# to get pca plot axis into the same scale create a dataframe of PC scores multiplied by their variance
d <-t(data.frame(t(mypca$x)*mypca$percentVar))
# Add spatial information as a numeric and plot
colData$Location<-as.number(colData$Pair)
# plot the PCA
pdf(paste(RHB,"PCA.pdf",sep="_"))
plotOrd(d,colData,design="Condition",xlabel="PC1",ylabel="PC2")
plotOrd(d,colData,shape="Condition",design="Location",continuous=T,xlabel="PC1",ylabel="PC2")
dev.off()
ggsave(paste(RHB,"PCA_loc.pdf",sep="_"),plotOrd(d,colData,shape="Condition",design="Location",continuous=T,xlabel="PC1",ylabel="PC2",alpha=0.75,pointSize=2))
### remove spatial information (this uses the factor "Pair" not the numeric "Location") and plot
pc.res <- resid(aov(mypca$x~colData$Pair,colData))
d <- t(data.frame(t(pc.res*mypca$percentVar)))
ggsave(paste(RHB,"PCA_deloc.pdf",sep="_"),plotOrd(d,colData,shape="Condition",design="Location",continuous=T,xlabel="PC1",ylabel="PC2"))
# ANOVA
sink(paste(RHB,"PCA_ANOVA.txt",sep="_"))
print("ANOVA")
lapply(seq(1:3),function(x) summary(aov(mypca$x[,x]~Pair+Condition,colData(dds))))
print("PERMANOVA")
lapply(seq(1:3),function(x) summary(aovp(mypca$x[,x]~Pair+Condition,colData(dds))))
sink()
# Sum of variances in PC scores
sum_squares <- t(apply(mypca$x,2,function(x)
t(summary(aov(x~Pair+Condition,colData(dds)))[[1]][2]))
)
colnames(sum_squares) <- c("Pair","Condition","residual")
x<-t(apply(sum_squares,1,prop.table))
perVar <- x * mypca$percentVar
colSums(perVar)
colSums(perVar)/sum(colSums(perVar))*100
### NMDS ###
# phyloseq has functions (using Vegan) for making NMDS plots
myphylo <- ubiom_to_phylo(list(counts(dds,normalize=T),taxData,colData))
# add tree to phyloseq object
phy_tree(myphylo) <- njtree
# calculate NMDS ordination using weighted unifrac scores
ordu = ordinate(myphylo, "NMDS", "unifrac", weighted=TRUE)
# plot with plotOrd (or use plot_ordination)
ggsave(paste(RHB,"Unifrac_NMDS.pdf",sep="_"),plotOrd(ordu$points,colData,shape="Condition",design="Location",continuous=T,xlabel="NMDS1",ylabel="NMDS2",alpha=0.75,pointSize=2))
# permanova of unifrac distance
sink(paste(RHB,"PERMANOVA_unifrac.txt",sep="_"))
adonis(distance(myphylo,"unifrac",weighted=T)~Pair+Condition,colData(dds),parallel=12,permutations=9999)
sink()
# calculate NMDS ordination with bray-curtis distance matrix
ordu = ordinate(myphylo, "NMDS", "bray",stratmax=50)
# plot with plotOrd (or use plot_ordination)
ggsave(paste(RHB,"BRAY_NMDS.pdf",sep="_"),plotOrd(ordu$points,colData,shape="Condition",design="Location",continuous=T,xlabel="NMDS1",ylabel="NMDS2",alpha=0.75,pointSize=2))
# phyla plots - keep top 7 phyla only
phylum.sum = tapply(taxa_sums(myphylo), tax_table(myphylo)[, "phylum"], sum, na.rm=TRUE)
top7phyla = names(sort(phylum.sum, TRUE))[1:7]
myphylo_slim = prune_taxa((tax_table(myphylo)[, "phylum"] %in% top7phyla), myphylo)
# split phylo into H and S
H <- prune_samples(colData$Condition=="H",myphylo_slim)
S <- prune_samples(colData$Condition=="S",myphylo_slim)
# calculate ordination
Hord <- ordinate(H, "NMDS", "bray")
Sord <- ordinate(S, "NMDS", "bray")
# plot with plot_ordination
theme_set(theme_facet_blank(angle=0,vjust=0,hjust=0.5))
pdf(paste(RHB,"NMDS_taxa_by_Condition.pdf",sep="_"))
plot_ordination(H, Hord, type="taxa", color="phylum")+ facet_wrap(~phylum, 3)
plot_ordination(S, Sord, type="taxa", color="phylum")+ facet_wrap(~phylum, 3)
dev.off()
#===============================================================================
# differential analysis
#===============================================================================
# filter for low counts - this can affect the FD probability and DESeq2 does apply its own filtering for genes/otus with no power
# but, no point keeping OTUs with 0 count
dds<-dds[rowSums(counts(dds,normalize=T))>0,]
# p value for FDR cutoff
alpha <- 0.1
# the full model
full_design <- ~Pair + Condition
# add full model to dds object
design(dds) <- full_design
# calculate fit
dds <- DESeq(dds,parallel=T)
# calculate results for default contrast (S vs H)
res <- results(dds,alpha=alpha,parallel=T)
# merge results with taxonomy data
res.merge <- data.table(inner_join(data.table(OTU=rownames(res),as.data.frame(res)),data.table(OTU=rownames(taxData),taxData)))
write.table(res.merge, paste(RHB,"diff.txt",sep="_"),quote=F,sep="\t",na="",row.names=F)
# output sig fasta
writeXStringSet(readDNAStringSet(paste0(RHB,".otus.fa"))[res.merge[padj<=0.05]$OTU],paste0(RHB,".sig.fa"))
|
162b8375e5a5b2fd72795ed754369b93c99aaaca
|
a175d771d8fbe234ca1ca55f8ca7bf13aa8d847e
|
/Linear regression on Housing Price dataset.R
|
d812181ebf94116d360f5e312c924cc196d0780b
|
[] |
no_license
|
HarshadaPatil19/Linear-Regression
|
0633aebe2e33d3ac087cbce3870c2883a0c5e7ae
|
c2d6430e3d5eb67ff530e04519d9b5cb11fa88bc
|
refs/heads/main
| 2023-03-25T15:21:05.620903
| 2021-03-13T19:00:07
| 2021-03-13T19:00:07
| 347,454,123
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 13,234
|
r
|
Linear regression on Housing Price dataset.R
|
#
library(ggplot2)
library(ggthemes)
library(scales)
library(dplyr)
library(gridExtra)
library(corrplot)
library(GGally)
library(e1071)
library(DAAG)
# Read Data File
data = read.csv("C:/Users/harsh/Downloads/Property_Price_Train.csv", na.strings=c("","NA"))
data
# Data dim
dim(data)
# structure of the data
str(data)
# Summary
summary(data)
# Data View
View(data)
# Remove ID variable containing unique values
data <- data[-1]
data
# Detecting NA values
colSums(is.na(data))
# Missing value analysis
Missing_index <- data %>% is.na() %>% colSums() * 100
Missing_index <- sort(round(Missing_index[Missing_index > 0], 2), decreasing = TRUE)
Missing_index
View(Missing_index)
# % of NA's in dataframe
sum(is.na(data))/prod(dim(data)) * 100
# % of NA's contains as rows
nrow(data[!complete.cases(data),]) / nrow(data) * 100
# Segregating numeric and factor data
data.numeric <- data[sapply(data, is.numeric)]
data.numeric
data.factor <- data[sapply(data, is.factor)]
data.factor
dim(data.numeric)
dim(data.factor)
# Numerical data analysis for cleaning
str(data.numeric)
summary(data.numeric)
# Need to convert datatype from numeic to factor
# 1. Overall_Material
# 2. House_Condition
# 3. Construction_Year
# 4. Remodel_Year
# 5. Kitchen_Above_Grade
# 6. Rooms_Above_Grade
# 7. Fireplaces
# 8. Garage_Size
# 9. Garage_Built_Year
# 10. Month_Sold
# 11. Year_Sold
# 12. "Underground_Full_Bathroom",
# 13. "Underground_Half_Bathroom",
# 14. "Full_Bathroom_Above_Grade",
# 15. "Half_Bathroom_Above_Grade",
# 16. "Bedroom_Above_Grade",
data$Overall_Material <- as.factor(data$Overall_Material)
data$House_Condition <- as.factor(data$House_Condition)
data$Construction_Year <- as.factor(data$Construction_Year)
data$Remodel_Year <- as.factor(data$Remodel_Year)
data$Kitchen_Above_Grade <- as.factor(data$Kitchen_Above_Grade)
data$Rooms_Above_Grade <- as.factor(data$Rooms_Above_Grade)
data$Fireplaces <- as.factor(data$Fireplaces)
data$Garage_Size <- as.factor(data$Garage_Size)
data$Garage_Built_Year <- as.factor(data$Garage_Built_Year)
data$Month_Sold <- as.factor(data$Month_Sold)
data$Year_Sold <- as.factor(data$Year_Sold)
data$Underground_Full_Bathroom <- as.factor(data$Underground_Full_Bathroom)
data$Underground_Half_Bathroom <- as.factor(data$Underground_Half_Bathroom)
data$Full_Bathroom_Above_Grade <- as.factor(data$Full_Bathroom_Above_Grade)
data$Half_Bathroom_Above_Grade <- as.factor(data$Half_Bathroom_Above_Grade)
data$Bedroom_Above_Grade <- as.factor(data$Bedroom_Above_Grade)
## Again data segregation
data.numeric <- data[sapply(data, is.numeric)]
data.numeric
dim(data.numeric)
data.factor <- data[sapply(data, is.factor)]
data.factor
dim(data.factor)
# Factor data analysis for cleaning
str(data.factor)
summary(data.factor)
# Remove the highly biassed variables "Road_Type" and "Utility_Type"
data <- data[(!colnames(data) %in% c("Utility_Type","Road_Type"))]
dim(data)
# Again data segregation
data.numeric <- data[sapply(data, is.numeric)]
data.numeric
dim(data.numeric)
data.factor <- data[sapply(data, is.factor)]
data.factor
dim(data.factor)
# Checking NA values in target variable
any(is.na(data$Sale_Price))
#Imputation with mean
for(i in seq(data.numeric)) {
data.numeric[i]<- ifelse(is.na(data.numeric[,i]),
median(data.numeric[,i], na.rm = T), data.numeric[,i])
}
colSums(is.na(data.numeric))
# Imputation with mode
# Mode function
#mode function
getmode <- function(x) {
x <- x[!is.na(x)]
uniqv <- unique(x)
uniqv[which.max(tabulate(match(x, uniqv)))]
}
#imputation with mode
for(i in seq(data.factor))
data.factor[,i][is.na(data.factor[,i])] <- getmode(data.factor[,i])
colSums(is.na(data))
str(data.factor)
summary(data.factor)
# Analysing histogram of each numeric values
numplot <- function(column, data)
{
ggplot(data, aes_string(x=column)) +
geaom_histogram(aes(y=..density..), fill = "grey", color = "black") +
geaom_density(fill = 'blue', alpha=0.2) +
xlab(column)
}
np <- lapply(colnames(data.numeric), numplot, df=data.numeric)
np
do.call("grid.arrange", np)
# Check with skewness
data.skewed <- apply(data.numeric, c(2), skewness)
data.skewed
drops <- data.numeric[c("Lot_Size",
"Brick_Veneer_Area",
"BsmtFinSF2",
"Second_Floor_Area",
"LowQualFinSF",
"Three_Season_Lobby_Area",
"Screen_Lobby_Area",
"Pool_Area",
"Miscellaneous_Value")]
drops
np_1 <- lapply(colnames(drops), numplot, data=drops);np_1
do.call("grid.arrange", np_1)
#Outlier
out_std_check = function(x){
m=mean(x)
s=sd(x)
lc=m-3*s #lower cut-off
uc=m+3*s
n=list( val=sum(x>uc | x<lc), lc=lc, uc=uc)
return(n)
}
np <- apply(data.numeric, c(2), out_std_check)
np
out_std_fix = function(x){
m=mean(x)
s=sd(x)
lc=m-3*s #lower cut-off
uc=m+3*s
out_value <- which(x > uc | x < lc)
x[out_value] <- m
return(x)
}
data.numeric <- apply(data.numeric, c(2), out_std_fix)
data.numeric <- as.data.frame(data.numeric)
View(data.numeric)
np <- lapply(colnames(data.numeric), numplot, data=data.numeric)
do.call("grid.arrange", np)
apply(data.numeric, c(2), skewness)
corrplot::corrplot(cor(data.numeric))
corrplot.mixed(cor(data.numeric), lower.col = "black", number.cex = .7)
colnames(data.numeric)
data.numeric <- data.numeric[!colnames(data.numeric) %in% c("Garage_Area","W_Deck_Area","Open_Lobby_Area","Enclosed_Lobby_Area")]
#Now factor analysis
#bar plot for categorical varibale etc.
factplot <- function(column, df)
{
ggplot(df, aes_string(x=column))+
geom_bar(fill = "blue", color = "black", alpha= 0.2)+
xlab(column)
}
#calling all bar plot
fp <- lapply(colnames(data.factor), factplot, df=data.factor)
fp
do.call("grid.arrange", fp)
drps <- c("Land_Outline", "Property_Slope", "Condition1","Condition2"
,"House_Type", "Roof_Quality","Heating_Type" ,
"BsmtFinType2","Functional_Rate", "Kitchen_Above_Grade",
"Garage_Quality","Garage_Condition")
data.factor <- data.factor[!colnames(data.factor) %in% drps]
factors <- c("Construction_Year", "Remodel_Year","Neighborhood","Garage_Built_Year",
"Month_Sold")
data.dummies <- data.factor[!colnames(data.factor) %in% factors]
data.dummies
#Significance ananlysis
annova <- function(x) {
y <- data.numeric$Sale_Price
q <- list(summary(aov(y~x)))
return(q)
}
y <- data.numeric$Sale_Price
q <- (summary(aov(y~data.factor$Sale_Condition)))
q
signify <- apply(data.factor, c(2),annova)
signify
#dealing with year factor.
#these will need transformation like life of house by substraction built & remodled year
#for now just converting them into numeric
data.factor$Construction_Year <- as.numeric(data.factor$Construction_Year)
data.factor$Remodel_Year <- as.numeric(data.factor$Remodel_Year)
data.factor$Garage_Built_Year <- as.numeric(data.factor$Garage_Built_Year)
data.factor$Garage_Finish_Year <- as.numeric(data.factor$Garage_Finish_Year)
data.factor$Month_Sold <- as.numeric(data.factor$Month_Sold)
data.factor$Year_Sold <- as.numeric(data.factor$Year_Sold)
library(dummies)
data.factor <- dummy.data.frame(data.factor)
data.factor
# dim(data.dummies)
# dim(aq)
# data.factor <- data.factor[colnames(data.factor) %in% factors]
# data.factor <- cbind(data.factor, aq)
#
data<- cbind(data.numeric, data.factor)
data
str(data)
dim(data)
colnames(data)
### Model 1
#sampling
set.seed(10)
s=sample(1:nrow(data),0.70*nrow(data))
train=data[s,]
test=data[-s,]
dim(train)
dim(test)
colnames(test1)
test1 <- test[!colnames(test) %in% "Sale_Price"]
#Applying lm model
model <- lm(Sale_Price~., data=train)
summary(model)
pred <- predict(model, test1)
View(pred)
results <- cbind(pred,test$Sale_Price)
colnames(results) <- c('pred','real')
results <- as.data.frame(results)
View(results)
# Grab residuals
res <- residuals(model)
res <- as.data.frame(res)
head(res)
ggplot(res,aes(res)) + geom_histogram(fill='blue',alpha=0.5)
plot(model)
#Stepwise modeling
#stepwise sleection:
nullModel<- lm(Sale_Price ~ 1, train)
#summary(nullModel)
fullmodel <- lm(Sale_Price~.,data = (train))
#summary(fullmodel)
fit <- step(nullModel, scope=list(lower=nullModel, upper=fullmodel), direction="both")
#revel the model
fit
model <- lm(formula = Sale_Price ~ Grade_Living_Area + Construction_Year +
Total_Basement_Area + Overall_Material8 + Overall_Material7 +
BsmtFinSF1 + Fireplaces0 + House_Condition7 + Overall_Material9 +
Kitchen_QualityGd + Overall_Material6 + Zoning_ClassRMD +
Sale_ConditionNormal + NeighborhoodTimber + NeighborhoodSomerst +
Building_Class160 + House_Condition2 + Exterior_ConditionEx +
Garage_Size0 + House_Condition8 + Rooms_Above_Grade12 + NeighborhoodCrawfor +
NeighborhoodBrkSide + House_Condition6 + House_Condition9 +
House_Condition5 + NeighborhoodNridgHt + Sale_TypeConLI +
Rooms_Above_Grade11 + Full_Bathroom_Above_Grade3 + Property_ShapeIR3 +
Underground_Full_Bathroom0 + Building_Class75 + Garage_Size4 +
NeighborhoodBrDale + Exterior_MaterialGd + Exposure_LevelAv +
NeighborhoodClearCr + Garage_Size1 + NeighborhoodNoRidge +
Rooms_Above_Grade9 + Foundation_TypeW + Exposure_LevelNo +
Zoning_ClassCommer + NeighborhoodIDOTRR + Bedroom_Above_Grade0 +
Exterior1stStone + Lot_ConfigurationC + Rooms_Above_Grade5 +
Bedroom_Above_Grade1 + Exterior1stHdBoard + Exterior2ndVinylSd +
Bedroom_Above_Grade8 + Building_Class90 + Underground_Full_Bathroom1 +
Overall_Material10 + Garage_Size2 + `Exterior2ndWd Shng` +
BsmtFinType1GLQ + Basement_HeightEx + NeighborhoodBlmngtn +
GarageAttchd + House_Design2.5Unf + Exterior1stBrkComm, data = train)
summary(model)
plot(model)
#
# x <- factor(c("A","B","A","C","D","E","A","E","C"))
# x
# library(car)
# x <- recode(x, "c('A', 'B')='A+B';c('D', 'E') = 'D+E'")
# x
### Model 2
#sampling
set.seed(20)
s=sample(1:nrow(data),0.70*nrow(data))
train=data[s,]
test=data[-s,]
dim(train)
dim(test)
colnames(test1)
test1 <- test[!colnames(test) %in% "Sale_Price"]
#Applying lm model
model <- lm(Sale_Price~., data=train)
summary(model)
pred <- predict(model, test1)
View(pred)
results <- cbind(pred,test$Sale_Price)
colnames(results) <- c('pred','real')
results <- as.data.frame(results)
View(results)
# Grab residuals
res <- residuals(model)
res <- as.data.frame(res)
head(res)
ggplot(res,aes(res)) + geom_histogram(fill='blue',alpha=0.5)
plot(model)
#Stepwise modeling
#stepwise sleection:
nullModel<- lm(Sale_Price ~ 1, train)
summary(nullModel)
fullmodel <- lm(Sale_Price~.,data = (train))
summary(fullmodel)
fit <- step(nullModel, scope=list(lower=nullModel, upper=fullmodel), direction="both")
#revel the model
fit
model <- lm(formula = Sale_Price ~ Grade_Living_Area + Construction_Year +
Total_Basement_Area + Overall_Material8 + Overall_Material7 +
BsmtFinSF1 + Fireplaces0 + House_Condition7 + Overall_Material9 +
Kitchen_QualityGd + Overall_Material6 + Zoning_ClassRMD +
Sale_ConditionNormal + NeighborhoodTimber + NeighborhoodSomerst +
Building_Class160 + House_Condition2 + Exterior_ConditionEx +
Garage_Size0 + House_Condition8 + Rooms_Above_Grade12 + NeighborhoodCrawfor +
NeighborhoodBrkSide + House_Condition6 + House_Condition9 +
House_Condition5 + NeighborhoodNridgHt + Sale_TypeConLI +
Rooms_Above_Grade11 + Full_Bathroom_Above_Grade3 + Property_ShapeIR3 +
Underground_Full_Bathroom0 + Building_Class75 + Garage_Size4 +
NeighborhoodBrDale + Exterior_MaterialGd + Exposure_LevelAv +
NeighborhoodClearCr + Garage_Size1 + NeighborhoodNoRidge +
Rooms_Above_Grade9 + Foundation_TypeW + Exposure_LevelNo +
Zoning_ClassCommer + NeighborhoodIDOTRR + Bedroom_Above_Grade0 +
Exterior1stStone + Lot_ConfigurationC + Rooms_Above_Grade5 +
Bedroom_Above_Grade1 + Exterior1stHdBoard + Exterior2ndVinylSd +
Bedroom_Above_Grade8 + Building_Class90 + Underground_Full_Bathroom1 +
Overall_Material10 + Garage_Size2 + `Exterior2ndWd Shng` +
BsmtFinType1GLQ + Basement_HeightEx + NeighborhoodBlmngtn +
GarageAttchd + House_Design2.5Unf + Exterior1stBrkComm, data = train)
summary(model)
plot(model)
|
3ca9693d9201e2105c67fadad71cf4d05e795ce6
|
502d84d512114b0fff16811e50941ca9e99e82d9
|
/tests/testthat/test-02_grow_leaves.R
|
c43713f21042acaa1b7607884552ffe4366e1bea
|
[] |
no_license
|
empalmer/leafart
|
49e270c8c1ac78df4635e9ca792d93d1f3b04dc3
|
5cd139cb40ddf0df4df6802232bcd5dd2be02f7c
|
refs/heads/main
| 2023-02-08T01:43:22.308199
| 2021-01-04T20:40:25
| 2021-01-04T20:40:25
| 326,800,945
| 2
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 615
|
r
|
test-02_grow_leaves.R
|
test_that("Number of layers correct", {
param <- get_params(n_leaves = 1,
n_layer = 3,
scale = c(.8,.9),
angle = c(-30,10,20),
split = 3)
expect_length(grow_leaf(param), param$n_layer +1)
})
test_that("number of branches correct",{
param <- get_params(n_layer = 3,split = 3)
leaf <- grow_leaf(param)
expect_equal(map_dbl(leaf,nrow),rep(param$split)^(0:param$n_layer))
param <- get_params(n_layer = 6,split = 5)
leaf <- grow_leaf(param)
expect_equal(map_dbl(leaf,nrow),rep(param$split)^(0:param$n_layer))
})
|
f50b9ad87562b0dbcc88f0b463555b333360e0c2
|
08048efe5325fe4930e3c9fe9694a95a02b10c0f
|
/R/zakup_zwierzecia.R
|
9ad9f032ac6f2d5a169f3ff8025a95b3603826a2
|
[] |
no_license
|
nkneblewska/SuperFarmerRCNK
|
0412245e48a93f43885bbc6fc44062bf249776a2
|
be69ebe194f2811c98415a4c591d3a2d6d752a7e
|
refs/heads/master
| 2020-06-19T15:57:29.529074
| 2016-11-27T16:03:26
| 2016-11-27T16:03:26
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 357
|
r
|
zakup_zwierzecia.R
|
zakup_zwierzecia <- function(stan_gracza, stan_stada, zwierzak) {
wartosci_zwierzat <- c(R = 1, S = 6, P = 12, C = 36, H = 72, SD = 6, BD = 36)
if (sum(stan_gracza[1:5]*wartosci_zwierzat[1:5]) >= wartosci_zwierzat[zwierzak]) {
return (kup_zwierze_yolo(zwierzak, stan_gracza, stan_stada))
} else {
return (cbind(stan_gracza, stan_stada))
}
}
|
e931a873842e17231f86df927dbac2b2a22ee50b
|
2bec5a52ce1fb3266e72f8fbeb5226b025584a16
|
/Opt5PL/man/sMultiple.Rd
|
03c6336b29246b814bdee6a6e5b379901db72b0b
|
[] |
no_license
|
akhikolla/InformationHouse
|
4e45b11df18dee47519e917fcf0a869a77661fce
|
c0daab1e3f2827fd08aa5c31127fadae3f001948
|
refs/heads/master
| 2023-02-12T19:00:20.752555
| 2020-12-31T20:59:23
| 2020-12-31T20:59:23
| 325,589,503
| 9
| 2
| null | null | null | null |
UTF-8
|
R
| false
| false
| 231
|
rd
|
sMultiple.Rd
|
\name{sMultiple}
\alias{sMultiple}
\title{Multiply a constant to a matrix}
\usage{
sMultiple(s, M)
}
\arguments{
\item{s}{Numeric}
\item{M}{A numeric matrix}
}
\description{
Multiply a constant to a matrix: s*M.
}
|
dd0d63121a3eba8061033d8ded44316dc9f68d0d
|
4dcd968ca1104a67e366179474240a2c5c9b9f0e
|
/R/tp_Bayes.R
|
03b9f55e05642f5ef1a389de491a31a8047e21f9
|
[] |
no_license
|
ThomasDavenel/TP_ACI
|
27edb80623c4d58a9ac219d640c0f0921a2ab824
|
d3efd5007c2d6f73074e80465d447f806bb8e7e2
|
refs/heads/main
| 2023-03-18T17:25:27.572962
| 2021-03-07T15:34:12
| 2021-03-07T15:34:12
| 337,032,440
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 4,033
|
r
|
tp_Bayes.R
|
# ---------- Chargement des packages nécéssaires
library(e1071)
source("fonctions_utiles.R")
# ---------- Chargement des données
ex1 = read.table("./data/data_exam.txt", header = T)
ex1$Reussite = as.factor(ex1$Reussite)
str(ex1)
table(ex1$Reussite)
# ---------- Separation Apprentissage/Test
nall = nrow(ex1) #total number of rows in data
ntrain = floor(0.7 * nall) # number of examples (rows) for train: 70% (vous pouvez changer en fonction des besoins)
ntest = nall - ntrain # number of examples for test: le reste
set.seed(20) # choix d'une graine pour le tirage aléatoire
index = sample(nall) # permutation aléatoire des nombres 1, 2, 3 , ... nall
ex1_app = ex1[index[1:ntrain],] # création du jeu d'apprentissage
ex1_test = ex1[index[(ntrain+1):(ntrain+ntest)],] # création du jeu de test
table(ex1_app$Reussite)
table(ex1_test$Reussite)
# ----- Visualisation des données apprentissage et validation
#Affichage des données d apprentissage
plot(ex1_app[which(ex1_app$Reussite==0),1:2], col = "red", xlim = c(0,105), ylim = c(0,105))
points(ex1_app[which(ex1_app$Reussite==1),1:2], col = "blue")
# Affichage des données de test (avec un triangle , pch = 2)
points(ex1_test[which(ex1_test$Reussite==1),1:2], col = "blue", pch = 2)
points(ex1_test[which(ex1_test$Reussite==0),1:2], col = "red", pch = 2)
# ---------- Construction du classifieur Bayesien Naif
# Pour le bayesien naif, il n y a pas de paramètres à ajuster donc on apprend le modèle sur
# l'ensemble d'apprentissage uniquement (pas d'ensemble de validation)
bayes = naiveBayes(Reussite~., data = ex1_app)
# Vous pouvez voir les probabilites a priori, ainsi que les paramètres des gaussiennes estimées en tapant le nom du modèle :
print(bayes$apriori)
print(bayes$tables)
# ou print(bayes)
# A-priori probabilities:
# Y
# 0 1
# 0.3882353 0.6117647
# Conditional probabilities:
# moyenne et ecart-type sachant Y dans les 2 colonnes des matrices données à l'écran
# Note1 = x1
# Y [,1] [,2]
# 0 53.07455 17.69572 -> moyenne [,1] et ecart-type [,2] de x1|C1
# 1 73.91385 14.68127 -> moyenne et ecart-type de x1|C2
# Note2 = x2
# Y [,1] [,2]
# 0 53.12424 15.36159
# 1 74.77731 16.17977
#### Visualisation des vraisemblances
# Affichage des densites de probabilités pour la variable x1=Note1
note1_c1=as.numeric(unlist(ex1_app[ex1_app$Reussite==0,][1])) #conversion to numeric for density plot
note1_c2=as.numeric(unlist(ex1_app[ex1_app$Reussite==1,][1]))
# Pour la classe 0
plot(density(note1_c1),lty=2,col="red", xlab="Note 1", main="Density estimation for Note1") # estimation par KDE
points(note1_c1, y= rep(0.00,length(note1_c1)),col="red") # affichage des points de l'ensemble d'apprentissage
curve(dnorm(x, bayes$tables$Note1[1,1], bayes$tables$Note1[1,2]), add=TRUE, col="red") # gaussienne estimée par MV
# Idem pour la classe 1
lines(density(note1_c2),lty=2,col="blue")
points(note1_c2, y= rep(0.00,length(note1_c2)),col="blue")
curve(dnorm(x, bayes$tables$Note1[2,1], bayes$tables$Note1[2,2]), add=TRUE, col="blue")
legend("topright", c("KDE class 0", "gaussian class 0","KDE class 1", "gaussian class 1"), col = c("red","red","blue","blue"), lty=c(2,1,2,1), cex=0.5)
# ---------- Prediction avec Bayes
###### Question 1: Calculer la classe du premier exemple de l'ensemble de test. Donnez les valeurs numériques calculées.
# Aide:
# - dnorm(x, mean = ??, sd = ??) calcul la densité de proba au point x
# d'une distribution gaussienne de moyenne 'mean' et d'ecart-type 'sd'
# - bayes$tables$Note1[1,1] et bayes$tables$Note1[1,2] pour accéder aux paramètres appris
# La fonction predict marche avec Bayes
# Vous pouvez prédire la classe de tous les individus de l'ensemble de test d'un coup:
predict(bayes, ex1_test)
# ---------- Affichage de la frontière de décision
# idem tree
dessiner_frontiere_tree(ex1_app, ex1_test, bayes, 0,105,0,105,c("red", "blue"))
###### Question 2: Estimez l'erreur de généralisation faite par bayes sur ces données.
|
03678cf781d7187df1e614702115f5156decf3f7
|
16502827f4c63a4dbb1a52517d777740b4253dcf
|
/R/taxa_summary.R
|
28f1da2828681dc6ce8abedb9114027f06c84205
|
[
"LicenseRef-scancode-warranty-disclaimer"
] |
no_license
|
microsud/microbiomeutilities
|
62a3dd12fa5396dff29a17f8f7612d25251e0269
|
046a9f928f35fc2a02dd6cfb9175a0c7ddf65c4b
|
refs/heads/master
| 2022-12-06T14:44:11.259855
| 2022-11-23T08:07:05
| 2022-11-23T08:07:05
| 104,251,264
| 32
| 8
|
NOASSERTION
| 2021-04-14T09:31:29
| 2017-09-20T18:10:49
|
R
|
UTF-8
|
R
| false
| false
| 2,094
|
r
|
taxa_summary.R
|
#' @title Give taxa summary at specified taxonomic level
#' @description Data frame with mean, max, median standard deviation of relative abundance.
#' @param x \code{\link{phyloseq-class}} object
#' @param level Taxonomic level for which summary is required
#' @return returns a data frame with relative abundance summary.
#' @import utils
#' @export
#' @author Contact: Sudarshan A. Shetty \email{sudarshanshetty9@@gmail.com}
#' @examples
#' \dontrun{
#' # Example data
#' library(microbiomeutilities)
#' data("zackular2014")
#' p0 <- zackular2014
#' p0.rel <- microbiome::transform(p0, "compositional")
#' tx.sum1 <- taxa_summary(p0, "Phylum")
#'
#' tx.sum2 <- taxa_summary(p0.rel, "Phylum")
#' }
#'
#' @keywords utilities
taxa_summary <- function(x, level) {
pobj <- taxdf <- pobj.ag <- otudf <- outputdf <- NULL
pobj <- x
# taxdf <- as.data.frame(pobj@tax_table)
taxdf <- tax_table(pobj) %>%
as("matrix") %>%
as.data.frame()
taxdf$OTU <- rownames(tax_table(pobj))
tax_table(pobj) <- tax_table(as.matrix(taxdf, quote = FALSE))
pobj.ag <- microbiome::aggregate_taxa(pobj, level)
com <- all(sample_sums(pobj.ag) == 1)
if (com == TRUE) {
message("Data provided is compositional \n will use values directly")
otudf2 <- as.data.frame(abundances(pobj.ag))
rownames(otudf2) <- tax_table(pobj.ag)[, level]
} else {
message("Data provided is not compositional \n will first transform")
otudf2 <- as.data.frame(abundances(pobj.ag, "compositional"))
rownames(otudf2) <- tax_table(pobj.ag)[, level]
}
output <- NULL
for (j in 1:nrow(otudf2)) {
x2 <- as.numeric(otudf2[j, ])
mx.rel <- max(x2, na.rm = TRUE)
mean.rel <- mean(x2, na.rm = TRUE)
med.rel <- median(x2, na.rm = TRUE)
Std.dev <- sd(x2, na.rm = TRUE)
output <- rbind(output, c(row.names(otudf2)[j], mx.rel, mean.rel, med.rel, Std.dev))
}
outputdf <- as.data.frame(output)
colnames(outputdf) <- c("Taxa", "Max.Rel.Ab", "Mean.Rel.Ab", "Median.Rel.Ab", "Std.dev")
return(outputdf)
}
|
7447db084a331dd5f279d53c296f7e889e465417
|
eb41c31ea2ec86dd0638aee11754a78c82260c4f
|
/attemp1.R
|
98e2c2cd390a2c2e95bf0197c77a7cc1caec23c8
|
[] |
no_license
|
SimSid2312/DataMiningClassifiers
|
4f2c9db4ffa2a6dd1ad08fb14a8bd93af467374e
|
699913b6198ae50a835e22db4f2cec8f94f3cd1f
|
refs/heads/master
| 2020-04-07T18:49:23.889432
| 2018-12-30T07:32:07
| 2018-12-30T07:32:07
| 158,625,036
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 492
|
r
|
attemp1.R
|
#read the dataset
dataset=read.csv('data.csv',as.is = TRUE)
dataset<- iris
#splitting the dataset - 60% will be training data and 40 % will be test data
training_rowCount= (nrow(dataset)*0.6)
test_rowCount=nrow(dataset)-training_rowCount
training_dataset= dataset[1:training_rowCount,]
test_dataset=tail(dataset,n=test_rowCount)
source("D:/data mining/tutPoint/naive_bayes.R")
results_training <- My_NaiveBayes(training_dataset)
results_test <- My_NaiveBayes(test_dataset)
|
adfeb35912f5bc73c8acefe2b5a8063298260aab
|
278057fd9b095a2805639d1c5f2cb7afdd33a44f
|
/man/read.pipe.Rd
|
fa2a7e09257b084d6ba82be976d32f530512085e
|
[] |
no_license
|
tlcaputi/tlcPack
|
6301c14540d02c35f05d163f6600d96b2c4fac67
|
d8d42ce3e3a807ec0e12bd20549fd1e3ac1861d4
|
refs/heads/master
| 2020-03-24T11:23:48.352052
| 2020-03-23T22:47:13
| 2020-03-23T22:47:13
| 142,684,188
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 452
|
rd
|
read.pipe.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/testingFunctions.R, R/trackProjectFunctions.r
\name{read.pipe}
\alias{read.pipe}
\title{Read Pipe Delimited Files}
\usage{
read.pipe(filename, ...)
read.pipe(filename, ...)
}
\arguments{
\item{filename}{filename for pipe-delimited file}
}
\description{
This function creates a table of summary statistics.
}
\examples{
read.pipe(filename)
}
\keyword{pipe}
\keyword{read}
|
0e3993b89f8a57a76c101f1626dc4e0abcb4e519
|
7e0f4777f4e06b0ac72b90422ac0d9c765767755
|
/projects/josm-shf/pif-ab-popsize.R
|
95aea614037c74c5164dca4a8342596b5bcbcda7
|
[] |
no_license
|
psolymos/abmianalytics
|
edd6a040082260f85afbf4fc25c4f2726b369392
|
9e801c2c564be155124109b4888d29c80bd1340d
|
refs/heads/master
| 2023-01-30T05:00:32.776882
| 2023-01-21T05:36:23
| 2023-01-21T05:36:23
| 34,713,422
| 0
| 7
| null | 2017-01-20T19:07:59
| 2015-04-28T06:39:37
|
R
|
UTF-8
|
R
| false
| false
| 45,374
|
r
|
pif-ab-popsize.R
|
library(mefa4)
ROOT <- "d:/abmi/AB_data_v2016"
e <- new.env()
#load(file.path(ROOT, "data", "data-full-withrevisit.Rdata"), envir=e)
load(file.path(ROOT, "out", "birds", "data", "data-wrsi.Rdata"), envir=e)
TAX <- droplevels(e$TAX)
TAX$Fn <- droplevels(TAX$English_Name)
levels(TAX$Fn) <- nameAlnum(levels(TAX$Fn), capitalize="mixed", collapse="")
rm(e)
e <- new.env()
load(file.path(ROOT, "out", "birds", "data", "data-josmshf.Rdata"), envir=e)
xn <- e$DAT
mods <- e$mods
OFF <- e$OFF
yy <- e$YY
BB <- e$BB
rm(e)
fln <- list.files(file.path(ROOT, "out", "birds", "results", "josmshf"))
fln <- sub("birds_abmi-josmshf_", "", fln)
fln <- sub(".Rdata", "", fln)
SPP <- fln
tax <- droplevels(TAX[SPP,])
tv0 <- read.csv("~/repos/abmianalytics/lookup/lookup-veg-hf-age.csv")
tv0$Sector2 <- factor(ifelse(is.na(tv0$Sector), "NATIVE", as.character(tv0$Sector)),
c("NATIVE", "Agriculture", "Energy", "Forestry", "Misc", "RuralUrban", "Transportation"))
tv0$HAB <- paste0(tv0$Type, ifelse(tv0$AGE %in% c("5", "6", "7", "8", "9"), "O", ""))
tv0$HAB[tv0$Combined %in% c("RoadTrailVegetated", "RoadVegetatedVerge",
"RailVegetatedVerge")] <- "Verges"
#tv0[tv0$HAB=="SoftLin",c("Combined", "HAB")]
if (FALSE) {
source("~/Dropbox/courses/st-johns-2017/R/diagnostics-functions.R")
xn$counts <- as.numeric(yy[,"ALFL"])
xn$counts01 <- ifelse(xn$counts>0, 1, 0)
plot(counts ~ wtAge + pAspen + ClosedCanopy + pWater + X + Y +
xPET + xAHM + Succ_KM + Alien_KM, xn, B=0)
siplot(counts01 ~ wtAge + pAspen + ClosedCanopy + pWater + X + Y +
xPET + xAHM + Succ_KM + Alien_KM, xn, B=0)
}
## --- calculating total pop size based on predictions ---
STAGE <- list(veg = 7) # hab=5, hab+clim=6, hab+clim+shf=7
OUTDIR1 <- paste0("e:/peter/josm/2017/stage", STAGE$veg, "/pred1")
OUTDIRB <- paste0("e:/peter/josm/2017/stage", STAGE$veg, "/predB")
load(file.path(ROOT, "out", "kgrid", "kgrid_table.Rdata"))
#source("~/repos/bragging/R/glm_skeleton.R")
#source("~/repos/abmianalytics/R/results_functions.R")
#source("~/repos/bamanalytics/R/makingsense_functions.R")
source("~/repos/abmianalytics/R/maps_functions.R")
regs <- levels(kgrid$LUFxNSR)
kgrid$useN <- !(kgrid$NRNAME %in% c("Grassland", "Parkland") | kgrid$NSRNAME == "Dry Mixedwood")
kgrid$useN[kgrid$NSRNAME == "Dry Mixedwood" & kgrid$POINT_Y > 56.7] <- TRUE
kgrid$useS <- kgrid$NRNAME == "Grassland"
kgrid$useBCR6 <- kgrid$BCRCODE == " 6-BOREAL_TAIGA_PLAINS"
AREA_ha <- (1-kgrid$pWater) * kgrid$Area_km2 * 100
#AREA_ha <- kgrid$Area_km2 * 100
AREA_ha <- AREA_ha[kgrid$useBCR6]
names(AREA_ha) <- rownames(kgrid)[kgrid$useBCR6]
PREDS <- matrix(0, sum(kgrid$useBCR6), length(SPP))
rownames(PREDS) <- rownames(kgrid)[kgrid$useBCR6]
colnames(PREDS) <- SPP
PREDS0 <- PREDS
for (spp in SPP) {
cat(spp, "--------------------------------------\n");flush.console()
fl <- list.files(file.path(OUTDIR1, spp))
ssRegs <- gsub("\\.Rdata", "", fl)
pxNcr <- NULL
#pxNrf <- NULL
for (i in ssRegs) {
cat(spp, i, "\n");flush.console()
load(file.path(OUTDIR1, spp, paste0(i, ".Rdata")))
rownames(pxNcr1) <- rownames(pxNrf1) <- names(Cells)
pxNcr <- rbind(pxNcr, pxNcr1)
#pxNrf <- rbind(pxNrf, pxNrf1)
}
PREDS[,spp] <- pxNcr[rownames(PREDS),]
#PREDS0[,spp] <- pxNrf[rownames(PREDS0),]
}
N <- colSums(PREDS*AREA_ha) / 10^6
save(AREA_ha, N, PREDS, file=file.path(OUTDIR1, "predictions.Rdata"))
## making pretty maps
library(sp)
library(rgeos)
library(raster)
library(cure4insect)
opar <- set_options(path = "w:/reports")
load_common_data()
stopifnot(all(rownames(KT)==rownames(kgrid)))
KT <- cure4insect:::.c4if$KT
rt <- cure4insect:::.read_raster_template()
#INSIDE <- KT$reg_nr!="Grassland" & coordinates(cure4insect:::.c4if$XY)[,2] > 50
INSIDE <- kgrid$useBCR6
r0 <- cure4insect:::.make_raster(ifelse(INSIDE, 1, 0), KT, rt)
v <- values(r0)
values(r0)[!is.na(v) & v==0] <- NA
cf <- function(n) rev(viridis::viridis(2*n)[(1+n):(n*2)])
pdf(file.path(ROOT, "out", "birds", "josmshf", "BCR6-maps.pdf"),
onefile=TRUE, width=6, height=9)
for (spp in SPP) {
cat(spp, "--------------------------------------\n");flush.console()
fl <- list.files(file.path(OUTDIR1, spp))
ssRegs <- gsub("\\.Rdata", "", fl)
pxNcr <- NULL
#pxNrf <- NULL
for (i in ssRegs) {
cat(spp, i, "\n");flush.console()
load(file.path(OUTDIR1, spp, paste0(i, ".Rdata")))
rownames(pxNcr1) <- rownames(pxNrf1) <- names(Cells)
pxNcr <- rbind(pxNcr, pxNcr1)
#pxNrf <- rbind(pxNrf, pxNrf1)
}
PREDS <- pxNcr[rownames(KT[INSIDE,]),]
PREDS <- PREDS[match(rownames(KT), names(PREDS))]
PREDS[is.na(PREDS)] <- 0
r1 <- .make_raster(PREDS, KT, rt)
r1 <- mask(r1, r0)
plot(rt, col="darkgrey", axes=FALSE, box=FALSE, legend=FALSE, main=spp)
#plot(r1, col=cf(100), axes=FALSE, box=FALSE, main=spp)
plot(r1, col=cf(100), add=TRUE)
}
dev.off()
## getting habitat stuf
fl <- list.files(file.path(OUTDIR1, SPP[1]))
ssRegs <- gsub("\\.Rdata", "", fl)
Aveg <- NULL
for (i in ssRegs) {
load(file.path(ROOT, "out", "transitions", paste0(i, ".Rdata")))
tmp <- trVeg[rownames(trVeg) %in% names(AREA_ha),]
tmp <- (1-kgrid[rownames(tmp), "pWater"]) * tmp
Aveg <- rbind(Aveg, colSums(tmp))
}
rownames(Aveg) <- ssRegs
Aveg <- Aveg / 10^4
ch2veg <- t(sapply(strsplit(colnames(trVeg), "->"),
function(z) if (length(z)==1) z[c(1,1)] else z[1:2]))
ch2veg <- data.frame(ch2veg)
colnames(ch2veg) <- c("rf","cr")
rownames(ch2veg) <- colnames(Aveg)
ch2veg$isHF <- ch2veg$cr %in% c("BorrowpitsDugoutsSumps",
"Canals", "CCConif0", "CCConif1", "CCConif2", "CCConif3", "CCConif4",
"CCConifR", "CCDecid0", "CCDecid1", "CCDecid2", "CCDecid3", "CCDecid4",
"CCDecidR", "CCMixwood0", "CCMixwood1", "CCMixwood2", "CCMixwood3",
"CCMixwood4", "CCMixwoodR", "CCPine0", "CCPine1", "CCPine2",
"CCPine3", "CCPine4", "CCPineR",
"CultivationCropPastureBareground", "HighDensityLivestockOperation",
"IndustrialSiteRural",
"MineSite",
"MunicipalWaterSewage", "OtherDisturbedVegetation",
"PeatMine", "Pipeline", "RailHardSurface",
"RailVegetatedVerge", "Reservoirs", "RoadHardSurface", "RoadTrailVegetated",
"RoadVegetatedVerge", "RuralResidentialIndustrial", "SeismicLine",
"TransmissionLine", "Urban", "WellSite",
"WindGenerationFacility")
ch2veg$HAB <- tv0$HAB[match(ch2veg$cr, tv0$Combined)]
ch2veg$HAB[is.na(ch2veg$HAB)] <- "Swamp"
ch2veg$HAB0 <- tv0$HAB[match(ch2veg$rf, tv0$Combined)]
ch2veg$HAB0[is.na(ch2veg$HAB0)] <- "Swamp"
AvegH <- groupSums(Aveg, 2, ch2veg$HAB)
sum(colSums(AvegH))/100
sum(AREA_ha)/100
ch2veg$Area_ha <- colSums(Aveg)
Nhab <- list()
for (spp in SPP) {
cat(spp, "--------------------------------------\n");flush.console()
# fl <- list.files(file.path(OUTDIR1, spp))
# ssRegs <- gsub("\\.Rdata", "", fl)
hbNcr <- 0
for (i in ssRegs) {
cat(spp, i, "\n");flush.console()
aa <- AvegH[i,]
ee <- new.env()
load(file.path(OUTDIR1, spp, paste0(i, ".Rdata")), envir=ee)
e <- new.env()
load(file.path(OUTDIRB, spp, paste0(i, ".Rdata")), envir=e)
if (FALSE) {
sum(aa)
sum(AREA_ha[names(e$Cells)])
str(ee$pxNcr1)
str(e$pxNcrB)
sum(ee$pxNcr1[,1], na.rm=TRUE)
sum(e$pxNcrB[,1], na.rm=TRUE)
str(ee$hbNcr1)
str(e$hbNcrB)
sum(ee$hbNcr1[,1], na.rm=TRUE)
sum(e$hbNcrB[,1], na.rm=TRUE)
sum(ee$pxNcr1[,1]*AREA_ha[names(ee$Cells)], na.rm=TRUE)
sum(e$pxNcrB[,1]*AREA_ha[names(e$Cells)], na.rm=TRUE)
sum(ee$hbNcr1[,1]*Aveg[i,], na.rm=TRUE)
sum(e$hbNcrB[,1]*Aveg[i,], na.rm=TRUE)
}
tmp <- e$hbNcrB
tmp[is.na(tmp)] <- 0
tmp <- groupSums(tmp*Aveg[i,], 1, ch2veg$HAB)
hbNcr <- hbNcr + tmp
}
Nhab[[spp]] <- hbNcr
}
save(AvegH, Nhab, file=file.path(OUTDIRB, "predictions_HAB.Rdata"))
## getting CIs for Stage 7
ssRegs <- gsub("\\.Rdata", "", list.files(file.path(OUTDIR1, SPP[1])))
PREDSCI <- array(0, c(length(ssRegs), length(SPP), 100))
dimnames(PREDSCI) <- list(ssRegs, SPP, NULL)
#PREDSCI0 <- PREDSCI
## do not need to subtract water: it was not accounted for in predictions
#AA <- (1-kgrid$pWater) * kgrid$Area_km2 * 100
AA <- kgrid$Area_km2 * 100
names(AA) <- rownames(kgrid)
for (i in ssRegs) {
cat(i, "--------------------------------------\n");flush.console()
for (spp in SPP) {
cat(i, spp, "\n");flush.console()
e <- new.env()
load(file.path(OUTDIRB, spp, paste0(i, ".Rdata")), envir=e)
pxNcr <- e$pxNcrB
#pxNrf <- e$pxNrfB
rownames(pxNcr) <- names(e$Cells)
#rownames(pxNrf) <- names(e$Cells)
PREDSCI[i,spp,] <- colSums(pxNcr*AA[names(e$Cells)])
#PREDSCI0[i,spp,] <- colSums(pxNrf*AA[names(e$Cells)])
}
}
save(PREDSCI, file=file.path(OUTDIRB, "predictionsCI.Rdata"))
## looking at results
e <- new.env()
load("e:/peter/josm/2017/stage5/pred1/predictions.Rdata", envir=e)
N5 <- e$N
e <- new.env()
load("e:/peter/josm/2017/stage6/pred1/predictions.Rdata", envir=e)
N6 <- e$N
e <- new.env()
load("e:/peter/josm/2017/stage7/pred1/predictions.Rdata", envir=e)
N7 <- e$N
rm(e)
NN <- data.frame(N5=N5, N6=N6, N7=N7)
NN$spp <- rownames(NN)
write.csv(NN, row.names=FALSE, file="~/Dropbox/bam/PIF-AB/results/PopSize567.csv")
summary(NN)
pairs(NN[NN$N5 < 20 & NN$N6 < 20 & NN$N7 < 20,])
## --- evaluating AUC based on external data ---
pr_fun_for_gof <- function(est, X, off=0) {
if (is.null(dim(est))) {
mu0 <- drop(X %*% est)
exp(mu0 + off)
} else {
mu0 <- apply(est, 1, function(z) X %*% z)
rowMeans(exp(mu0 + off))
}
}
simple_roc <- function(labels, scores){
labels <- labels[order(scores, decreasing=TRUE)]
data.frame(TPR=cumsum(labels)/sum(labels), FPR=cumsum(!labels)/sum(!labels), labels)
}
simple_auc <- function(ROC) {
ROC$inv_spec <- 1-ROC$FPR
dx <- diff(ROC$inv_spec)
sum(dx * ROC$TPR[-1]) / sum(dx)
}
## out-of-sample set
bunique <- unique(BB)
INTERNAL <- 1:nrow(xn) %in% bunique
ss <- which(INTERNAL)
ss1 <- which(!INTERNAL)
bid <- xn$bootid
levels(bid) <- sapply(strsplit(levels(bid), "\\."), "[[", 1)
up <- function() {
source("~/repos/bragging/R/glm_skeleton.R")
source("~/repos/abmianalytics/R/results_functions.R")
source("~/repos/bamanalytics/R/makingsense_functions.R")
# source("~/repos/abmianalytics/R/wrsi_functions.R")
# source("~/repos/abmianalytics/R/results_functions1.R")
# source("~/repos/abmianalytics/R/results_functions2.R")
invisible(NULL)
}
up()
Terms <- getTerms(mods, "list")
Xn <- model.matrix(getTerms(mods, "formula"), xn)
colnames(Xn) <- fixNames(colnames(Xn))
#spp <- "OVEN"
res_AUC <- list()
for (spp in fln) {
cat(spp, "\n");flush.console()
y1sp <- yy[,spp]
y10sp <- ifelse(y1sp > 0, 1, 0)
off1sp <- OFF[,spp]
resn <- loadSPP(file.path(ROOT, "out", "birds", "results", "josmshf",
paste0("birds_abmi-josmshf_", spp, ".Rdata")))
est0 <- getEst(resn, stage=0, na.out=FALSE, Xn)
est5 <- getEst(resn, stage=5, na.out=FALSE, Xn)
est6 <- getEst(resn, stage=6, na.out=FALSE, Xn)
est7 <- getEst(resn, stage=7, na.out=FALSE, Xn)
j <- 1:nrow(est7)
pr0o <- pr_fun_for_gof(est0[j,], Xn[ss1,], off=off1sp[ss1])
pr5o <- pr_fun_for_gof(est5[j,], Xn[ss1,], off=off1sp[ss1])
pr6o <- pr_fun_for_gof(est6[j,], Xn[ss1,], off=off1sp[ss1])
pr7o <- pr_fun_for_gof(est7[j,], Xn[ss1,], off=off1sp[ss1])
pr0i <- pr_fun_for_gof(est0[j,], Xn[ss,], off=off1sp[ss])
pr5i <- pr_fun_for_gof(est5[j,], Xn[ss,], off=off1sp[ss])
pr6i <- pr_fun_for_gof(est6[j,], Xn[ss,], off=off1sp[ss])
pr7i <- pr_fun_for_gof(est7[j,], Xn[ss,], off=off1sp[ss])
auc0o <- simple_auc(simple_roc(y10sp[ss1], pr0o))
auc5o <- simple_auc(simple_roc(y10sp[ss1], pr5o))
auc6o <- simple_auc(simple_roc(y10sp[ss1], pr6o))
auc7o <- simple_auc(simple_roc(y10sp[ss1], pr7o))
auc0i <- simple_auc(simple_roc(y10sp[ss], pr0i))
auc5i <- simple_auc(simple_roc(y10sp[ss], pr5i))
auc6i <- simple_auc(simple_roc(y10sp[ss], pr6i))
auc7i <- simple_auc(simple_roc(y10sp[ss], pr7i))
res_AUC[[spp]] <- c(
auc0o=auc0o, auc5o=auc5o, auc6o=auc6o, auc7o=auc7o,
auc0i=auc0i, auc5i=auc5i, auc6i=auc6i, auc7i=auc7i)
}
AUC <- data.frame(do.call(rbind, res_AUC))
AUC$k5 <- (AUC$auc5i-AUC$auc0i) / (1-AUC$auc0i)
AUC$k6 <- (AUC$auc6i-AUC$auc0i) / (1-AUC$auc0i)
AUC$k7 <- (AUC$auc7i-AUC$auc0i) / (1-AUC$auc0i)
AUC$spp <- rownames(AUC)
write.csv(AUC, row.names=FALSE, file="~/Dropbox/bam/PIF-AB/results/AUC.csv")
write.csv(NN, row.names=FALSE, file="~/Dropbox/bam/PIF-AB/results/PopSize567.csv")
## road effects
pr_fun_for_road <- function(est, X0, X1) {
lam1 <- exp(apply(est, 1, function(z) X1 %*% z))
Lam1 <- unname(colMeans(lam1))
lam0 <- exp(apply(est, 1, function(z) X0 %*% z))
Lam0 <- unname(colMeans(lam0))
out <- rbind(Lam1, Lam0, Ratio=Lam1 / Lam0)
cbind(mean=rowMeans(out), t(apply(out, 1, quantile, c(0.5, 0.025, 0.975))))
}
roadside_bias <- list()
xn$BCR6
Xn_onroad <- Xn_offroad <- Xn[Xn[,"ROAD01"] == 1,]
Xn_offroad[,c("ROAD01","habCl:ROAD01")] <- 0
for (spp in SPP) {
cat(spp, "\n");flush.console()
resn <- loadSPP(file.path(ROOT, "out", "birds", "results", "josmshf",
paste0("birds_abmi-josmshf_", spp, ".Rdata")))
est7 <- getEst(resn, stage=7, na.out=FALSE, Xn)
roadside_bias[[spp]] <- pr_fun_for_road(est7, X0=Xn_offroad, X1=Xn_onroad)
}
#rsb <- data.frame(do.call(rbind, roadside_bias))
#rsb$spp <- SPP
#write.csv(rsb, row.names=FALSE, file="~/Dropbox/bam/PIF-AB/results/roadside_bias.csv")
#save(roadside_bias, file=file.path(ROOT, "josmshf", "roadside_bias.Rdata"))
## not conclusive: but habCl effect pulls the contrast to more neutral for forest species
roadside_coef <- list()
for (spp in SPP) {
cat(spp, "\n");flush.console()
resn <- loadSPP(file.path(ROOT, "out", "birds", "results", "josmshf",
paste0("birds_abmi-josmshf_", spp, ".Rdata")))
est7 <- getEst(resn, stage=7, na.out=FALSE, Xn)
roadside_coef[[spp]] <- est7[,c("ROAD01","habCl:ROAD01")]
}
rcf <- t(exp(sapply(roadside_coef, function(z) c(RdOp=mean(z[,1]), RdCl=mean(rowSums(z))))))
boxplot(rcf,ylim=c(0,10))
abline(h=1,col=2)
hist(rcf[,2]/rcf[,1])
## roadside avoidance index
rai_data <- data.frame(HAB=xn$hab1ec, ROAD=xn$ROAD01)
rai_pred <- matrix(0, nrow(Xn), nrow(tax))
rownames(rai_pred) <- rownames(rai_data) <- rownames(xn)
colnames(rai_pred) <- SPP
for (spp in SPP) {
cat(spp, "\n");flush.console()
resn <- loadSPP(file.path(ROOT, "out", "birds", "results", "josmshf",
paste0("birds_abmi-josmshf_", spp, ".Rdata")))
est7 <- getEst(resn, stage=7, na.out=FALSE, Xn)
rai_pred[,spp] <- pr_fun_for_gof(est7, Xn, off=0)
}
save(roadside_bias, rai_pred, rai_data,
file="e:/peter/josm/2017/roadside_avoidance.Rdata")
## --- unifying the bits and pieces ---
source("~/repos/bamanalytics/R/makingsense_functions.R")
## PIF table
pif <- read.csv("~/GoogleWork/bam/PIF-AB/popBCR-6AB_v2_22-May-2013.csv")
mefa4::compare_sets(tax$English_Name, pif$Common_Name)
setdiff(tax$English_Name, pif$Common_Name)
pif <- pif[match(tax$English_Name, pif$Common_Name),]
rownames(pif) <- rownames(tax)
AUC <- read.csv("~/GoogleWork/bam/PIF-AB/results/AUC.csv")
rownames(AUC) <- AUC$spp
AUC$spp <- NULL
AUC <- AUC[rownames(tax),]
if (FALSE) {
NN <- read.csv("~/GoogleWork/bam/PIF-AB/results/PopSize567.csv")
rownames(NN) <- NN$spp
NN$spp <- NULL
NN <- NN[rownames(tax),]
load("e:/peter/josm/2017/stage7/predB/predictionsCI.Rdata")
rm(PREDSCI0)
PREDSCI <- PREDSCI[,rownames(tax),] / 10^6
N7B <- apply(PREDSCI, 2, colSums)
N7CI <- t(apply(N7B, 2, quantile, c(0.5, 0.025, 0.975)))
N7mean <- colMeans(N7B)
}
#load("e:/peter/AB_data_v2016/out/birds/data/mean-qpad-estimates.Rdata")
#qpad_vals <- qpad_vals[rownames(tax),]
## roadside_bias, rai_pred, rai_data
#load("e:/peter/josm/2017/roadside_avoidance.Rdata")
#rsb <- t(sapply(roadside_bias, function(z) z[,1]))
## AvegH, Nhab
load("e:/peter/josm/2017/stage7/predB/predictions_HAB.Rdata")
## bootstrap averaged pop size estimate
b_fun <- function(h) {
N7tb <- c(Mean=mean(colSums(h) / 10^6),
quantile(colSums(h) / 10^6, c(0.5, 0.025, 0.975)))
N7hb <- t(apply(h, 1, function(z)
c(Mean=mean(z / 10^6), quantile(z / 10^6, c(0.5, 0.025, 0.975)))))
N7b <- rbind(N7hb, TOTAL=N7tb)
N7b
}
NestAll <- lapply(Nhab, b_fun)
NestTot <- t(sapply(NestAll, function(z) z["TOTAL",]))
library(mefa4)
library(rgdal)
library(rgeos)
library(sp)
library(gstat)
library(raster)
#library(viridis)
load(file.path("e:/peter/AB_data_v2016", "out", "kgrid", "kgrid_table.Rdata"))
r <- raster(file.path("~/Dropbox/courses/st-johns-2017",
"data", "ABrasters", "dem.asc"))
slope <- terrain(r, opt="slope")
aspect <- terrain(r, opt="aspect")
hill <- hillShade(slope, aspect, 40, 270)
od <- setwd("e:/peter/AB_data_v2017/data/raw/xy/bcr/")
BCR <- readOGR(".", "BCR_Terrestrial_master") # rgdal
BCR <- spTransform(BCR, proj4string(r))
BCR <- gSimplify(BCR, tol=500, topologyPreserve=TRUE)
setwd(od)
od <- setwd("~/Dropbox/courses/st-johns-2017/data/NatRegAB")
AB <- readOGR(".", "Natural_Regions_Subregions_of_Alberta") # rgdal
AB <- spTransform(AB, proj4string(r))
AB <- gUnaryUnion(AB, rep(1, nrow(AB))) # province
AB <- gSimplify(AB, tol=500, topologyPreserve=TRUE)
setwd(od)
BCR2AB <- gIntersection(AB, BCR, byid=TRUE)
For <- c("Decid", "Mixwood", "Pine", "Conif", "BSpr", "Larch")
xn$HAB <- paste0(xn$hab1, ifelse(xn$hab1 %in% For & xn$wtAge*200 >= 80, "O", ""))
compare_sets(colnames(AvegH), xn$HAB)
setdiff(colnames(AvegH), xn$HAB)
xnss <- nonDuplicated(xn, SS, TRUE)
xy <- xnss
coordinates(xy) <- ~ POINT_X + POINT_Y
proj4string(xy) <-
CRS("+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0")
xy <- spTransform(xy, proj4string(r))
xy2BCR <- over(xy, BCR)
tmp <- xnss[!is.na(xy2BCR) & xy2BCR==24,]
tmp <- tmp[!(tmp$PCODE != "BBSAB" & tmp$ROAD01 > 0),]
tab <- table(tmp$HAB, tmp$ROAD01)
xypt <- xn
coordinates(xypt) <- ~ POINT_X + POINT_Y
proj4string(xypt) <-
CRS("+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0")
xypt <- spTransform(xypt, proj4string(r))
xypt2BCR <- over(xypt, BCR)
tabpt <- table(xn$HAB)
#Ahab <- tab[,"0"] / sum(tab[,"0"])
Ahab <- colSums(AvegH[,rownames(tab)]) / sum(AvegH[,rownames(tab)])
Whab <- tab[,"1"] / sum(tab[,"1"])
AWhab <- data.frame(Ahab, Whab)
NAM <- names(Ahab)
h_fun <- function(h) {
NN <- h[NAM, "50%"] * 10^6 # back to individuals
DD <- NN / colSums(AvegH)[NAM] # density: males / ha
sum(DD * Whab) / sum(DD * Ahab)
}
H <- sapply(NestAll, h_fun)
d_fun <- function(h) {
NN <- h[NAM, "50%"] * 10^6 # back to individuals
DD <- NN / colSums(AvegH)[NAM] # density: males / ha
DD
}
## forest classes
NAM_for <- c("BSpr", "BSprO", "Conif", "ConifO", "Decid", "DecidO",
"Larch", "LarchO", "Mixwood", "MixwoodO", "Pine", "PineO")
pref_fun <- function(h) {
NN <- h[NAM, "50%"] * 10^6 # back to individuals
NN_for <- h[NAM[NAM %in% NAM_for], "50%"] * 10^6 # back to individuals
sum(NN_for) / sum(NN)
}
pref <- sapply(NestAll, pref_fun)
quantile(pref,seq(0,1,0.25))
## roadside count effect within BCR 6
source("~/repos/abmianalytics/R/results_functions.R")
pr_fun_for_road <- function(est, X0, X1) {
lam1 <- exp(apply(est, 1, function(z) X1 %*% z))
Lam1 <- unname(colMeans(lam1))
lam0 <- exp(apply(est, 1, function(z) X0 %*% z))
Lam0 <- unname(colMeans(lam0))
out <- rbind(Lam1, Lam0, Ratio=Lam1 / Lam0)
cbind(mean=rowMeans(out), t(apply(out, 1, quantile, c(0.5, 0.025, 0.975))))
}
roadside_bias <- list()
tmp <- xn[!is.na(xypt2BCR) & xypt2BCR==24,]
tmp <- tmp[tmp$PCODE == "BBSAB",]
Xn_offroad <- model.matrix(getTerms(mods, "formula"), tmp)
colnames(Xn_offroad) <- fixNames(colnames(Xn_offroad))
Xn_onroad <- Xn_offroad
Xn_offroad[,c("ROAD01","habCl:ROAD01")] <- 0
for (spp in SPP) {
cat(spp, "\n");flush.console()
resn <- loadSPP(file.path(ROOT, "out", "birds", "results", "josmshf",
paste0("birds_abmi-josmshf_", spp, ".Rdata")))
est7 <- getEst(resn, stage=7, na.out=FALSE, Xn_offroad)
roadside_bias[[spp]] <- pr_fun_for_road(est7, X0=Xn_offroad, X1=Xn_onroad)
}
rsb <- t(sapply(roadside_bias, function(z) z[,1]))
## offsets
load("e:/peter/AB_data_v2016/out/birds/data/data-offset-covars.Rdata")
tmp <- xn[!is.na(xypt2BCR) & xypt2BCR==24,]
offdat <- offdat[rownames(offdat) %in% rownames(tmp),]
Xp <- cbind("(Intercept)"=1, as.matrix(offdat[,c("TSSR","JDAY","TSSR2","JDAY2")]))
Xq <- cbind("(Intercept)"=1, TREE=offdat$TREE,
LCC2OpenWet=ifelse(offdat$LCC2=="OpenWet", 1, 0),
LCC4Conif=ifelse(offdat$LCC4=="Conif", 1, 0),
LCC4Open=ifelse(offdat$LCC4=="Open", 1, 0),
LCC4Wet=ifelse(offdat$LCC4=="Wet", 1, 0))
library(QPAD)
load_BAM_QPAD(3)
getBAMversion()
meanphi <- meantau <- meanphi0 <- meantau0 <- structure(rep(NA, length(SPP)), names=SPP)
for (spp in SPP) {
cf0 <- exp(unlist(coefBAMspecies(spp, 0, 0)))
mi <- bestmodelBAMspecies(spp, type="BIC", model.sra=0:8)
cat(spp, unlist(mi), "\n");flush.console()
cfi <- coefBAMspecies(spp, mi$sra, mi$edr)
Xp2 <- Xp[,names(cfi$sra),drop=FALSE]
OKp <- rowSums(is.na(Xp2)) == 0
Xq2 <- Xq[,names(cfi$edr),drop=FALSE]
OKq <- rowSums(is.na(Xq2)) == 0
phi1 <- exp(drop(Xp2[OKp,,drop=FALSE] %*% cfi$sra))
tau1 <- exp(drop(Xq2[OKq,,drop=FALSE] %*% cfi$edr))
meanphi[spp] <- mean(phi1)
meantau[spp] <- mean(tau1)
meanphi0[spp] <- cf0[1]
meantau0[spp] <- cf0[2]
}
qpad_vals <- data.frame(Species=SPP, phi0=meanphi0, tau0=meantau0,
phi=meanphi, tau=meantau)
## taxonomy etc ---
pop <- tax[,c("Species_ID", "English_Name", "Scientific_Name", "Spp")]
## LH stuff
library(lhreg)
data(lhreg_data)
compare_sets(lhreg_data$spp, rownames(pop))
setdiff(rownames(pop), lhreg_data$spp)
pop <- data.frame(pop, lhreg_data[match(rownames(pop), lhreg_data$spp),
c("Mig", "Mig2", "Hab2", "Hab3", "Hab4")])
## pop size estimates ---
#pop$Npix <- NestTot[,"Mean"] # M males
#pop$Npix <- NestTot[,"50%"]
#pop$NpixLo <- NestTot[,"2.5%"]
#pop$NpixHi <- NestTot[,"97.5%"]
#pop$Npif <- (pif$Population_Estimate_unrounded / pif$Pair_Adjust) / 10^6 # M males
pop$Npix <- NestTot[,"50%"] * pif$Pair_Adjust # M inds
pop$NpixLo <- NestTot[,"2.5%"] * pif$Pair_Adjust
pop$NpixHi <- NestTot[,"97.5%"] * pif$Pair_Adjust
pop$Npif <- pif$Population_Estimate_unrounded / 10^6 # M inds
## roadside related metrics
pop$Y1 <- rsb[,"Lam1"]
pop$Y0 <- rsb[,"Lam0"]
## Tadj and EDR/MDD ---
pop$TimeAdj <- pif$Time_Adjust
#pop$p3 <- 1-exp(-3 * qpad_vals$phi0)
pop$p3 <- 1-exp(-3 * qpad_vals$phi)
pop$MDD <- pif$Detection_Distance_m
#pop$EDR <- qpad_vals$tau0 * 100
pop$EDR <- qpad_vals$tau * 100
## QAQC ---
pop$AUCin <- AUC$auc7i
pop$AUCout <- AUC$auc7o
pop$k7 <- AUC$k7
pop$DataQ <- pif$Data_Quality_Rating
pop$BbsVar <- pif$BBS_Variance_Rating
pop$SpSamp <- pif$Species_Sample_Rating
pop$H <- H
## Deltas ---
pop$DeltaObs <- log(pop$Npix / pop$Npif)
pop$DeltaR <- log(pop$Y0/pop$Y1)
pop$DeltaT <- log((1/pop$p3)/pop$TimeAdj)
pop$DeltaA <- log((1/pop$EDR^2) / (1/pop$MDD^2))
pop$DeltaH <- log(1/H) # we take inverse because H=1 is the PIF setup
pop$DeltaExp <- pop$DeltaR + pop$DeltaT + pop$DeltaA + pop$DeltaH
pop$epsilon <- pop$DeltaObs - pop$DeltaExp
## subset ---
pop <- droplevels(pop[rowSums(is.na(pop))==0,])
#pop <- droplevels(pop[!is.na(pop$Npif),])
pop <- pop[sort(rownames(pop)),]
Dall <- data.frame(Ahab=100*Ahab, Whab=100*Whab, sapply(NestAll, d_fun)[,rownames(pop)])
write.csv(pop, row.names=FALSE, file="~/GoogleWork/bam/PIF-AB/draft2/Table1-estimates.csv")
write.csv(Dall, file="~/GoogleWork/bam/PIF-AB/draft2/Table3-densities.csv")
write.csv(cbind(Dall[,1:2], n=tabpt), file="~/GoogleWork/bam/PIF-AB/draft2/Table2-habitats.csv")
## --- making the figures ---
## maps
pdf("~/GoogleWork/bam/PIF-AB/draft3/Fig1-maps.pdf", width=12, height=9)
op <- par(mfrow=c(1,2), mar=c(1,1,1,1))
plot(BCR2AB, col=c(NA, "grey", rep(NA, 11)), border=NA, main="Roadside surveys")
plot(AB, col=NA, border=1,add=TRUE)
plot(xy[xy@data$ROAD01 == 1,], add=TRUE, pch=19, col=1, cex=0.25)
plot(BCR2AB, col=c(NA, "grey", rep(NA, 11)), border=NA, main="Off-road surveys")
plot(AB, col=NA, border=1,add=TRUE)
plot(xy[xy@data$ROAD01 == 0,], add=TRUE, pch=19, col=1, cex=0.25)
par(op)
dev.off()
xylonlat <- spTransform(xy, CRS("+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0"))
plot(density(coordinates(xylonlat)[,2]), type="n")
lines(density(coordinates(xylonlat)[xylonlat@data$PCODE!="BBSAB",2]), col=2)
lines(density(coordinates(xylonlat)[xylonlat@data$PCODE=="BBSAB",2]), col=4)
library(MASS)
library(KernSmooth)
dots_box_plot <- function(mat, lines=FALSE, method="box", ...) {
set.seed(1)
rnd <- runif(nrow(mat), -0.05, 0.05)
boxplot(mat, range=0, border="white",...)
if (lines)
for (i in 2:ncol(mat))
segments(x0=i+rnd-1, x1=i+rnd, y0=mat[,i-1], y1=mat[,i], col="lightgrey")
for (i in 1:ncol(mat))
points(i+rnd, mat[,i], pch=19, col="#00000080")
if (method == "box") {
#boxplot(mat, range=0, add=TRUE, col="#ff000020", names=NA)
boxplot(mat, range=0, add=TRUE, col="#00000020", names=NA)
} else {
v <- 0.2
for (i in 1:ncol(mat)) {
xx <- sort(mat[,i])
st <- boxplot.stats(xx)
s <- st$stats
if (method == "kde")
d <- bkde(xx) # uses Normal kernel
if (method == "fft")
d <- density(xx) # uses FFT
if (method == "hist") {
h <- hist(xx, plot=FALSE)
xv <- rep(h$breaks, each=2)
yv <- c(0, rep(h$density, each=2), 0)
} else {
xv <- d$x
yv <- d$y
jj <- xv >= min(xx) & xv <= max(xx)
xv <- xv[jj]
yv <- yv[jj]
}
yv <- 0.4 * yv / max(yv)
polygon(c(-yv, rev(yv))+i, c(xv, rev(xv)), col="#00000020", border="#40404080")
polygon(c(-v,-v,v,v)+i, s[c(2,4,4,2)], col="#40404080", border=NA)
lines(c(-v,v)+i, s[c(3,3)], lwd=2, col="#00000020")
}
}
invisible(NULL)
}
pdf("~/GoogleWork/bam/PIF-AB/draft3/Fig2-popsize-old.pdf", width=8, height=8)
op <- par(mfrow=c(2,2), las=1, mar=c(4,4,1,2))
dots_box_plot(pop[,c("Npif", "Npix")], lines=TRUE,
ylab="Population Size (M singing inds.)", names=c("PIF", "PIX"))
dots_box_plot(log(pop[,c("Npif", "Npix")]), lines=TRUE,
ylab="log Population Size (M singing inds.)", names=c("PIF", "PIX"))
plot(pop[,c("Npif", "Npix")], xlab=expression(N[PIF]), ylab=expression(N[PIX]),
pch=19, col="#00000080", xlim=c(0, max(pop[,c("Npif", "Npix")])),
ylim=c(0, max(pop[,c("Npif", "Npix")])))
abline(0,1)
plot(log(pop[,c("Npif", "Npix")]), xlab=expression(log(N[PIF])), ylab=expression(log(N[PIX])),
pch=19, col="#00000080", xlim=range(log(pop[,c("Npif", "Npix")])),
ylim=range(log(pop[,c("Npif", "Npix")])))
abline(0,1)
par(op)
dev.off()
pdf("~/GoogleWork/bam/PIF-AB/draft3/Fig2-popsize.pdf", width=7, height=7)
op <- par(mfrow=c(1,1), las=1, mar=c(4,4,1,2))
pch <- 19 # ifelse(pop$Npif %[]% list(pop$NpixLo, pop$NpixHi), 21, 19)
plot(pop[,c("Npif", "Npix")], xlab=expression(N[PIF]), ylab=expression(N[PIX]),
type="n",
pch=pch, col="#00000080", xlim=c(0, max(pop[,c("Npif", "Npix")])),
ylim=c(0, max(pop[,c("Npif", "Npix")])))
abline(0,1, lty=2)
Min <- 3*2
Siz <- 18*2
di <- sqrt(pop[,"Npif"]^2+pop[,"Npix"]^2) > Min
pp <- pop[pop[,"Npif"] < Min & pop[,"Npix"] < Min, c("Npif", "Npix")]
ppp <- pop[pop[,"Npif"] < Min & pop[,"Npix"] < Min, ]
pch2 <- 19 # ifelse(ppp$Npif %[]% list(ppp$NpixLo, ppp$NpixHi), 21, 19)
di2 <- sqrt(pp[,"Npif"]^2+pp[,"Npix"]^2) > 1
pp <- pp*Siz/Min
pp[,1] <- pp[,1] + (60-Siz)
lines(c(0,60-Siz), c(0, 0), col="grey")
lines(c(0,60-Siz), c(Min, Siz), col="grey")
rect(0, 0, Min, Min)
rect(60-Siz, 0, 60-Siz+Min*Siz/Min, Min*Siz/Min, col="white")
lines(c(60-Siz, 60), c(0, Siz), col=1, lty=2)
points(pp, pch=pch2, col="#00000080")
points(pop[,c("Npif", "Npix")], pch=pch, col="#00000080")
text(pop[,"Npif"]+1.2*2, pop[,"Npix"]+0, labels=ifelse(di, rownames(pop), ""), cex=0.5)
text(pp[,"Npif"]+1.2*2, pp[,"Npix"]+0, labels=ifelse(di2, rownames(pp), ""), cex=0.5)
segments(x0=c(60-Siz+c(0, 1, 2, 3)*Siz/3), y0=rep(Siz, 4), y1=rep(Siz, 4)+0.5)
text(c(60-Siz+c(0, 1, 2, 3)*Siz/3), rep(Siz, 4)+1, 0:3)
dev.off()
pdf("~/GoogleWork/bam/PIF-AB/draft3/Fig3-poprank.pdf", width=7, height=7)
op <- par(mfrow=c(1,1), las=1, mar=c(4,4,1,2))
rnk <- cbind(rank(pop$Npif), rank(pop$Npix))
#rnk <- 100 * rnk / max(rnk)
plot(rnk,
#xlab="PIF rank quantile (%)", ylab="PIX rank quantile (%)",
xlab=expression(N[PIF]~rank), ylab=expression(N[PIX]~rank),
#xlim=c(0,100), ylim=c(0,100),
type="n", axes=FALSE)
abline(0,1, lty=2)
text(rnk, labels=rownames(pop), cex=0.8)
axis(1, c(1, 20, 40, 60, 80, 95), c(1, 20, 40, 60, 80, 95))
axis(2, c(1, 20, 40, 60, 80, 95), c(1, 20, 40, 60, 80, 95))
box()
#abline(h=c(24,48,72),v=c(24,48,72))
par(op)
dev.off()
pdf("~/GoogleWork/bam/PIF-AB/draft3/Fig4-components.pdf", width=10, height=7)
op <- par(las=1)
#mat <- pop[,c("DeltaObs", "DeltaExp", "DeltaR", "DeltaT", "DeltaA", "DeltaH")]
#colnames(mat) <- c("OBS", "EXP", "R", "T", "A", "H")
mat <- pop[,c("DeltaObs", "DeltaExp", "DeltaT", "DeltaA", "DeltaR", "DeltaH")]
colnames(mat) <- c("OBS", "EXP", "T", "A", "R", "H")
par(las=1)
dots_box_plot(mat, ylab="Log Ratio", method="kde")
abline(h=0, col=1, lwd=1,lty=2)
off <- 0.2
i <- 1
z <- mat[order(mat[,i], decreasing=TRUE),]
text(i+off, head(z[,i], 1), cex=0.8, rownames(z)[1])
text(i+off, tail(z[,i], 2), cex=0.8, rownames(z)[(nrow(z)-1):nrow(z)])
i <- 2
z <- mat[order(mat[,i], decreasing=TRUE),]
text(i+off, head(z[,i], 1), cex=0.8, rownames(z)[1])
text(i+off, tail(z[,i], 1), cex=0.8, rownames(z)[nrow(z)])
i <- 5
z <- mat[order(mat[,i], decreasing=TRUE),]
text(i+off, head(z[,i], 2), cex=0.8, rownames(z)[1:2])
text(i+off, tail(z[,i], 3), cex=0.8, rownames(z)[(nrow(z)-2):nrow(z)])
i <- 4
z <- mat[order(mat[,i], decreasing=TRUE),]
text(i+off, head(z[,i], 1), cex=0.8, rownames(z)[1])
text(i+off, tail(z[,i], 1), cex=0.8, rownames(z)[nrow(z)])
i <- 6
z <- mat[order(mat[,i], decreasing=TRUE),]
text(i+off, head(z[,i], 1), cex=0.8, rownames(z)[1])
#text(i+off, tail(z[,i], 1), cex=0.8, rownames(z)[nrow(z)])
for (i in 1:6) {
zz1 <- format(mean(mat[,i]), trim = TRUE, scientific = FALSE, digits = 2)
zz2 <- format(sd(mat[,i]), trim = TRUE, scientific = FALSE, digits = 2)
mtext(paste("Mean =", zz1), side=1,at=i,line=3, cex=0.8)
mtext(paste("SD =", zz2), side=1,at=i,line=4, cex=0.8)
}
par(op)
dev.off()
mod <- lm(DeltaObs ~ DeltaR + DeltaT + DeltaA + DeltaH, pop)
an <- anova(mod)
an$Percent <- 100 * an[["Sum Sq"]] / sum(an[["Sum Sq"]])
an <- an[c("Df", "Sum Sq", "Percent", "Mean Sq", "F value", "Pr(>F)")]
an
summary(mod)
summary(mod)$sigma^2
zval <- c(coef(summary(mod))[,1] - c(0, 1, 1, 1, 1))/coef(summary(mod))[,2]
round(cbind(coef(summary(mod))[,1:2], ph=2 * pnorm(-abs(zval))), 3)
round(an$Percent,1)
mod2 <- step(lm(DeltaObs ~ (DeltaR + DeltaT + DeltaA + DeltaH)^2, pop), trace=0)
summary(mod2)
an2 <- anova(mod2)
an2$Percent <- 100 * an2[["Sum Sq"]] / sum(an2[["Sum Sq"]])
an2 <- an2[c("Df", "Sum Sq", "Percent", "Mean Sq", "F value", "Pr(>F)")]
an2
## looking at shared variation
vpfun <- function (x, cutoff = 0, digits = 1, Xnames, showNote=FALSE, ...)
{
x <- x$part
vals <- x$indfract[, 3]
is.na(vals) <- vals < cutoff
if (cutoff >= 0)
vals <- round(vals, digits + 1)
labs <- format(vals, digits = digits, nsmall = digits + 1)
labs <- gsub("NA", "", labs)
showvarparts(x$nsets, labs, Xnames=Xnames, ...)
if (any(is.na(vals)) && showNote)
mtext(paste("Values <", cutoff, " not shown", sep = ""),
1)
invisible()
}
prt <- vegan::varpart(Y=pop$DeltaObs, ~DeltaR, ~DeltaT, ~DeltaA, ~DeltaH, data=pop)
pdf("~/GoogleWork/bam/PIF-AB/draft3/FigX-varpart.pdf", width=6, height=6)
vpfun(prt, cutoff = 0, digits = 2, Xnames=c("R", "T", "A", "H"))
dev.off()
## road avoidance and ordination
library(vegan)
DD <- as.matrix(t(Dall[,-(1:2)]))
NN <- t(t(DD) * Dall$Ahab)
NN <- t(NN / rowSums(NN))
Cex <- pop$DeltaObs
names(Cex) <- rownames(pop)
br <- c(-Inf, -2, -1, -0.1, 0.1, 1, 2, Inf)
#c00 <- c('#d7191c','#fdae61','#ffffbf','#abdda4','#2b83ba')
c00 <- c('#d7191c','darkgrey','#2b83ba')
#c00 <- c("red", "darkgrey", "blue")
Col0 <- colorRampPalette(c00)(7)
Col <- Col0[cut(Cex, br)]
names(Col) <- names(Cex)
o <- cca(NN)
round(100*eigenvals(o)/sum(eigenvals(o)), 1)
round(cumsum(100*eigenvals(o)/sum(eigenvals(o))), 1)
pdf("~/GoogleWork/bam/PIF-AB/draft3/Fig6-ordination3.pdf", width=9, height=9)
op <- par(las=1)
plot(0, type="n", xlim=c(-0.8,1.2), ylim=c(-1,1), xlab="Axis 1", ylab="Axis 2")
s2 <- scores(o)$sites
s2 <- s2 / max(abs(s2))
for (i in 1:nrow(s2))
arrows(x0=0,y0=0,x1=s2[i,1],y1=s2[i,2], angle=20, length = 0.1, col="darkgrey")
text(s2*1.05, labels=rownames(NN),cex=1,col=1)
abline(h=0,v=0,lty=2)
s1 <- scores(o)$species
s1 <- s1 / max(abs(s1))
text(s1[names(Col),]*0.8, labels=colnames(NN),cex=0.75, col=Col)
#text(s1[names(Col),]*0.8, labels=colnames(NN),cex=0.75, col=4)
for (ii in 1:200)
lines(c(0.85, 0.9), rep(seq(-0.55, -0.95, len=200)[ii], 2), col=colorRampPalette(c00)(200)[ii])
text(c(1,1,1)+0.1, c(-0.6, -0.75, -0.9),
c(expression(N[PIX] < N[PIF]),expression(N[PIX] == N[PIF]),expression(N[PIX] > N[PIF])))
par(op)
dev.off()
pdf("~/GoogleWork/bam/PIF-AB/draft3/Fig6-report.pdf", width=8, height=8)
op <- par(las=1)
Colg <- colorRampPalette(c("red","yellow"))(7)[cut(Cex, br)]
names(Colg) <- names(Cex)
plot(0, type="n", xlim=c(-0.8,1.2), ylim=c(-1,1), xlab="", ylab="")
s1 <- scores(o)$species
s1 <- s1 / max(abs(s1))
points(s1[names(Colg),]*0.8, cex=2, col=Colg, pch=19)
points(s1[names(Colg),]*0.8, cex=2, col="orange")
s2 <- scores(o)$sites
s2 <- s2 / max(abs(s2))
for (i in 1:nrow(s2))
arrows(x0=0,y0=0,x1=s2[i,1],y1=s2[i,2], angle=20, length = 0.1, col="darkgrey")
text(s2*1.05, labels=rownames(NN),cex=1,col=1)
abline(h=0,v=0,lty=2)
#text(s1[names(Col),]*0.8, labels=colnames(NN),cex=0.75, col=4)
for (ii in 1:200)
lines(c(0.85, 0.9), rep(seq(-0.55, -0.95, len=200)[ii], 2), col=colorRampPalette(c("red","yellow"))(200)[ii])
text(c(1,1,1)+0.1, c(-0.6, -0.75, -0.9),
c(expression(N[PIX] < N[PIF]),expression(N[PIX] == N[PIF]),expression(N[PIX] > N[PIF])))
par(op)
dev.off()
pdf("~/GoogleWork/bam/PIF-AB/draft3/Fig6-ordination-all.pdf", width=9, height=9, onefile=TRUE)
for (ii in c("DeltaObs", "DeltaExp", "DeltaR", "DeltaT", "DeltaA", "DeltaH")) {
Cex <- pop[[ii]]
names(Cex) <- rownames(pop)
br <- c(-Inf, -2, -1, -0.1, 0.1, 1, 2, Inf)
#c00 <- c('#d7191c','#fdae61','#ffffbf','#abdda4','#2b83ba')
c00 <- c('#d7191c','darkgrey','#2b83ba')
#c00 <- c("red", "darkgrey", "blue")
Col0 <- colorRampPalette(c00)(7)
Col <- Col0[cut(Cex, br)]
names(Col) <- names(Cex)
op <- par(las=1)
plot(0, type="n", xlim=c(-0.8,1.2), ylim=c(-1,1), xlab="Axis 1", ylab="Axis 2",
main=ii)
s2 <- scores(o)$sites
s2 <- s2 / max(abs(s2))
for (i in 1:nrow(s2))
arrows(x0=0,y0=0,x1=s2[i,1],y1=s2[i,2], angle=20, length = 0.1, col="darkgrey")
text(s2*1.05, labels=rownames(NN),cex=1,col=1)
abline(h=0,v=0,lty=2)
s1 <- scores(o)$species
s1 <- s1 / max(abs(s1))
text(s1[names(Col),]*0.8, labels=colnames(NN),cex=0.75, col=Col)
for (ii in 1:200)
lines(c(0.85, 0.9), rep(seq(-0.55, -0.95, len=200)[ii], 2), col=colorRampPalette(c00)(200)[ii])
text(c(1,1,1)+0.1, c(-0.6, -0.75, -0.9),
c(expression(N[PIX] < N[PIF]),expression(N[PIX] == N[PIF]),expression(N[PIX] > N[PIF])))
par(op)
}
dev.off()
## roadside count/habitat bias figure
library(intrval)
rd_over <- sapply(roadside_bias[rownames(pop)], function(z) z[1,3:4] %[o]% z[2,3:4])
rd_roadhigher <- sapply(roadside_bias[rownames(pop)], function(z) z[1,3:4] %[<o]% z[2,3:4])
rd_sign <- ifelse(rd_roadhigher, 1, -1)
rd_sign[rd_over] <- 0
table(rd_over, rd_roadhigher)
table(rd_sign)
plot(pop$DeltaR, pop$DeltaH, col=rd_sign+2)
abline(h=0, v=0)
Cex <- pop$DeltaObs
names(Cex) <- rownames(pop)
br <- c(-Inf, -2, -1, -0.1, 0.1, 1, 2, Inf)
#c00 <- c('#d7191c','#fdae61','#ffffbf','#abdda4','#2b83ba')
c00 <- c('#d7191c','darkgrey','#2b83ba')
#c00 <- c("red", "darkgrey", "blue")
Col0 <- colorRampPalette(c00)(7)
Col <- Col0[cut(Cex, br)]
names(Col) <- names(Cex)
pdf("~/GoogleWork/bam/PIF-AB/draft3/Fig5-count-habitat.pdf", width=7, height=7)
op <- par(las=1)
cx <- 1 # pop$EDR/100
topl <- pop[,c("DeltaR", "DeltaH")]
plot(topl,
xlab="R", ylab="H",
pch=c(19, 21, 19)[rd_sign+2],
cex=cx,
col=Col)
abline(h=0,v=0,lty=2)
#points(topl, cex=cx, pch=21)
text(topl[,1], topl[,2]-0.06,
labels=ifelse(pop$DeltaR %][% c(-1.5, 0.9), rownames(topl), ""), cex=0.6)
for (ii in 1:200)
lines(c(1.7, 1.9), rep(seq(-0.55, -0.95, len=200)[ii], 2), col=colorRampPalette(c00)(200)[ii])
text(c(2.5,2.5,2.5)+0.1, c(-0.6, -0.75, -0.9),
c(expression(N[PIX] < N[PIF]),expression(N[PIX] == N[PIF]),expression(N[PIX] > N[PIF])))
dev.off()
## ranking
pdf("~/GoogleWork/bam/PIF-AB/draft2/Fig2-popsize.pdf", width=7, height=7)
op <- par(mfrow=c(1,1), las=1, mar=c(4,4,1,2))
plot(rank(pop$Npif), rank(pop$Npix), xlab="PIF rank", ylab="PIX rank",
type="n",
pch=19, col="#00000080",
ylim=c(0, nrow(pop)), xlim=c(0, nrow(pop)))
abline(0,1, lty=2)
text(rank(pop$Npif), rank(pop$Npix), labels=rownames(pop), cex=0.4)
par(op)
dev.off()
## results numbers
summary(exp(pop$DeltaObs))
table(pop$Npif < pop$NpixLo)
table(pop$Npif > pop$NpixHi)
table(pop$Npif %[]% list(pop$NpixLo, pop$NpixHi))
# AMCR AMGO AMRO BARS BBMA BRBL CCSP EAPH EUST HOSP HOWR SAVS SOSP VESP
rownames(pop)[pop$Npif > pop$NpixHi]
# BANS BAOR CLSW EAKI GRCA LCSP MODO NESP RWBL TRESVEER YHBL
rownames(pop)[pop$Npif %[]% list(pop$NpixLo, pop$NpixHi)]
# ---
plot(Ahab, Whab, type="n", ylim=c(0,max(AWhab)), xlim=c(0,max(AWhab)))
abline(0,1)
text(Ahab, Whab, names(Ahab), cex=0.7)
summary(round(rowSums(mat[,-(1:2)]) - mat[,1], 12))
summary(round(mat[,2]+mat[,"epsilon"] - mat[,1], 12))
mod <- lm(DeltaObs ~ DeltaR + DeltaT + DeltaA + DeltaH, pop)
an <- anova(mod)
an$Percent <- 100 * an[["Sum Sq"]] / sum(an[["Sum Sq"]])
an <- an[c("Df", "Sum Sq", "Percent", "Mean Sq", "F value", "Pr(>F)")]
summary(mod)
an
cf <- coef(mod)
mat2 <- t(t(model.matrix(mod)) * cf)
mat2 <- cbind(Obs=pop$DeltaObs, mat2[,-1], eps=pop$DeltaObs-rowSums(mat2))
par(mfrow=c(2,1))
dots_box_plot(mat2)
abline(h=0, col=2, lwd=2)
dots_box_plot(mat)
abline(h=0, col=2, lwd=2)
par(las=1)
mat2 <- pop[,c("Npif", "Npix")]
colnames(mat2) <- c("PIF", "'Pixel'")
dots_box_plot(mat2, col="grey", "Population Size (M singing inds.)")
library(intrval)
table(OVER <- pop$Npif %[]% pop[,c("NpixLo", "NpixHi")])
rownames(pop)[OVER]
#v <- tanh(pop$RAI * 0.5)
#v <- plogis(pop$RAI)*2-1
#v <- pop$RAI
Cex <- pop$DeltaObs
names(Cex) <- rownames(pop)
br <- c(-Inf, -2, -1, -0.1, 0.1, 1, 2, Inf)
Col <- colorRampPalette(c("red", "black", "blue"))(7)[cut(Cex, br)]
names(Col) <- rownames(pop)
with(pop, plot(log(H), DeltaObs-DeltaExp, type="n", #xlim=c(-0.4, 0.4), ylim=c(-10,10),
ylab=expression(Delta[OBS]-Delta[EXP]), xlab=expression(-Delta[H])))
abline(h=0, v=0, col=1, lwd=1, lty=2)
abline(lm(I(DeltaObs-DeltaExp) ~ I(log(H)), pop), col=1)
with(pop, text(log(H), DeltaObs-DeltaExp, rownames(pop),
cex=0.8, col=Col))
legend("topright", bty="n", fill=c(4,2), border=NA, legend=c("QPAD > PIF", "QPAD < PIF"))
with(pop, plot(DeltaObs-log(H), DeltaExp, type="n", #xlim=c(-0.4, 0.4), ylim=c(-10,10),
ylab=expression(Delta[OBS]-Delta[H]), xlab=expression(Delta[R]+Delta[T]+Delta[A]+epsilon)))
abline(h=0, v=0, col=1, lwd=1, lty=2)
abline(0, 1)
with(pop, text(DeltaObs-log(H), DeltaExp, rownames(pop), cex=0.8, col=Col))
legend("bottomright", bty="n", fill=c(4,2), border=NA, legend=c("QPAD > PIF", "QPAD < PIF"))
with(pop, plot(DeltaObs, log(H)))
with(pop, plot(epsilon, log(H)))
with(pop, plot(PropRd, log(DeltaRes), type="n",
ylab=expression(log(Delta[Res])), xlab="Road Avoidance Index"))
abline(h=0, v=0, col=1, lwd=1, lty=2)
abline(lm(log(DeltaRes) ~ PropRd, pop), col=1)
with(pop, text(PropRd, log(DeltaRes), rownames(pop),
cex=0.8, col=Col))
legend("topleft", bty="n", fill=c(4,2), border=NA, legend=c("QPAD > PIF", "QPAD < PIF"))
o <- cca(rai)
plot(0,type="n", xlim=c(-1,1), ylim=c(-1,1))
s1 <- scores(o)$species
s1 <- s1 / max(abs(s1))
text(s1[names(Col),], labels=colnames(rai),cex=0.75, col=Col)
s2 <- scores(o)$sites
s2 <- s2 / max(abs(s2))
text(s2, labels=rownames(rai),cex=1,col=3)
points(s1[1,,drop=F],pch=3,col=4,cex=5)
abline(h=0,v=0,lty=2)
legend("topleft", bty="n", fill=c(4,2), border=NA, legend=c("QPAD > PIF", "QPAD < PIF"))
plot(Ahab, Whab, type="n", ylim=c(0,max(AWhab)), xlim=c(0,max(AWhab)))
abline(0,1)
text(Ahab, Whab, names(Ahab), cex=0.7)
op <- par(mar=c(1,1,1,1))
plot(hill, col=grey(0:100/100), legend=FALSE, bty="n",
box=FALSE, axes=FALSE)
plot(r, legend=FALSE, col=topo.colors(50, alpha=0.35)[26:50], add=TRUE)
plot(BCR2AB, col=c("#00000060", NA, rep("#00000060", 11)), add=TRUE)
plot(xy[xy@data$PCODE!="BBSAB" & !(!is.na(xy2BCR) & xy2BCR==24),], add=TRUE, pch=19, col="white", cex=0.5)
plot(xy[xy@data$PCODE=="BBSAB" & !(!is.na(xy2BCR) & xy2BCR==24),], add=TRUE, pch=19, col="lightblue", cex=0.5)
plot(xy[xy@data$PCODE!="BBSAB" & !is.na(xy2BCR) & xy2BCR==24,], add=TRUE, pch=19, cex=0.5)
plot(xy[xy@data$PCODE=="BBSAB" & !is.na(xy2BCR) & xy2BCR==24,], add=TRUE, pch=19, col=4, cex=0.5)
legend("bottomleft", title="Surveys", pch=c(19, 19, 19, 21), bty="n",
col=c("blue", "lightblue", "black", "black"),
legend=c("BBS in BCR 6", "BBS outside", "Off road in BCR 6", "Off road outside"))
par(op)
tab0 <- table(xy@data$HAB)
ii1 <- xy@data$PCODE=="BBSAB" & !is.na(xy2BCR) & xy2BCR==24
tab1 <- table(xy@data$HAB, ii1)
summary(xnss$POINT_Y[ii1 & xnss$POINT_Y < 58]) # 56.51 latitude
ii2 <- xnss$POINT_Y < 56.51
tab2 <- table(xy@data$HAB, ii2)
df <- data.frame(tab0/sum(tab0), w1=tab1[,"TRUE"]/sum(tab1[,"TRUE"]),
w2=tab2[,"TRUE"]/sum(tab2[,"TRUE"]))
all(NAM==rownames(df))
tmp <- t(as.matrix(df[,-1]))
rownames(tmp)[1] <- "a"
tmp <- tmp[,order(tmp[1,])]
par(las=1, mar=c(5,6,2,2))
barplot(100*tmp[1:2,], beside=TRUE, horiz=TRUE, xlab="% representation",
legend.text=TRUE)
h_fun <- function(h, a, w) {
NN <- h[NAM, "50%"] * 10^6 # back to individuals
DD <- NN / colSums(AvegH)[NAM] # density: males / ha
sum(DD * w) / sum(DD * a)
}
H1 <- sapply(NestAll, h_fun, a=df$Freq, w=df$w1)
H2 <- sapply(NestAll, h_fun, a=df$Freq, w=df$w2)
H3 <- sapply(NestAll, h_fun, a=df$Freq, w=df$w1*df$w2/sum(df$w1*df$w2))
names(H1) <- names(H2) <- names(H3) <- names(NestAll)
par(mfrow=c(2,2))
plot(H1,H2)
plot(log(H1[rownames(pop)]), pop$epsilon)
plot(log(H2[rownames(pop)]), pop$epsilon)
plot(log(H3[rownames(pop)]), pop$epsilon)
cor(cbind(pop$epsilon, H1[rownames(pop)], H2[rownames(pop)], H3[rownames(pop)]))
par(mfrow=c(1,3))
logH <- log(H1[rownames(pop)])
with(pop, plot(logH, epsilon, type="n", #xlim=c(-0.4, 0.4), ylim=c(-10,10),
ylab=expression(Delta[RES]), xlab=expression(Delta[H])))
abline(h=0, v=0, col=1, lwd=1, lty=2)
abline(lm(epsilon ~ logH, pop), col=1)
with(pop, text(logH, epsilon, rownames(pop),
cex=0.8, col=Col))
legend("topleft", bty="n", fill=c(4,2), border=NA, legend=c("QPAD > PIF", "QPAD < PIF"))
logH <- log(H2[rownames(pop)])
with(pop, plot(logH, epsilon, type="n", #xlim=c(-0.4, 0.4), ylim=c(-10,10),
ylab=expression(Delta[RES]), xlab=expression(Delta[H])))
abline(h=0, v=0, col=1, lwd=1, lty=2)
abline(lm(epsilon ~ logH, pop), col=1)
with(pop, text(logH, epsilon, rownames(pop),
cex=0.8, col=Col))
legend("topleft", bty="n", fill=c(4,2), border=NA, legend=c("QPAD > PIF", "QPAD < PIF"))
logH <- log(H3[rownames(pop)])
with(pop, plot(logH, epsilon, type="n", #xlim=c(-0.4, 0.4), ylim=c(-10,10),
ylab=expression(Delta[RES]), xlab=expression(Delta[H])))
abline(h=0, v=0, col=1, lwd=1, lty=2)
abline(lm(epsilon ~ logH, pop), col=1)
with(pop, text(logH, epsilon, rownames(pop),
cex=0.8, col=Col))
legend("topleft", bty="n", fill=c(4,2), border=NA, legend=c("QPAD > PIF", "QPAD < PIF"))
par(mfrow=c(1,3))
plot(pop$Npix, pop$Npif, ylim=c(0,7));abline(0,1)
plot(pop$Npix, pop$Npif/H1[rownames(pop)], ylim=c(0,7));abline(0,1)
plot(pop$Npix, pop$Npif/H2[rownames(pop)], ylim=c(0,7));abline(0,1)
plot(log(pop$Npix/pop$Npif), log(H1[rownames(pop)]));abline(h=0,v=0)
plot(pop$epsilon, log(H1[rownames(pop)]));abline(h=0,v=0)
plot(pop$epsilon+log(H1[rownames(pop)]), log(H1[rownames(pop)]));abline(h=0,v=0)
pop$DeltaH <- log(H1[rownames(pop)])
pop$Res1 <- pop$DeltaObs - pop$DeltaT
pop$Res2 <- pop$DeltaObs - pop$DeltaT - pop$DeltaA
pop$Res3 <- pop$DeltaObs - pop$DeltaT - pop$DeltaA - pop$DeltaR
pop$Res4 <- pop$DeltaObs - pop$DeltaT - pop$DeltaA - pop$DeltaR - pop$DeltaH
par(mfrow=c(2,1))
mat <- pop[,c("DeltaObs", "DeltaR", "DeltaT", "DeltaA", "DeltaH", "epsilon")]
dots_box_plot(mat, col="grey", ylab=expression(Delta))
abline(h=0, col=2, lwd=2)
mat <- pop[,c("DeltaObs", "Res1", "Res2", "Res3", "Res4")]
dots_box_plot(mat, col="grey", ylab=expression(Delta))
abline(h=0, col=2, lwd=2)
yyy <- as.matrix(t(groupMeans(yy, 1, xn$ROAD01)))
r2 <- yyy[,"0"]/yyy[,"1"]
r2 <- r2[rownames(pop)]
plot(r2, exp(pop$DeltaR))
|
abe355e2694387f6c8bc77ac044ca6312da70aaf
|
32b18b6d88a47691a6a8d4275268b2c197e90b99
|
/run_analysis.R
|
ceb5f412c0c6aeeeffddf48ee5a618ab0230dc79
|
[] |
no_license
|
BBeise/GettingCleansingData
|
039e04eee578a2fe53b6089974a3c8b233cb55f2
|
d09b4cf049212e41d947d09d18443df49bedadfe
|
refs/heads/master
| 2020-05-18T12:35:03.204294
| 2015-03-22T23:25:00
| 2015-03-22T23:25:00
| 32,700,516
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 3,009
|
r
|
run_analysis.R
|
# preliminary stuff
library(dplyr)
library(tidyr)
# read in the datasets
activity_labels <- read.table("~/Google Drive/Training/Data Science/Getting Cleansing Data/Class Project/UCI HAR Dataset/activity_labels.txt", quote="\"")
subject_test <- read.table("~/Google Drive/Training/Data Science/Getting Cleansing Data/Class Project/UCI HAR Dataset/test/subject_test.txt", quote="\"")
subject_train <- read.table("~/Google Drive/Training/Data Science/Getting Cleansing Data/Class Project/UCI HAR Dataset/train/subject_train.txt", quote="\"")
features <- read.table("~/Google Drive/Training/Data Science/Getting Cleansing Data/Class Project/UCI HAR Dataset/features.txt", quote="\"")
y_test <- read.table("~/Google Drive/Training/Data Science/Getting Cleansing Data/Class Project/UCI HAR Dataset/test/y_test.txt", quote="\"")
y_train <- read.table("~/Google Drive/Training/Data Science/Getting Cleansing Data/Class Project/UCI HAR Dataset/train/y_train.txt", quote="\"")
X_train <- read.table("~/Google Drive/Training/Data Science/Getting Cleansing Data/Class Project/UCI HAR Dataset/train/X_train.txt", quote="\"")
X_test <- read.table("~/Google Drive/Training/Data Science/Getting Cleansing Data/Class Project/UCI HAR Dataset/test/X_test.txt", quote="\"")
# Step 1 from Project Instruction - Merge the test and training datasets
# SubStep 1.1 - creating a Test/Train identifier to the subject tables
subj_test_in<-data.frame(subject_test)
subj_test_in$group<-"TEST"
subj_train_in<-data.frame(subject_train)
subj_train_in$group<-"TRAIN"
# SubStep 1.2 - rowbinding the test/train subject files to the subject_files
# for each row - output: a_subject
a_subject<-data.frame(rbind(subj_test_in,subj_train_in))
# SubStep 1.3 - rowbinding and labelling the test/train activity files - output: b_activity
b_activity<-data.frame(rbind(y_test,y_train))
colnames(b_activity)<-c("actcode")
# SubStep 1.4 - rowbinding the test and training data files and applying colnames - output: c_data
c_data<-data.frame(rbind(X_test,X_train))
names.cdata<-t(features)
colnames(c_data)<-names.cdata[2,]
# SubStep 1.5 - colbinding the subject file to the activity file to the data file - output: comb_raw
comb_raw<-cbind(a_subject,b_activity,c_data)
# Step 2 - Extract the measurements on the mean and standard deviation for each measurement
# SubStep 2.1 - identify the variables of interest and subset on that list
variables <- grep("std|mean|Mean",features[,2],value=TRUE)
comb_sub1<-comb_raw[,1:3]
comb_sub2<-comb_raw[,variables]
comb_dat<-cbind(comb_sub1,comb_sub2)
# Step 3 - apply activity name through merge
names(activity_labels)<-c("actcode","ACTIVITY")
mergefile<-merge(activity_labels,comb_dat,by.x="actcode",by.y="actcode",all=TRUE)
# Step 4 - label data set with appropriate variable names
colnames(mergefile)[3] <- "SUBJID"
# Step 5 - create tidy data set of average of each variable for each activity and each subject
############# unable to complete step 5 by submission date
|
9adaa066e4024d2e441af2a2920383a5b1c92967
|
c539d183c64dfd4206676803a38e2b1b87022e3f
|
/qrsvm/man/predict.qrsvm.Rd
|
f2a2af2d02eb4ccac3eff78743f10a1d98971068
|
[] |
no_license
|
wothad/qrsvm
|
c26a97c0702842daa922b8b5da6a2aeaa6cd6b3a
|
3606547b79116895f0f64a266723c9e0e058312e
|
refs/heads/master
| 2021-01-20T12:41:41.771990
| 2017-05-05T16:22:28
| 2017-05-05T16:22:28
| 90,394,299
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 469
|
rd
|
predict.qrsvm.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/predict.qrsvm.R
\name{predict.qrsvm}
\alias{predict.qrsvm}
\title{Predict ann Object oc class "qrsvm"}
\usage{
predict.qrsvm(model, newdata)
}
\arguments{
\item{model}{An object of class "qrsvm"}
\item{newdata}{The predictors of the predictable data in an n X m Matrix}
}
\value{
A numeric vector of predicted values
}
\description{
Predict ann Object oc class "qrsvm"
}
|
8bb567f4a58d89494ef09622d913cec6bc56c436
|
d0c44734ab7c3ea3a1a14e2769f175c8dfd60302
|
/run_analysis.R
|
d80497230a8cfba496d9f2d4ce63194009009c9d
|
[] |
no_license
|
rakeshds/runanalysis
|
fd1e1f5f6ee490b8573b1b37389e020d1a145716
|
a1bf7a16615ef45f9c530fe8007102e8b51d6419
|
refs/heads/master
| 2021-01-01T04:45:08.652049
| 2016-04-28T08:52:27
| 2016-04-28T08:52:27
| 56,919,566
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,331
|
r
|
run_analysis.R
|
##load package
library(resahpe2)
library(dplyr)
##downlaoding of data
url<-"https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip"
f<-file.path(getwd(),"gdpf.zip")
download.file(url,f)
##data make up
actname<-read.table("./UCIHARDataset/activity_labels.txt")
name_feature<-read.table("./UCIHARDataset/features.txt")
name_feature[,2] <- as.character(name_feature[,2])
extract_feature<-grep(".*mean.*|.*std.*",name_feature[,2])
extract_feature.names<-name_feature[extract_feature,2]
##training
train_x<- read.table("./UCIHARDataset/train/X_train.txt")[extract_feature]
train_y<-read.table("./UCIHARDataset/train/y_train.txt")
train_sub<-read.table("./UCIHARDataset/train/subject_train.txt")
train<-cbind(train_x,train_y,train_sub)
##testing
test_x<- read.table("./UCIHARDataset/test/X_test.txt")[extract_feature]
test_y<-read.table("./UCIHARDataset/test/y_test.txt")
test_sub<-read.table("./UCIHARDataset/test/subject_test.txt")
test<-cbind(test_x,test_y,test_sub)
##merge data
merge_data<-rbind(test,train)
colnames(merge_data)<-c(extract_feature.names,"activity","subject")
##tidy data
average.df <- aggregate(x=merge_data, by=list(act=merge_data$activity, subj=merge_data$subject), FUN=mean)
write.table(average.df, './UCIHARDataset/average.txt', row.names = F)
|
6179e0deaac1df8aeb181afc3e6f2d87a6fec575
|
24acd216a8755a84e072cc10401f0e495461af26
|
/EDAcasestudy.R
|
12f285359fbd34f84269378b42f773bdeb2fadb1
|
[] |
no_license
|
PrudviBilakanti/Exploratory-Data-Analysis-on-Lending-Club-data
|
2a72c78bc046d51a7ac8fafa2fa4ff1d71f1a94b
|
ef0dfb50a39041344d325872fdcad12b86de917d
|
refs/heads/master
| 2020-04-09T19:39:45.250801
| 2018-12-05T16:51:44
| 2018-12-05T16:51:44
| 160,549,799
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 31,852
|
r
|
EDAcasestudy.R
|
library(dplyr)
library(tidyr)
library(stringr)
library(ggplot2)
#install ggcorrplot you dont have any such package
install.packages("ggcorrplot")
library(ggcorrplot)
#set working directory
df <- read.csv('loan.csv',stringsAsFactors = F,na.strings=c(""," ","NA","n/a","N/A"))
# Lets subset the data for the output variable loan_status with value Charged off or Fully paid.
df <- subset(df,df$loan_status == 'Charged Off' | df$loan_status == 'Fully Paid')
# Lets do some cleaning.
# Lets clean columsn with Zero variance
df <- df[sapply(df, function(x) n_distinct(x,na.rm = T) > 1)]
# Lets get rid of columns which doesnt provide any insights about why user defaults
# Below Coumns doesnt have any values that differentiate between Charged off and Fully paid loans or these columns represent values after declaring a loan as charged off
# out_prncp,out_prncp_inv,next_pymnt_d,recoveries
df <- df[,!(names(df) %in% c('out_prncp','out_prncp_inv','next_pymnt_d','recoveries','id','member_id','url','desc','title','mths_since_last_record','mths_since_last_delinq'))]
# Bucket funded_amount into buckets
min(df$funded_amnt)
max(df$funded_amnt)
summary(df$revol_bal)
df$funded_amnt_range[between(df$funded_amnt,0,10000)] <- 'Low_fund'
df$funded_amnt_range[between(df$funded_amnt,10001,20000)] <- 'Medium_fund'
df$funded_amnt_range[df$funded_amnt > 20000] <- 'High_fund'
summary(df)
# Bucket funded_amount into buckets
min(df$funded_amnt_inv)
max(df$funded_amnt_inv)
df$funded_amnt_inv_range[between(df$funded_amnt_inv,0,10000)] <- 'Low_inv_fund'
df$funded_amnt_inv_range[between(df$funded_amnt_inv,10001,20000)] <- 'Medium_inv_fund'
df$funded_amnt_inv_range[df$funded_amnt_inv > 20000] <- 'High_inv_fund'
# Remove 'months' from term
df$term <- gsub(" months","",df$term)
# Lets format int_rate column so that we can bucket into segments for further analysis.
df$int_rate <- gsub("%","",df$int_rate)
df$int_rate <- as.double(df$int_rate)
max(df$int_rate)
min(df$int_rate)
df$int_rate_category[between(df$int_rate,0,10)] <- 'Low_interest'
df$int_rate_category[between(df$int_rate,11,20)] <- 'Medium_interest'
df$int_rate_category[df$int_rate > 20] <- 'High_interest'
# Lets also bucket installment into buckets
summary(df$installment)
df$installment_category[between(df$installment,0,450)] <- 'Low_installment'
df$installment_category[between(df$installment,451,900)] <- 'Medium_installment'
df$installment_category[df$installment > 900] <- 'High_intallment'
# bucket dti into low medium and high
df$dti_category[between(df$dti,0,10)] <- 'Low_dti'
df$dti_category[between(df$dti,11,20)] <- 'Medium_dti'
df$dti_category[df$dti > 20] <- 'High_dti'
# Format the revolving credit utilization revol_util. remove % from the value
df$revol_util <- gsub("%","",df$revol_util)
df$revol_util <- as.double(df$revol_util)
#Lets also bucket the revol_util
summary(df$revol_util)
df$rev_util_rate[between(df$revol_util,0,25)] <- 'Low_revol_credit'
df$rev_util_rate[between(df$revol_util,26,50)] <- 'Average_revol_credit'
df$rev_util_rate[between(df$revol_util,51,75)] <- 'above_avg_revol_credit'
df$rev_util_rate[between(df$revol_util,76,100)] <- 'High_revol_credit'
# extracting the years (so the 0-1 year exp will be represented as 1, 10 or 10+ as 10)
df$emp_length <- str_extract(df$emp_length, pattern = '[0-9]+')
# round funded amount by investors to match it with other rows
df$funded_amnt_inv <- round(df$funded_amnt_inv,0)
df$annual_inc <- round(df$annual_inc,0)
df$issue_d <- as.Date(paste('1-',df$issue_d,sep = ''),format ='%d-%b-%y')
df$issued_month <- as.numeric(format(df$issue_d, "%m"))
df$earliest_cr_line <- as.Date(paste('1-',df$earliest_cr_line,sep = ''),format ='%d-%b-%y')
df$last_pymnt_d <-as.Date(paste('1-',df$last_pymnt_d,sep = ''),format ='%d-%b-%y')
df$issue_year <- format(df$issue_d,"%Y")
#sub-grade has number and alphabet associated so we can split to get better insights
#subgrade spitting
split_sub_grade <- function(x){
return(unlist(str_split(x,''))[2])
}
df$sub_grade_no <- as.numeric(unlist(lapply(df$sub_grade, split_sub_grade)))
#filling na revo_util with mean
df$revol_util[is.na(df$revol_util)] <- 48.7
# Delete title column.
########################################################## UNI VARIATE ANALYSIS ###########################################################
#Funded amout by investor distribution
ggplot(df,aes(x=funded_amnt_inv,fill=loan_status))+
geom_histogram(bins = 50)+ggtitle('Funded amount by Investor Distribution')
#we can see that we have some outliers in distribution
mean(df$funded_amnt_inv)
ggplot(df, aes(x="",y=funded_amnt_inv))+
geom_boxplot()+ggtitle('Funded amount by Investor Boxplot')
#We can identify outliers in boxplot easily
summary(df$funded_amnt_inv)
#catogery distribution of term
ggplot(df,aes(x=term,fill=term))+
geom_bar()+
geom_text(aes(label=paste(round((..count..)/sum(..count..)*100,1),'%'),vjust=-0.3),stat="count")+
ggtitle('Terms distribution')
# Plotting Interest rate distribution
ggplot(df, aes(x=int_rate))+
geom_histogram(binwidth = 5)+ggtitle('Interest rate Distribution')
# We can see that interest rate is around 10,15 have high count
#Installment Distribution
ggplot(df, aes(x=installment))+
geom_histogram(binwidth = 100)+ggtitle('Installment Distribution')
#Understanding outliers using boxplot
ggplot(df, aes(x="",y=installment))+
geom_boxplot()+ggtitle('Installment Boxplot')
#central tendency are affected due to outliers here
summary(df$installment)
# Analyse Grade catogery using bar plot
ggplot(df,aes(x=grade,fill=grade))+
geom_bar()+
geom_text(aes(label=paste(round((..count..)/sum(..count..)*100,1),'%'),vjust=-0.3),stat="count")+
ggtitle('grade distribution')
#Sub Grade
ggplot(df,aes(x=sub_grade_no,fill=factor(sub_grade_no)))+
geom_bar()+
geom_text(aes(label=paste(round((..count..)/sum(..count..)*100,1),'%'),vjust=-0.3),stat="count")+
ggtitle('sub_grade_no distribution')
# emp
df$emp_title <- tolower(df$emp_title)
df$emp_title <- gsub("^\\s+|\\s+$", "", df$emp_title)
#exp length
ggplot(df,aes(x=emp_length,fill=loan_status))+
geom_bar()+theme(axis.text.x = element_text(angle = 90, hjust = 1))+
ggtitle('Employee Experience Bar plot')
table(df$home_ownership)
# MORTGAGE NONE OTHER OWN RENT
# 17659 3 98 3058 18899
###
ggplot(df,aes(x=home_ownership,fill=home_ownership))+
geom_bar()+
geom_text(aes(label=paste(round((..count..)/sum(..count..)*100,1),'%'),vjust=-0.3),stat="count")+
ggtitle('Home Ownership Bar plot')
# 14 of all egments are defaulted
# annual income box plot and histogram
#since values are very large log transformation would give better insight
ggplot(df,aes(x="",y=annual_inc))+
geom_boxplot()+ coord_trans(y = "log10")+ggtitle('Log Transformed annual income boxplot')
ggplot(df,aes(x=annual_inc))+
geom_histogram(bins = 30)+ggtitle('Annual income Histogram')
summary(df$annual_inc)
#Outliers are affecting central tendency in this feature
# Verification Status
ggplot(df, aes(x=verification_status,fill=verification_status))+
geom_bar()+
geom_text(aes(label=paste(round((..count..)/sum(..count..)*100,1),'%'),vjust=-0.3),stat="count")+
ggtitle('Verification Status Bar plot')
#There are high people with not verified as status around 43
# loan status
ggplot(df, aes(x=loan_status,fill=loan_status))+
geom_bar()+geom_text(aes(label=paste(round((..count..)/sum(..count..)*100,1),'%'),vjust=-0.3),stat="count")+
ggtitle('Loan Status Bar plot')
#Here we can see that bank can have losses due to 14.6% of individuals tend to default
# Charged Off Current Fully Paid
# 0.14167737 0.02870307 0.82961956
# Purpose
ggplot(df, aes(x=purpose,fill=purpose))+
geom_bar()+
geom_text(aes(label=paste(round((..count..)/sum(..count..)*100,1),'%'),vjust=-0.3),stat="count")+
ggtitle('Purpose Bar plot')+ theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank())
# majority people opted loan to consolidate debts(46%), credit card (12%),
#home improvements(7.4), major purchase(5.5%) and small bussiness(4.6%)
#zip code commented plot for zip as its hard to infer
#ggplot(df,aes(x=zip_code,fill=loan_status))+
# geom_bar()
# addr_state
ggplot(df,aes(x=addr_state,fill=addr_state))+
geom_bar()+ theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank())
sort(table(df$addr_state),decreasing = T)
#These are the top 5 states. most of the loans are from California region
# CA NY FL TX NJ
# 7099 3812 2866 2727 1850
# dti
ggplot(df, aes(x=dti))+
geom_histogram(bins = 50)+ggtitle('DTI Distribution')
#we can see a sudden drop in count around 28
ggplot(df, aes(x='',y=dti))+geom_boxplot()
# delinq_2yrs
ggplot(df,aes(x=factor(delinq_2yrs),fill=factor(delinq_2yrs)))+
geom_bar()
sort(table(df$delinq_2yrs),decreasing = T)
# Majority of the poeple were not delin1 in last 2 years
# Pending formatting on Date column
table(df$issue_d)
ggplot(df,aes(x=issue_d))+geom_bar()+ggtitle('Issue date histogram')
#inq_last_6mths
table(df$inq_last_6mths,df$loan_status)
ggplot(df, aes(x=inq_last_6mths))+
geom_bar()+ggtitle('inq_last_6mths Distribution')
# people with less inq in last 6 months are more
#mths_since_last_delinq
#ggplot(df,aes(x=mths_since_last_delinq,fill=loan_status))+
# geom_bar()
# mths_since_last_delinq dosent seem to effect loan status directly
#droped column due to high number of na
#mths_since_last_record
#ggplot(df,aes(x=mths_since_last_record,fill=loan_status))+
# geom_bar()
#we can drop this column as it contains more number of na and is not directly affecting loan status
# open_acc Distribution
ggplot(df, aes(x=(open_acc),fill=loan_status))+
geom_histogram(bins = 50)
# Most of the poeple have credit lines between 2 and 15
#pub_rec
ggplot(df,aes(x=factor(pub_rec)))+
geom_bar()+ggtitle('Number of derogatory public records distribution')
# Majority of the people had zero derogatory public records
# revol_bal
ggplot(df, aes(x="",y=revol_bal))+
geom_boxplot()+coord_trans(x = "log")+ggtitle('Transformed annual income boxplot')
#we can see many outliers lets plot distribution plot
ggplot(df,aes(x=revol_bal))+
geom_histogram(binwidth = 10000)
# Most of the poeple had the revol balance between 0 and 25000
# revol_util distribution
ggplot(df, aes(x="",y=revol_util))+
geom_boxplot()
summary(df$revol_util)
# Most of the people utilised 50 % of their revlving credit
#after loking at summary and distribution we can impute na values with mean
# Total credit lines in borrowers accoun
ggplot(df,aes(x=total_acc))+
geom_histogram(bins = 10)+ggtitle('total_acc distribution')
# There are more number of people with credit line between 10 to 40
# Outstanding Principle#
#ggplot(df, aes(x=out_prncp))+
# geom_histogram()#
# This is expected as most of the users have cleared their loans, there will be zero pending principle amount
#we can remove feature
# Outstanding principle for the amount funded by investors#
#ggplot(df,aes(x=out_prncp_inv))+
# geom_histogram()
# Same trend with the amount that is funded by investors.
#total_pymnt
ggplot(df,aes(x=total_pymnt,fill=loan_status))+
geom_histogram(bins = 50)
#total payment distribution and its effect on loan status
ggplot(df,aes(x=total_pymnt,fill=loan_status))+
geom_histogram(bins = 50)+ggtitle('Total payment and loan status Distribution')
##################### Continues variables Analysis #######
ggplot(df, aes(x=dti, fill = factor(loan_status))) +
geom_histogram(aes(y=..density..),bins = 30, position="identity", alpha=0.5,fill="white", color="black")+
geom_density(alpha=0.6) +
labs(title = "DTI vs Status",
x = "DTI",
y = "Status")
ggplot(df, aes(x=loan_amnt, fill = factor(loan_status))) +
geom_histogram(aes(y=..density..), bins = 30,position="identity", alpha=0.5)+
geom_density(alpha=0.6) +
labs(title = "Loan Amount vs Status",
x = "Loan Amount",
y = "Status")
######################################## BI variate Analysis and Multi Variate analysis #########################################################
#plotting term vs loanstatus
ggplot(df,aes(x=term,fill=loan_status))+geom_bar()+ggtitle('Term vs Loan status')
#multi variate on term and loan status
ggplot(df,aes(x=term,fill=loan_status))+geom_bar()+ggtitle('Term vs Loan status vs grade')+facet_grid(.~grade )
ggplot(df,aes(x=term,fill=loan_status))+geom_bar()+ggtitle('Term vs Loan status vs Employee experience')+facet_grid(.~emp_length )
#for 10 year experience 60 month has high default rate
ggplot(df,aes(x=term,fill=loan_status))+geom_bar()+ggtitle('Term vs Loan status vs verification_status')+facet_grid(.~verification_status )
ggplot(df,aes(x=term,fill=loan_status))+geom_bar()+ggtitle('Term vs Loan status vs purpose')+facet_grid(.~purpose )
#debt consolidation has high amount of loan default in both terms
ggplot(df,aes(x=term,fill=loan_status))+geom_bar()+ggtitle('Term vs Loan status vs home_ownership')+facet_grid(.~home_ownership )
ggplot(df,aes(x=term,fill=loan_status))+geom_bar()+ggtitle('Term vs Loan status vs dti_category')+facet_grid(.~dti_category )
ggplot(df,aes(x=term,fill=loan_status))+geom_bar()+ggtitle('Term vs Loan status vs installment_category')+facet_grid(.~installment_category )
#low installments has high defaulters
ggplot(df,aes(x=term,fill=loan_status))+geom_bar()+ggtitle('Term vs Loan status vs sub_grade_no')+facet_grid(.~sub_grade_no )
#plot home ownership vs loanstatus
ggplot(df,aes(x=home_ownership,fill=loan_status))+ggtitle('Home Ownership vs Loan Status')+geom_bar()
#mortgage and rent has high amount of defaulters
ggplot(df,aes(x=home_ownership,fill=loan_status))+geom_bar()+facet_grid(.~grade)+theme(axis.text.x = element_text(angle = 90, hjust = 1))
ggplot(df,aes(x=home_ownership,fill=loan_status))+ggtitle('Home Ownership vs Loan Status Vs Experiance')+geom_bar()+facet_grid(.~emp_length)+theme(axis.text.x = element_text(angle = 90, hjust = 1))
ggplot(df,aes(x=home_ownership,fill=loan_status))+geom_bar()+facet_grid(.~purpose)+theme(axis.text.x = element_text(angle = 90, hjust = 1))
#mortgage,rent purpose has high default in debt consolidation
ggplot(df,aes(x=home_ownership,fill=loan_status))+geom_bar()+facet_grid(.~dti_category)+theme(axis.text.x = element_text(angle = 90, hjust = 1))
ggplot(df,aes(x=home_ownership,fill=loan_status))+geom_bar()+ggtitle('Home Ownership vs Loan Status vs installment catogery')+facet_grid(.~installment_category)+theme(axis.text.x = element_text(angle = 90, hjust = 1))
#low installment and rent mortage are showing significant pattern
ggplot(df,aes(x=home_ownership,fill=loan_status))+geom_bar()+facet_grid(.~sub_grade_no)+theme(axis.text.x = element_text(angle = 90, hjust = 1))
#plot emp_length vs loan status and multi varient plots
ggplot(df,aes(x=emp_length,fill=loan_status))+geom_bar()
ggplot(df,aes(x=emp_length,fill=loan_status))+geom_bar()+facet_grid(.~grade)+theme(axis.text.x = element_text(angle = 90, hjust = 1))
ggplot(df,aes(x=emp_length,fill=loan_status))+geom_bar()+facet_grid(.~emp_length)+theme(axis.text.x = element_text(angle = 90, hjust = 1))
#mortgage,rent purpose has high default in debt consolidation
ggplot(df,aes(x=emp_length,fill=loan_status))+geom_bar()+facet_grid(.~dti_category)+theme(axis.text.x = element_text(angle = 90, hjust = 1))
ggplot(df,aes(x=emp_length,fill=loan_status))+geom_bar()+facet_grid(.~installment_category)+theme(axis.text.x = element_text(angle = 90, hjust = 1))
#low installment and rent mortage are showing significant pattern
ggplot(df,aes(x=emp_length,fill=loan_status))+geom_bar()+facet_grid(.~sub_grade_no)+theme(axis.text.x = element_text(angle = 90, hjust = 1))
#plot grade vs loan status
ggplot(df,aes(x=loan_status,fill=loan_status))+geom_bar()+facet_grid(.~grade)+theme(axis.text.x = element_text(angle = 90, hjust = 1))
#grade B,C,D has high amount of defaulters
ggplot(df,aes(x=grade,fill=loan_status))+geom_bar()+facet_grid(.~verification_status)+theme(axis.text.x = element_text(angle = 90, hjust = 1))
ggplot(df,aes(x=grade,fill=loan_status))+geom_bar()+facet_grid(.~emp_length)+theme(axis.text.x = element_text(angle = 90, hjust = 1))
ggplot(df,aes(x=grade,fill=loan_status))+geom_bar()+facet_grid(.~dti_category)+theme(axis.text.x = element_text(angle = 90, hjust = 1))
ggplot(df,aes(x=grade,fill=loan_status))+geom_bar()+facet_grid(.~df$home_ownership)+theme(axis.text.x = element_text(angle = 90, hjust = 1))
ggplot(df,aes(x=grade,fill=loan_status))+geom_bar()+facet_grid(.~installment_category)+theme(axis.text.x = element_text(angle = 90, hjust = 1))
ggplot(df,aes(x=grade,fill=loan_status))+geom_bar()+facet_grid(.~sub_grade_no)+theme(axis.text.x = element_text(angle = 90, hjust = 1))
#multi variate on term and loan status
ggplot(df,aes(x=purpose,fill=loan_status))+geom_bar()+facet_grid(.~grade )+theme(axis.text.x = element_text(angle = 90, hjust = 1))
ggplot(df,aes(x=purpose,fill=loan_status))+geom_bar()+facet_grid(.~verification_status )+theme(axis.text.x = element_text(angle = 90, hjust = 1))
ggplot(df,aes(x=purpose,fill=loan_status))+geom_bar()+facet_grid(.~dti_category )+theme(axis.text.x = element_text(angle = 90, hjust = 1))
#medium dti has high rate of defaulters
ggplot(df,aes(x=purpose,fill=loan_status))+geom_bar()+facet_grid(.~installment_category )+theme(axis.text.x = element_text(angle = 90, hjust = 1))
#low installments has high defaults
ggplot(df,aes(x=purpose,fill=loan_status))+geom_bar()+facet_grid(.~sub_grade_no )+theme(axis.text.x = element_text(angle = 90, hjust = 1))
ggplot(df,aes(x=loan_status,fill=loan_status))+geom_bar()+facet_grid(.~installment_category )+theme(axis.text.x = element_text(angle = 90, hjust = 1))
#low installments has high fully paid and high defaulters
ggplot(df,aes(x=loan_amnt,y=purpose , col=factor(loan_status)))+geom_point()+geom_jitter()
#we can see that debt considolation and small bussiness have good amount of defaulterss
ggplot(df,aes(x=loan_amnt,y=purpose , col=loan_status))+geom_point()+ggtitle('Purpose vs Loan Status vs Loan amount vs Installment catogery')+geom_jitter()+facet_grid(.~installment_category )
#medium and low installments with dc and sb has high defaulters
ggplot(df,aes(x=loan_amnt,y=purpose , col=loan_status))+geom_point()+geom_jitter()+facet_grid(.~dti_category )
#lets look address
ggplot(df,aes(x=addr_state,fill=loan_status))+geom_bar()+ggtitle('Address vs loan Status')
#CA and NY has high amount of
#
ggplot(df,aes(factor(issued_month),fill=loan_status))+
geom_bar(position = 'fill')
ggplot(df,aes(x=purpose,fill=loan_status))+
geom_bar(position = 'fill')
#small business reltively high defaulters
ggplot(df,aes(x=grade,fill=loan_status))+
geom_bar(position = 'fill')
#as grade increases charged off increases
ggplot(df,aes(x=sub_grade,fill=loan_status))+
geom_bar(position = 'fill')
#f5 has highest amount of defaulters
ggplot(df,aes(x=home_ownership,fill=loan_status))+
geom_bar(position = 'fill')
#none seems to haveno defaulters whihc is good from banks as then can confidently provide loans
ggplot(df,aes(x=zip_code,fill=loan_status))+
geom_bar(position = 'fill')
############################################# scatter plot for continues variables#####
## Loan amount - by grade and loan status
Grade_total <- df %>% select(grade,loan_status,loan_amnt) %>%
group_by(grade,loan_status) %>%
summarise(loan_amnt = sum(loan_amnt))
ggplot(Grade_total, aes(x = grade, y = loan_amnt, col = factor(loan_status))) + geom_point(aes(size=loan_amnt)) +
labs(title = "Loan amount distribution among loan status and Grade",
x = "grade",
y = "Amount")
### loan amount by purpose and loan status
purpose_total <- df %>% select(purpose,loan_status,loan_amnt) %>%
group_by(purpose,loan_status) %>%
summarise(loan_amnt = sum(loan_amnt))
ggplot(purpose_total, aes(x = purpose, y = loan_amnt, col = factor(loan_status))) +
geom_point(aes(size=loan_amnt)) + theme(axis.text.x = element_text(angle = 90, hjust = 1))+
labs(title = "Loan amount distribution among loan status and purpose",
x = "purpose",
y = "Amount")
##########################################################################
select_if(df, is.numeric) -> df_numeric
#correlation plot
ggcorrplot(round(cor(df_numeric,use = 'pairwise.complete.obs'),2), hc.order = TRUE,
type = "lower",
lab = TRUE,
lab_size = 3,
method="circle",
colors = c("tomato2", "white", "springgreen3"),
title="Correlogram of variables",
ggtheme=theme_bw)+
theme(plot.title = element_text(hjust = 0.5))
# Funded Amount buckets
df %>%
group_by(funded_amnt_range,loan_status) %>%
summarise(cnt = n()) %>%
mutate(percent = paste0(round(100 * cnt/sum(cnt), 0), "%")) %>%
ungroup() %>%
as.data.frame() %>%
ggplot(.,aes(x=funded_amnt_range,y=percent,fill=loan_status))+
geom_bar(stat = 'identity')+
labs(title='Defaulters ratio by the funded amount range',
x='Funded amount range',
y='Percent',
fill="Loan_Status")+
geom_text(aes(y=(percent),label=(percent)),hjust = 0.5,vjust=1,size=3,
position = position_stack(vjust = .5))+
theme(plot.title = element_text(hjust = 0.5))
# funded amount by investor
df %>%
group_by(funded_amnt_inv_range,loan_status) %>%
summarise(cnt = n()) %>%
mutate(percent = paste0(round(100 * cnt/sum(cnt), 0), "%")) %>%
ungroup() %>%
as.data.frame() %>%
ggplot(.,aes(x=funded_amnt_inv_range,y=percent,fill=loan_status))+
geom_bar(stat = 'identity')+
labs(title='Defaulters ratio by the investors funded amount range',
x='Funded amount by investors range',
y='Percent',
fill="Loan_Status")+
geom_text(aes(y=(percent),label=(percent)),hjust = 0.5,vjust=1,size=3,
position = position_stack(vjust = .5))+
theme(plot.title = element_text(hjust = 0.5))
# As the funded amount and funded amount by investors are nearly the same we clearly see that poeple
# with high amount of loans tend to default.
# Term and loan status
df %>%
group_by(term,loan_status) %>%
summarise(cnt = n()) %>%
mutate(percent = paste0(round(100 * cnt/sum(cnt), 0), "%")) %>%
ungroup() %>%
as.data.frame() %>%
ggplot(.,aes(x=term,y=percent,fill=loan_status))+
geom_bar(stat = 'identity')+
labs(title='Default ratio by loan term period',
x='Term',
y='Percent',
fill="Loan_Status")+
geom_text(aes(y=(percent),label=(percent)),hjust = 0.5,vjust=1,size=3,
position = position_stack(vjust = .5))+
theme(plot.title = element_text(hjust = 0.5))
# *** Loans with higher term (60 month) tend to default more when compared to 36 months term.
# Int rate bucket
df %>%
filter(int_rate_category != 'NA') %>%
group_by(int_rate_category,loan_status) %>%
summarise(cnt = n()) %>%
mutate(percent = round(100 * cnt/sum(cnt), 0)) %>%
ungroup() %>%
as.data.frame() %>%
ggplot(.,aes(x=int_rate_category,y=as.numeric(as.character(percent)),fill=loan_status))+
geom_bar(stat = 'identity')+
labs(title='Default ratio by loan term period',
x='Term',
y='Percent',
fill="Loan_Status")+
geom_text(aes(label=paste(percent,'%')),hjust = 0.5,vjust=1,size=3,
position = position_stack(vjust = .5))+
theme(plot.title = element_text(hjust = 0.5))
# We can see that people with High interest rinterest rates default more
# installment_category
df %>%
filter(installment_category != 'NA') %>%
group_by(installment_category,loan_status) %>%
summarise(cnt = n()) %>%
mutate(percent = round(100 * cnt/sum(cnt), 0)) %>%
ungroup() %>%
as.data.frame() %>%
ggplot(.,aes(x=installment_category,y=as.numeric(as.character(percent)),fill=loan_status))+
geom_bar(stat = 'identity')+
labs(title='Default ratio by loan installment category',
x='installment category',
y='Percent',
fill="Loan_Status")+
geom_text(aes(label=paste(percent,'%')),hjust = 0.5,vjust=1,size=3,
position = position_stack(vjust = .5))+
theme(plot.title = element_text(hjust = 0.5))
# Even here we can see that people with high installment tend to default more.
# Grade
df %>%
group_by(grade,loan_status) %>%
summarise(cnt = n()) %>%
mutate(percent = round(100 * cnt/sum(cnt), 0)) %>%
ungroup() %>%
as.data.frame() %>%
ggplot(.,aes(x=grade,y=as.numeric(as.character(percent)),fill=loan_status))+
geom_bar(stat = 'identity')+
labs(title='Default ratio by Grade',
x='Grade',
y='Percent',
fill="Loan_Status")+
geom_text(aes(label=paste(percent,'%')),hjust = 0.5,vjust=1,size=3,
position = position_stack(vjust = .5))+
theme(plot.title = element_text(hjust = 0.5))
# From the plot it is evident that the loan defaulters are high in the groups D to G
df %>%
filter(emp_length != 'NA') %>%
group_by(emp_length,loan_status) %>%
summarise(cnt = n()) %>%
mutate(percent = paste0(round(100 * cnt/sum(cnt), 0), "%")) %>%
ungroup() %>%
as.data.frame()
df %>%
filter(emp_length != 'NA') %>%
group_by(emp_length,loan_status) %>%
summarise(cnt = n()) %>%
mutate(percent = round(100 * cnt/sum(cnt), 0)) %>%
ungroup() %>%
as.data.frame() %>%
ggplot(.,aes(x=as.factor(emp_length),y=as.numeric(as.character(percent)),fill=loan_status))+
geom_bar(stat = 'identity')+
labs(title='Default ratio by Employent Length',
x='Employment Length',
y='Percent',
fill="Loan_Status")+
geom_text(aes(label=(percent)),hjust = 0.5,vjust=1,size=3,
position = position_stack(vjust = .5))+
theme(plot.title = element_text(hjust = 0.5))
# The default ration is common across all the employment length.
# Home ownership
df %>%
filter(home_ownership != 'NONE') %>%
group_by(home_ownership,loan_status) %>%
summarise(cnt = n()) %>%
mutate(percent = round(100 * cnt/sum(cnt), 0)) %>%
ungroup() %>%
as.data.frame() %>%
ggplot(.,aes(x=home_ownership,y=as.numeric(as.character(percent)),fill=loan_status))+
geom_bar(stat = 'identity')+
labs(title='Default ratio by home ownership status',
x='Home Ownership',
y='Percent',
fill="Loan_Status")+
geom_text(aes(label=paste(percent,'%')),hjust = 0.5,vjust=1,size=3,
position = position_stack(vjust = .5))+
theme(plot.title = element_text(hjust = 0.5))
# Home ownership Rent and Mortgage to contribute to defaulters ratio.
# Verification Status
df %>%
filter(verification_status != 'NA') %>%
group_by(verification_status,loan_status) %>%
summarise(cnt = n()) %>%
mutate(percent = round(100 * cnt/sum(cnt), 0)) %>%
ungroup() %>%
as.data.frame() %>%
ggplot(.,aes(x=verification_status,y=as.numeric(as.character(percent)),fill=loan_status))+
geom_bar(stat = 'identity')+
labs(title='Default ratio by LC verification of income source and others',
x='verification Status',
y='Percent',
fill="Loan_Status")+
geom_text(aes(label=paste(percent,'%')),hjust = 0.5,vjust=1,size=3,
position = position_stack(vjust = .5))+
theme(plot.title = element_text(hjust = 0.5))
# In contrast to the general belief, loan defaults are higher in verfied pool.
# Purpose
df %>%
filter(purpose != 'NA') %>%
group_by(purpose,loan_status) %>%
summarise(cnt = n()) %>%
mutate(percent = round(100 * cnt/sum(cnt), 0)) %>%
ungroup() %>%
as.data.frame() %>%
ggplot(.,aes(x=purpose,y=as.numeric(as.character(percent)),fill=loan_status))+
geom_bar(stat = 'identity')+
labs(title='Default ratio by purpose of the Loans',
x='Purpose of the Loan',
y='Percent',
fill="Loan_Status")+
geom_text(aes(label=paste(percent,'%')),hjust = 0.5,vjust=1,size=3,
position = position_stack(vjust = .5))+
theme(plot.title = element_text(hjust = 0.5))
# People who took loans to setup small business tend to default ( 27% ) more,
# Debit to Income ratio
df %>%
filter(dti_category != 'NA') %>%
group_by(dti_category,loan_status) %>%
summarise(cnt = n()) %>%
mutate(percent = round(100 * cnt/sum(cnt), 0)) %>%
ungroup() %>%
as.data.frame() %>%
ggplot(.,aes(x=dti_category,y=as.numeric(as.character(percent)),fill=loan_status))+
geom_bar(stat = 'identity')+
labs(title='Default ratio by Debit to income ration',
x='Debit to income ratio',
y='Percent',
fill="Loan_Status")+
geom_text(aes(label=paste(percent,'%')),hjust = 0.5,vjust=1,size=3,
position = position_stack(vjust = .5))+
theme(plot.title = element_text(hjust = 0.5))
# People with high Debt to income ratio tend to default more, 17% of the people defaulted from the higher dt bucket.
# delinq_2yrs
df %>%
filter(delinq_2yrs != 'NA') %>%
group_by(delinq_2yrs,loan_status) %>%
summarise(cnt = n()) %>%
mutate(percent = round(100 * cnt/sum(cnt), 0)) %>%
ungroup() %>%
as.data.frame() %>%
ggplot(.,aes(x=factor(delinq_2yrs),y=as.numeric(as.character(percent)),fill=loan_status))+
geom_bar(stat = 'identity')+
labs(title='Default ratio by delinq in last 2yrs',
x='Number of Delinq in last 2 years',
y='Percent',
fill="Loan_Status")+
geom_text(aes(label=paste(percent,'%')),hjust = 0.5,vjust=1,size=3,
position = position_stack(vjust = .5))+
theme(plot.title = element_text(hjust = 0.5))
# We have less data with high delinq rate ni last 2 years. So we cannot firmly say that Defaulters ratio is high for the people who were delinq in last 2 years.
#inq_last_6mths
df %>%
filter(inq_last_6mths != 'NA') %>%
group_by(inq_last_6mths,loan_status) %>%
summarise(cnt = n()) %>%
mutate(percent = round(100 * cnt/sum(cnt), 0)) %>%
ungroup() %>%
as.data.frame() %>%
ggplot(.,aes(x=factor(inq_last_6mths),y=as.numeric(as.character(percent)),fill=loan_status))+
geom_bar(stat = 'identity')+
labs(title='Default ratio by number of inquiries in last 6 months',
x='Number of inquiries in last 6 months',
y='Percent',
fill="Loan_Status")+
geom_text(aes(label=paste(percent,'%')),hjust = 0.5,vjust=1,size=3,
position = position_stack(vjust = .5))+
theme(plot.title = element_text(hjust = 0.5))
df %>%
filter(inq_last_6mths != 'NA') %>%
group_by(inq_last_6mths,loan_status) %>%
summarise(cnt = n()) %>%
mutate(percent = round(100 * cnt/sum(cnt), 0)) %>%
ungroup() %>%
as.data.frame() %>%
filter(loan_status == 'Charged Off') %>%
ggplot(.,aes(x=inq_last_6mths,y=percent,col=loan_status))+
geom_point()+
geom_smooth(method = 'loess')
# The Defaulters ratio is slightly high with high number of inquiries.
##################################END of EDA#############################
|
479302d49ea0b98c679ae071a77907a47283176d
|
d73a21c656c4b75f0e26ae376209077e739df76c
|
/public/blog/maps/map_functions.R
|
acff05ab4680af2631471ce5a4271aaa0525c888
|
[
"MIT"
] |
permissive
|
hsowards/hayleysowards
|
af45a572c844b0a153cb6ab87d1f3cac7190ae03
|
34b62defd6b0260f464c15284d97433941ac66e9
|
refs/heads/master
| 2021-07-15T06:08:22.705628
| 2021-02-01T05:31:08
| 2021-02-01T05:31:08
| 232,381,620
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 781
|
r
|
map_functions.R
|
library(tidyverse)
library(osmdata)
get_streets <- function(city) {
getbb(paste0(city, " United States")) %>%
opq() %>%
add_osm_feature(key = "highway",
value = c("motorway", "primary",
"secondary", "tertiary")) %>%
osmdata_sf()
}
get_small_streets <- function(city) {
getbb(paste0(city, " United States")) %>%
opq() %>%
add_osm_feature(key = "highway",
value = c("residential", "living_street",
"unclassified",
"service", "footway")) %>%
osmdata_sf()
}
get_river <- function(city){
getbb(paste0(city, " United States"))%>%
opq()%>%
add_osm_feature(key = "waterway", value = "river") %>%
osmdata_sf()
}
|
43239af51bdfd1482109d1b0ca1cfd65fab7765d
|
0d5fdf5238436ab70990eb931ca5d9356536882c
|
/selftrain_gradativo_epoca/treinamento.R
|
4d4a268a8a4234dd889616ead2a0a015a78591c3
|
[] |
no_license
|
karlianev/projeto_karliane
|
632360ae3a86a2c49a9d9d3e0d80777d11e6ed09
|
38a5a729707425300113d21372f8ca070075cd4c
|
refs/heads/master
| 2021-06-30T10:47:01.838081
| 2021-01-12T21:50:06
| 2021-01-12T21:50:06
| 94,321,582
| 1
| 4
| null | 2017-08-02T12:53:09
| 2017-06-14T11:01:50
|
R
|
UTF-8
|
R
| false
| false
| 1,329
|
r
|
treinamento.R
|
#chamando a funcao selfTrain adaptada
print("Iniciando Treinamento")
#naive
if(k==1){
if (t==1){
nbST_gra<- funcSelfTrainGradativo(as.formula(paste(classe,'~', '.')), base_treino_self_training,learner("naiveBayes", list()),'func',0.9,100,1,TRUE,grad)
}else if (t==2){
nbST_gra<- funcSelfTrainGradativo(as.formula(paste(classe,'~', '.')), base_treino_self_training,learner("naiveBayes", list()),'func',0.95,100,1,TRUE,grad)
}
matriz_confusao_gra<-table(predict(nbST_gra, base_teste), base_teste$class)
}
if(k==2){
if (t==1){
ST_gra <- funcSelfTrainGradativo(as.formula(paste(classe,'~', '.')), base_treino_self_training,learner('rpartXse',list(se=0.5)),'f',0.9,100,1,TRUE,grad)
}else if (t==2){
ST_gra <- funcSelfTrainGradativo(as.formula(paste(classe,'~', '.')), base_treino_self_training,learner('rpartXse',list(se=0.5)),'f',0.95,100,1,TRUE,grad)
}
matriz_confusao_gra=table(predict(ST_gra,base_teste,type='class'),base_teste$class)
}
n <- length(base_teste$class)
acc_gra<-((sum(diag(matriz_confusao_gra)) / n) * 100)
acc_g_gra <- c(acc_g_gra, acc_gra)
grad_g_acc<-c(grad_g_acc,grad)
bd <- c(bd, bd_nome)
tx <- c(tx, taxa)
cat("\n Acerto global original (%) =", acc_gra)
cat('FIM') #, '\t base de dados ', i, '\n', 'total rotulados: ', total_rotulados, '\n')
|
4cec405f0f9f7b9e8bb6f9e3f594c138f4ca8caa
|
26151b679705ae098e79fab6c98ca68c093a4f09
|
/Classification_Techniques/ADiscriminante_Practica_8.R
|
f11dd60b5c47b1a8438b63bfcc6bb4d188e63e54
|
[] |
no_license
|
octadelsueldo/Master_DS_CUNEF
|
2808e5f5fbf564c7c3fdf209d699056ecd75a8af
|
354f9d4e43cbcf9d47edd85cfcb5010b417a8b3a
|
refs/heads/main
| 2023-08-21T22:54:16.712139
| 2021-10-06T22:33:32
| 2021-10-06T22:33:32
| 414,352,378
| 0
| 0
| null | null | null | null |
WINDOWS-1250
|
R
| false
| false
| 2,866
|
r
|
ADiscriminante_Practica_8.R
|
#----------------------------------------------------------------------
# MDS - Tecnicas de Clasificacion
# Modelos de AD para Datos del MC. Modelos LDA - QDA
# Práctica 8
#----------------------------------------------------------------------
rm(list=ls())
datos<-read.table("Cotizaciones2020.txt",header=T)
names(datos)
attach(datos)
head(datos)
#----------------------------------------------------------------------
# Calculo de los Rendimientos
#----------------------------------------------------------------------
n <- dim(datos)[1]
n
RIBEX <- IBEX[2:n]/IBEX[1:n-1] - 1
RSAN <- SAN.MC[2:n] / SAN.MC[1:n-1] - 1
RBBVA <- BBVA.MC[2:n] / BBVA.MC[1:n-1] - 1
RREP <- REP.MC[2:n] / REP.MC[1:n-1] - 1
RITX <- ITX.MC[2:n] / ITX.MC[1:n-1] - 1
RTL5 <- TL5.MC[2:n] / TL5.MC[1:n-1] - 1
#----------------------------------------------------------------------
# Variable RSAN en cuatro categorias
#----------------------------------------------------------------------
summary(RSAN)
RSANBIN <- cut(RSAN, breaks=c(-Inf, -0.015468,-0.003004, 0.011675, Inf),
labels=c("lo", "med", "high", "vhigh"))
table(RSANBIN)
datos1<-cbind(RSANBIN, RSAN, RIBEX, RBBVA, RREP, RITX, RTL5)
datos1<-as.data.frame(datos1)
summary(datos1)
#----------------------------------------------------------------------
# Analisis discriminante lineal
#----------------------------------------------------------------------
library(MASS)
sant.lda <- lda(RSANBIN ~ RIBEX+RBBVA, data=datos1)
sant.lda
plot(sant.lda, pch=16)
#Probabilidades a priori
sant.lda$prior
#Probabilidades a posteriori
predict(sant.lda)$posterior
# Matriz de confusion
predicted.lda <- predict(sant.lda, data = datos1)
tabla1<-table(RSANBIN, predicted.lda$class, dnn = c("Grupo real","Grupo Pronosticado"))
tabla1
# Precision del modelo
sum(diag(tabla1))/sum(tabla1) # Precision de 0.66
# Graficos de Particion
library(klaR)
partimat(datos1[,c(3,4)],RSANBIN,data=datos1,method="lda",main="Partition Plots")
#----------------------------------------------------------------------
# Analisis discriminante cuadratico
#----------------------------------------------------------------------
library(MASS)
sant.qda <- qda(RSANBIN ~ RIBEX+RBBVA, data=datos1)
sant.qda
# Comprobar la hipotesis para el QDA
library(biotools)
boxM(datos1[,c(3,4)],RSANBIN)
#Probabilidades a priori
sant.qda$prior
#Probabilidades a posteriori
predict(sant.qda)$posterior
# Matriz de confusion
predicted.qda <- predict(sant.qda, data = datos1)
tabla2<-table(RSANBIN, predicted.qda$class, dnn = c("Grupo real","Grupo Pronosticado"))
tabla2
# Precision del modelo
sum(diag(tabla2))/sum(tabla2) # 0.67
# Graficos de Particion
library(klaR)
partimat(datos1[,c(3,4)],RSANBIN,data=datos1,method="qda",main="Partition Plots")
|
a2fcf2bd936baf0899d916d529df0eb45a11f677
|
a2099edf0795624d588faae2095f6104944977ab
|
/inst/shiny/read_delim_dygraph/ui.R
|
404c9c28518ae9fb9a6a1c18363eaf4aa0511196
|
[] |
no_license
|
ijlyttle/shinychord
|
00343338bb7e0fdf33ee703201bc89ecf82e4726
|
71c6abd6b05d012f1fb54a227f6ca24adb760911
|
refs/heads/master
| 2020-05-17T03:58:11.967469
| 2016-01-29T23:52:00
| 2016-01-29T23:52:00
| 40,910,215
| 14
| 2
| null | 2016-01-29T23:52:00
| 2015-08-17T16:27:03
|
HTML
|
UTF-8
|
R
| false
| false
| 421
|
r
|
ui.R
|
library("shiny")
library("shinyBS")
library("shinyjs")
library("shinychord")
shinyUI(
fluidPage(
useShinyjs(),
sidebarLayout(
sidebarPanel(
h4("CSV"),
chord_tbl_csv$ui_controller,
h4("Dygraph"),
chord_dygraph$ui_controller
),
mainPanel(
h3("CSV"),
chord_tbl_csv$ui_view,
h3("Dygraph"),
chord_dygraph$ui_view
)
)
)
)
|
9f999e63e6491832acaa2af16e1224bc16ee8d47
|
14f57999626b31adc1d5c68dde59ee9b7428580c
|
/scripts/safecracker_40.R
|
a53e2e8523efc5d27bf641aea04a7a8af4c2f4cf
|
[] |
no_license
|
rocalabern/r-safecracker_40_puzzle
|
2dcd2cab4fe2a6106b22185c9669b7bd43c20f1b
|
fc8884340cb71f120c29fde8a42893dc1fb32116
|
refs/heads/main
| 2023-07-18T21:45:27.552870
| 2021-09-19T08:07:36
| 2021-09-19T08:07:36
| 407,860,541
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 411
|
r
|
safecracker_40.R
|
# runApp ----
library(shiny)
runApp()
# Test local ----
rm(list=ls())
library(plotly)
source('tools/load_tools.R', encoding = "utf8")
load_tools('tools')
c_angles <- c(-9, -4, 4, 0) # solution
c_angles <- c(0, 0, 0, 0)
c_totals <- calc_totals(c_Layers, c_angles)
fig <- plotly_puzzle(c_Layers, c_angles, c_totals)
# fig <- fig %>% layout(height = 800, width = 800)
fig
c_angles
c_totals
|
aee8250a8ca2a1025474aa1a118120c941024af8
|
d17100a3f8887e627fb33eebd2feac71dbbf5f02
|
/anomalous_subsequence.R
|
99f509d73b7b3fa2f334417ebdd642df329ea696
|
[] |
no_license
|
jeffung/coursework-cypersecurity
|
28da95476c2cd94cc139def3caf7a1a58cabe5f7
|
336844efc06022de223ccc3f52232766b845a9b1
|
refs/heads/master
| 2020-03-28T21:55:34.250107
| 2017-08-03T23:29:03
| 2017-08-03T23:29:03
| 149,192,504
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,228
|
r
|
anomalous_subsequence.R
|
sampleSize <- 1000
subsequenceSize <- 100
randIndex <- sample(1400616:length(train$Global_active_power), sampleSize)
lLikelihood <- as.data.frame(cbind(c(1:1000), c(1:1000), c(1:1000), c(1:1000)))
interval <- as.data.frame(cbind(c(1:2), c(1:2), c(1:2), c(1:2)))
LLmean <- c(1:4)
for (i in 1:1000) {
if (randIndex[i] < (length(train$Global_active_power) - sampleSize + 1)) {
start <- randIndex[i]
end <- start + sampleSize
}
else {
end <- randIndex[i]
start <- end - sampleSize
}
subSeq1 <- data.frame(train$Global_active_power[start:(start + 450)])
colnames(subSeq1) <- c("global_active_power")
subSeq2 <- data.frame(train$Global_active_power[(start + 550):end])
colnames(subSeq2) <- c("global_active_power")
tmp <- formatMhsmm(data.frame(rbind(subSeq1, subSeq2)))
yhat1 <- predict(hmm_8, tmp)
lLikelihood[i, 1] <- yhat1$loglik
yhat2 <- predict(hmm_10, tmp)
lLikelihood[i, 2] <- yhat2$loglik
yhat3 <- predict(hmm_12, tmp)
lLikelihood[i, 3] <- yhat3$loglik
yhat4 <- predict(hmm_14, tmp)
lLikelihood[i, 4] <- yhat4$loglik
}
for (i in 1:4) {
interval[1,i] <- max(lLikelihood[, i])
interval[2,i] <- min(lLikelihood[, i])
LLmean[i] <- mean(lLikelihood[, i])
}
interval
LLmean
|
f3494cad00e6f1ed3612dd17d84a0556bb23bc77
|
0500ba15e741ce1c84bfd397f0f3b43af8cb5ffb
|
/cran/paws.networking/man/cloudfront_list_invalidations.Rd
|
67561caa9d03c7c4deeff20bfd657da610faef12
|
[
"Apache-2.0"
] |
permissive
|
paws-r/paws
|
196d42a2b9aca0e551a51ea5e6f34daca739591b
|
a689da2aee079391e100060524f6b973130f4e40
|
refs/heads/main
| 2023-08-18T00:33:48.538539
| 2023-08-09T09:31:24
| 2023-08-09T09:31:24
| 154,419,943
| 293
| 45
|
NOASSERTION
| 2023-09-14T15:31:32
| 2018-10-24T01:28:47
|
R
|
UTF-8
|
R
| false
| true
| 1,139
|
rd
|
cloudfront_list_invalidations.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/cloudfront_operations.R
\name{cloudfront_list_invalidations}
\alias{cloudfront_list_invalidations}
\title{Lists invalidation batches}
\usage{
cloudfront_list_invalidations(DistributionId, Marker = NULL, MaxItems = NULL)
}
\arguments{
\item{DistributionId}{[required] The distribution's ID.}
\item{Marker}{Use this parameter when paginating results to indicate where to begin in
your list of invalidation batches. Because the results are returned in
decreasing order from most recent to oldest, the most recent results are
on the first page, the second page will contain earlier results, and so
on. To get the next page of results, set \code{Marker} to the value of the
\code{NextMarker} from the current page's response. This value is the same as
the ID of the last invalidation batch on that page.}
\item{MaxItems}{The maximum number of invalidation batches that you want in the response
body.}
}
\description{
Lists invalidation batches.
See \url{https://www.paws-r-sdk.com/docs/cloudfront_list_invalidations/} for full documentation.
}
\keyword{internal}
|
8e5fadd67721404ec4d6732f60884fc6d38cd123
|
c3eba48b456943d39f0ff66ef9288159ef4ce4ab
|
/milestone_1.r
|
c897c140340d1eda2d52c2b9edf527b7f2b4cee5
|
[] |
no_license
|
MakotoAsami/INST737
|
fcd93414e27e412c99d37fe1569f8a89ef80da11
|
45bf28510267e59762ab30473956d45a05dd3bde
|
refs/heads/master
| 2021-04-04T19:21:49.346693
| 2020-03-19T11:45:37
| 2020-03-19T11:45:37
| 248,480,863
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 3,348
|
r
|
milestone_1.r
|
# load csv file to data frame
bank <- read.csv("Databank_1.csv", stringsAsFactors=FALSE)
# delete eccessive rows
bank <- bank[-(4921:4925),]
# create new data frame for 2012 data with 1 row per country
bank2012 <- data.frame(Country.Name=c("Afghanistan"),Country.Code=c("AFG"),NY.GDP.MKTP.KD.ZG=c(0),
NY.GDP.MKTP.CD=c(0),NY.GDP.PCAP.CD=c(0),NY.GNP.PCAP.CD=c(0),NE.EXP.GNFS.ZS=c(0),BN.KLT.DINV.CD=c(0),
NY.GNP.PCAP.PP.CD=c(0),SI.POV.GINI=c(0),FP.CPI.TOTL.ZG=c(0),NY.GDP.DEFL.KD.ZG=c(0),IT.NET.USER.P2=c(0),
NE.IMP.GNFS.ZS=c(0),SP.DYN.LE00.IN=c(0),SE.ADT.LITR.ZS=c(0),SL.UEM.TOTL.ZS=c(0),SI.POV.NAHC=c(0),
NV.AGR.TOTL.ZS=c(0),EN.ATM.CO2E.PC=c(0),GC.DOD.TOTL.GD.ZS=c(0),SP.POP.TOTL=c(0),stringsAsFactors=FALSE)
# transfer data from original dataset to new format using for loop
for(i in 1:nrow(bank)) {
# when country code matches, copy 2012 data to applicable column
if(bank[i,4] %in% bank2012[,2]) {
bank2012[bank2012[,1]==bank[i,3],c(bank[i,2])] <- bank[i,c("X2012")]
# when country code doesn't match, add a row for the country and copy 2012 data to applicable column
} else {
newRow <- data.frame(Country.Name=bank[i,3],Country.Code=bank[i,4],NY.GDP.MKTP.KD.ZG=c(0),
NY.GDP.MKTP.CD=c(0),NY.GDP.PCAP.CD=c(0),NY.GNP.PCAP.CD=c(0),NE.EXP.GNFS.ZS=c(0),
BN.KLT.DINV.CD=c(0),NY.GNP.PCAP.PP.CD=c(0),SI.POV.GINI=c(0),FP.CPI.TOTL.ZG=c(0),
NY.GDP.DEFL.KD.ZG=c(0),IT.NET.USER.P2=c(0),NE.IMP.GNFS.ZS=c(0),SP.DYN.LE00.IN=c(0),
SE.ADT.LITR.ZS=c(0),SL.UEM.TOTL.ZS=c(0),SI.POV.NAHC=c(0),NV.AGR.TOTL.ZS=c(0),EN.ATM.CO2E.PC=c(0),
GC.DOD.TOTL.GD.ZS=c(0),SP.POP.TOTL=c(0),stringsAsFactors=FALSE)
bank2012 <- rbind(bank2012,newRow)
bank2012[bank2012[,1]==bank[i,3],c(bank[i,2])] <- bank[i,c("X2012")]
}
}
# convert chr type to numeric type
bank2012$NY.GDP.MKTP.KD.ZG <- as.numeric(bank2012$NY.GDP.MKTP.KD.ZG)
bank2012$NY.GDP.MKTP.CD <- as.numeric(bank2012$NY.GDP.MKTP.CD)
bank2012$NY.GDP.PCAP.CD <- as.numeric(bank2012$NY.GDP.PCAP.CD)
bank2012$NY.GNP.PCAP.CD <- as.numeric(bank2012$NY.GNP.PCAP.CD)
bank2012$NE.EXP.GNFS.ZS <- as.numeric(bank2012$NE.EXP.GNFS.ZS)
bank2012$BN.KLT.DINV.CD <- as.numeric(bank2012$BN.KLT.DINV.CD)
bank2012$NY.GNP.PCAP.PP.CD <- as.numeric(bank2012$NY.GNP.PCAP.PP.CD)
bank2012$SI.POV.GINI <- as.numeric(bank2012$SI.POV.GINI)
bank2012$FP.CPI.TOTL.ZG <- as.numeric(bank2012$FP.CPI.TOTL.ZG)
bank2012$NY.GDP.DEFL.KD.ZG <- as.numeric(bank2012$NY.GDP.DEFL.KD.ZG)
bank2012$IT.NET.USER.P2 <- as.numeric(bank2012$IT.NET.USER.P2)
bank2012$NE.IMP.GNFS.ZS <- as.numeric(bank2012$NE.IMP.GNFS.ZS)
bank2012$SP.DYN.LE00.IN <- as.numeric(bank2012$SP.DYN.LE00.IN)
bank2012$SE.ADT.LITR.ZS <- as.numeric(bank2012$SE.ADT.LITR.ZS)
bank2012$SL.UEM.TOTL.ZS <- as.numeric(bank2012$SL.UEM.TOTL.ZS)
bank2012$SI.POV.NAHC <- as.numeric(bank2012$SI.POV.NAHC)
bank2012$NV.AGR.TOTL.ZS <- as.numeric(bank2012$NV.AGR.TOTL.ZS)
bank2012$EN.ATM.CO2E.PC <- as.numeric(bank2012$EN.ATM.CO2E.PC)
bank2012$GC.DOD.TOTL.GD.ZS <- as.numeric(bank2012$GC.DOD.TOTL.GD.ZS)
bank2012$SP.POP.TOTL <- as.numeric(bank2012$SP.POP.TOTL)
# delete rows for aggregated data
bank2012 <- bank2012[-(215:246),]
# install ggplots package
install.packages("ggplot2")
library(ggplot2)
# plot each country's population and GDP
ggplot(data=bank2012,aes(x=SP.POP.TOTL,y=NY.GDP.MKTP.CD))+geom_point(size=3)
|
bfc6f47f02c9850f429a2b74a6352af214e27ebb
|
24c417f8bbfff92fec2d7025816348d0fec31ae1
|
/geneinteraction/mergesum.r
|
74757aaf7b0116d2d6d481006d9983a90272351b
|
[] |
no_license
|
Wenxue-PKU/script
|
3532d2d3a56b5a31c0d841fdeed274e3c149b89b
|
4d85c218b70422f034a9c4367fa01274f0206d73
|
refs/heads/master
| 2022-03-26T07:02:52.857563
| 2020-01-07T03:20:15
| 2020-01-07T03:20:15
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,075
|
r
|
mergesum.r
|
#!/usr/bin/R
# 2018-9-7
# this is used to sum the interaction, only for these data
command=matrix(c("Input","i",1,"character",
"Output","o",1,"character",
"help","h",0,"logical"),byrow=T,ncol=4)
args=getopt::getopt(command)
if (!is.null(args$help) || is.null(args$Input) || is.null(args$Output)) {
cat(paste(getopt::getopt(command, usage = T), "\n"))
q()
}
#suppressMessages(datatest <- readr::read_tsv(args$Input,col_names = c("ID1","ID2","interaction","chr1","chr2")))
#sum(datatest$interaction)
suppressMessages(library(dplyr))
uniqw <- read.table(args$Input)
colnames(uniqw) <- c("ID1","ID2","NUM")
uniqw <- mutate(uniqw, ID3=paste0(ID1,ID2))
total <- summarise(group_by(uniqw,ID3),sum(NUM))
uniquniq <- select(uniqw, ID1,ID2,ID3)
uniquniq <- unique(uniquniq)
mergeuniq <- merge(y = total, x = uniquniq,by.x = "ID3",by.y = "ID3")
colnames(mergeuniq) <- c("ID3","ID1","ID2","NUM")
mergeuniq <- select(mergeuniq, ID1,ID2,NUM)
write.table(mergeuniq,args$Output,col.names = F, row.names = F,quote = F)
|
29f29cd298ab0209d6397ccd1ccdf4e9ed76e5f8
|
dfbdc3b9ef4942f7dd8822c0a67c2da933e009f9
|
/man/unByteCode.Rd
|
576c5f60ed02e3a0edd5114ed0c4491bdac364d2
|
[] |
no_license
|
r-gregmisc/gtools
|
50453e16f6887cd878a6f10fd1acfba6a1f5ab01
|
bdabe5340501e3b73f63aaa26ab5c1fa9475066f
|
refs/heads/master
| 2023-05-11T11:00:16.825040
| 2023-05-06T19:01:07
| 2023-05-06T19:01:07
| 245,282,085
| 18
| 6
| null | 2023-08-19T17:16:53
| 2020-03-05T22:40:00
|
R
|
UTF-8
|
R
| false
| true
| 2,708
|
rd
|
unByteCode.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/unByteCode.R
\name{unByteCode}
\alias{unByteCode}
\alias{unByteCodeAssign}
\alias{assignEdgewise}
\title{Convert a byte-code function to an interpreted-code function}
\usage{
unByteCode(fun)
assignEdgewise(name, env, value)
unByteCodeAssign(fun)
}
\arguments{
\item{fun}{function to be modified}
\item{name}{object name}
\item{env}{namespace}
\item{value}{new function body}
}
\value{
All three functions return a copy of the modified function or
assigned value.
}
\description{
The purpose of these functions is to allow a byte coded function to be
converted back into a fully interpreted function as a \emph{temporary} work
around for issues in byte-code interpretation.
}
\details{
\code{unByteCode} returns a copy of the function that is directly
interpreted from text rather than from byte-code.
\code{assignEdgewise} makes an assignment into a locked environment.
\code{unByteCodeAssign} changes the specified function \emph{in its source
environment} to be directly interpreted from text rather than from
byte-code.
The latter two functions no longer work out of the box because \code{assignEdgewise}
(which \code{unByteCodeAssign} uses) makes use of an unsafe \code{unlockBinding}
call, but running \code{assignEdgewise()} will
}
\note{
These functions are not intended as a permanent solution to issues
with byte-code compilation or interpretation. Any such issues should be
promptly reported to the R maintainers via the R Bug Tracking System at
\url{https://bugs.r-project.org} and via the R-devel mailing list
\url{https://stat.ethz.ch/mailman/listinfo/r-devel}.
}
\examples{
data(badDend)
dist2 <- function(x) as.dist(1 - cor(t(x), method = "pearson"))
hclust1 <- function(x) hclust(x, method = "single")
distance <- dist2(badDend)
cluster <- hclust1(distance)
dend <- as.dendrogram(cluster)
\dontrun{
## In R 2.3.0 and earlier crashes with a node stack overflow error
plot(dend)
## Error in xy.coords(x, y, recycle = TRUE) : node stack overflow
}
## convert stats:::plotNode from byte-code to interpreted-code
## (no longer available unless assignEdgewise is defined by the user)
## unByteCodeAssign(stats:::plotNode)
## illustrated in https://stackoverflow.com/questions/16559250/error-in-heatmap-2-gplots
# increase recursion limit
options("expressions" = 5e4)
# now the function does not crash
plot(dend)
}
\references{
These functions were inspired as a work-around to R bug
\url{https://bugs.r-project.org/show_bug.cgi?id=15215}.
}
\seealso{
\code{\link[compiler]{disassemble}}, \code{\link{assign}}
}
\author{
Gregory R. Warnes \email{greg@warnes.net}
}
\keyword{programming}
\keyword{utilites}
|
47e3f4148a611698977547c9a7ec9735aad8b24d
|
efd0d6bec42aa38c1e62b6eecd5b1f4234581ec2
|
/man/apeff_bife.Rd
|
b4f92de5b8cdabb09ee98d050b2c0e513b71c24d
|
[] |
no_license
|
jlegewie/bife
|
5bdabfd799f8075ea1f55f60002d3d427d45c2c5
|
2206789a3a8bc157fbd7c8d10aca6243d626dc1d
|
refs/heads/master
| 2020-04-06T16:52:13.632534
| 2018-11-15T02:03:50
| 2018-11-15T02:03:50
| 157,637,657
| 0
| 0
| null | 2018-11-15T02:01:55
| 2018-11-15T02:01:55
| null |
UTF-8
|
R
| false
| true
| 4,059
|
rd
|
apeff_bife.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/apeff_bife.R
\name{apeff_bife}
\alias{apeff_bife}
\title{Average Partial Effects for Binary Choice Models with Fixed Effects}
\usage{
apeff_bife(mod, discrete = NULL, bias_corr = "ana", iter_demeaning = 100,
tol_demeaning = 1e-05, iter_offset = 1000, tol_offset = 1e-05)
}
\arguments{
\item{mod}{an object of class \code{bife}.}
\item{discrete}{a description of the variables that are discrete regressors.
For \code{apeff_bife} this has to be a character string naming the discrete regressors.
Default is \code{NULL} (no discrete regressor(s)).}
\item{bias_corr}{an optional string that specifies the type of the bias correction:
semi or analytical. The value should be any of the values \code{"semi"} or
\code{"ana"}. Default is \code{"ana"} (analytical bias-correction). Details
are given under \code{Details}.}
\item{iter_demeaning}{an optional integer value that specifies the maximum number
of iterations of the demeaning algorithm. Default is \code{100}. Details
are given under \code{Details}.}
\item{tol_demeaning}{an optional number that specifies the tolerance level of the
demeaning algorithm. Default is \code{1e-5}. Details are given under \code{Details}.}
\item{iter_offset}{an optional integer value that specifies the maximum number of
iterations of the offset algorithm for the computation of bias-adjusted fixed
effects. Default is \code{1000}. Details are given under \code{Details}.}
\item{tol_offset}{an optional number that specifies the tolerance level of the
offset algorithm for the computation of bias-adjusted fixed effects. Default
is \code{1e-5}. Details are given under \code{Details}.}
}
\value{
An object of \code{apeff_bife} returns a named matrix with at least a first
column "apeff" containing the uncorrected average partial effects of the
structural variables. An optional second column "apeff_corrected" is returned
containing the corrected average partial effects of the structural variables.
}
\description{
\code{apeff_bife} is a function used to compute average partial effects
for fixed effects binary choice models. It is able to compute
bias-corrected average partial effects derived by Newey and Hahn (2004) to
account for the incidental parameters bias.
}
\details{
The semi bias-corrected average partial effects are computed as usual partial
effects with the bias-adjusted fixed effects and the bias-corrected structural
parameters.
The analytical bias-corrected average partial effects follow
Newey and Hahn (2004). For further details consult the description of \code{bife}.
\strong{Note:} Bias-corrected partial effects can be only returned if the
object \code{mod} returns bias-corrected coefficients, i.e. if a bias-correction
has been used in the previous \code{bife} command.
}
\examples{
library("bife")
# Load 'psid' dataset
dataset <- psid
head(dataset)
# Fixed effects logit model w/o bias-correction
mod_no <- bife(LFP ~ AGE + I(INCH / 1000) + KID1 + KID2 + KID3 | ID,
data = dataset, bias_corr = "no")
# Compute uncorrected average partial effects for mod_no
# Note: bias_corr does not affect the result
apeff_bife(mod_no, discrete = c("KID1", "KID2", "KID3"))
# Fixed effects logit model with analytical bias-correction
mod_ana <- bife(LFP ~ AGE + I(INCH / 1000) + KID1 + KID2 + KID3 | ID,
data = dataset)
# Compute semi-corrected average partial effects for mod_ana
apeff_bife(mod_ana, discrete = c("KID1", "KID2", "KID3"),
bias_corr = "semi")
# Compute analytical bias-corrected average partial effects
# for mod_ana
apeff_bife(mod_ana, discrete = c("KID1", "KID2", "KID3"))
}
\references{
Hahn, J., and W. Newey (2004). "Jackknife and analytical bias reduction
for nonlinear panel models." Econometrica 72(4), 1295-1319.
Stammann, A., F. Heiss, and D. McFadden (2016). "Estimating Fixed Effects
Logit Models with Large Panel Data". Working paper.
}
\seealso{
\code{\link{bife}}
}
\author{
Amrei Stammann, Daniel Czarnowske, Florian Heiss, Daniel McFadden
}
|
7aff9b861139fb8d4f7f7dcdd937b02e7158b77c
|
ca577d7c18719c05bed95bd510d13bdd0c7c13d0
|
/script(7).R
|
5cb0592f922314572f60a87ded276d99ec141095
|
[] |
no_license
|
fredyulie123/R-programming-
|
eb2b163480510a11626215647b6e3d2c0258a0ed
|
44c564869f91b13217527c23f030399dc7b71707
|
refs/heads/main
| 2023-03-27T10:22:53.510694
| 2021-03-30T05:06:08
| 2021-03-30T05:06:08
| 352,876,241
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 3,432
|
r
|
script(7).R
|
#########################################
####### R Statistical Programming #######
#### Lesson 10: Model Validation #####
#########################################
# Install and load the caret package
install.packages("caret")
install.packages("e1071")
library(e1071)
library(caret)
library(ggplot2)
library(lattice)
# Bring the sonar dataset into our environment
library(mlbench)
data("Sonar")
?Sonar
# We'll take a subset of these sonar columns
Sonar <- Sonar[, c(1:20, 61)]
library(Metrics)
library(tidyverse)
#### Building a Basic GLM ##
# We can use caret's create data partition to split our data into a
# training and test set
data.split <- createDataPartition(Sonar$Class, p = .75, list = F)
# Index Sonar by data.split to get a training and testing set
training <- Sonar[data.split, ]
testing <- Sonar[-data.split, ]
view(training)
# Use glm to try and predict class
sonar.glm1 <- glm(Class ~ ., data = training, family = "binomial")
summary(sonar.glm1)
# Get the predicted probabilities and classes
glm1.prob.train <- predict(sonar.glm1, type = "response")
glm1.prob.test <- predict(sonar.glm1, newdata = testing, type = "response")
glm1.class.train <- ifelse(glm1.prob.train >= .5, "R", "M")
glm1.class.test <- ifelse(glm1.prob.test >= .5, "R", "M")
#find accuracy of model
accuracy(actual = training$Class, predicted = glm1.class.train)
accuracy(actual = testing$Class, predicted = glm1.class.test)
#### Using Caret for Model Building ####
# Use train to develop a model and specify what method you want to use
set.seed(1234)
sonar.glm.cv <- train(Class ~ ., data = training,
method = "glm", family = "binomial",
trControl = trainControl(method = "repeatedcv",
repeats = 5, number = 3,
savePredictions = T))
sonar.glm.cv$results
sonar.glm.cv
## train original data
set.seed(1234)
sonar.cv <- train(Class ~ ., data = Sonar,
method = "glm", family = "binomial",
trControl = trainControl(method = "repeatedcv",
repeats = 5, number = 3, p=.75,
savePredictions = T))
sonar.cv$results
# By calling resample, we can see how caret performed on each held out fold
sonar.glm.cv$resample
# We can even plot the accuracy distribution
ggplot(data = sonar.glm.cv$resample, aes(x = Accuracy)) +
geom_density(alpha = .2, fill="red")
# If we needed to get a non-caret object, we can extract the final model
sonar.glm2 <- sonar.glm.cv$finalModel
summary(sonar.glm2)
# We can try different validation methods by specifying them in
# the control parameter
set.seed(123)
sonar.glm.cv2 <- train(Class ~ ., data = training,
method = "glm", family = "binomial",
trControl=trainControl(method = "boot", number = 5,
savePredictions = T))
sonar.glm.cv2
sonar.glm.cv2$resample
# Calling predict on the caret output brings back class predictions
class.predictions <- predict(sonar.glm.cv, newdata = testing)
# We can create confusion matrices with the caret package
confusionMatrix(data = class.predictions, reference = testing$Class,
positive = "R")
|
35f631473b76aafd0f33fc865ac960ec2f9b19be
|
9984fa5e343a7810ae8da2ee3933d1acce9a9657
|
/twitter_profiles_DB_prep/prepSeveralFiles.R
|
dea5b6b16874becb0a005f8aef600347770afb91
|
[] |
no_license
|
kennyjoseph/twitter_matching
|
4757e7cff241d90167888ce24625be36015b5a93
|
9fe3a2b970e0ff2f559261f89d29b3202db25920
|
refs/heads/master
| 2022-01-05T15:45:19.643082
| 2019-07-11T20:28:54
| 2019-07-11T20:28:54
| 84,259,451
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 6,799
|
r
|
prepSeveralFiles.R
|
source("cleanProfilesIndexNames.R")
# Code in this file: makes calls to preprocessProfileTextFileParallel() from cleanProfilesIndexNames.R. Many times.
# See the very bottom for live / best version, which calls runOnManyFiles().
runOnManyFiles = function(inDir, inFiles, outDir) {
fullInFiles = file.path(inDir, inFiles)
outFs = vector(mode="character", length=length(inFiles))
outFDs = vector(mode="character", length=length(inFiles))
# do a little loop as the easiest way to fill in outFs and outFDs
messageFiles = vector(mode="character", length=length(inFiles))
fileCnt = 0
for (filePiece in inFiles) {
fileCnt = fileCnt + 1
f2 = filePiece
substr(f2, start=nchar(f2) - 2, stop=nchar(f2)) = "csv"
outF = file.path(outDir, f2)
outFs[fileCnt] = outF
substr(f2, start=nchar(f2) - 2, stop=nchar(f2)) = "out"
messageF = file.path(outDir, f2)
messageFiles[fileCnt] = messageF
substr(f2, start=nchar(f2) - 13, stop=nchar(f2)) = "foreign.csv.gz"
outFD = file.path(outDir, f2)
outFDs[fileCnt] = outFD
}
print("Created list of args, about to initialize the cluster")
initSnowfall()
print("About to make the big call! Check *.out files in output directory to see stdout from each run")
files = cbind(fullInFiles, outFs, outFDs, messageFiles)
#sfClusterApplyLB(files, 1, function(x) safeClusterDoOneFile(x[1], x[2], x[3])) <-- wrong b/c it doesn't do margins, only single vector arg
sfApply(files, 1, function(x) capture.output(safeClusterDoOneFile(x[1], x[2], x[3]), file=x[4]))
print("finished running!!")
sfStop()
}
initSnowfall = function() {
require(snowfall)
sfInit(parallel=TRUE,cpus=10)
# components for making isDefinitelyForeign work:
sfLibrary(stringi)
sfExport("onlyForeignAlphabet", "isDefinitelyForeign", "isDefinitelyForeignVectorized")
sfExport("USTimeZoneStrings")
# components for making putThemTogether work:
sfExport("putThemTogether", "getWordsFromTwitterHandle", "splitHandleNative", "getWordsFromTwitterName",
"getWordsFromTwitterNameForHandleMatching", "processNameText", "getHandleChunksUsingNameWords",
"assembleAlignment", "getContinguousChunksFromDataStructure", "getMatchedChunksFromDataStructure",
"itemizeAvailableChunks")
# the big function itself
sfLibrary(data.table)
sfExport("preprocessProfileTextFileParallel", "safeClusterDoOneFile")
# helpers I didn't export before
sfExport("makeDate", "fixGPS")
}
safeClusterDoOneFile = function(inFile, outF, outFD) {
print(paste("At", Sys.time(), "starting to work on inFile", inFile, ", with outF=", outF, "and outFD=", outFD))
# todo: surround by try/catch
result = tryCatch( {
preprocessProfileTextFileParallel(inFile, outF, outFileForDroppedForeign=outFD,
appendToOutputFiles=F, compressOutput=F,
chunkSize=-1, pruneMoreForeign=T, timeProfiling=T, inParallel=F)
# while testing/debugging
#chunkSize=1000, stopAfterChunk=1, pruneMoreForeign=T, timeProfiling=T, inParallel=F)
print(paste("Finished successfully with inFile", inFile))
},
error = function(err) {
print(paste("Died on inFile", inFile, "with the following error:"))
print(err)
}
)
}
# Example usage / small tests
if (F) {
inFile = "/home/kjoseph/profile_study/collection_1_22/100_user_info.txt"
inFile = "/home/lfriedl/100_noNul-head.txt" # or a small one
outF = "/home/lfriedl/twitter_matching/twitter_profiles_DB_prep/data/100_prep.csv"
outFD = "/home/lfriedl/twitter_matching/twitter_profiles_DB_prep/data/100_foreign.csv.gz"
preprocessProfileTextFileParallel(inFile, outF, outFileForDroppedForeign=outFD,
appendToOutputFiles=F, compressOutput=F,
chunkSize=5000, pruneMoreForeign=T, timeProfiling=T, inParallel=F, stopAfterChunk=4, needCleanInfile=F)
# or
outF = "/home/lfriedl/twitter_matching/twitter_profiles_DB_prep/data/100_prepPar.csv"
outFD = "/home/lfriedl/twitter_matching/twitter_profiles_DB_prep/data/100_foreignPar.csv.gz"
preprocessProfileTextFileParallel(inFile, outF, outFileForDroppedForeign=outFD,
appendToOutputFiles=F, compressOutput=F,
chunkSize=5000, pruneMoreForeign=T, timeProfiling=T, inParallel=T, stopAfterChunk=4, needCleanInfile=F)
# can also experiment with useFread=F flag, but there's no reason to ever use it
# test on a whole file
inFile = "/home/lfriedl/100_noNul.txt"
outF = "/home/lfriedl/twitter_matching/twitter_profiles_DB_prep/data/100_bigPrep.2.csv"
outFD = "/home/lfriedl/twitter_matching/twitter_profiles_DB_prep/data/100_bigForeign.2.csv.gz"
preprocessProfileTextFileParallel(inFile, outF, outFileForDroppedForeign=outFD,
appendToOutputFiles=F, compressOutput=F,
chunkSize=-1, pruneMoreForeign=T, timeProfiling=T, inParallel=T, needCleanInfile=F)
}
# One way to run everything: 1 file at a time, parallelized. This turns out not to be the fast way.
if (F) { # actual processing of everything in a directory
inDir = "/home/kjoseph/profile_study/collection_20170306"
outDir = "/home/lfriedl/twitter_matching/twitter_profiles_DB_prep/data_collection_20170306"
fileList = list.files(path=inDir, pattern="_user_info.txt", no..=T)
fileCnt = 0
startAtFile = 23
for (filePiece in fileList) {
fileCnt = fileCnt + 1
if (fileCnt < startAtFile) {
next
}
inFile = file.path(inDir, filePiece)
f2 = filePiece
substr(f2, start=nchar(f2) - 2, stop=nchar(f2)) = "csv"
outF = file.path(outDir, f2)
substr(f2, start=nchar(f2) - 13, stop=nchar(f2)) = "foreign.csv.gz"
outFD = file.path(outDir, f2)
print(paste("At", Sys.time(), "starting to work on file", fileCnt, ":", filePiece))
preprocessProfileTextFileParallel(inFile, outF, outFileForDroppedForeign=outFD,
appendToOutputFiles=F, compressOutput=F,
chunkSize=-1, pruneMoreForeign=T, timeProfiling=T, inParallel=T)
gc()
}
print("finished!!!")
}
# notes:
# -error on file 10 : 108_user_info.txt. during putThemTogether, got "Character conversion: Unmappable input sequence / Invalid character. (U_INVALID_CHAR_FOUND)"
# -ditto, file 14: 111_user_info.txt
# -ditto, file 22 : 119_user_info.txt
# -ditto, file 23 : 11_user_info.txt
# -ditto, file 27 : 123_user_info.txt
if (F) { # a new way to run things
inDir = "/home/kjoseph/profile_study/collection_20170306"
outDir = "/home/lfriedl/twitter_matching/twitter_profiles_DB_prep/data_collection_20170306"
fileList = list.files(path=inDir, pattern="_user_info.txt", no..=T)
infiles = rev(sort(fileList)) # start at the bottom, so as not to conflict with other version
# we put the first 100 of the reversed list on achtung04.
# on achtung03, send the broken ones from before: 14, 22, 23, and 27; then the rest through #130. (100 + 130 = total number of input files)
infiles = infiles[1:130]
infiles = infiles[c(14, 22, 23, 27:130)]
runOnManyFiles(inDir, infiles, outDir)
}
|
1ba3c9bf0c51b4ce31ab40cba123f9669fece645
|
a5b8244731689344004c67af107b1a531f7e9e2f
|
/src/07_dashboard_aggs/00_player_ratings.R
|
d5caa61302228d9ab2d8e1f4943a2bc51e41721b
|
[] |
no_license
|
jvenzor23/DefensiveCoverageNet
|
4efcb0f36d6806c71a1750fa9b58ba63c55e3929
|
85eef09aeede123aa32cb8ad3a8075cd7b7f3e43
|
refs/heads/master
| 2023-02-13T22:14:23.396421
| 2021-01-07T22:52:32
| 2021-01-07T22:52:32
| 317,361,746
| 4
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 21,265
|
r
|
00_player_ratings.R
|
# Clean workspace
rm(list=ls())
# Setting Working Directory
setwd("~/Desktop/CoverageNet/inputs/")
# Calling Necessary Libraries
library(tidyverse)
library(dplyr)
library(ggplot2)
library(lubridate)
library(reticulate)
library(gganimate)
library(magick)
library(fitdistrplus)
library(skewt)
library(sn)
library(broom)
library(dplyr)
# Reading in The Data -----------------------------------------------------
man_ratings = read.csv("~/Desktop/CoverageNet/src/04_evaluate_players/outputs/overall_player_skills_summary.csv")
zone_ratings = read.csv("~/Desktop/CoverageNet/src/05_evaluate_players_zone/outputs/overall_player_skills_summary.csv")
sos_ratings = read.csv("~/Desktop/CoverageNet/src/06_evaluate_receivers/outputs/overall_player_sos_skills_summary.csv")
player_info = read.csv("~/Desktop/CoverageNet/src/00_data_wrangle/helper_tables/dashboard_player_info.csv") %>%
dplyr::select(nflId, displayName, team, games, plays, position) %>%
rename(pass_snaps = plays,
Pos = position,
G = games,
`Pass Snaps` = plays) %>%
mutate(Player = paste0(displayName, " (", team, ")")) %>%
dplyr::select(nflId, Player, G, `Pass Snaps`, Pos)
man_zone_classification = rbind(
read.csv("~/Desktop/CoverageNet/src/01_identify_man_coverage/outputs/all_positions_pass_attempts_man_zone_classes.csv") %>%
dplyr::select(gameId, playId, nflId, zone_probability),
read.csv("~/Desktop/CoverageNet/src/01_identify_man_coverage/outputs/all_positions_sacks_man_zone_classes.csv") %>%
dplyr::select(gameId, playId, nflId, zone_probability)
) %>%
arrange(gameId, playId, nflId) %>%
distinct(gameId, playId, nflId, .keep_all = TRUE)
man_coverage = read.csv("~/Desktop/CoverageNet/src/01_identify_man_coverage/outputs/man_defense_off_coverage_assignments_all_lbs.csv")
zone_coverage = man_zone_classification %>%
anti_join(man_coverage,
by = c("gameId", "playId", "nflId" = "nflId_def"))
man_zone_perc = man_coverage %>%
distinct(gameId, playId, nflId_def) %>%
group_by(nflId_def) %>%
summarize(man_plays = n()) %>%
full_join(zone_coverage %>%
rename(nflId_def = nflId) %>%
distinct(gameId, playId, nflId_def) %>%
group_by(nflId_def) %>%
summarize(zone_plays = n()))
man_zone_perc[is.na(man_zone_perc)] = 0
man_zone_perc = man_zone_perc %>%
mutate(`%Man` = man_plays/(zone_plays + man_plays)) %>%
dplyr::select(nflId_def, `%Man`)
# Overall Aggregation ---------------------------------------------
overall_table = man_ratings %>%
dplyr::select(nflId_def, eps_man_coverage, routes, accurate_targets, completions_allowed,
PB, ball_hawk_pbus, INT, ball_hawk_ints, T, FF, penalties_count,
penalties_eps) %>%
rename(man_routes = routes,
man_accurate_targets = accurate_targets,
man_completions_allowed = completions_allowed,
man_T = T,
man_INTs = INT,
man_ball_hawk_ints = ball_hawk_ints,
man_PB = PB,
man_ball_hawk_pbus = ball_hawk_pbus,
man_FF = FF,
man_penalties = penalties_count,
man_penalties_eps = penalties_eps,
`Man EPS` = eps_man_coverage) %>%
dplyr::select(nflId_def, `Man EPS`, starts_with("man")) %>%
full_join(zone_ratings %>%
dplyr::select(nflId_def, eps_zone_coverage, zone_covers, accurate_targets, completions_allowed,
PB, ball_hawk_pbus, INT, ball_hawk_ints, T, FF, penalties_count,
penalties_eps) %>%
rename(zone_accurate_targets = accurate_targets,
zone_completions_allowed = completions_allowed,
zone_INTs = INT,
zone_ball_hawk_ints = ball_hawk_ints,
zone_PB = PB,
zone_ball_hawk_pbus = ball_hawk_pbus,
zone_T = T,
zone_FF = FF,
zone_penalties = penalties_count,
zone_penalties_eps = penalties_eps,
`Zone EPS` = eps_zone_coverage) %>%
dplyr::select(nflId_def, `Zone EPS`, starts_with("zone"))) %>%
full_join(sos_ratings %>%
dplyr::select(nflId_def, eps_tot_sos_adj, eps_zone_sos_adj, eps_man_sos_adj) %>%
rename(`SOS EPS` = eps_tot_sos_adj,
`SOS Man EPS` = eps_man_sos_adj,
`SOS Zone EPS` = eps_zone_sos_adj))
overall_table[is.na(overall_table)] = 0
overall_table = overall_table %>%
inner_join(player_info %>%
rename(nflId_def = nflId)) %>%
mutate(EPS = `Man EPS` + `Zone EPS`,
`EPS Per Game` = EPS/G,
`EPS Per Snap` = EPS/`Pass Snaps`,
`SOS EPS Per Game` = `SOS EPS`/G,
`SOS EPS Per Snap` = `SOS EPS`/`Pass Snaps`,
`EPS Penalties` = man_penalties_eps + zone_penalties_eps,
Covers = man_routes + zone_covers,
`Accurate TAR` = man_accurate_targets + zone_accurate_targets,
C = man_completions_allowed + zone_completions_allowed,
INT = man_INTs + zone_INTs,
`Ball Hawk INT` = man_ball_hawk_ints + zone_ball_hawk_ints,
PB = man_PB + zone_PB,
`Ball Hawk PB` = man_ball_hawk_pbus + zone_ball_hawk_pbus,
T = man_T + zone_T,
FF = man_FF + zone_FF,
Penalties = man_penalties + zone_penalties,
`Targeted C%` = replace_na(C/`Accurate TAR`, 0),
`Hands on Ball % of Targets` = replace_na((INT + PB)/`Accurate TAR`, 0),
`Hands on Ball % of Plays` = replace_na((INT + `Ball Hawk INT` +
PB + `Ball Hawk PB`)/`Pass Snaps`, 0)) %>%
inner_join(man_zone_perc) %>%
dplyr::select(nflId_def, Pos, Player, G, `Pass Snaps`, `%Man`,
EPS, `Man EPS`, `Zone EPS`, `EPS Per Game`, `EPS Per Snap`,
`SOS EPS`, `SOS Man EPS`, `SOS Zone EPS`, `SOS EPS Per Game`,
`SOS EPS Per Snap`,
Covers, `Accurate TAR`, C, `Targeted C%`,
INT, `Ball Hawk INT`,
PB, `Ball Hawk PB`, `Hands on Ball % of Targets`,
`Hands on Ball % of Plays`, T, FF, Penalties) %>%
arrange(desc(EPS)) %>%
# dplyr::mutate_if(is.numeric, round, digits=4) %>%
# mutate(`%Man` = scales::percent(`%Man`, accuracy = .1),
# `Targeted C%` = scales::percent(`Targeted C%`, accuracy = .1),
# `Hands on Ball % of Targets` = scales::percent(`Hands on Ball % of Targets`, accuracy = .1),
# `Hands on Ball % of Plays` = scales::percent(`Hands on Ball % of Plays`, accuracy = .1)) %>%
dplyr::mutate_if(is.numeric, round, digits=2)
write.csv(overall_table,
"~/Desktop/CoverageNet/src/07_dashboard_aggs/outputs/player_ratings/player_ratings_overall.csv",
row.names = FALSE)
# Man/Overall Aggregation --------------------------------------------------
man_overall_table = man_ratings %>%
dplyr::select(nflId_def, eps_man_coverage, eps_tracking, eps_closing,
eps_ball_skills, eps_tackling, eps_int_returns, eps_ball_hawk,
routes, accurate_targets, completions_allowed,
PB, ball_hawk_pbus, INT, ball_hawk_ints, T, FF, penalties_count,
penalties_eps, man_tracking_win_rate) %>%
rename(man_routes = routes,
man_accurate_targets = accurate_targets,
man_completions_allowed = completions_allowed,
man_T = T,
man_INTs = INT,
man_ball_hawk_ints = ball_hawk_ints,
man_PB = PB,
man_ball_hawk_pbus = ball_hawk_pbus,
man_FF = FF,
man_penalties = penalties_count,
man_penalties_eps = penalties_eps,
`Man EPS` = eps_man_coverage,
`Man EPS Tracking` = eps_tracking,
`Man EPS Closing` = eps_closing,
`Man EPS Ball Skills` = eps_ball_skills,
`Man EPS Tackling` = eps_tackling,
`Man EPS Ball Hawk` = eps_ball_hawk,
`Man EPS INT Returns` = eps_int_returns,
`Man Tracking Win Rate` = man_tracking_win_rate) %>%
dplyr::select(nflId_def, `Man EPS`, starts_with("man")) %>%
full_join(sos_ratings %>%
rename(`SOS Man EPS` = eps_man_sos_adj,
`SOS Man EPS Tracking` = eps_man_tracking_sos_adj,
`SOS Man EPS Closing` = eps_man_closing_sos_adj,
`SOS Man EPS Ball Skills` = eps_man_ball_skills_sos_adj,
`SOS Man EPS Tackling` = eps_man_tackling_sos_adj))
man_overall_table[is.na(man_overall_table)] = 0
man_overall_table = man_overall_table %>%
inner_join(player_info %>%
rename(nflId_def = nflId)) %>%
mutate(`EPS Penalties` = man_penalties_eps,
Covers = man_routes,
`Man EPS Per Game` = `Man EPS`/G,
`Man EPS Per Cover` = `Man EPS`/Covers,
`SOS Man EPS Per Game` = `SOS Man EPS`/G,
`SOS Man EPS Per Cover` = `SOS Man EPS`/Covers,
`Accurate TAR` = man_accurate_targets,
C = man_completions_allowed,
INT = man_INTs,
`Ball Hawk INT` = man_ball_hawk_ints,
PB = man_PB,
`Ball Hawk PB` = man_ball_hawk_pbus,
T = man_T,
FF = man_FF,
Penalties = man_penalties,
`Targeted C%` = replace_na(C/`Accurate TAR`, 0),
`Hands on Ball % of Targets` = replace_na((INT + PB)/`Accurate TAR`, 0),
`Hands on Ball % of Plays` = replace_na((INT + `Ball Hawk INT` +
PB + `Ball Hawk PB`)/`Covers`, 0)) %>%
inner_join(man_zone_perc) %>%
dplyr::select(nflId_def, Pos, Player, G, `Pass Snaps`, `%Man`,
`Man EPS`,`Man EPS Per Game`, `Man EPS Per Cover`,
`Man EPS Tracking`, `Man EPS Closing`,
`Man EPS Ball Skills`, `Man EPS Tackling`,
`Man EPS Ball Hawk`, `Man EPS INT Returns`,
`SOS Man EPS`,`SOS Man EPS Per Game`,`SOS Man EPS Per Cover`,
`SOS Man EPS Tracking`, `SOS Man EPS Closing`,
`SOS Man EPS Ball Skills`, `SOS Man EPS Tackling`,
Covers, `Accurate TAR`, C, `Targeted C%`,
INT, `Ball Hawk INT`, PB, `Ball Hawk PB`,
`Hands on Ball % of Targets`,
`Hands on Ball % of Plays`,
T, FF, Penalties, `Man Tracking Win Rate`) %>%
arrange(desc(`Man EPS`)) %>%
# dplyr::mutate_if(is.numeric, round, digits=4) %>%
# mutate(`%Man` = scales::percent(`%Man`, accuracy = .1),
# `Targeted C%` = scales::percent(`Targeted C%`, accuracy = .1),
# `Hands on Ball % of Targets` = scales::percent(`Hands on Ball % of Targets`, accuracy = .1),
# `Hands on Ball % of Plays` = scales::percent(`Hands on Ball % of Plays`, accuracy = .1),
# `Man Tracking Win Rate` = scales::percent(`Man Tracking Win Rate`, accuracy = .1)) %>%
dplyr::mutate_if(is.numeric, round, digits=2)
write.csv(man_overall_table,
"~/Desktop/CoverageNet/src/07_dashboard_aggs/outputs/player_ratings/player_ratings_man.csv",
row.names = FALSE)
# Zone Overall Table ------------------------------------------------------
zone_overall_table = zone_ratings %>%
dplyr::select(nflId_def, eps_zone_coverage, eps_closing,
eps_ball_skills, eps_tackling, eps_int_returns, eps_ball_hawk,
zone_covers, accurate_targets, completions_allowed,
PB, ball_hawk_pbus, INT, ball_hawk_ints, T, FF, penalties_count,
penalties_eps) %>%
rename(zone_covers = zone_covers,
zone_accurate_targets = accurate_targets,
zone_completions_allowed = completions_allowed,
zone_T = T,
zone_INTs = INT,
zone_ball_hawk_ints = ball_hawk_ints,
zone_PB = PB,
zone_ball_hawk_pbus = ball_hawk_pbus,
zone_FF = FF,
zone_penalties = penalties_count,
zone_penalties_eps = penalties_eps,
`Zone EPS` = eps_zone_coverage,
`Zone EPS Closing` = eps_closing,
`Zone EPS Ball Skills` = eps_ball_skills,
`Zone EPS Tackling` = eps_tackling,
`Zone EPS Ball Hawk` = eps_ball_hawk,
`Zone EPS INT Returns` = eps_int_returns) %>%
dplyr::select(nflId_def, `Zone EPS`, starts_with("zone")) %>%
full_join(sos_ratings %>%
rename(`SOS Zone EPS` = eps_zone_sos_adj,
`SOS Zone EPS Closing` = eps_zone_closing_sos_adj,
`SOS Zone EPS Ball Skills` = eps_zone_ball_skills_sos_adj,
`SOS Zone EPS Tackling` = eps_zone_tackling_sos_adj))
zone_overall_table[is.na(zone_overall_table)] = 0
zone_overall_table = zone_overall_table %>%
inner_join(player_info %>%
rename(nflId_def = nflId)) %>%
mutate(`EPS Penalties` = zone_penalties_eps,
Covers = zone_covers,
`Zone EPS Per Game` = `Zone EPS`/G,
`Zone EPS Per Cover` = `Zone EPS`/Covers,
`SOS Zone EPS Per Game` = `SOS Zone EPS`/G,
`SOS Zone EPS Per Cover` = `SOS Zone EPS`/Covers,
`Accurate TAR` = zone_accurate_targets,
C = zone_completions_allowed,
INT = zone_INTs,
`Ball Hawk INT` = zone_ball_hawk_ints,
PB = zone_PB,
`Ball Hawk PB` = zone_ball_hawk_pbus,
T = zone_T,
FF = zone_FF,
Penalties = zone_penalties,
`Targeted C%` = replace_na(C/`Accurate TAR`, 0),
`Hands on Ball % of Targets` = replace_na((INT + PB)/`Accurate TAR`, 0),
`Hands on Ball % of Plays` = replace_na((INT + `Ball Hawk INT` +
PB + `Ball Hawk PB`)/`Covers`, 0)) %>%
inner_join(man_zone_perc) %>%
mutate(`%Zone` = 1 - `%Man`) %>%
dplyr::select(nflId_def, Pos, Player, G, `Pass Snaps`, `%Zone`,
`Zone EPS`, `Zone EPS Per Game`, `Zone EPS Per Cover`,
`Zone EPS Closing`,
`Zone EPS Ball Skills`, `Zone EPS Tackling`,
`Zone EPS Ball Hawk`, `Zone EPS INT Returns`,
`SOS Zone EPS`, `SOS Zone EPS Per Game`,`SOS Zone EPS Per Cover`,
`SOS Zone EPS Closing`,
`SOS Zone EPS Ball Skills`, `SOS Zone EPS Tackling`,
Covers, `Accurate TAR`, C, `Targeted C%`,,
INT, `Ball Hawk INT`, PB, `Ball Hawk PB`,
`Hands on Ball % of Targets`,
`Hands on Ball % of Plays`,
T, FF, Penalties) %>%
arrange(desc(`Zone EPS`)) %>%
# dplyr::mutate_if(is.numeric, round, digits=4) %>%
# mutate(`%Zone` = scales::percent(`%Zone`, accuracy = .1),
# `Targeted C%` = scales::percent(`Targeted C%`, accuracy = .1),
# `Hands on Ball % of Targets` = scales::percent(`Hands on Ball % of Targets`, accuracy = .1),
# `Hands on Ball % of Plays` = scales::percent(`Hands on Ball % of Plays`, accuracy = .1)) %>%
dplyr::mutate_if(is.numeric, round, digits=2)
write.csv(zone_overall_table,
"~/Desktop/CoverageNet/src/07_dashboard_aggs/outputs/player_ratings/player_ratings_zone.csv",
row.names = FALSE)
# by route
route_man_ratings = read.csv("~/Desktop/CoverageNet/src/04_evaluate_players/outputs/overall_player_skills_summary_by_route.csv")
route_zone_ratings = read.csv("~/Desktop/CoverageNet/src/05_evaluate_players_zone/outputs/overall_player_skills_summary_by_route.csv")
route_overall_table = route_man_ratings %>%
dplyr::select(nflId_def, route, eps_man_coverage, routes, accurate_targets, completions_allowed,
PB, INT, T, FF) %>%
rename(man_routes = routes,
man_accurate_targets = accurate_targets,
man_completions_allowed = completions_allowed,
man_T = T,
man_INTs = INT,
man_PB = PB,
man_FF = FF,
`Man EPS` = eps_man_coverage) %>%
dplyr::select(nflId_def, route, `Man EPS`, starts_with("man")) %>%
full_join(route_zone_ratings %>%
dplyr::select(nflId_def, route, eps_zone_coverage,
zone_covers, accurate_targets, completions_allowed,
PB, INT, T, FF) %>%
rename(zone_accurate_targets = accurate_targets,
zone_completions_allowed = completions_allowed,
zone_INTs = INT,
zone_PB = PB,
zone_T = T,
zone_FF = FF,
`Zone EPS` = eps_zone_coverage) %>%
dplyr::select(nflId_def, route, `Zone EPS`, starts_with("zone")))
route_overall_table[is.na(route_overall_table)] = 0
route_overall_table = route_overall_table %>%
mutate(EPS = `Man EPS` + `Zone EPS`,
Covers = man_routes + zone_covers,
`Accurate TAR` = man_accurate_targets + zone_accurate_targets,
C = man_completions_allowed + zone_completions_allowed,
INT = man_INTs + zone_INTs,
PB = man_PB + zone_PB,
T = man_T + zone_T,
FF = man_FF + zone_FF) %>%
inner_join(player_info %>%
rename(nflId_def = nflId)) %>%
inner_join(man_zone_perc) %>%
dplyr::select(nflId_def, Pos, Player, G, `Pass Snaps`, route, `%Man`,
EPS, `Man EPS`, `Zone EPS`,
Covers, `Accurate TAR`, C, INT,
PB, T, FF) %>%
arrange(desc(EPS)) %>%
dplyr::mutate_if(is.numeric, round, digits=2)
write.csv(route_overall_table,
"~/Desktop/CoverageNet/src/07_dashboard_aggs/outputs/player_ratings/player_ratings_route_overall.csv",
row.names = FALSE)
# Man/Overall Aggregation --------------------------------------------------
route_man_overall_table = route_man_ratings %>%
dplyr::select(nflId_def, route, eps_man_coverage, eps_tracking, eps_closing,
eps_ball_skills, eps_tackling,
routes, accurate_targets, completions_allowed,
PB, INT, T, FF) %>%
rename(man_routes = routes,
man_accurate_targets = accurate_targets,
man_completions_allowed = completions_allowed,
man_T = T,
man_INTs = INT,
man_PB = PB,
man_FF = FF,
`Man EPS` = eps_man_coverage,
`Man EPS Tracking` = eps_tracking,
`Man EPS Closing` = eps_closing,
`Man EPS Ball Skills` = eps_ball_skills,
`Man EPS Tackling` = eps_tackling) %>%
dplyr::select(nflId_def, route, `Man EPS`, starts_with("man"))
route_man_overall_table[is.na(route_man_overall_table)] = 0
route_man_overall_table = route_man_overall_table %>%
mutate(Covers = man_routes,
`Accurate TAR` = man_accurate_targets,
C = man_completions_allowed,
INT = man_INTs,
PB = man_PB,
T = man_T,
FF = man_FF) %>%
inner_join(player_info %>%
rename(nflId_def = nflId)) %>%
inner_join(man_zone_perc) %>%
dplyr::select(nflId_def, Pos, Player, G, `Pass Snaps`, `%Man`,route,
`Man EPS`,`Man EPS Tracking`, `Man EPS Closing`,
`Man EPS Ball Skills`, `Man EPS Tackling`,
Covers, `Accurate TAR`, C, INT,
PB, T, FF) %>%
arrange(desc(`Man EPS`)) %>%
dplyr::mutate_if(is.numeric, round, digits=2)
write.csv(route_man_overall_table,
"~/Desktop/CoverageNet/src/07_dashboard_aggs/outputs/player_ratings/player_ratings_route_man.csv",
row.names = FALSE)
# Zone Overall Table ------------------------------------------------------
route_zone_overall_table = route_zone_ratings %>%
dplyr::select(nflId_def, route, eps_zone_coverage, eps_closing,
eps_ball_skills, eps_tackling,
zone_covers, accurate_targets, completions_allowed,
PB, INT, T, FF) %>%
rename(zone_covers = zone_covers,
zone_accurate_targets = accurate_targets,
zone_completions_allowed = completions_allowed,
zone_T = T,
zone_INTs = INT,
zone_PB = PB,
zone_FF = FF,
`Zone EPS` = eps_zone_coverage,
`Zone EPS Closing` = eps_closing,
`Zone EPS Ball Skills` = eps_ball_skills,
`Zone EPS Tackling` = eps_tackling) %>%
dplyr::select(nflId_def, route, `Zone EPS`, starts_with("zone"))
route_zone_overall_table[is.na(route_zone_overall_table)] = 0
route_zone_overall_table = route_zone_overall_table %>%
mutate(Covers = zone_covers,
`Accurate TAR` = zone_accurate_targets,
C = zone_completions_allowed,
INT = zone_INTs,
PB = zone_PB,
T = zone_T,
FF = zone_FF) %>%
inner_join(player_info %>%
rename(nflId_def = nflId)) %>%
inner_join(man_zone_perc) %>%
mutate(`%Zone` = 1 - `%Man`) %>%
dplyr::select(nflId_def, Pos, Player, G, `Pass Snaps`, `%Zone`, route,
`Zone EPS`, `Zone EPS Closing`,
`Zone EPS Ball Skills`, `Zone EPS Tackling`,
Covers, `Accurate TAR`, C, INT,
PB, T, FF) %>%
arrange(desc(`Zone EPS`)) %>%
dplyr::mutate_if(is.numeric, round, digits=2)
write.csv(route_zone_overall_table,
"~/Desktop/CoverageNet/src/07_dashboard_aggs/outputs/player_ratings/player_ratings_route_zone.csv",
row.names = FALSE)
|
2e5b3cb3341bcf7b2a2d6030fcd45ad5c8982f15
|
02e3ab7b73e3a57f0e63849b4ab6cc9f62f3ebc1
|
/tests/oprobit2.R
|
ed23861cfee9214d3cd3463da2b5507a8dd6cf60
|
[] |
no_license
|
zeligdev/ZeligOrdinal
|
6f0317768b923052d70adf542c773667a4aa8a18
|
b4348d6bc413faab0402f42811f62ff8e8615896
|
refs/heads/master
| 2016-09-06T17:58:53.002123
| 2011-12-12T20:16:52
| 2011-12-12T20:16:52
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 393
|
r
|
oprobit2.R
|
#
library(ZeligOrdinal)
data(sanction)
sanction$ncost <- factor(sanction$ncost, ordered = TRUE,
levels = c("net gain", "little effect",
"modest loss", "major loss"))
z.out2 <- zelig(ncost ~ mil + coop, model = "oprobit", data = sanction)
x.out2 <- setx(z.out2, fn = NULL)
s.out2 <- sim(z.out2, x = x.out2)
summary(z.out2)
plot(s.out2)
|
6da5956dec35a8d4b1882a505d1b502c90f8e8fe
|
29585dff702209dd446c0ab52ceea046c58e384e
|
/ssh.utils/R/mkdir.R
|
ba00a45b6197db653f6672a164d2db6ad07e80df
|
[] |
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
| 2,481
|
r
|
mkdir.R
|
#-------------------------------------------------------------------------------
#
# Package ssh.utils
#
# Implementation.
#
# Sergei Izrailev, 2011-2014
#-------------------------------------------------------------------------------
# Copyright 2011-2014 Collective, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#-------------------------------------------------------------------------------
#' Creates a remote directory with the specified group ownership and permissions.
#'
#' If the directory already exists, attempts to set the group ownership to the
#' \code{user.group}. The allowed group permissions are one of
#' \code{c("g+rwx", "g+rx", "go-w", "go-rwx")}, or \code{"-"}. The value
#' \code{"-"} means "don't change permissions".
#' @param path Directory path. If using \code{remote}, this should be a full path or
#' a path relative to the user's home directory.
#' @param user.group The user group. If NULL, the default group is used.
#' @param remote Remote machine specification for ssh, in format such as \code{user@@server} that does not
#' require interactive password entry. For local execution, pass an empty string "" (default).
#' @param permissions The group permissions on the directory. Default is 'rwx'.
#' @rdname mkdir.remote
#' @note This may not work on Windows.
# COMPATIBILITY WARNING: WINDOWS
mkdir.remote <- function(path, user.group = NULL, remote = "",
permissions = c("g+rwx", "g+rx", "go-w", "go-rwx", "-"))
{
permissions <- match.arg(permissions)
if (!file.exists.remote(path, remote = remote))
{
run.remote(paste("mkdir -p", path), remote = remote)
}
if (!is.null(user.group))
{
run.remote(paste("chgrp -R", user.group, path), remote = remote)
}
if (permissions != "-")
{
run.remote(paste("chmod ", permissions, " ", path, sep = ""), remote = remote)
}
}
#-------------------------------------------------------------------------------
|
a18c2672cb66eff89cf1e184962aa2b75e45f5b4
|
9548b1f23cad26a5a5c68cd9b67d45b8280c7040
|
/man/survival.Rd
|
0ef3f37c2464ba1780a356b848e09f7582c5afb3
|
[] |
no_license
|
socale/frailtypack
|
1e802fe02f735df54655d361512ddef163ec2dd9
|
fc55e2f58b8f0c972405ae3049e0a21f5d2074fb
|
refs/heads/master
| 2021-08-11T02:56:31.967342
| 2021-06-10T10:06:54
| 2021-06-10T10:06:54
| 156,885,473
| 5
| 16
| null | 2020-03-31T13:35:48
| 2018-11-09T16:03:01
|
Fortran
|
UTF-8
|
R
| false
| true
| 698
|
rd
|
survival.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/survival.R
\name{survival}
\alias{survival}
\title{Survival function}
\usage{
survival(t, ObjFrailty)
}
\arguments{
\item{t}{time for survival function.}
\item{ObjFrailty}{an object from the frailtypack fit.}
}
\value{
return the value of survival function in t.
}
\description{
Let t be a continuous variable, we determine the value of the survival
function to t after run fit.
}
\examples{
\dontrun{
#-- a fit Shared
data(readmission)
fit.shared <- frailtyPenal(Surv(time,event)~dukes+cluster(id)+
strata(sex),n.knots=10,kappa=c(10000,10000),data=readmission)
#-- calling survival
survival(20,fit.shared)
}
}
|
773d4d363704e5727071ebffdc2641d11e2efa97
|
2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0
|
/fuzzedpackages/adpss/man/adaptive_analysis_norm_global.Rd
|
cc3f32aeccf9f0038b1ec5d784ae1cb7f1cd09c7
|
[] |
no_license
|
akhikolla/testpackages
|
62ccaeed866e2194652b65e7360987b3b20df7e7
|
01259c3543febc89955ea5b79f3a08d3afe57e95
|
refs/heads/master
| 2023-02-18T03:50:28.288006
| 2021-01-18T13:23:32
| 2021-01-18T13:23:32
| 329,981,898
| 7
| 1
| null | null | null | null |
UTF-8
|
R
| false
| true
| 3,043
|
rd
|
adaptive_analysis_norm_global.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/work_test_norm_global.R
\name{adaptive_analysis_norm_global}
\alias{adaptive_analysis_norm_global}
\title{Analyze data according to a globally efficient adaptive design.}
\usage{
adaptive_analysis_norm_global(initial_test = 0, times = 0, stats = 0,
costs = 0, final_analysis = TRUE, estimate = TRUE, ci_coef = 0.95,
tol_est = 1e-08, input_check = TRUE)
}
\arguments{
\item{initial_test}{Designate the initial working test generated by \code{work_test_norm_global} function.}
\item{times}{The sequence of times (sample size or information level) at which analyses were conducted.}
\item{stats}{The sequence of test statistics.}
\item{costs}{The sequence of loss required to construct working tests. Specification is optional. Partial specification is allowed, in which non-specification may be represented by \code{0}.}
\item{final_analysis}{If \code{TRUE}, the result input will be regarded as complete (no more data will be obtained) and the significance level will be exhausted. If \code{FALSE}, the current analysis will be regarded as an interim analysis and the significance level will be preserved.}
\item{estimate}{If \code{TRUE}, p-value, median unbiased estimator and upper and lower confidence limits will be calculated.}
\item{ci_coef}{The confidence coefficient. Default is 0.95.}
\item{tol_est}{The precision of the calculated results.}
\item{input_check}{Indicate whether or not the arguments input by user contain invalid values.}
}
\value{
It returns whether or not the result was statistically significant, a p-value and an exact confidence limits.
}
\description{
\code{adaptive_analysis_norm_global} performs an globally efficient adaptive test,
a Frequentist adaptive test with the specified significance level
with full flexibility.
Normality with known variance is assumed for the test statistic
(more accurately, the test statistic is assumed to follow Brownian motion.)
Null hypothesis is fixed at 0 without loss of generality.
Exact p-value, median unbiased estimate and confidence limits proposed by Gao et al. (2013) can also be calculated.
For detailed illustration, see \code{vignette("adpss_ex")}.
}
\examples{
# Construct an initial working test
# Note: cost_type_1_err will be automatically calculated when 0 is specified.
init_work_test <- work_test_norm_global(min_effect_size = -log(0.65), cost_type_1_err=1683.458)
# Sample size calculation
sample_size_norm_global(
initial_test = init_work_test,
effect_size = 11.11 / 20.02, # needs not be MLE
time = 20.02,
target_power = 0.75,
sample_size = TRUE
)
}
\references{
Kashiwabara, K., Matsuyama, Y. An efficient adaptive design approximating fixed sample size designs. In preparation.
Gao, P., Liu, L., Mehta, C. (2013) Exact inference for adaptive group sequential designs. Stat Med 32: 3991-4005.
}
\seealso{
\code{\link{work_test_norm_global}} and \code{\link{sample_size_norm_global}}.
}
|
e2dce9d131ed7e512734d18563b84e0eee8ac752
|
c5a08892d45ce23f54771eafe379ed843363f27e
|
/man/octashift.Rd
|
f9fe4f5f5676d9f9f78872670cd44e7b874bcaec
|
[] |
no_license
|
cran/StratigrapheR
|
9a995ea399e97a449bb94a5c8bb239935b108da0
|
aff0937f9ee8d0976fc67a46768b32379cf0274b
|
refs/heads/master
| 2023-07-26T21:02:30.211546
| 2023-07-05T23:14:06
| 2023-07-05T23:14:06
| 163,700,147
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 1,326
|
rd
|
octashift.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/octashift.R
\name{octashift}
\alias{octashift}
\title{Shifts the order of polygon points}
\usage{
octashift(x, y, i, pos, clockwise = NA)
}
\arguments{
\item{x, y}{the coordinates of the polygons}
\item{i}{the identification of the polygons if there are multiple ones}
\item{pos}{an integer from 1 to 8 identifying a points, based on the
formalism of the \code{\link{octapos}} function}
\item{clockwise}{whether to have the points in the polygon be ordered
clockwise (T), counterclockwise (F). If NA (which is the default), this will
not be addressed}
}
\value{
a data frame with $x, $y and $i of the polygons as columns
}
\description{
Shifts the order of polygon points based on octagon-like
reference
}
\examples{
xy <- c(-3,-4,-3,0,-1,-2,-1,0,1,2,1,3,4,5,4,3)
dt <- c(1,1.5,2,1,1,1.5,2,2,1,1.5,2,1,1,1.5,2,2)
id <- c(rep("B1",3), rep("B2",5), rep("B3",3), rep("B4",5))
out <- octashift(xy, dt, id, pos = 3, clockwise = TRUE)
par(mfrow = c(2,1))
plot.new()
plot.window(xlim = range(xy) + c(-1, 1), ylim = range(dt) + 0.5 * c(-1, 1))
axis(1)
axis(2)
multilines(i = id, x = xy, y = dt)
plot.new()
plot.window(xlim = range(xy) + c(-1, 1), ylim = range(dt) + 0.5 * c(-1, 1))
axis(1)
axis(2)
multilines(i = out$i, x = out$x, y = out$y)
}
|
eeed3063df995ce81312f2bf3bff2e46290d660d
|
10c2dbb072e022a11a61f3a86d7f327cae85aa6e
|
/man/lookup_symbol.Rd
|
1572b4802c0149acb6c12d61bf8481e4331d3c4b
|
[] |
no_license
|
dwinter/rensembl
|
3dda8f10637de87d1663b4a4771baddc2322c8f4
|
2225df9968dfb1ad8bc3c9c6d1cc8c950cd65453
|
refs/heads/master
| 2020-05-30T05:44:51.784053
| 2016-10-25T21:20:21
| 2016-10-25T21:22:56
| 29,898,025
| 2
| 3
| null | 2015-10-12T18:13:29
| 2015-01-27T04:55:32
|
R
|
UTF-8
|
R
| false
| true
| 1,224
|
rd
|
lookup_symbol.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/info.r
\name{lookup_symbol}
\alias{lookup_symbol}
\title{Retrieve information about a sequence via gene symbol and species}
\usage{
lookup_symbol(symbol, expand = FALSE, species = "homo_sapiens",
format = "full", return_format = "json")
}
\arguments{
\item{symbol}{character, gene symbol(s) from which to retreive information.}
\item{species}{character, species from which to retreieve information. See
\code{info_species} for a list of avaliable species. Defaults to homo_sapiens}
\item{format, }{character, type of record to return either 'full' or 'condensed'}
\item{return_format, }{character method by which record is returned. Defaults
to \code{json}}
\item{symbol}{logical, if \code{TRUE} return information about transcripts/proteins
associated with these record. Defaults to \code{FALSE}}
}
\description{
Retrieve information about a sequence via gene symbol and species
}
\examples{
human_brca <- lookup_symbol("BRCA2")
chimp_brca <- lookup_symbol("BRCA2", species="ptro")
chimp_brca_expanded <- lookup_symbol("BRCA2", species="ptro", expand=TRUE)
}
\references{
\url{lhttp://rest.ensembl.org/documentation/info/symbol_lookup}
}
|
dce67268fd5e8b667f3a87a035143c97a5f7c268
|
81a2fa3228451179b12779bb0149398cbfc8e9b1
|
/man/firstOfRepeated.Rd
|
812db8335f7248743fd4e64895e69d86aeec1777
|
[] |
no_license
|
cran/wrMisc
|
c91af4f8d93ad081acef04877fb7558d7de3ffa2
|
22edd90bd9c2e320e7c2302460266a81d1961e31
|
refs/heads/master
| 2023-08-16T21:47:39.481176
| 2023-08-10T18:00:02
| 2023-08-10T19:30:33
| 236,959,523
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 1,614
|
rd
|
firstOfRepeated.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/firstOfRepeated.R
\name{firstOfRepeated}
\alias{firstOfRepeated}
\title{Find first of repeated elements}
\usage{
firstOfRepeated(x, silent = FALSE, debug = FALSE, callFrom = NULL)
}
\arguments{
\item{x}{(charcter or numeric) main input}
\item{silent}{(logical) suppress messages}
\item{debug}{(logical) display additional messages for debugging}
\item{callFrom}{(character) allow easier tracking of message(s) produced}
}
\value{
list with indices: $indRepeated, $indUniq, $indRedund
}
\description{
This function works similar to \code{unique}, but provides additional information about which elements of original input \code{'x'} are repeatd by providing indexes realtoe to the input.
\code{firstOfRepeated} makes list with 3 elements : $indRepeated.. index for first of repeated 'x', $indUniq.. index of all unique + first of repeated, $indRedund.. index of all redundant entries, ie non-unique (wo 1st).
Used for reducing data to non-redundant status, however, for large numeric input the function nonAmbiguousNum() may perform better/faster.
NAs won't be considered (NAs do not appear in reported index of results), see also firstOfRepLines() .
}
\examples{
x <- c(letters[c(3,2:4,8,NA,3:1,NA,5:4)]); names(x) <- 100+(1:length(x))
firstOfRepeated(x)
x[firstOfRepeated(x)$indUniq] # only unique with names
}
\seealso{
\code{\link[base]{duplicated}}, \code{\link{nonAmbiguousNum}}, \code{\link{firstOfRepLines}} gives less detail in output (lines/elements/indexes of omitted not directly accessible) and works fsster
}
|
a78420cc40013b9790ca3774633b404066473f2f
|
447332bec1373c67f98fff08313d85e89f3c8944
|
/usefullcode.R
|
7408ffd975667a4bc727d6ef2157552aa1f18c87
|
[] |
no_license
|
bitterbalpiraat/random_bear_trajectories
|
8a254d64529448bc86f8002c56d2034f5f523a88
|
ee82d3d28c5beb2877b6de14fbd5dfaf4d9ef5c0
|
refs/heads/master
| 2021-01-17T10:15:17.921784
| 2016-03-04T14:20:15
| 2016-03-04T14:20:15
| 53,140,136
| 1
| 0
| null | 2016-03-04T14:17:56
| 2016-03-04T14:17:56
| null |
UTF-8
|
R
| false
| false
| 3,549
|
r
|
usefullcode.R
|
mypoints <- SpatialPointsDataFrame(cbind(Koski2007$Locale_E, Koski2007$Locale_N), data =Koski2007,
proj4string=CRS("+init=epsg:2400"))
mypoints@data$tspannum<- as.numeric(mypoints@data$tspan)
mypoints<-mypoints[1:10,]
# 74:150 bij V<-2000
#tinterval <- 3
mypoints<-SpatialPointsDataFrame(mypoints@coords,as.data.frame(mypoints@data$tspannum))
names(mypoints)<-'tspannum'
V<-5000
tinterval <- 2
allpoints<-mypoints[length(mypoints),]
xy<-c()
time<-c()
tvector<- sample(seq(1:(tinterval-1)))
plot(mypoints,col='blue',xlim=c(bbox(mypoints)[1][1]-(V/3),bbox(mypoints)[,2][1]+(V/3)),axes=T,
ylim=c(bbox(mypoints)[2][1]-(V/3),bbox(mypoints)[,2][2]+(V/3)),xlab='X (meters)', ylab= 'Y (meters)',
main='Random trajectory between known points')
for(j in seq(1:(length(mypoints)-1))){
startpoint<-mypoints[j,]
endpoint<- mypoints[j+1,]
newpoints<-mypoints[j,]
npoint<-startpoint
last_t<-0
t<-(mypoints[j+1,]@data$tspannum-mypoints[j,]@data$tspannum)/tinterval
for(i in tvector){
if (i<last_t)
{ endpoint<-npoint
}
if (i>last_t)
{ startpoint<-npoint
}
buffer1<-gBuffer(startpoint,width=(V*(t*i)),quadsegs=7)
buffer2<-gBuffer(endpoint,width=(V*(t*(tinterval-i))),quadsegs=7)
plot(buffer1,add=T)
plot(buffer2,add=T,border='red')
PpA<-gIntersection(buffer1,buffer2)
plot(PpA,add=T,col='green')
npoint<-spsample(PpA,1,type = 'random')
npoint<-SpatialPointsDataFrame(npoint,data=as.data.frame(mypoints@data$tspannum[j]+(i*t)))
names(npoint)<-'tspannum'
newpoints<-spRbind(newpoints,npoint)
last_t<-i
}
xy<-rbind(xy,newpoints@coords)
time <- c(time,newpoints@data$tspannum)
}
allpoints<-SpatialPointsDataFrame(xy[order(time),],data=as.data.frame(c(time[order(time)])))
names(allpoints)<- 'tspannum'
allpoints<-spRbind(allpoints,mypoints[length(mypoints),])
plot(allpoints,add=T,col='blue',pch='*')
lines(allpoints@coords)
plot(mypoints,add=T,col='red')
## spplot
spplot(mypoints,zcol='tspannum',colorkey = list(
right = list( # see ?levelplot in package trellis, argument colorkey:
fun = draw.colorkey,
args = list(
key = list(
at = seq(0, max(mypoints@data$tspannum), 10), # colour breaks
col = bpy.colors(length(seq(0, max(mypoints@data$tspannum), 10))), # colours
labels = list(
at = c(0, median(seq(0, max(mypoints@data$tspannum), 10)),740),
labels = c("0 hour", "370 hour", "740 hour")
)
)
)
)
))
#### original function
V<-100
tinterval <- 16
allpoints<-mypoints[length(mypoints),]
xy<-c()
time<-c()
for(j in seq(1:(length(mypoints)-1))){
npoint<-mypoints[j,]
newpoints<-mypoints[j,]
plot(mypoints,col='blue')
t<-(mypoints[j+1,]@data$tspannum-mypoints[j,]@data$tspannum)/tinterval
m=1
for(i in seq(tinterval,2,-1)){
buffer1<-gBuffer(npoint,width=(V*t),quadsegs=7)
buffer2<-gBuffer(mypoints[j+1,],width=(V*t)*i,quadsegs=7)
PpA<-gIntersection(buffer1,buffer2)
npoint<-spsample(PpA,1,type = 'random')
npoint<-SpatialPointsDataFrame(npoint,data=as.data.frame(mypoints@data$tspannum[j]+m*(t)))
names(npoint)<-'tspannum'
newpoints<-spRbind(newpoints,npoint)
m <- m+1
}
xy<-rbind(xy,newpoints@coords)
time <- c(time,newpoints@data$tspannum)
}
allpoints<-SpatialPointsDataFrame(xy,data=as.data.frame(c(time)))
names(allpoints)<- 'tspannum'
allpoints<-spRbind(allpoints,mypoints[length(mypoints),])
plot(allpoints)
lines(allpoints@coords)
plot(mypoints,add=T,col='red')
|
a7a24610205721eb019fc82b11dc14d73df75979
|
c2c3cec8f61b1cca97326dea30d70f7912c37987
|
/man/dot-mergeAndReturn.Rd
|
a92fdf305d44a86c1815330767bccf681d0ceb41
|
[] |
no_license
|
malnirav/hiAnnotator
|
549747d5c601349a2bf732e28b3d7462e9e1f7c2
|
9f52365c3e8f396fba947eca377a2c5b5fe87f47
|
refs/heads/master
| 2021-07-29T19:14:19.164306
| 2021-07-27T20:27:19
| 2021-07-27T20:27:19
| 3,418,947
| 3
| 3
| null | null | null | null |
UTF-8
|
R
| false
| true
| 543
|
rd
|
dot-mergeAndReturn.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/hiAnnotator.R
\name{.mergeAndReturn}
\alias{.mergeAndReturn}
\title{Merge results back to the query object and perform additional post
processing steps.}
\usage{
.mergeAndReturn()
}
\description{
This function merges all the calculation results back to the query object.
Additionally, if any flags were set, the function does the necessary checks
and processing to format the return object as required. Evaluation of this
function happens in the parent function.
}
|
f32c6e9ed3c0b1d6f0946b8343fe48bee738cecf
|
d96c0d0c39d1e96fa98154e1206c136ccef7686f
|
/code/09-write_fns.R
|
eeaa806c248945895aa48c7a342c97eb25031e08
|
[] |
no_license
|
HelenaLC/simulation-comparison
|
d94df6f9a68e46f974d6342a81c75066aad7e1d7
|
f89e6650f8acae1c32b8fe27d644ec7c7be63051
|
refs/heads/master
| 2023-04-17T22:06:50.698560
| 2022-08-10T09:32:28
| 2022-08-10T09:32:28
| 344,418,980
| 7
| 3
| null | null | null | null |
UTF-8
|
R
| false
| false
| 103
|
r
|
09-write_fns.R
|
pat <- paste0("^", wcs$pat)
fns <- list.files("outs", pat, full.names = TRUE)
writeLines(fns, args$txt)
|
70cd3888c6e7c7affc0c601ff6e693796d349758
|
580587aeff946a705babe263bb1d52a059a6614b
|
/_scripts/MFA_RTBcrops.R
|
a1209109acecc929f03800dfad53b35346ec2296
|
[] |
no_license
|
CIAT-DAPA/gfsf_project
|
cdb1b8716624f4cb59f7781800e62b134d4914c0
|
49c525a9e30368358b18400681a0160dc97dae7f
|
refs/heads/master
| 2021-01-12T12:45:42.612328
| 2019-06-19T17:08:58
| 2019-06-19T17:08:58
| 69,292,875
| 0
| 1
| null | null | null | null |
ISO-8859-2
|
R
| false
| false
| 30,509
|
r
|
MFA_RTBcrops.R
|
#MFA RTB
### PCA for RTB crops
### Carlos Eduardo Gonzalez R.
### RTB Analysis
g=gc;rm(list = ls())
# librerias------------
suppressMessages(library(reshape))
suppressMessages(library(ggplot2))
suppressMessages(library(plyr))
suppressMessages(library(grid))
suppressMessages(library(gridExtra))
suppressMessages(library(dplyr))
suppressMessages(library(tidyverse))
suppressMessages(library(modelr))
suppressMessages(library(purrr))
suppressMessages(library(broom))
suppressMessages(library(tidyr))
suppressMessages(library(corrplot))
suppressMessages(library(FactoMineR))
suppressMessages(library(factoextra))
suppressMessages(library(cluster))
suppressMessages(library(RCurl))
suppressMessages(library(ggthemes))
suppressMessages(library(tidyquant))
suppressMessages(library(devtools))
suppressMessages(library(mvoutlier))
suppressMessages(library(R.utils))
# if(!require(devtools)) install.packages("devtools")
# devtools::install_github("kassambara/factoextra")
#
abbreviate("percentage")
options(warn = -1); options(scipen = 999)
options(digits=3)
############################################################# BIG Regions ####################################################################
rdsFiles<-c("//dapadfs/workspace_cluster_6/Socioeconomia/GF_and_SF/USAIDForGFSF/RTB_files/")
# Big Regions[
r<- c("EAP", "EUR","FSU", "LAC", "MEN", "NAM", "SAS", "SSA")
s<- c("SSP2-HGEM-HiYld2","SSP2-HGEM-RegYld2","SSP2-HGEM-HiNARS2", "SSP2-HGEM-MMEFF2","SSP2-HGEM2")
# Parametro 2 All Countries.
r2<- c("EAP", "EUR","FSU", "LAC", "MEN", "NAM", "SAS", "SSA", "Africa","Americas","DVD", "DVG","WLD")
r3<- c("Africa","Americas", "Asia","Europe", "Oceania")
r4<- c("Australia and New Zealand","Caribbean","Central America", "Central Asia","Eastern Africa","Eastern Asia","Eastern Europe","Melanesia",
"Middle Africa","Northern Africa","Northern America","Northern Europe","South America","South-Eastern Asia","Southern Africa","Southern Asia",
"Southern Europe","Western Africa","Western Asia", "Western Europe", "Western and Central Asia")
r5<- c("MENg","EAPg")
rall<- c(r2,r3,r4, r5)
jrtb<- c("jbana","jcass", "jpota", "jswpt","jyams","jorat")
t<- c(2010, 2030,2050)
# Vector con los cultivos para RTB incluyendo Bananas
rtb<- c("R&T-Potato","R&T-Sweet Potato","R&T-Yams","R&T-Other Roots","R&T-Cassava","F&V-Banana")
cfiles<-list.files(path = rdsFiles, pattern = "Blue.rds|dataagg.rds|datatotal.rds|precios.rds|TradeFood.rds",full.names = T)
cfiles<- lapply(cfiles, readRDS)
cdata<-cfiles
# primer grupo de variables------------
for(i in 1:length(cdata)){
cdata[[i]]$Scenarios<- gsub("'",'',cdata[[i]]$Scenarios)
cdata[[i]]$Commodity<- gsub("'", '',cdata[[i]]$Commodity)
cdata[[i]]$Regions<- gsub("'", '',cdata[[i]]$Regions)
cdata[[i]]$Year<- gsub("'",'',cdata[[i]]$Year)
cdata[[i]]$Scenarios<- as.character( cdata[[i]]$Scenarios)
cdata[[i]]$Commodity<- as.character( cdata[[i]]$Commodity)
cdata[[i]]$Regions<- as.character( cdata[[i]]$Regions)
cdata[[i]]<- filter(cdata[[i]], Scenarios %in% s)
cdata[[i]]<- filter(cdata[[i]], !Regions %in% rall)
cdata[[i]]<- filter(cdata[[i]], Commodity %in% rtb)
cdata[[i]]<- cdata[[i]]%>% spread(Year, Val)
cdata[[i]]$change<- ((cdata[[i]]$`2050`-cdata[[i]]$`2010`)/cdata[[i]]$`2010`)*100
cdata[[i]]<- cdata[[i]][,c("Scenarios", "Commodity", "Regions", "parameter", "change" )]
cdata[[i]]<- cdata[[i]]%>% spread(Scenarios, change)
colnames(cdata[[i]])<- c("Commodity", "Regions","parameter", "HIGH+NARS","HIGH","RMM", "REGION", "REF" )
print(i)
}
crbind<- do.call(rbind, cdata)
# Por sistema --------
jfiles<-list.files(path = rdsFiles, pattern = "datasys.rds|green.rds",full.names = T) #|shock.rds|ipr.rds
jfiles<- lapply(jfiles, readRDS)
jdata<-jfiles
countries <- read.csv(file = paste(rdsFiles,"IPRsLabelsRegions.csv", sep=""), header = T)
colnames(countries)<- c("Regions", "IMPACT.Name")
#i=3
for(i in 1:length(jdata)){
jdata[[i]]$Scenarios<- gsub("'",'',jdata[[i]]$Scenarios)
jdata[[i]]$Commodity<- gsub("'", '',jdata[[i]]$Commodity)
jdata[[i]]$Regions<- gsub("'", '',jdata[[i]]$Regions)
jdata[[i]]$Year<- gsub("'",'',jdata[[i]]$Year)
jdata[[i]]$Sys<- gsub("'",'',jdata[[i]]$Sys)
jdata[[i]]$parameter<- gsub("'",'',jdata[[i]]$parameter)
jdata[[i]]<- filter(jdata[[i]], Scenarios %in% s)
jdata[[i]]<- filter(jdata[[i]], !Regions %in% rall)
jdata[[i]]<- filter(jdata[[i]], Commodity %in% rtb)
jdata[[i]]<- jdata[[i]][,c("Scenarios", "Commodity","Regions","parameter", "Sys", "Year","Val") ]
jdata[[i]]<- jdata[[i]]%>% spread(Year, Val)
jdata[[i]]$change<- ((jdata[[i]]$`2050`-jdata[[i]]$`2010`)/jdata[[i]]$`2010`)*100
jdata[[i]]<- jdata[[i]][,c("Scenarios", "Commodity", "Regions", "parameter","Sys", "change" )]
jdata[[i]]<- jdata[[i]]%>% spread(Scenarios, change)
colnames(jdata[[i]])<- c("Commodity", "Regions","parameter","Sys", "HIGH+NARS","HIGH","RMM", "REGION", "REF" )
print(i)
}
jrbind<- do.call(rbind, jdata)
# Economic Variables1--------------
efiles<-list.files(path = rdsFiles, pattern = "EcoFood.rds",full.names = T)
efiles<- lapply(efiles, readRDS)
erbind<- do.call(rbind, efiles)
#ajuste y corregir asuntos de texto
erbind$Scenarios<- gsub("'",'',erbind$Scenarios)
erbind$Regions<- gsub("'", '',erbind$Regions)
erbind$Year<- gsub("'",'',erbind$Year)
erbind$Scenarios<- as.character( erbind$Scenarios)
erbind$Regions<- as.character(erbind$Regions)
erbind<- filter(erbind, Scenarios %in% s) %>% filter(., !Regions %in% rall) %>% spread(Year, Val)
erbind$change<- ((erbind$`2050`-erbind$`2010`)/erbind$`2010`)*100
erbind<- erbind[,c("Scenarios","Regions","parameter","change")]
erbind<- erbind %>% spread(Scenarios, change)
colnames(erbind)<- c("Regions","parameter","HIGH+NARS","HIGH","RMM", "REGION", "REF")
# Economic Variables2--------------
efiles2<-list.files(path = rdsFiles, pattern = "EcoFood2.rds",full.names = T)
efiles2<- lapply(efiles2, readRDS)
erbind2<- do.call(rbind, efiles2)
#ajuste y corregir asuntos de texto
erbind2$Scenarios<- gsub("'",'',erbind2$Scenarios)
erbind2$Regions<- gsub("'", '',erbind2$Regions)
erbind2$Year<- gsub("'",'',erbind2$Year)
erbind2$Scenarios<- as.character( erbind2$Scenarios)
erbind2$Regions<- as.character(erbind2$Regions)
erbind2<- erbind2 %>% filter(Scenarios %in% s) %>%
filter(., !Regions %in% rall) %>%
spread(Year, Val)
erbind2$change<- ((log10(erbind2$`2050`)- log10(erbind2$`2010`))/(2050-2010))
erbind2<- erbind2[,c("Scenarios","Regions","parameter","change")]
erbind2<- erbind2 %>% spread(Scenarios, change)
colnames(erbind2)<- c("Regions","parameter","HIGH+NARS","HIGH","RMM", "REGION", "REF")
#Economic variables3-------------
efiles3<-list.files(path = rdsFiles, pattern = "EcoFood3.rds",full.names = T)
efiles3<- lapply(efiles3, readRDS)
erbind3<- do.call(rbind, efiles3)
#ajuste y corregir asuntos de texto
erbind3$Scenarios<- gsub("'",'',erbind3$Scenarios)
erbind3$Regions<- gsub("'", '',erbind3$Regions)
erbind3$Year<- gsub("'",'',erbind3$Year)
erbind3$Scenarios<- as.character( erbind3$Scenarios)
erbind3$Regions<- as.character(erbind3$Regions)
erbind3<- erbind3 %>% filter(Scenarios %in% s) %>%
filter(., !Regions %in% rall) %>%
spread(Year, Val)
erbind3$change<- (erbind3$`2050`- erbind3$`2010`)
erbind3<- erbind3[,c("Scenarios","Regions","parameter","change")]
erbind3<- erbind3 %>% spread(Scenarios, change)
colnames(erbind3)<- c("Regions","parameter","HIGH+NARS","HIGH","RMM", "REGION", "REF")
############################### Tratamiento y construccion de grupos de variables ######################
#detection and deleted of NA
crbind[is.na(crbind)]<- 0
erbind[is.na(erbind)]<- 0
jrbind[is.na(jrbind)]<- 0
erbind2[is.na(erbind2)]<- 0
erbind3[is.na(erbind3)]<- 0
crbind<- crbind %>% gather(Sce, change, 4:8)
erbind<- erbind %>% gather(Sce, change, 3:7)
erbind2<- erbind2 %>% gather(Sce, change, 3:7)
erbind3<- erbind3 %>% gather(Sce, change, 3:7)
jrbind<- jrbind %>% gather(Sce, change, 5:9)
###################################### Tratamiento de datos economicos #################################
#economic1
ad_erbind<- erbind %>% split(erbind$Sce)
efiles<- list()
for(i in 1:length(ad_erbind)){
efiles[[i]] <- ad_erbind[[i]] %>% spread(parameter, change) %>%
gather(Variable, Summary, -(Regions:Sce)) %>%
unite(Temp, Sce, Variable)%>% spread(Temp, Summary)
}
#economic2
ad_erbind2<- erbind2 %>% split(erbind2$Sce)
efiles2<- list()
for(i in 1:length(ad_erbind2)){
efiles2[[i]] <- ad_erbind2[[i]] %>% spread(parameter, change) %>%
gather(Variable, Summary, -(Regions:Sce)) %>%
unite(Temp, Sce, Variable)%>% spread(Temp, Summary)
}
#economic3
ad_erbind3<- erbind3 %>% split(erbind3$Sce)
efiles3<- list()
for(i in 1:length(ad_erbind3)){
efiles3[[i]] <- ad_erbind3[[i]] %>% spread(parameter, change) %>%
gather(Variable, Summary, -(Regions:Sce)) %>%
unite(Temp, Sce, Variable)%>% spread(Temp, Summary)
}
######################################## Irrigacion ###################################################
ad_jrbind<- jrbind %>% split(jrbind$Sce)
jfiles<- list()
#i=1
for(i in 1:length(ad_jrbind)){
jfiles[[i]] <- ad_jrbind[[i]] %>% spread(parameter, change) %>%
gather(Variable, Summary, -(Commodity:Sce)) %>%
unite(Temp, Sys, Variable)%>% spread(Temp, Summary) %>%
gather(Variable, Summary, -(Commodity:Sce))%>%
unite(Temp, Sce, Variable)%>% spread(Temp, Summary)
}
######################################### Analisis PCA #################################################
ad_crbind<- crbind %>% split(crbind$Sce)
cfiles<- list()
usaid<- c("HIGH","HIGH+NARS","REF","REGION","RMM")
c=1
s=1
for(s in 1:length(ad_crbind)){
cfiles[[s]]<- ad_crbind[[s]] %>% spread(parameter, change)
##### By Sce
cfiles[[s]] <- cfiles[[s]] %>% gather(Variable, Summary, -(Commodity:Sce)) %>%
unite(Temp, Sce, Variable)%>% spread(Temp, Summary)
dfjoin<- left_join(cfiles[[s]],efiles[[s]], by=("Regions"))
dfjoin<- left_join(dfjoin,efiles2[[s]], by=("Regions"))
dfjoin<- left_join(dfjoin,efiles3[[s]], by=("Regions"))
dfjoin<- left_join(dfjoin, jfiles[[s]], by=c("Commodity","Regions"))
##### By crops
ad_crops<- dfjoin %>% split(dfjoin$Commodity)
lapply(1:length(ad_crops), function(c){
crop<- unique(as.character(ad_crops[[c]]$Commodity))
sce<- usaid[s]
zones<- unique(ad_crops[[c]]$Regions)
# Creating directories
wk_dir <- "//dapadfs/workspace_cluster_6/Socioeconomia/GF_and_SF/USAIDForGFSF/RTB_files/RTBAnalysis"
if(!dir.exists(paths = paste(wk_dir, "/",crop, sep = ""))){ dir.create(path = paste(wk_dir, "/", crop, sep = "")) }
# Creando data frame
dff<- ad_crops[[c]]
dff<- dff[grep(pattern ="MEN|SSA|SAS|LAC|EAP" ,x = dff$Regions, ignore.case = T),]
#eliminando variables con baja correlación
dff<- dff[,-c(5,6,7,8,10,11,12,17,19,21)]
dbox<- dff
dbox$Sce<- usaid[s]
write.csv(x = dbox, file = paste(wk_dir, "/", crop,"_", usaid[s],"_boxplot.csv", sep = ""))
# # boxplot
boxplot(dff[,3:ncol(dff)], horizontal = T)
# Deleting columns with SD = 0
rownames(dff) <- dff$Regions
dff$Regions <- dff$Commodity <- NULL
##Calcular porcentaje de datos faltantes por variable
miss <- apply(X=dff, MARGIN = 2, FUN = function(x){ y <- sum(is.na(x))/nrow(dff); return(y*100) })
dff <- dff[,names(which(miss <= 30))]
dff[is.na(dff)]<- 0
dff <- Filter(function(x) sd(x) != 0, dff)
#detection outluiers, multivariado
mahal<- mahalanobis(dff,colMeans(dff, na.rm = TRUE),cov(dff), use="pairwise.complete.obs")
cuotoff<- qchisq(0.999, ncol(dff))
summary(mahal < cuotoff) # identificar outliners
outMvar<- dff[mahal>cuotoff,]
if(nrow(outMvar)>1){
outMvar<- as.data.frame(outMvar)
outMvar$sce<- sce
outMvar$crop<- crop
outMvar$Regions<- row.names(outMvar)
row.names(outMvar)<- NULL
outMvar<- outMvar %>% gather(Var, Val, 1:(ncol(outMvar)-3))
outMvar$Var<- gsub("HIGH_", '',outMvar$Var)
outMvar$Var<- gsub("REF_", '',outMvar$Var)
outMvar$Var<- gsub("REGION_", '',outMvar$Var)
outMvar$Var<- gsub("RMM_", '',outMvar$Var)
outMvar$Var<- gsub("RMM_", '',outMvar$Var)
outMvar$Var<- gsub("[[:punct:]]", '',outMvar$Var)
outMvar$Var<- gsub("HIGHNARS",'', outMvar$Var)
outMvar$Regions<- gsub("LAC-", '',outMvar$Regions)
outMvar$Regions<- gsub("FSU-", '',outMvar$Regions)
outMvar$Regions<- gsub("SSA-", '',outMvar$Regions)
outMvar$Regions<- gsub("MEN-", '',outMvar$Regions)
outMvar$Regions<- gsub("SAS-", '',outMvar$Regions)
outMvar$Regions<- gsub("EAP-", '',outMvar$Regions)
write.csv(x = outMvar, file = paste(wk_dir, "/", crop,"_",sce,"_OutlierMV.csv", sep = "") )
}else{cat("no pasa nada de nada")}
dff<- dff[mahal< cuotoff,]
write.csv(x = dff, file = paste(wk_dir, "/", crop,"/",sce,"dataGenesis.csv", sep = "") )
# Correlation matrix
if(!file.exists(paste(wk_dir, "/", crop,"/",sce,"_corrMatrix.png", sep = ""))){
M<- cor(dff)
png(file = paste(wk_dir, "/", crop,"/",sce,"_corrMatrix.png", sep = ""), height = 8, width = 8, units = "in", res = 300)
corrplot.mixed(M)
dev.off()
}
# Principal Component Analysis
res.pca <- FactoMineR::PCA(dff, graph = F,scale.unit = T) #quanti.sup =8, ind.sup =12
if(!file.exists(paste(wk_dir, "/", crop,"/",sce,"_eigenValuesPCA.png", sep = ""))){
gg <- fviz_eig(res.pca, addlabels = TRUE, hjust = -0.3) + theme_bw() # Visualize eigenvalues/variances
ggsave(filename = paste(wk_dir, "/", crop,"/",sce,"_eigenValuesPCA.png", sep = ""), plot = gg, width = 8, height = 8, units = "in")
}
if(!file.exists(paste(wk_dir, "/", crop,"/",sce,"_varQuality.png", sep = ""))){
png(paste(wk_dir, "/", crop, "/",sce,"_varQuality.png", sep = ""), height = 8, width = 16, units = "in", res = 300)
par(mfrow = c(1, 3))
corrplot(res.pca$var$cos2[,1:2], is.corr = FALSE, title = "Representation quality", mar = c(1, 0, 1, 0)) # Representation quality of each variable
corrplot(res.pca$var$contrib[,1:2], is.corr = FALSE, title = "Contribution", mar = c(1, 0, 1, 0)) # Contribution of each variable to dimension
corrplot(res.pca$var$cor[,1:2], method = "ellipse", is.corr = TRUE, title = "Correlation", mar = c(1, 0, 1, 0)) # Correlation of each variable to dimension
dev.off()
}
# Hierarchical Clustering on Principle Components
res.hcpc <- FactoMineR::HCPC(res.pca, nb.clust = -1, graph = F)
if(!file.exists(paste(wk_dir, "/", crop, "/",sce,"_biplotPCA.png", sep = ""))){
gg <- fviz_pca_biplot(res.pca, label = "var", habillage = res.hcpc$data.clust$clust, addEllipses = TRUE, ellipse.level = 0.95) + theme_bw() # Biplot of individuals and variables. Only variables are labelled (var, ind)
ggsave(filename = paste(wk_dir, "/", crop, "/",sce,"_biplotPCA.png", sep = ""), plot = gg, width = 8, height = 8, units = "in")
}
# Getting coordinates by cluster
coordClust<- res.hcpc$call$X
coordClust$Regions<- rownames(coordClust)
rownames(coordClust)<- NULL
coordClust$sce<- sce
coordClust$crop<- crop
coordClust<- coordClust[,c("Regions","sce","crop","Dim.1","Dim.2","clust")]
interClus<- coordClust %>% split(coordClust$clust)
lapply(1: length(interClus), function(i){
d<- interClus[[i]]
write.csv(x = d,paste(wk_dir, "/",crop,"_",sce,"Inter_clusT",i,".csv" ,sep = ""))
})
# Getting countries farthest
disFar<- res.hcpc$desc.ind$dist
disClos<- (res.hcpc$desc.ind$para)
#cluster grafico1
png(paste(wk_dir, "/", crop, "/",sce,"_1ClusterPLOTPCA.png", sep = ""))
plot(res.hcpc, choice = "3D.map", angle=60)
dev.off()
#Cluster grafico2
gg1 <- fviz_cluster(res.hcpc,ellipse.alpha = 0.1,main = paste("Scenario: ",sce, " ","crop: ",crop, sep = "" )) #labelsize = T, ggtheme = theme_minimal()
ggsave(filename = paste(wk_dir, "/", crop, "/",sce,"_2ClusterPLOTPCA.png", sep = ""), plot = gg1, width = 8, height = 8, units = "in")
#dendrogram
gg1<- fviz_dend(res.hcpc,main = paste("Scenario: ",sce, " ","crop: ",crop, sep = "" ),horiz = T ,cex = 0.5, color_labels_by_k = FALSE, rect = TRUE)
ggsave(filename = paste(wk_dir, "/", crop, "/",sce,"_DendogramPCA.png", sep = ""), plot = gg1, width = 10, height = 8, units = "in")
# Individuals factor map
if(!file.exists(paste(wk_dir, "/", crop, "/",sce,"_IndividualsFactorPCA.png", sep = ""))){
gg <- fviz_pca_ind(res.pca, habillage = res.hcpc$data.clust$clust, addEllipses = TRUE, ellipse.level = 0.95) + theme_bw() # Biplot of individuals and variables. Only variables are labelled (var, ind)
ggsave(filename = paste(wk_dir, "/", crop, "/",sce,"_IndividualsFactorPCA.png", sep = ""), plot = gg, width = 8, height = 8, units = "in")
}
#deteccion de datos por cluster
desClus<- res.hcpc$desc.var$quanti
for(i in 1:length(desClus)){
if(!is.null(desClus[[i]])){
cfilesClust<- as.data.frame(desClus[[i]])
cfilesClust$Var<- rownames(cfilesClust)
rownames(cfilesClust) <- NULL
cfilesClust$cv <- cfilesClust$`sd in category`/cfilesClust$`Mean in category`
cfilesClust$crop<- crop
cfilesClust$clust<- i
cfilesClust$Sce<- sce
write.csv(x =cfilesClust, file = paste(wk_dir, "/", crop,"/",sce, "cluster_",i,"_desCluster.csv", sep = ""))
}else{}
}
# Contribution individual
indcontri<- data.frame(res.pca$ind$contrib)
indcontri$Sce<- sce
indcontri$crop<- crop
indcontri$Regions<- rownames(indcontri)
rownames(indcontri) <- NULL
indcontri<- indcontri[,c("Sce","crop","Regions","Dim.1","Dim.2","Dim.3","Dim.4","Dim.5")]
write.csv(x =indcontri, file = paste(wk_dir, "/", crop,"/",sce,"_IndContribution.csv", sep = ""))
# Contribution variable
indcontriVar<- data.frame(res.pca$var$contrib)
indcontriVar$Sce<- sce
indcontriVar$crop<- crop
indcontriVar$var<- rownames(indcontriVar)
rownames(indcontriVar) <- NULL
indcontriVar<- indcontriVar[,c("Sce","crop","var","Dim.1","Dim.2","Dim.3","Dim.4","Dim.5")]
write.csv(x =indcontriVar, file = paste(wk_dir, "/", crop,"/",sce,"_VarContribution.csv", sep = ""))
# Index based on PCA
script <- getURL("https://raw.githubusercontent.com/haachicanoy/r_scripts/master/calculate_index_by_pca.R", ssl.verifypeer = FALSE); eval(parse(text = script)); rm(script)
#cluster
df_cluster <- res.hcpc$data.clust
dataCluster<- res.hcpc$data.clust
dataCluster$Sce<- sce
dataCluster$crop<- crop
dataCluster$Regions<- rownames(dataCluster)
rownames(dataCluster) <- NULL
dataCluster<- dataCluster[,c("crop","Regions","Sce","clust")]
write.csv(x =dataCluster, file = paste(wk_dir, "/", crop,"/",sce,"_DataCluster.csv", sep = ""))
cat(paste(crop, " done\n", sep = ""))
})
cat(paste("terminos el escenario ", s, " cool it's done\n", sep = ""))
}
############################# Analisis de cluster #############
wk_dir <- "//dapadfs/workspace_cluster_6/Socioeconomia/GF_and_SF/USAIDForGFSF/RTB_files/RTBAnalysis"
for(i in 1:length(rtb)){
c_clus<- list.files(path =paste(wk_dir,"/",rtb[i],sep = ""), pattern = "DataCluster.csv", full.names = T)
c_clus<- lapply(c_clus, read.csv)
rr<- do.call(rbind, c_clus)
rr$X<- NULL
write.csv(x = rr, file = paste(wk_dir, "/", rtb[i],"/","sumCluster.csv", sep = ""))
write.csv(x = rr, file = paste(wk_dir, "/", rtb[i],"_sumCluster.csv", sep = ""))
png(filename = paste(wk_dir,"/",rtb[i],"_HeatMapCluster",".png",sep=""),
width = 10, height = 10, units = 'in', res = 100)
gg<- ggplot(rr, aes(Sce, Regions)) +
geom_tile(aes(fill = as.factor(clust)), colour = "white") +
scale_fill_brewer(palette = "Set1")+
labs(x=NULL, y=NULL, title=paste( "Clustering of\n ", rtb[i], " by scenarios",sep = ""))+
coord_equal()+
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
theme_grey() + labs(x = "",y = "")+
theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 12))+
theme(axis.text.y = element_text(hjust = 1, size = 12))+
theme(strip.text.x = element_text(size = 12, face = "bold.italic"))+
theme(strip.text=element_text(size=8))+
theme(strip.text.y = element_text(angle = 0,size = 12))+
guides(fill=guide_legend(title="Cluster"))
plot(gg)
dev.off()
print(i)
}
############################################ total subregions
c_clus<- list.files(path =wk_dir, pattern = "_sumCluster", full.names = T)
c_clus<- lapply(c_clus, read.csv)
rrsubregions<- do.call(rbind, c_clus)
rrsubregions$X<- NULL
write.csv(x = rrsubregions, file = paste(wk_dir, "/","subregionsTotalCluster.csv", sep = ""))
#correr
rrsubregions$Regions<- gsub("LAC-", '',rrsubregions$Regions)
rrsubregions$Regions<- gsub("FSU-", '',rrsubregions$Regions)
rrsubregions$Regions<- gsub("SSA-", '',rrsubregions$Regions)
rrsubregions$Regions<- gsub("MEN-", '',rrsubregions$Regions)
rrsubregions$Regions<- gsub("SAS-", '',rrsubregions$Regions)
rrsubregions$Regions<- gsub("EAP-", '',rrsubregions$Regions)
rrsubregions$Sce <- factor(rrsubregions$Sce, levels = c("REF","HIGH","HIGH+NARS","REGION","RMM"))
png(filename = paste(wk_dir,"/","HeatMapClustersuper",".png",sep=""),
width = 12, height =16 , units = 'in', res = 100)
gg<- ggplot(rrsubregions, aes(Sce, Regions)) +
geom_tile(aes(fill = as.factor(clust)), colour = "white") +
scale_fill_brewer(palette = "Set1")+ facet_grid(~crop,scales = "free")+
labs(x=NULL, y=NULL, title=paste( "RTB crops clustering of\n ", " by scenarios",sep = ""))+
coord_equal()+
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
theme_grey() + labs(x = "",y = "")+
theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 10))+
theme(axis.text.y = element_text(hjust = 1, size = 9))+
theme(strip.text.x = element_text(size = 9, face = "bold.italic"))+
theme(strip.text=element_text(size=8))+
theme(strip.text.y = element_text(angle = 0,size = 9))+
guides(fill=guide_legend(title="Cluster"))
plot(gg)
dev.off()
########################################### groups by cluster
for(i in 1:6){
c_clusG<- list.files(path =paste(wk_dir,"/",rtb[i],sep = ""), pattern = "desCluster.csv", full.names = T)
c_clusG<- lapply(c_clusG, read.csv)
rrG<- do.call(rbind, c_clusG)
rrG$X<- NULL
rrG$v.test<- NULL
rrG<- rrG[,c("crop","clust", "Sce" ,"Var","Mean.in.category", "Overall.mean", "sd.in.category","Overall.sd","p.value", "cv")]
rrG$crop<- as.character(rrG$crop)
rrG$Var<- as.character(rrG$Var)
write.csv(x = rrG, file = paste(wk_dir, "/", rtb[i],"_sum_AllClustReport.csv", sep = ""))
print(i)
}
c_clusG<- list.files(path =wk_dir, pattern = "_sum_AllClustReport.csv", full.names = T)
c_clusG<- lapply(c_clusG, read.csv)
rrG<- do.call(rbind, c_clusG)
rrG$X<- NULL
rrG$Var<- gsub("HIGH_", '',rrG$Var)
rrG$Var<- gsub("REF_", '',rrG$Var)
rrG$Var<- gsub("REGION_", '',rrG$Var)
rrG$Var<- gsub("RMM_", '',rrG$Var)
rrG$Var<- gsub("RMM_", '',rrG$Var)
rrG$Var<- gsub("[[:punct:]]", '',rrG$Var)
rrG$Var<- gsub("HIGHNARS",'', rrG$Var)
write.csv(x = rrG, file = paste(wk_dir, "/","TotalCluster.csv", sep = ""))
########################## additional analysis ########################
#tabla con cluster y paises que corresponde a cada cluster
c_distri<- list.files(path =wk_dir, pattern = "boxplot", full.names = T)
c_distri<- lapply(c_distri, read.csv)
for(c in 1:length(c_distri)){
# d<- c_distri[[c]]
c_distri[[c]]$X<- NULL
c_distri[[c]]<- c_distri[[c]] %>% gather(Var, change, 3:(ncol(c_distri[[c]])-1))
c_distri[[c]]$Var<- gsub("HIGH_", '',c_distri[[c]]$Var)
c_distri[[c]]$Var<- gsub("REF_", '',c_distri[[c]]$Var)
c_distri[[c]]$Var<- gsub("REGION_", '',c_distri[[c]]$Var)
c_distri[[c]]$Var<- gsub("RMM_", '',c_distri[[c]]$Var)
c_distri[[c]]$Var<- gsub("[[:punct:]]", '',c_distri[[c]]$Var)
c_distri[[c]]$Var<- gsub("HIGHNARS",'', c_distri[[c]]$Var)
c_distri[[c]]$Regions<- gsub("LAC-", '',c_distri[[c]]$Regions)
c_distri[[c]]$Regions<- gsub("FSU-", '',c_distri[[c]]$Regions)
c_distri[[c]]$Regions<- gsub("SSA-", '',c_distri[[c]]$Regions)
c_distri[[c]]$Regions<- gsub("MEN-", '',c_distri[[c]]$Regions)
c_distri[[c]]$Regions<- gsub("SAS-", '',c_distri[[c]]$Regions)
c_distri[[c]]$Regions<- gsub("EAP-", '',c_distri[[c]]$Regions)
print(c)
}
ccc<- do.call(rbind, c_distri)
colnames(ccc)[1]<- "crop"
testC<- left_join(ccc, rrsubregions, by=c("crop", "Regions", "Sce"))
testXX<- left_join(rrG, testC, by=c("crop", "Sce", "Var","clust"))
testXX<- testXX[,c("crop","clust", "Sce","Var", "Regions","change" )]
cultivation<- unique(testXX$crop)
grupos<- unique(testXX$clust)
testXX$Sce <- factor(testXX$Sce, levels = c("REF","HIGH","HIGH+NARS","REGION","RMM"))
for(c in 1:length(cultivation)){
cfiles<- testXX %>% filter(crop==cultivation[c])
png(filename = paste(wk_dir,"/",cultivation[c],"_BloxPlot",".png",sep=""),
width = 12, height =16 , units = 'in', res = 100)
gg<- ggplot(cfiles, aes(y=change, x=droplevels(Sce),fill=Var)) +
geom_boxplot() +
facet_grid(clust~Var, scales = "free")+
labs(x=NULL, y=NULL, title=paste(cultivation[c]," cluster boxplot\n ", " by scenarios",sep = ""))+
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
theme_grey() + labs(x = "",y = "")+
theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 10))+
theme(axis.text.y = element_text(hjust = 1, size = 9))+
theme(strip.text.x = element_text(size = 9, face = "bold.italic"))+
theme(strip.text=element_text(size=8))+
theme(strip.text.y = element_text(angle = 0,size = 9))
plot(gg)
dev.off()
print(i)
}
### Paises con valores extremos
coutfiles<- list.files(path = wk_dir,pattern = "OutlierMV", full.names = T)
coutfiles<- lapply(coutfiles, read.csv, header=T)
coutfiles<- do.call(rbind, coutfiles)
coutfiles$X<- NULL
coutfiles<- coutfiles[,c("Regions","crop","sce", "Var", "Val")]
coutfiles<- coutfiles %>% spread (sce, Val)
write.csv(x = coutfiles,file = paste(wk_dir,"/","CountriesAtypical.csv", sep = ""))
### intersection across scenarios
it<- list.files(path = wk_dir,pattern = "Inter_clusT", full.names = T)
it<- lapply(it, read.csv)
it<- do.call(rbind, it)
it$X<- NULL
crosInter<- it%>% split(it$crop)
# c=1
intentM<- lapply(1:length(crosInter), function(c){
df<- crosInter[[c]]
crop<- droplevels(unique(df$crop))
df<- df[,c("Regions","sce","crop", "clust")]
cfcrops<- df %>% split(df$sce)
a<- left_join(cfcrops[[1]],cfcrops[[2]],by = c("Regions", "crop"))
b<- left_join(a,cfcrops[[3]],by = c("Regions", "crop"))
c<- left_join(b,cfcrops[[4]],by = c("Regions", "crop"))
d<- left_join(c,cfcrops[[5]],by = c("Regions", "crop"))
d$inter <- apply(d[,c(4,6,8,10,12)], 1, function(x)( all(x==1) || all(x==2) || all(x==3) || all(x==4) ))
d$inter<- ifelse(d$inter=="TRUE",1,0)
# d<- d[,c("Regions","crop", "inter")]
rateStab<- d %>% group_by(inter) %>% summarise(n = n())
write.csv(rateStab, paste(wk_dir,"/" ,crop,"_consisting.csv", sep = ""))
cat(paste("Cultivation: ", crop, " is complete\n", sep = ""))
})
|
ec3e2b142f67fb4392952f29efd6630c9fa70d7d
|
7a602fb8fd4df2cac0002d0522f3ffa8c23a3e05
|
/data_prep/scheduler.R
|
9ca925ccc43b3f3e0cdead7bb351c06731ec4f1e
|
[] |
no_license
|
mkaranja/btract1
|
7f7add53cb6b7341b869c18a252d9de091706139
|
a582692bc5fb6e5b667c48ce014c49bf820042f1
|
refs/heads/main
| 2023-04-30T16:54:02.498744
| 2021-05-14T23:10:01
| 2021-05-14T23:10:01
| 367,032,252
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 6,038
|
r
|
scheduler.R
|
#source("data_prep/cleantable.R", local = T)
dt <- cleantable()%>%
arrange(Date = as.Date(Date, "%d-%m-%Y"))
df <- dt[!duplicated(dt[,1:4]),]
df <- df[!duplicated(df$Accession),] %>%
setDT()
df <- df[Activity != "Flowering"][,-c(5:6)]
# days elapsed
df$`Days Elapsed` <- Sys.Date()-df$Date
# mean number of days between activities
dff <- banana %>%
dplyr::left_join(
repeatpollination()
)
colnames(dff) <- gsub("_"," ", names(dff))
dff$`Days to Maturity` <- lubridate::date(dff$`Bunch Harvest Date`) - lubridate::date(dff$`First Pollination Date`)
dff$`Days in ripening shed` <- lubridate::date(dff$`Seed Extraction Date`) - lubridate::date(dff$`Bunch Harvest Date`)
dff$`Days to Germination` <- lubridate::date(dff$`Germination Date`) - lubridate::date(dff$`Embryo Rescue Date`)
dff$`Days to Embryo Rescue` <- lubridate::date(dff$`Embryo Rescue Date`) - lubridate::date(dff$`Seed Extraction Date`)
dff$`Days to Repeat Pollination` <- as.Date(dff$`Repeat Pollination Date`, "1970-01-01") -
as.Date(dff$`First Pollination Date`, "1970-01-01")
mean_days_to_repeatpollination <- mean(as.integer(na.omit(dff$`Days to Repeat Pollination`))) %>% floor() # to repeat
mean_days_to_maturity <- mean(as.integer(na.omit(dff$`Days to Maturity`))) %>% floor() # to harvest
mean_days_in_ripening <- mean(as.integer(na.omit(dff$`Days in ripening shed`))) %>% floor() # to extraction
mean_days_to_embryo_rescue <- mean(as.integer(na.omit(dff$`Days to Embryo Rescue`))) %>% floor() # to rescue
mean_days_to_germination <- mean(as.integer(na.omit(dff$`Days to Germination`))) %>% floor() # to germination
df$status <- ifelse(df$Activity=='First pollination' & df$`Days Elapsed` > (mean_days_to_repeatpollination+5), "Overdue",
ifelse(df$Activity=='First pollination' & df$`Days Elapsed` >= mean_days_to_repeatpollination-5 & df$`Days Elapsed` <= mean_days_to_repeatpollination+5, "Ready",
ifelse(df$Activity=='First pollination' & df$`Days Elapsed` >= mean_days_to_repeatpollination-10 & df$`Days Elapsed` <= mean_days_to_repeatpollination-5, "Approaching",
ifelse(df$Activity=='First pollination' & df$`Days Elapsed` < mean_days_to_repeatpollination-10, "Wait",
ifelse(df$Activity=='Repeat pollination' & df$`Days Elapsed` > (mean_days_to_maturity+10),"Overdue",
ifelse(df$Activity=='Repeat pollination' & df$`Days Elapsed` <= mean_days_to_maturity-10 & df$`Days Elapsed` >= mean_days_to_maturity+10, "Ready",
ifelse(df$Activity=='Repeat pollination' & df$`Days Elapsed` >= mean_days_to_maturity-30 & df$`Days Elapsed` <= mean_days_to_maturity-10, "Approaching",
ifelse(df$Activity=='Repeat pollination' & df$`Days Elapsed` < mean_days_to_maturity-30, "Wait",
# harvested
ifelse(df$Activity=='Harvested bunches' & df$`Days Elapsed` > mean_days_in_ripening+3,"Overdue",
ifelse(df$Activity=='Harvested bunches' & df$`Days Elapsed` >= mean_days_in_ripening-3 & df$`Days Elapsed` <= mean_days_in_ripening+3, "Ready",
ifelse(df$Activity=='Harvested bunches' & df$`Days Elapsed` >= mean_days_in_ripening-5 & df$`Days Elapsed` <= mean_days_in_ripening-3, "Approaching",
ifelse(df$Activity=='Harvested bunches' & df$`Days Elapsed` < mean_days_in_ripening-5, "Wait",
# Seed extraction
ifelse(df$Activity=='Seed extraction' & df$`Total Seeds` > 0 & df$`Days Elapsed` > (mean_days_to_embryo_rescue+3), "Overdue",
ifelse(df$Activity=='Seed extraction' & df$`Total Seeds` > 0 & df$`Days Elapsed` >= mean_days_to_embryo_rescue-2 & df$`Days Elapsed` <= mean_days_to_embryo_rescue+3, "Ready",
ifelse(df$Activity=='Seed extraction' & df$`Total Seeds` > 0 & df$`Days Elapsed` >= 3 & df$`Days Elapsed` <= mean_days_to_embryo_rescue-3, "Approaching",
ifelse(df$Activity=='Seed extraction' & df$`Total Seeds` > 0 & df$`Days Elapsed` < 3, "Wait",
# EMbryo rescue
ifelse(df$Activity=='Embryo Rescue' & df$`Number of Embryo Rescued` >0 & df$`Days Elapsed` > 56, "Overdue",
ifelse(df$Activity=='Embryo Rescue' & df$`Number of Embryo Rescued` >0 & df$`Days Elapsed` >= 50 & df$`Days Elapsed` <= 56, "Ready",
ifelse(df$Activity=='Embryo Rescue' & df$`Number of Embryo Rescued` >0 & df$`Days Elapsed` >= 45 & df$`Days Elapsed` <= 49, "Approaching",
ifelse(df$Activity=='Embryo Rescue' & df$`Number of Embryo Rescued` >0 & df$`Days Elapsed` < 45, "Wait",NA
))))))))))))))))))))
schedulerdata = df[!is.na(df$status),]
schedulerdata = schedulerdata[status !='Wait']
schedulerdata$NextActivity = ifelse(schedulerdata$Activity=="First pollination","Repeat pollination",
ifelse(schedulerdata$Activity=="Repeat pollination","Bunch Harvesting",
ifelse(schedulerdata$Activity=="Harvested bunches","Seed Extraction",
ifelse(schedulerdata$Activity=="Seed extraction","Embryo Rescue",
ifelse(schedulerdata$Activity=="Embryo Rescue","Germination",'')))))
#schedulerdata <- schedulerdata[,c("Location", "Accession","CurrentActivity","Days Elapsed", "status", "NextActivity")]
|
4a25abc42d38269d03cbe8d437a73c6579af2205
|
e278fbe0caf74403a865b6e2daffa680517d9886
|
/tests/testthat/test-search_sorting.R
|
0aca0db6b686bfab7005ec0e04a4fac062b2d298
|
[] |
no_license
|
amandaJayanetti/RXapian
|
cb129fd9527e582a7e897da878e8523260fa40c2
|
6595c3ab26f4efb55050b6e44b645eb715a10218
|
refs/heads/master
| 2021-01-20T17:58:22.156647
| 2018-03-21T07:01:35
| 2018-03-21T07:01:35
| 59,979,615
| 8
| 1
| null | 2016-08-18T17:23:29
| 2016-05-30T04:08:03
|
C++
|
UTF-8
|
R
| false
| false
| 1,819
|
r
|
test-search_sorting.R
|
context("Sorting Search Results")
test_that("Search results are sorted as expected", {
# preparing arguments for xapian_index()
id<-c(1,
2,
3)
name<-c("State of Montana",
"State of Iowa",
"State of Texas")
motto<-c("Oro y Plata (Spanish: Gold and Silver)",
"Our liberties we prize and our rights we will maintain.",
"Equality Before the Law")
description<-c("This geographical fact is reflected in the state's name, derived from the Spanish word montaña (mountain).",
"It is located in the Midwestern United States and Great Lakes Region.",
"The name was applied by the Spanish to the Caddo themselves")
admitted<-c(1889,
1959,
1845)
data<-data.frame(id, name, motto, description, admitted)
db<- tempfile(pattern="RXapian-")
id<-c(0)
vs1<-list(slot=c(1),serialise=TRUE,name="admitted")
valueS<-list(vs1)
indexFields<-list(list(name="name",prefix="S"),
list(name="description",prefix="XD"),
list(name="motto",prefix="XM"))
xapian_index(dbpath = db,
dataFrame = data,
idColumn = id,
indexFields = indexFields,
valueSlots = valueS,
stemmer = "en")
# preparing arguments for xapian_search()
enq<-list(sortby="value_then_relevance",valueNo=1,reverse_sort_order=FALSE)
preF<-list(list(name="name", prefix="S"),
list(name="description", prefix="XD"))
query<-list(queryString="spanish", stemmer="en", prefix.fields=preF)
result<-xapian_search(db, enq, query)
# comparing results
expect_equal(name[3],as.character(result$b[1]))
expect_equal(name[1],as.character(result$b[2]))
})
|
83c399e6c21314b836bed1cfeb6ab312707b1ccc
|
838a6ac041e68dbe497016af0762e7c19994dce8
|
/CompData/best.R
|
40560d35b71c3b0d4fb28a7c8fe989112e066eeb
|
[] |
no_license
|
htx1219/R
|
e844243cf9a794ec04440b3b260c9fb07196a4f6
|
475a68d66387532058d885bcc0606fc1db9e8be1
|
refs/heads/master
| 2021-01-19T14:11:10.195519
| 2013-02-05T01:12:41
| 2013-02-05T01:12:41
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 815
|
r
|
best.R
|
best<-function(state, outcome){
k<-read.csv("outcome-of-care-measures.csv", colClasses="character")
k[,11]<-as.numeric(k[,11])
k[,17]<-as.numeric(k[,17])
k[,23]<-as.numeric(k[,23])
if(!state %in% dimnames(table(k$State))[[1]]){
stop("invalid state")
}
if(!outcome %in% c("heart failure", "heart attack", "pneumonia")){
stop("invalid outcome")
}
if(outcome == "heart attack"){
k2<-k[k$State == state,]
w<-k2$Hospital.Name[which(k2[,11] == min(k2[,11], na.rm=T))]
w<-sort(w)
return(w[1])
}
if(outcome == "heart failure"){
k2<-k[k$State == state,]
w<-k2$Hospital.Name[which(k2[,17] == min(k2[,17], na.rm=T))]
w<-sort(w)
return(w[1])
}
if(outcome == "pneumonia"){
k2<-k[k$State == state,]
w<-k2$Hospital.Name[which(k2[,23] == min(k2[,23], na.rm=T))]
w<-sort(w)
return(w[1])
}
}
|
1b75520cc0d9591c71ba2b6125494bbbcf44b0b9
|
c7906993c18bef9eeefccf183a3b6563af9f5500
|
/Lab12/WINFREY_Lab12.R
|
86f9620e3de17e85450fe3c31de59a4daba52f33
|
[] |
no_license
|
clairecwinfrey/CompBioLabsAndHomework
|
2fbfa091f050f98bfd2df1bb3ccf3b1ab7de7d87
|
67f76cea4406702e58984c1e2ab3ce96e2239772
|
refs/heads/master
| 2023-04-12T10:53:41.256988
| 2021-05-01T10:34:44
| 2021-05-01T10:34:44
| 334,553,486
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 4,809
|
r
|
WINFREY_Lab12.R
|
# Script for Lab #12
# Claire Winfrey
# April 2, 2021
# (also worked on in class April 5th and 7th)
# Working with some data from the Colorado Department of Public Health
# and Environment (CDPHE) on COVID-19 in Colorado.
# Changed so that it works on my computer
#setwd("~/Desktop/Spring_2021/Comp_Bio/CompBio_sandbox/CompBio_on_git/Datasets/COVID-19/CDPHE_Data/CDPHE_Data_Portal/")
stateStatsData <- read.csv("DailyStateStats2/CDPHE_COVID19_Daily_State_Statistics_2_2021-04-02.csv",
stringsAsFactors = F)
library("tidyverse")
library("dplyr")
####################################################
## Explore the data
####################################################
names(stateStatsData)
str(stateStatsData)
summary(stateStatsData)
unique(stateStatsData$Name)
unique(stateStatsData$Desc_)
table(stateStatsData$Name)
View(stateStatsData)
#############################################
# Part 1: (Finish) Getting the data into shape
#############################################
# (going off of what we did together in class):
# 1. subset the data so that we only keep the rows where the text in the column (variable) named "Name" is "Colorado"
ColoradoData <- filter(stateStatsData, Name == "Colorado")
# 2. subset to keep (select) only the columns "Date", "Cases", and "Deaths"
substateStats <-ColoradoData %>%
select("Date", "Cases", "Deaths")
# 3. change the data in the "Date" column to be actual dates rather than a character
substateStats$Date <- strptime(substateStats$Date, format = "%m/%d/%Y", tz = "")
## my group in class ##
# 4. sort the data so that the rows are in order by date from earliest to latest
require("lubridate")
substateStats <- substateStats %>%
arrange(Date)
# 5. subset the data so that we only have dates prior to May 15th, 2020
substateStats$Date <- as.POSIXlt(substateStats$Date, format = "%m/%d/%Y", tz = "")
dt <- as.Date("2020-05-15")
dt <- as.POSIXlt("2020-05-15") #get dates in right format for ggplot later?
index <- which(as.Date(substateStats$Date) < dt)
COearlyCovid <- substateStats[index , ]
View(COearlyCovid) #looks as expected!
# or using lubridate
# require("lubridate")
# datesTimes <- parse_date_time(x = substateStatsArr$Date, orders = c("mdy"))
# Now do it all in a pipeline with pipes (Sam's example from class)
ColoradoData <- stateStatsData %>%
filter(Name == "Colorado") %>% #this removed 5 rowd
select(Date, Cases, Deaths) %>%
mutate(Date = strptime(Date, format = "%m/%d/%Y", tz = "")) %>% #Could not specify stateStatsData$Date becasue then it would go back to the beginning!
arrange(Date) %>%
filter( Date < as.Date("2020-05-15"))
View(ColoradoData)
####################################################
# Part 2: Make plots in R using the data from Part 1
####################################################
# Plot 1: Cases on y axis, Date on x
ggplot(data = ColoradoData,
mapping = aes(x=Date, y=Cases)) +
geom_point() +
geom_line()
# Get error "Error: Invalid input: time_trans works with objects of class POSIXct only"
# I'll try a few ways to get around this:
myPlot <- ggplot(data = ColoradoData,
mapping = aes(x=as.Date(Date), y=Cases)) +
geom_line() +
xlab("Date")
# This above looks like the example in the Lab12 document on GitHub,
# (except that my data shows twice as many cases by May 15th!)
#########
# Plot #2: Date on x-axis, Deaths on y-axis:
myPlot2 <- ggplot(data = ColoradoData,
mapping = aes(x=as.Date(Date), y=Deaths)) +
geom_line() +
xlab("Date")
# Huh, again the shape of my line in the date is quite different than the example
# that we were asked to replicate, but it looks correct based on viewing the ColoradoData
# data frame.
####################################################
# Part 3: Write a function for adding doubling times
####################################################
addDoublingTimeRefLines <- function( myPlot, doublingTimeVec, timeVar, ObsData, startFrom ) {
# myPlot is the starting plot, where time or dates are on the x- axis and observations of something are on the y axis.
# doublingTimeVec is vector which
# timeVar is the time or date data you are working with, e.g. ColoradoData$Date
# ObsData is the observational data you are working with, e.g. ColoradoData$Cases from above
# Instead of having "startFrom", I make the starting time as below with "timeZero"
require("ggplot2")
timeZero <- min(timeVar)
# I think that for the code below, I wouldn't have to specify the data, because it would work
# off of whatever the starting plot "myPlot" had as data...
RefLine1 <- 2^ #the power that 2 is to should reflect
myNewPlot <- myPlot +
geom_line(mapping = aes(x = timeVar - timeZero, y = RefLine1),
color = "maroon",
linetype = "dashed" ) )
return( myNewPlot )
}
|
a68aa377f0e4f88be0f2a7b4d046d9603dee98e2
|
fdc7245741d314f963d42c185845168046f9cf98
|
/R/BinStat.R
|
c8d17d5504e41946641e9b5f0d4b65bb6ef998cd
|
[] |
no_license
|
kkeenan02/MsatAllele
|
c08db0e279b05a9d5cd966741ec04b6a46bdf458
|
0379a16db5b4a968b27e989302edb03e86e986d9
|
refs/heads/master
| 2020-05-17T17:50:35.658051
| 2014-10-02T13:33:56
| 2014-10-02T13:33:56
| 24,057,348
| 4
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 891
|
r
|
BinStat.R
|
BinStat<-function(DataBase,loci){
o<-order(DataBase[DataBase[,1]==loci,3])
Frag<-DataBase[DataBase[,1]==loci,3]
Frag<-Frag[o]
Bin<-1:length(Frag)
i<-1
repeat{
Bin[i]<-get.allele(DataBase,loci,Frag[i])
i<-i+1
if(i>length(Frag))break}
Bins <-levels(as.factor(Bin))
N <-tapply(Bin,as.factor(Bin),length)
Min <-tapply(Frag,as.factor(Bin),min)
Max <-tapply(Frag,as.factor(Bin),max)
Range <-Max-Min
Sd <-round(tapply(Frag,as.factor(Bin),sd),digits=3)
MEAN <-round(tapply(Frag,as.factor(Bin),mean),digits=2)
MEDIAN<-round(tapply(Frag,as.factor(Bin),median),digits=2)
Binstats<-data.frame(
Bins =Bins ,
N =N ,
Min =Min ,
Max =Max ,
Range =Range,
Sd =Sd ,
MEAN =MEAN ,
MEDIAN=MEDIAN)
Binstats
}
|
965fa1d130762d545eca288ebb8e741c58f42ad1
|
c855f7b5003c49ee0bdb2c5405226c3d27a81d1d
|
/man/get_germplasm_data.Rd
|
d47d7904c2adcc5d06fc85e6f036d30b893a8f5d
|
[] |
no_license
|
khaled-alshamaa/QBMS
|
971bd3f29d62f6f516c7652d7556b8241ef0477e
|
021abe36f76b3aae3a9ac0e2b9e2f63b7fb270ac
|
refs/heads/master
| 2021-07-18T11:48:09.806213
| 2020-08-09T09:49:28
| 2020-08-09T09:49:28
| 198,709,256
| 1
| 1
| null | null | null | null |
UTF-8
|
R
| false
| true
| 1,192
|
rd
|
get_germplasm_data.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/qbms.R
\name{get_germplasm_data}
\alias{get_germplasm_data}
\title{Get the observations data of a given germplasm name}
\usage{
get_germplasm_data(germplasm_name)
}
\arguments{
\item{germplasm_name}{the name of the germplasm}
}
\value{
a data frame of the germplasm observations data aggregate from all trials
}
\description{
This function will retrieve the observations data of the current active study
as configured in the internal state object using `set_study()` function.
}
\examples{
# config your BMS connection
set_qbms_config(server = "bms.icarda.org", port = 18443, protocol = "https://")
# login using your BMS account (interactive mode)
# you can pass BMS username and password as parameters (batch mode)
login_bms()
set_crop("Tutorial1")
# select a breeding program by name
set_program("Training Breeding Program")
# retrive observations data of a given germplasm aggregated from all trials
germplasm_observations <- get_germplasm_data("FLIP10-3C")
}
\seealso{
\code{\link{login_bms}}, \code{\link{set_crop}}, \code{\link{set_program}}
}
\author{
Khaled Al-Shamaa, \email{k.el-shamaa@cgiar.org}
}
|
7d1984bc84acbfddb82c33a6f0a63a76b3b16a19
|
ffdea92d4315e4363dd4ae673a1a6adf82a761b5
|
/data/genthat_extracted_code/alphashape3d/examples/components_ashape3d.Rd.R
|
d7e1603373c98535ed0d5f3676a8676ca658c8bf
|
[] |
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
| 767
|
r
|
components_ashape3d.Rd.R
|
library(alphashape3d)
### Name: components_ashape3d
### Title: Connected subsets computation
### Aliases: components_ashape3d
### ** Examples
T1 <- rtorus(1000, 0.5, 2)
T2 <- rtorus(1000, 0.5, 2, ct = c(2, 0, 0), rotx = pi/2)
x <- rbind(T1, T2)
alpha <- c(0.25, 2)
ashape3d.obj <- ashape3d(x, alpha = alpha)
plot(ashape3d.obj, indexAlpha = "all")
# Connected components of the alpha-shape for both values of alpha
comp <- components_ashape3d(ashape3d.obj, indexAlpha = "all")
class(comp)
# Number of components and points in each component for alpha=0.25
table(comp[[1]])
# Number of components and points in each component for alpha=2
table(comp[[2]])
# Plot the connected components for alpha=0.25
plot(ashape3d.obj, byComponents = TRUE, indexAlpha = 1)
|
6e916788abf74d992018f2119b41d2d5c7528f11
|
9752a371fb29d70be9b4fc7efb7baab3d763c50f
|
/man/sub-sub-plan-method.Rd
|
cc36fd1eb537746e593838eaa648dc9a845ea3d9
|
[] |
no_license
|
cran/plan
|
5ba2e21c768c322f689a57e1cc287a9a88df0c1e
|
88a1dd89fcfa562da2f98f88dd9f91e56ada0d4c
|
refs/heads/master
| 2023-08-31T07:46:12.007363
| 2023-08-19T11:10:02
| 2023-08-19T12:41:36
| 17,698,551
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 519
|
rd
|
sub-sub-plan-method.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/AllClass.R
\name{[[,plan-method}
\alias{[[,plan-method}
\title{Extract Something From a plan Object}
\usage{
\S4method{[[}{plan}(x, i, j, ...)
}
\arguments{
\item{x}{A \linkS4class{plan} object.}
\item{i}{The item to extract.}
\item{j}{Optional additional information on the \code{i} item.}
\item{...}{Optional additional information (ignored).}
}
\description{
Extract something from a plan object, avoiding using the "slot" notation.
}
|
a8db74305c09d151262204d0bf98b03184b157d0
|
44e97a4f153fede9ad36444426382599a0b5ad34
|
/man/visualize_terms.Rd
|
0a2a12ce1c71b4f250831741d0fa655fec2772f9
|
[
"MIT"
] |
permissive
|
egeulgen/pathfindR
|
3f58046ad48ecb7698c604bc9959e7a70d49b53a
|
857f4b70566ea3a0413c015fd9580c15137a1615
|
refs/heads/master
| 2023-08-22T15:13:58.185804
| 2023-08-22T12:57:44
| 2023-08-22T12:57:44
| 115,432,311
| 149
| 28
|
NOASSERTION
| 2023-08-27T15:21:31
| 2017-12-26T15:11:22
|
R
|
UTF-8
|
R
| false
| true
| 2,358
|
rd
|
visualize_terms.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/visualization.R
\name{visualize_terms}
\alias{visualize_terms}
\title{Create Diagrams for Enriched Terms}
\usage{
visualize_terms(
result_df,
input_processed = NULL,
hsa_KEGG = TRUE,
pin_name_path = "Biogrid",
...
)
}
\arguments{
\item{result_df}{Data frame of enrichment results. Must-have columns for
KEGG human pathway diagrams (\code{hsa_kegg = TRUE}) are: "ID" and "Term_Description".
Must-have columns for the rest are: "Term_Description", "Up_regulated" and
"Down_regulated"}
\item{input_processed}{input data processed via \code{\link{input_processing}},
not necessary when \code{hsa_KEGG = FALSE}}
\item{hsa_KEGG}{boolean to indicate whether human KEGG gene sets were used for
enrichment analysis or not (default = \code{TRUE})}
\item{pin_name_path}{Name of the chosen PIN or absolute/path/to/PIN.sif. If PIN name,
must be one of c("Biogrid", "STRING", "GeneMania", "IntAct", "KEGG", "mmu_STRING"). If
path/to/PIN.sif, the file must comply with the PIN specifications. (Default = "Biogrid")}
\item{...}{additional arguments for \code{\link{visualize_hsa_KEGG}} (used
when \code{hsa_kegg = TRUE}) or \code{\link{visualize_term_interactions}}
(used when \code{hsa_kegg = FALSE})}
}
\value{
Depending on the argument \code{hsa_KEGG}, creates visualization of
interactions of genes involved in the list of enriched terms in
\code{result_df} and saves them in the folder "term_visualizations" under
the current working directory.
}
\description{
Create Diagrams for Enriched Terms
}
\details{
For \code{hsa_KEGG = TRUE}, KEGG human pathway diagrams are created,
affected nodes colored by up/down regulation status.
For other gene sets, interactions of affected genes are determined (via a shortest-path
algorithm) and are visualized (colored by change status) using igraph.
}
\examples{
\dontrun{
visualize_terms(result_df, input_processed)
visualize_terms(result_df, hsa_KEGG = FALSE, pin_name_path = "IntAct")
}
}
\seealso{
See \code{\link{visualize_hsa_KEGG}} for the visualization function
of human KEGG diagrams. See \code{\link{visualize_term_interactions}} for the
visualization function that generates diagrams showing the interactions of
input genes in the PIN. See \code{\link{run_pathfindR}} for the wrapper
function of the pathfindR workflow.
}
|
ae08a37cadacee79b1a961231751ecbc7e1bde2b
|
3dafbf5ca0374492ee3a0d16dec014cdb6b814fe
|
/group_map/comparison.do.group_map.R
|
057aecb5efb53e4b92879cac3d5a0f64f752f170
|
[] |
no_license
|
razielar/Tidyverse-examples
|
1174225d4c13db4922c31c8bb56bc87e1efac3c8
|
e1dbf5e4a970011ab90cf8bc16a777541a836e99
|
refs/heads/master
| 2023-08-26T01:06:58.736992
| 2021-11-03T20:09:49
| 2021-11-03T20:09:49
| 232,620,563
| 1
| 0
| null | 2021-08-04T09:09:01
| 2020-01-08T17:32:39
|
R
|
UTF-8
|
R
| false
| false
| 1,174
|
r
|
comparison.do.group_map.R
|
### Comparison between do and group_map
### April 10th 2020
### Libraries:
library(tidyverse)
library(magrittr)
library(lubridate)
library(scales) #dollar_format
library(reshape2)
options(stringsAsFactors = F)
setwd('/home/razielar/Documents/SIRIS/SIRIS_internal_dashboard/')
######### 1) Input:
proj_income <- readRDS("Data//proj_income.RDS")
######### 2) Using do:
projects_second <- proj_income %>%
mutate(amount_month= round(Income/round_project_length,
digits=2)) %>%
mutate(new_end_date=start_date+months(round_project_length)) %>%
select(project, new_status, start_date,
new_end_date, round_project_length, Income, amount_month) %>%
group_by(project, new_status, start_date, new_end_date,
round_project_length, Income) %>%
do(
data.frame( amount_month= rep(.$amount_month,
.$round_project_length) )
) %>%
arrange(start_date, project) %>% ungroup %>% group_by(project) %>%
mutate(start_date=start_date+months( row_number()-1 ) ) %>%
mutate(new_end_date=start_date+months(1)) %>% ungroup
######### 3) Using group_map:
|
170ae084ce038b9ea8c8dad233fcd6b3926ccc13
|
29585dff702209dd446c0ab52ceea046c58e384e
|
/FisherEM/R/sfem.R
|
52587e7aaf6f95f24b892bcdab48eab7f20a9657
|
[] |
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
| 1,531
|
r
|
sfem.R
|
sfem <- function(Y,K=2:6,model='AkjB',method='reg',crit='icl',maxit=50,eps=1e-6,init='kmeans',nstart=25,Tinit=c(),kernel='',disp=F,l1=0.1,l2=0,nbit=2){
call = match.call()
if (max(l1)>1 || min(l1)<0 || max(l2)>1 || min(l2)<0) stop("Parameters l1 and l2 must be within [0,1]\n",call.=FALSE)
else{
bic = aic = icl = c()
RES = list()
try(res0 <- fem(Y,K,init=init,nstart=nstart,maxit=maxit,eps=eps,Tinit=Tinit,model=model,kernel=kernel,method='reg',crit=crit))
for (i in 1:length(l1)){
try(RES[[i]] <- fem.sparse(Y,res0$K,model=res0$model,maxit=15,eps=eps,Tinit=res0$P,l1=l1[i],l2=l2,nbit=nbit))
try(bic[i] <- RES[[i]]$bic)
try(aic[i] <- RES[[i]]$aic)
try(icl[i] <- RES[[i]]$icl)
#try(fish[i] <- RES[[i]]$fish)
}
if (crit=='bic'){ id_max = which.max(bic); crit_max = RES[[id_max]]$bic}
if (crit=='aic'){ id_max = which.max(aic); crit_max = RES[[id_max]]$aic}
if (crit=='icl'){ id_max = which.max(icl); crit_max = RES[[id_max]]$icl}
#if (crit=='fisher'){ id_max = which.max(diff(fish)); crit_max = RES[[id_max]]$fish}
res = RES[[id_max]]
res$call = res0$call
res$plot = res0$plot
res$plot$l1 = list(aic=aic,bic=bic,icl=icl,l1=l1)
res$call = call
res$crit = crit
res$l1 = l1[id_max]
res$l2 = l2
if (disp){ if (length(l1)>1) cat('The best sparse model is with lambda =',l1[id_max],'(',crit,'=',crit_max,')\n')
else cat('The sparse model has a lambda =',l1[id_max],'(',crit,'=',crit_max,')\n')}
}
class(res)='fem'
res
}
|
877babad9da9ab66819c3d89f2e771271fbc1b33
|
06afced918b4cb9fcce7b227b994d8442c83713d
|
/redesBayesianas/S2/0_distribuciones_de_probabilidad_con_R.R
|
c7ab72d258dfbbab25101d93af22686ab9a0845b
|
[] |
no_license
|
BigDataFred/MasterComputationalEnginerring
|
20040278446b088d47e8e043266ed856d7f2a658
|
b383a1feaaa0e7ff39411ca1683595a5febee0c2
|
refs/heads/master
| 2020-04-20T16:52:08.864432
| 2020-01-28T21:45:55
| 2020-01-28T21:45:55
| 168,971,382
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 3,545
|
r
|
0_distribuciones_de_probabilidad_con_R.R
|
## ----plot_normal_density,prompt=TRUE,message=FALSE,fig.path="./figures/",fig.keep='last',fig.show='hide',tidy=FALSE----
#Generamos los puntos en los que evaluar (entre -5 y 5)
x<-seq(-5,5,0.01)
#Obtenemos los valores de las 3 densidades
densidad1<-dnorm(x,mean=0, sd=0.5)
densidad2<-dnorm(x,mean=0,sd=1)
densidad3<-dnorm(x,mean=2,sd=1)
#Las representamos gráficamente
plot(x,densidad1,type="l",main="Ejemplo de tres densidades Gausianas",
xlab="X", ylab="Densidad")
lines(x,densidad2,col=2)
lines(x,densidad3,col=3)
## ----plot_lognormal_cummulative,prompt=TRUE,message=FALSE,fig.path="./figures/",fig.keep='last',fig.show='hide',tidy=FALSE----
#Obtenemos los valores de las 3 densidades
#Generamos los puntos en los que evaluar (entre -5 y 5)
x<-seq(0,100,0.1)
cdf1<-plnorm(x,meanlog=0, sdlog=1)
cdf2<-plnorm(x,meanlog=0,sdlog=2)
cdf3<-plnorm(x,meanlog=3,sdlog=1)
#Las representamos gráficamente
library(ggplot2)
df<-rbind(data.frame(X=x,Y=cdf1,Distribucion="CDF #1"),
data.frame(X=x,Y=cdf2,Distribucion="CDF #2"),
data.frame(X=x,Y=cdf3,Distribucion="CDF #3"))
ggplot(df,aes(x=X, y=Y, col=Distribucion)) + geom_line(size=1.1) +
labs(x="X", y="CDF")
## ----integral,prompt=TRUE,message=FALSE,tidy=FALSE-----------------------
#Para poder hacer la integral hay que definir la función a integrar
f<-function (x){
dchisq(x,df=5)
}
#Hecho esto la integral será ...
integrate(f = f,lower = 4, upper = 6)
## ----sampling,prompt=TRUE,message=FALSE,tidy=FALSE-----------------------
muestra<-rbeta(250,1,10)
## ----qteorico,prompt=TRUE,message=FALSE,tidy=FALSE-----------------------
#Definimos los cuantiles a usar
p<-seq(0.1,0.9,0.1)
#Obtenemos los valores teóricos
cuantiles_teoricos<-qbeta(p,1,10)
## ----qmuestral,prompt=TRUE,message=FALSE,tidy=FALSE----------------------
#Creamos una función para obtener los cuantiles muestrales
qmuestral<-function(p, muestra){
muestra_ordenada<-sort(muestra)
posiciones<-p*length(muestra)
muestra_ordenada[posiciones]
}
cuantiles_muestrales<-qmuestral(p,muestra)
## ----qqplot,prompt=TRUE,message=FALSE,fig.path="./figures/",fig.keep='last',fig.show='hide',tidy=FALSE----
plot(cuantiles_muestrales, cuantiles_teoricos,
main="Gráfico cuantil-cuantil", xlab="Cuantil muestral", ylab="Cuantil del modelo",
pch=19, col=3)
lines(c(0,1),c(0,1))
## ----qqplot_normal,prompt=TRUE,message=FALSE,fig.path="./figures/",fig.keep='last',fig.show='hide',tidy=FALSE----
cuantiles_normal<-qnorm(p,mean(muestra),sd(muestra))
plot(cuantiles_muestrales, cuantiles_normal,
main="Gráfico cuantil-cuantil", xlab="Cuantil muestral", ylab="Cuantil del modelo",
pch=19, col=3)
lines(c(0,1),c(0,1))
## ----verosimilitud,prompt=TRUE,message=FALSE,tidy=FALSE------------------
#Creamos la muestra
sample<-rpois(1000,5)
#Determinamos la probabilidad asociada a cada valor muestreado
probabilidades<-dpois(sample,5)
#La probabilidad la computamos como el producto
prod(probabilidades)
## ----loglikelihood,prompt=TRUE,message=FALSE,tidy=FALSE------------------
#El logaritmo del producto de probabilidades es la suma de los logaritmos
sum(log(probabilidades))
## ----logveros_plot,prompt=TRUE,message=FALSE,fig.path="./figures/",fig.keep='last',fig.show='hide',tidy=FALSE----
f<-function(lambda){
sum(log(dpois(sample,lambda)))
}
lambdas<-seq(1,10,0.1)
logveros<-sapply(lambdas, FUN = f)
df<-data.frame(Lambda=lambdas, Logverosimilitud=logveros)
ggplot(df,aes(x=Lambda, y=Logverosimilitud)) + geom_line(size=1.1) +
labs(x=expression(lambda))
|
c22e84a77cb1b25cb9310c040da43d1deb132d14
|
60437c90873385bdf7256e2b3dc1b7a6e60e2228
|
/Scripts/ejercicio_practico.R
|
6cd012ace13fac7f6d9733ab6fb73ed7b7306d67
|
[] |
no_license
|
FredysMD/Modeling-and-Simulation
|
6c725422b0a8e3c7fcca27c0d9865d43f63c345c
|
3419803157acd517e9ec7ac031254329d7af4700
|
refs/heads/master
| 2023-01-12T01:10:39.481052
| 2020-11-20T17:05:43
| 2020-11-20T17:05:43
| 294,872,153
| 1
| 0
| null | 2020-10-09T18:01:07
| 2020-09-12T04:48:19
|
R
|
ISO-8859-1
|
R
| false
| false
| 1,102
|
r
|
ejercicio_practico.R
|
moda <- function(dato){
fa <- data.frame(table(dato))
moda <- fa[which.max(fa$Freq), 1]
}
zona1<-rbind(194, 199, 191, 202, 215, 214, 197, 204, 199, 202, 230, 193, 194, 209)
zona2<-rbind(158, 161, 143, 174, 220, 156, 156, 156, 198, 161, 188, 139, 147, 116)
datos <- data.frame("zona1"= zona1, "zona2" = zona2)
#----------------------------------
(summary(datos))
paste("moda de zona 1: ",moda(datos$zona1),sep="")
paste("varianza de zona 1: ",var(datos$zona1),sep="")
paste("varianza de zona 2: ",var(datos$zona2),sep="")
#----------------------------------
#----------------------------------
zona1 <- datos$zona1
zona2 <- datos$zona2
hist(zona1,main = "Histograma de zona 1", xlab = "número de colonia/ 1000mm agua", ylab = "Frecuencia")
hist(zona2,main = "Histograma de zona 2",
xlab = "número de colonia/ 1000mm agua", ylab = "Frecuencia")
#----------------------------------
#----------------------------------
boxplot(x = datos, main = "Niveles de colonias de bacterias",
col = c("orange3", "yellow3", "green3", "grey"))
#----------------------------------
|
18a9d5163e5743103e67c35ceb949314227d7212
|
25eed88db72bc6161c4725a210e13bce63cfe95a
|
/R/cdtStnCoords_Procs.R
|
59eba9192276cf496a17e04e0d09b569db67c179
|
[] |
no_license
|
heureux1985/CDT
|
68fcfb02636cb9473b09bb2be918afd3880dde52
|
876c5f527628527b6d3923b59c710469c1db2dd2
|
refs/heads/master
| 2020-04-25T16:13:30.020982
| 2019-02-01T06:49:02
| 2019-02-01T06:49:02
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 11,079
|
r
|
cdtStnCoords_Procs.R
|
StnChkCoordsProcs <- function(GeneralParameters){
if(!dir.exists(GeneralParameters$output)){
Insert.Messages.Out(paste(GeneralParameters$output, "did not find"), format = TRUE)
return(NULL)
}
if(GeneralParameters$data.type == "cdtcoords")
{
don0 <- getStnOpenData(GeneralParameters$infile)
if(is.null(don0)) return(NULL)
nom.col <- names(don0)
don.disp <- don0
coords <- list(id = as.character(don0[, 1]),
lon = as.numeric(don0[, 3]),
lat = as.numeric(don0[, 4]))
}
if(GeneralParameters$data.type == "cdtstation")
{
don0 <- getStnOpenData(GeneralParameters$infile)
if(is.null(don0)) return(NULL)
don <- splitCDTData0(don0)
if(is.null(don)) return(NULL)
don <- don[c('id', 'lon', 'lat', 'elv')]
nom.col <- c("ID", "Longitude", "Latitude", "Elevation")
if(is.null(don$elv)){
don <- don[c('id', 'lon', 'lat')]
nom.col <- nom.col[1:3]
}
don.disp <- as.data.frame(don)
names(don.disp) <- nom.col
coords <- don[c('id', 'lon', 'lat')]
rm(don)
}
############
outdir <- file.path(GeneralParameters$output, "CHECK.COORDS_data")
dir.create(outdir, showWarnings = FALSE, recursive = TRUE)
fileout <- file.path(outdir, paste0('Checked_Coords_', GeneralParameters$infile))
don.info <- getStnOpenDataInfo(GeneralParameters$infile)
sep <- don.info[[3]]$sepr
if(sep == "") sep <- " "
write.table(don0, file = fileout, sep = sep, na = don.info[[3]]$miss.val,
col.names = don.info[[3]]$header, row.names = FALSE, quote = FALSE)
rm(don0)
############
if(GeneralParameters$shpfile == "")
{
Insert.Messages.Out("No ESRI shapefile found", format = TRUE)
Insert.Messages.Out("The stations outside the boundaries will not be checked", format = TRUE)
shpd <- NULL
}else{
shpd <- getShpOpenData(GeneralParameters$shpfile)
if(is.null(shpd)){
Insert.Messages.Out(paste('Unable to open', GeneralParameters$shpfile, 'or it is not an ESRI shapefile'), format = TRUE)
Insert.Messages.Out("The stations outside the boundaries will not be checked", format = TRUE)
shpd <- NULL
}else{
shpd <- as(shpd[[2]], "SpatialPolygons")
shpd <- gUnaryUnion(shpd)
shpd <- gSimplify(shpd, tol = 0.05, topologyPreserve = TRUE)
shpd <- gBuffer(shpd, width = GeneralParameters$buffer/111)
}
}
############
output <- list(params = GeneralParameters, info = don.info, id = coords$id)
coords <- as.data.frame(coords)
coords$id <- as.character(coords$id)
don.disp$LonX <- coords$lon
don.disp$LatX <- coords$lat
don.disp$StatusX <- rep("blue", length(coords$lon))
don.table <- NULL
############
## Missing coords
imiss <- is.na(coords$lon) | is.na(coords$lat)
if(any(imiss)){
don.table$miss <- data.frame(State = 'Missing Coordinates', don.disp[imiss, , drop = FALSE])
don.disp <- don.disp[!imiss, , drop = FALSE]
coords <- coords[!imiss, , drop = FALSE]
}
## Wrong coords
iwrong <- coords$lon < -180 | coords$lon > 360 | coords$lat < -90 | coords$lat > 90
if(any(iwrong)){
don.table$wrong <- data.frame(State = 'Invalid Coordinates', don.disp[iwrong, , drop = FALSE])
don.disp <- don.disp[!iwrong, , drop = FALSE]
coords <- coords[!iwrong, , drop = FALSE]
}
## Duplicated ID
iddup <- duplicated(coords$id) | duplicated(coords$id, fromLast = TRUE)
if(any(iddup)){
don.table$iddup <- data.frame(State = 'Duplicate ID', don.disp[iddup, , drop = FALSE])
don.table$iddup <- don.table$iddup[order(coords$id[iddup]), , drop = FALSE]
don.disp$StatusX[iddup] <- "orange"
}
## Duplicated coordinates
crddup <- duplicated(coords[, c('lon', 'lat'), drop = FALSE]) |
duplicated(coords[, c('lon', 'lat'), drop = FALSE], fromLast = TRUE)
if(any(crddup)){
don.table$crddup <- data.frame(State = 'Duplicate Coordinates', don.disp[crddup, , drop = FALSE])
don.table$crddup <- don.table$crddup[order(paste0(coords$lon[crddup], coords$lat[crddup])), , drop = FALSE]
don.disp$StatusX[crddup] <- "orange"
}
## Coordinates outside boundaries
if(!is.null(shpd)){
spcoords <- coords
coordinates(spcoords) <- ~lon+lat
iout <- is.na(over(spcoords, geometry(shpd)))
if(any(iout)){
don.table$out <- data.frame(State = 'Coordinates Outside', don.disp[iout, , drop = FALSE])
don.table$out <- don.table$out[order(coords$id[iout]), , drop = FALSE]
don.disp$StatusX[iout] <- "red"
}
rm(spcoords, shpd)
}
############
if(!is.null(don.table)){
don.table <- do.call(rbind, don.table)
don.table <- don.table[, !names(don.table) %in% c('LonX', 'LatX', 'StatusX'), drop = FALSE]
rownames(don.table) <- NULL
}
output$coords <- coords
############
file.index <- file.path(outdir, 'CoordinatesCheck.rds')
dataOUT <- file.path(outdir, 'CDTDATASET')
dir.create(dataOUT, showWarnings = FALSE, recursive = TRUE)
file.table.csv <- file.path(outdir, 'Stations_to_Check.csv')
file.table.rds <- file.path(dataOUT, 'Table.rds')
file.display <- file.path(dataOUT, 'Display.rds')
saveRDS(output, file.index)
saveRDS(don.disp, file.display)
saveRDS(don.table, file.table.rds)
if(!is.null(don.table)) writeFiles(don.table, file.table.csv, col.names = TRUE)
############
.cdtData$EnvData$output <- output
.cdtData$EnvData$PathData <- outdir
.cdtData$EnvData$Table.Disp <- don.table
.cdtData$EnvData$Maps.Disp <- don.disp
return(0)
}
##########################################################################
StnChkCoordsDataStn <- function(GeneralParameters){
if(GeneralParameters$data.type == "cdtcoords")
{
don0 <- getStnOpenData(GeneralParameters$infile)
if(is.null(don0)) return(NULL)
nom.col <- names(don0)
don.orig <- don0
coords <- list(id = as.character(don0[, 1]),
lon = as.numeric(don0[, 3]),
lat = as.numeric(don0[, 4]))
}
if(GeneralParameters$data.type == "cdtstation")
{
don0 <- getStnOpenData(GeneralParameters$infile)
if(is.null(don0)) return(NULL)
don <- splitCDTData0(don0)
if(is.null(don)) return(NULL)
don <- don[c('id', 'lon', 'lat', 'elv')]
nom.col <- c("ID", "Longitude", "Latitude", "Elevation")
if(is.null(don$elv)){
don <- don[c('id', 'lon', 'lat')]
nom.col <- nom.col[1:3]
}
don.orig <- as.data.frame(don)
names(don.orig) <- nom.col
coords <- don[c('id', 'lon', 'lat')]
rm(don)
}
############
rm(don0)
coords <- as.data.frame(coords)
don.orig$LonX <- coords$lon
don.orig$LatX <- coords$lat
don.orig$StatusX <- rep("blue", length(coords$lon))
############
## Missing coords
imiss <- is.na(coords$lon) | is.na(coords$lat)
if(any(imiss)){
don.orig <- don.orig[!imiss, , drop = FALSE]
coords <- coords[!imiss, , drop = FALSE]
}
## Wrong coords
iwrong <- coords$lon < -180 | coords$lon > 360 | coords$lat < -90 | coords$lat > 90
if(any(iwrong)){
don.orig <- don.orig[!iwrong, , drop = FALSE]
coords <- coords[!iwrong, , drop = FALSE]
}
.cdtData$EnvData$output$coords <- coords
.cdtData$EnvData$Maps.Disp <- don.orig
return(0)
}
##########################################################################
StnChkCoordsCorrect <- function(){
if(is.null(.cdtData$EnvData$Table.Disp0)){
Insert.Messages.Out("No stations to be corrected")
return(NULL)
}
idx0 <- as.character(.cdtData$EnvData$Table.Disp0$ID)
fileTable <- file.path(.cdtData$EnvData$PathData, "CDTDATASET/Table.rds")
Table.Disp <- readRDS(fileTable)
if(!is.null(Table.Disp)){
idx <- as.character(Table.Disp$ID)
id.del0 <- idx0[!idx0 %in% idx]
change <- Table.Disp[, -1, drop = FALSE]
change <- as.matrix(change)
.cdtData$EnvData$Table.Disp <- Table.Disp
}else{
id.del0 <- idx0
change <- matrix(NA, 0, 3)
.cdtData$EnvData$Table.Disp <- NULL
}
######
info <- .cdtData$EnvData$output$info
fileout <- file.path(.cdtData$EnvData$PathData,
paste0('Checked_Coords_', .cdtData$EnvData$output$params$infile))
don0 <- read.table(fileout, header = info[[3]]$header,
sep = info[[3]]$sepr, na.strings = info[[3]]$miss.val,
stringsAsFactors = FALSE, colClasses = "character")
filemap <- file.path(.cdtData$EnvData$PathData, 'CDTDATASET', 'Display.rds')
map.disp <- readRDS(filemap)
nom1 <- names(map.disp)
nom1 <- which(!nom1 %in% c('LonX', 'LatX', 'StatusX'))
######
if(.cdtData$EnvData$output$params$data.type == "cdtcoords"){
if(nrow(change) > 0){
ix <- match(idx, .cdtData$EnvData$output$id)
don0[ix, ] <- change
pos.lon <- 3
pos.lat <- 4
}
if(length(id.del0)){
ix1 <- match(id.del0, .cdtData$EnvData$output$id)
don0 <- don0[-ix1, , drop = FALSE]
}
}
if(.cdtData$EnvData$output$params$data.type == "cdtstation"){
if(nrow(change) > 0){
ix <- match(idx, .cdtData$EnvData$output$id)
don0[1:ncol(change), ix + 1] <- t(change)
pos.lon <- 2
pos.lat <- 3
}
if(length(id.del0)){
ix1 <- match(id.del0, .cdtData$EnvData$output$id)
don0 <- don0[, -(ix1 + 1), drop = FALSE]
}
}
if(length(id.del0)){
.cdtData$EnvData$output$id <- .cdtData$EnvData$output$id[-ix1]
.cdtData$EnvData$Table.Disp0 <- .cdtData$EnvData$Table.Disp0[!idx0 %in% id.del0, , drop = FALSE]
if(nrow(.cdtData$EnvData$Table.Disp0) == 0) .cdtData$EnvData$Table.Disp0 <- NULL
}
######
idx1 <- .cdtData$EnvData$output$coords$id
id.del1 <- if(length(id.del0)) idx1[idx1 %in% id.del0] else NULL
if(nrow(change) > 0){
ix0 <- match(idx, idx1)
ina <- is.na(ix0)
if(any(ina)){
ix0 <- ix0[!ina]
change0 <- change[!ina, , drop = FALSE]
change1 <- change[ina, , drop = FALSE]
idx2 <- idx[ina]
}else change0 <- change
.cdtData$EnvData$output$coords$id[ix0] <- as.character(change0[, 1])
.cdtData$EnvData$output$coords$lon[ix0] <- as.numeric(change0[, pos.lon])
.cdtData$EnvData$output$coords$lat[ix0] <- as.numeric(change0[, pos.lat])
map.disp[ix0, nom1] <- change0
map.disp$LonX[ix0] <- as.numeric(change0[, pos.lon])
map.disp$LatX[ix0] <- as.numeric(change0[, pos.lat])
.cdtData$EnvData$Maps.Disp[ix0, nom1] <- change0
.cdtData$EnvData$Maps.Disp$LonX[ix0] <- as.numeric(change0[, pos.lon])
.cdtData$EnvData$Maps.Disp$LatX[ix0] <- as.numeric(change0[, pos.lat])
if(any(ina)){
idx1 <- c(idx1, idx2)
tmp <- data.frame(id = as.character(change1[, 1]),
lon = as.numeric(change1[, pos.lon]),
lat = as.numeric(change1[, pos.lat]),
stringsAsFactors = FALSE)
.cdtData$EnvData$output$coords <- rbind(.cdtData$EnvData$output$coords, tmp)
tmp1 <- data.frame(change1,
LonX = as.numeric(change1[, pos.lon]),
LatX = as.numeric(change1[, pos.lat]),
StatusX = "red", stringsAsFactors = FALSE)
map.disp <- rbind(map.disp, tmp1)
.cdtData$EnvData$Maps.Disp <- rbind(.cdtData$EnvData$Maps.Disp, tmp1)
}
}
if(length(id.del1)){
ix <- match(id.del1, idx1)
.cdtData$EnvData$output$coords <- .cdtData$EnvData$output$coords[-ix, , drop = FALSE]
map.disp <- map.disp[-ix, , drop = FALSE]
.cdtData$EnvData$Maps.Disp <- .cdtData$EnvData$Maps.Disp[-ix, , drop = FALSE]
}
######
sep <- info[[3]]$sepr
if(sep == "") sep <- " "
write.table(don0, file = fileout, sep = sep, na = info[[3]]$miss.val,
col.names = info[[3]]$header, row.names = FALSE, quote = FALSE)
saveRDS(map.disp, filemap)
return(0)
}
|
ef4cee9279be51d7e7ee82a2642e5e9200eb62b4
|
f38bace9fc1f70068f2119362b4184e3d7122078
|
/R/plot_top_pictures.R
|
b2b96f0bc84c1ec9f2a3a9b98411a1ef6212e51d
|
[] |
no_license
|
thies/paper-uk-vintages
|
9aafa11a558be8330cd9c69e5272b3cb432d322c
|
d31a7930725728efbb367d28d36502d205e58783
|
refs/heads/master
| 2021-06-24T17:34:10.390676
| 2020-11-14T12:57:46
| 2020-11-14T12:57:46
| 163,590,256
| 5
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,592
|
r
|
plot_top_pictures.R
|
# Table with pictures of estimate vintages
plot_top_pictures <- function( regsam=NA, n=10, labels_short=NA, figpath = "/home/thies/db/Cambridge/data/images/cambridge/", img.width="50px"){
library(xtable)
options(xtable.sanitize.text.function=identity)
# load data
if( is.na(regsam) ){
require(RCurl)
remote.file <- "https://www.dropbox.com/s/he3hfquk8claaa1/regsamples.csv?dl=1"
tmp.file <- tempfile()
download.file(remote.file, tmp.file)
regsam <- read.csv(tmp.file)
file.remove(tmp.file)
}
if( is.na(labels_short) ){
labels_short <- c("Georg.","Early Vic.","Late V./Edw.","Interwar","Postwar","Contemp.","Revival")
}
eras <- unique(regsam$era)
eras <- eras[grepl("^[a-z]", eras)]
eras <- eras[ order(eras) ]
names(labels_short) <- eras
files <- list.files( figpath )
files <- files[grepl(".jpg", files)]
tab <- matrix("", n , length(eras))
colnames(tab) <- eras
for(e in eras){
tmp <- unique( subset(regsam, era == e, select=c("TOID","max","era")))
tmp <- tmp[rev(order(tmp$max)),]
for(i in 1:n){
tab[i, e ] <- files[ grepl( as.character( tmp$TOID[i] ), files )]
tab[ i, e] <- paste("\\includegraphics[width=",img.width,"]{", figpath ,tab[i,e],"}", sep="")
}
}
colnames(tab) <- paste("\\emph{",labels_short,"}", sep="")
xtab <- xtable( tab,
booktabs=TRUE,
comment=FALSE,
row.names=FALSE)
print(xtab,
comment=FALSE,
include.rownames=FALSE,
floating=FALSE)
}
#plot_top_pictures(regsam=regsample)
|
219d5a5f640042c892494c39dd08281a75632a19
|
f8936f07724b6d7179e4bb7705f81d1008377420
|
/man/flag_dead_fish.Rd
|
cfe594952d0240fd325702028c60fbb0c61765e0
|
[] |
no_license
|
jBernardADFG/telprep
|
5c86cc7f247dc0f126095d958387c55cba423dca
|
8495cad097b2cc87b97f3339e46e1fed84761c65
|
refs/heads/master
| 2021-04-05T11:52:47.124274
| 2020-07-24T18:20:40
| 2020-07-24T18:20:40
| 248,377,136
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 902
|
rd
|
flag_dead_fish.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/flagDeadFish.R
\name{flag_dead_fish}
\alias{flag_dead_fish}
\title{Uses movement information to determine the living status of fish.}
\usage{
flag_dead_fish(best_detects, dist_thresh = 10)
}
\arguments{
\item{dist_thresh}{See description.}
}
\value{
Returns a data.frame where $MortFlag=T if a fish has been flagged as dead.
}
\description{
An ad hoc algorithm is used to determine the living status of fish. Basically, if a fish moves less than dist_thresh km for all consecutive detection periods following the detection, the fish will be flagged as dead. Euclidean distance is currently used. To determine the survival status of fish using a statistical approach that incorporates mortality sensor information use \code{\link{hmm_survival}}.
}
\examples{
flagged_fish <- flag_dead_fish(best_detects)
head(flagged_fish)
}
|
43df11290b62bb4821212071f31bc3bb024bb50d
|
8d28b939007e0887f3a1af5b54a24c68dd3d4204
|
/R/familyAR1TS.R
|
6620b24a77412669f1291da814e2abd63e86288c
|
[] |
no_license
|
cran/VGAMextra
|
897c59ab2b532b0aa1d4011130db79f5c95eb443
|
ac7e3df54136fd4c9e49b754f6747a11d7c3b122
|
refs/heads/master
| 2021-06-06T03:52:23.167971
| 2021-05-24T03:10:07
| 2021-05-24T03:10:07
| 138,900,855
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 23,368
|
r
|
familyAR1TS.R
|
##########################################################################
# These functions are
# Copyright (C) 2014-2020 V. Miranda & T. Yee
# Auckland University of Technology & University of Auckland
# All rights reserved.
### Exact EIms for the AR1 model.
# Here, theta = c(m*, serrors^2, theta)
AR1EIM.G2 <- function(y, drift, sdError, AR1coeff, var.arg = TRUE,
order = 1, nodrift = FALSE) {
y <- cbind(y); nn <- nrow(y)
RMat <- diag(c(sdError^2))
dRMat <- cbind( if(!nodrift) matrix(0, nrow = nn, ncol = 1) else NULL,
if (var.arg) matrix(1, nrow = nn, ncol = 1) else
2 * sdError,
matrix(0, nrow = nn, ncol = 1))
dmt0 <- matrix(1, nrow = nn, ncol = 1)
dmt <- cbind(if (!nodrift) dmt0 else NULL,
rep_len(0, nn), c(1 / (1 - AR1coeff[1])^2, y[-nn]))
M <- 3 # For AR1
try.comb <- combVGAMextra(1:M, nodrift = nodrift)
finMat <- apply(try.comb, 1, function(x) {
kl <- x
invRMat <- solve(RMat)
dRdthel <- diag(dRMat[, kl[1]])
dRdthek <- diag(dRMat[, kl[2]])
term.1 <- (0.5) * invRMat %*% dRdthel %*% invRMat %*% dRdthek
term.2 <- diag(dmt[, kl[1]]) %*%
invRMat %*% diag(dmt[, kl[2]])
diag(term.1 + term.2)
})
finMat[ , ncol(finMat)] <- (0.995) * finMat[ , ncol(finMat)]
finMat
}
### Density fo the AR1 model. dARff() may also work.
dAR1extra <- function(x,
drift = 0, # Stationarity is the default
var.error = 1, ARcoef1 = 0.0,
type.likelihood = c("exact", "conditional"),
log = FALSE) {
type.likelihood <- match.arg(type.likelihood,
c("exact", "conditional"))[1]
is.vector.x <- is.vector(x)
x <- as.matrix(x)
drift <- as.matrix(drift)
var.error <- as.matrix(var.error)
ARcoef1 <- as.matrix(ARcoef1)
LLL <- max(nrow(x), nrow(drift), nrow(var.error), nrow(ARcoef1))
UUU <- max(ncol(x), ncol(drift), ncol(var.error), ncol(ARcoef1))
x <- matrix(x, LLL, UUU)
drift <- matrix(drift, LLL, UUU)
var.error <- matrix(var.error, LLL, UUU)
rho <- matrix(ARcoef1, LLL, UUU)
if (any(abs(rho) > 1))
warning("Values of argument 'ARcoef1' are greater ",
"than 1 in absolute value")
if (!is.logical(log.arg <- log) || length(log) != 1)
stop("Bad input for argument 'log'.")
rm(log)
ans <- matrix(0.0, LLL, UUU)
var.noise <- var.error / (1 - rho^2)
ans[ 1, ] <- dnorm(x = x[1, ],
mean = drift[ 1, ] / (1 - rho[1, ]),
sd = sqrt(var.noise[1, ]), log = log.arg)
ans[-1, ] <- dnorm(x = x[-1, ],
mean = drift[-1, ] + rho[-1, ] * x[-nrow(x), ],
sd = sqrt(var.error[-1, ]), log = log.arg)
if (type.likelihood == "conditional")
ans[1, ] <- NA
if (is.vector.x) as.vector(ans) else ans
}
if (FALSE)
AR1extra.control <- function(epsilon = 1e-6,
maxit = 30,
stepsize = 1,...){
list(epsilon = epsilon,
maxit = maxit,
stepsize = stepsize,
...)
}
## Family function AR1extra() to fit order-1 Autoregressive models
AR1extra <-
function(zero = c(if (var.arg) "var" else "sd", "rho"),
type.EIM = c("exact", "approximate")[1],
var.arg = TRUE,
nodrift = FALSE,
ldrift = "identitylink",
lsd = "loglink",
lvar = "loglink",
lrho = "rhobitlink",
idrift = NULL,
isd = NULL,
ivar = NULL,
irho = NULL,
print.EIM = FALSE) {
if (length(isd) && !is.Numeric(isd, positive = TRUE))
stop("Bad input for argument 'isd'")
if (length(ivar) && !is.Numeric(ivar, positive = TRUE))
stop("Bad input for argument 'ivar'")
if (length(irho) &&
(!is.Numeric(irho) || any(abs(irho) > 1.0)))
stop("Bad input for argument 'irho'")
type.EIM <- match.arg(type.EIM, c("exact", "approximate"))[1]
poratM <- (type.EIM == "exact")
imethod = 1
if (!is.logical(nodrift) ||
length(nodrift) != 1)
stop("Argument 'nodrift' must be a single logical")
if (!is.logical(var.arg) ||
length(var.arg) != 1)
stop("Argument 'var.arg' must be a single logical")
if (!is.logical(print.EIM))
stop("Invalid 'print.EIM'.")
type.likelihood <- "exact"
ismn <- idrift
lsmn <- as.list(substitute(ldrift))
esmn <- link2list(lsmn)
lsmn <- attr(esmn, "function.name")
lsdv <- as.list(substitute(lsd))
esdv <- link2list(lsdv)
lsdv <- attr(esdv, "function.name")
lvar <- as.list(substitute(lvar))
evar <- link2list(lvar)
lvar <- attr(evar, "function.name")
lrho <- as.list(substitute(lrho))
erho <- link2list(lrho)
lrho <- attr(erho, "function.name")
n.sc <- if (var.arg) "var" else "sd"
l.sc <- if (var.arg) lvar else lsdv
e.sc <- if (var.arg) evar else esdv
new("vglmff",
blurb = c(ifelse(nodrift, "Two", "Three"),
"-parameter autoregressive process of order-1\n\n",
"Links: ",
if (nodrift) "" else
paste(namesof("drift", lsmn, earg = esmn), ", ",
sep = ""),
namesof(n.sc , l.sc, earg = e.sc), ", ",
namesof("rho", lrho, earg = erho), "\n",
"Model: Y_t = drift + rho * Y_{t-1} + error_{t},",
"\n",
" where 'error_{2:n}' ~ N(0, sigma^2) ",
"independently",
if (nodrift) ", and drift = 0" else "",
"\n",
"Mean: drift / (1 - rho)", "\n",
"Correlation: rho = ARcoef1", "\n",
"Variance: sd^2 / (1 - rho^2)"),
constraints = eval(substitute(expression({
M1 <- 3 - .nodrift
dotzero <- .zero
constraints <-
cm.zero.VGAM(constraints, x = x, zero = .zero , M = M,
predictors.names = parameter.names,
M1 = M1)
}), list( .zero = zero, .nodrift = nodrift ))),
infos = eval(substitute(function(...) {
list(M1 = 3 - .nodrift ,
Q1 = 1,
expected = TRUE,
multipleResponse = TRUE,
type.likelihood = .type.likelihood ,
ldrift = if ( .nodrift ) NULL else .lsmn ,
edrift = if ( .nodrift ) NULL else .esmn ,
lvar = .lvar ,
lsd = .lsdv ,
evar = .evar ,
esd = .esdv ,
lrho = .lrho ,
erho = .erho ,
zero = .zero )
}, list( .lsmn = lsmn, .lvar = lvar, .lsdv = lsdv, .lrho = lrho,
.esmn = esmn, .evar = evar, .esdv = esdv, .erho = erho,
.type.likelihood = type.likelihood,
.nodrift = nodrift, .zero = zero))),
initialize = eval(substitute(expression({
extra$M1 <- M1 <- 3 - .nodrift
check <- w.y.check(w = w, y = y,
Is.positive.y = FALSE,
ncol.w.max = Inf,
ncol.y.max = Inf,
out.wy = TRUE,
colsyperw = 1,
maximize = TRUE)
w <- check$w
y <- check$y
if ( .type.likelihood == "conditional") {
w[1, ] <- 1.0e-6
} else {
w[1, ] <- 1.0e-6
}
NOS <- ncoly <- ncol(y)
n <- nrow(y)
M <- M1 * NOS
extra$y <- y
extra$print.EIM <- FALSE
var.names <- param.names("var", NOS)
sdv.names <- param.names("sd", NOS)
smn.names <- if ( .nodrift ) NULL else
param.names("drift", NOS)
rho.names <- param.names("rho", NOS)
parameter.names <- c(smn.names,
if ( .var.arg ) var.names else sdv.names,
rho.names)
parameter.names <-
parameter.names[interleave.VGAM(M, M1 = M1)]
predictors.names <-
c(if ( .nodrift ) NULL else
namesof(smn.names, .lsmn , earg = .esmn , tag = FALSE),
if ( .var.arg )
namesof(var.names, .lvar , earg = .evar , tag = FALSE) else
namesof(sdv.names, .lsdv , earg = .esdv , tag = FALSE),
namesof(rho.names, .lrho , earg = .erho , tag = FALSE))
predictors.names <- predictors.names[interleave.VGAM(M, M1 = M1)]
if (!length(etastart)) {
init.smn <- matrix( if (length( .ismn )) .ismn else 0,
nrow = n, ncol = NOS, byrow = TRUE)
init.rho <- matrix(if (length( .irho )) .irho else 0.1,
n, NOS, byrow = TRUE)
init.sdv <- matrix(if (length( .isdv )) .isdv else 1.0,
n, NOS, byrow = TRUE)
init.var <- matrix(if (length( .ivar )) .ivar else 1.0,
n, NOS, byrow = TRUE)
for (jay in 1:NOS) {
mycor <- cov(y[-1, jay], y[-n, jay]) /
apply(y[, jay, drop = FALSE], 2, var)
init.smn[ , jay] <- mean(y[, jay]) * (1 - mycor)
if (!length( .irho ))
init.rho[, jay] <- sign(mycor) * min(0.95, abs(mycor))
if (!length( .ivar ))
init.var[, jay] <- var(y[, jay]) * (1 - mycor^2)
if (!length( .isdv ))
init.sdv[, jay] <- sqrt(init.var[, jay])
} # for
etastart <-
cbind(if ( .nodrift ) NULL else
theta2eta(init.smn, .lsmn , earg = .esmn ),
if ( .var.arg )
theta2eta(init.var, .lvar , earg = .evar ) else
theta2eta(init.sdv, .lsdv , earg = .esdv ),
theta2eta(init.rho, .lrho , earg = .erho ))
etastart <- etastart[, interleave.VGAM(M, M1 = M1), drop = FALSE]
}
}), list( .lsmn = lsmn, .lrho = lrho, .lsdv = lsdv, .lvar = lvar,
.esmn = esmn, .erho = erho, .esdv = esdv, .evar = evar,
.ismn = ismn, .irho = irho, .isdv = isd , .ivar = ivar,
.type.likelihood = type.likelihood,
.var.arg = var.arg, .nodrift = nodrift ))),
linkinv = eval(substitute(function(eta, extra = NULL) {
n <- nrow(eta)
M1 <- 3 - .nodrift
NOS <- ncol(eta)/M1
ar.smn <- if ( .nodrift ) 0 else
eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lsmn , earg = .esmn )
ar.rho <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lrho , earg = .erho )
y.lag <- matrix(0, nrow = n, ncol = NOS)
y.lag[-1, ] <- extra$y[-n, ]
ar.smn + ar.rho * y.lag
}, list ( .lsmn = lsmn, .lrho = lrho , .lsdv = lsdv, .lvar = lvar ,
.var.arg = var.arg, .type.likelihood = type.likelihood,
.esmn = esmn, .erho = erho , .esdv = esdv, .evar = evar ,
.nodrift = nodrift ))),
last = eval(substitute(expression({
if (any(abs(ar.rho) > 1))
warning("Regularity conditions are violated at the final",
"IRLS iteration, since 'abs(rho) > 1")
M1 <- extra$M1
temp.names <- parameter.names
temp.names <- temp.names[interleave.VGAM(M1 * ncoly, M1 = M1)]
misc$link <- rep( .lrho , length = M1 * ncoly)
misc$earg <- vector("list", M1 * ncoly)
names(misc$link) <- names(misc$earg) <- temp.names
for (ii in 1:ncoly) {
if ( !( .nodrift ))
misc$link[ M1*ii-2 ] <- .lsmn
misc$link[ M1*ii-1 ] <- if ( .var.arg ) .lvar else .lsdv
misc$link[ M1*ii ] <- .lrho
if ( !( .nodrift ))
misc$earg[[M1*ii-2]] <- .esmn
misc$earg[[M1*ii-1]] <- if ( .var.arg ) .evar else .esdv
misc$earg[[M1*ii ]] <- .erho
}
#if (( .poratM ) && any(flag.1) )
# warning("\nExact EIM approach currently implemented for",
# " AR(1) processes \n with stationary noise. Shifting",
# " to the 'approximate' EIM approach.")
misc$M1 <- M1
misc$var.arg <- .var.arg
misc$expected <- TRUE
misc$nodrift <- .nodrift
misc$poratM <- .poratM
misc$print.EIM <- FALSE
misc$type.likelihood <- .type.likelihood
misc$multipleResponses <- TRUE
}), list( .lsmn = lsmn, .lrho = lrho, .lsdv = lsdv, .lvar = lvar,
.esmn = esmn, .erho = erho, .esdv = esdv, .evar = evar,
.irho = irho, .isdv = isd , .ivar = ivar,
.nodrift = nodrift, .poratM = poratM,
.var.arg = var.arg, .type.likelihood = type.likelihood ))),
loglikelihood = eval(substitute(
function(mu, y, w, residuals= FALSE, eta,
extra = NULL, summation = TRUE) {
M1 <- 3 - .nodrift
NOS <- ncol(eta)/M1
if ( .var.arg ) {
ar.var <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lvar , earg = .evar )
ar.sdv <- sqrt(ar.var)
} else {
ar.sdv <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lsdv , earg = .esdv )
ar.var <- ar.sdv^2
}
ar.smn <- if ( .nodrift ) 0 else
eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lsmn , earg = .esmn )
ar.rho <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lrho , earg = .erho )
if (residuals) {
stop("Loglikelihood not implemented yet to handle",
"residuals.")
} else {
loglik.terms <-
c(w) * dAR1extra(x = y,
drift = ar.smn ,
var.error = ar.var,
type.likelihood = .type.likelihood ,
ARcoef1 = ar.rho, log = TRUE)
loglik.terms <- as.matrix(loglik.terms)
if (summation) {
sum(if ( .type.likelihood == "exact") loglik.terms else
loglik.terms[-1, ] )
} else {
loglik.terms
}
}
}, list( .lsmn = lsmn, .lrho = lrho , .lsdv = lsdv, .lvar = lvar ,
.var.arg = var.arg, .type.likelihood = type.likelihood,
.nodrift = nodrift,
.esmn = esmn, .erho = erho , .esdv = esdv, .evar = evar ))),
vfamily = c("AR1"),
validparams = eval(substitute(function(eta, y, extra = NULL) {
M1 <- 3 - .nodrift
n <- nrow(eta)
NOS <- ncol(eta)/M1
ncoly <- ncol(as.matrix(y))
if ( .var.arg ) {
ar.var <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lvar , earg = .evar )
ar.sdv <- sqrt(ar.var)
} else {
ar.sdv <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lsdv , earg = .esdv )
ar.var <- ar.sdv^2
}
ar.smn <- if ( .nodrift ) matrix(0, n, NOS) else
eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lsmn , earg = .esmn )
ar.rho <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lrho , earg = .erho )
okay1 <- all(is.finite(ar.sdv)) && all(0 < ar.sdv) &&
all(is.finite(ar.smn)) && all(is.finite(ar.rho))
okay1
}, list( .lsmn = lsmn, .lrho = lrho , .lsdv = lsdv, .lvar = lvar ,
.var.arg = var.arg, .type.likelihood = type.likelihood,
.nodrift = nodrift,
.esmn = esmn, .erho = erho , .esdv = esdv, .evar = evar ))),
simslot = eval(substitute(function(object, nsim) {
pwts <- if (length(pwts <- object@prior.weights) > 0)
pwts else weights(object, type = "prior")
if (any(pwts != 1))
warning("ignoring prior weights")
eta <- predict(object)
fva <- fitted(object)
M1 <- 3 - .nodrift
NOS <- ncol(eta)/M1
if ( .var.arg ) {
ar.var <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lvar , earg = .evar )
ar.sdv <- sqrt(ar.var)
} else {
ar.sdv <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lsdv , earg = .esdv )
ar.var <- ar.sdv^2
}
ar.smn <- if ( .nodrift ) matrix(0, n, NOS) else
eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lsmn , earg = .esmn )
ar.rho <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lrho , earg = .erho )
ans <- array(0, c(nrow(eta), NOS, nsim))
for (jay in 1:NOS) {
ans[1, jay, ] <- ar.smn[1, jay] +
rnorm(nsim, sd = sqrt(ar.var[1, jay]))
for (ii in 2:nrow(eta))
ans[ii, jay, ] <- ar.smn[ii, jay] +
ar.rho[ii, jay] * ans[ii-1, jay, ] +
rnorm(nsim, sd = sqrt(ar.var[ii, jay]))
}
ans <- matrix(c(ans), c(nrow(eta) * NOS, nsim))
ans
}, list( .lsmn = lsmn, .lrho = lrho , .lsdv = lsdv, .lvar = lvar ,
.var.arg = var.arg, .nodrift = nodrift,
.esmn = esmn, .erho = erho , .esdv = esdv, .evar = evar ))),
deriv = eval(substitute(expression({
M1 <- 3 - .nodrift
NOS <- ncol(eta)/M1
ncoly <- ncol(as.matrix(y))
if ( .var.arg ) {
ar.var <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lvar , earg = .evar )
ar.sdv <- sqrt(ar.var)
} else {
ar.sdv <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lsdv , earg = .esdv )
ar.var <- ar.sdv^2
}
ar.smn <- if ( .nodrift ) matrix(0, n, NOS) else
eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lsmn , earg = .esmn )
ar.rho <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lrho , earg = .erho )
if (any(abs(ar.rho) < 1e-2))
warning("Estimated values of 'rho' are too close to zero.")
y.lags <- apply(y, 2, function(x) WN.lags(y = cbind(x), lags = 1))
y.means <- y - (ar.smn + ar.rho * y.lags)
dl.dsmn <- y.means / ar.var
if ( .var.arg ) {
dl.dvarSD <- y.means^2 / ( 2 * ar.var^2) - 1 / (2 * ar.var)
} else {
dl.dvarSD <- y.means^2 / ar.sdv^3 - 1 / ar.sdv
}
dl.drho <- y.means * y.lags / ar.var
dsmn.deta <- dtheta.deta(ar.smn, .lsmn , earg = .esmn )
drho.deta <- dtheta.deta(ar.rho, .lrho , earg = .erho )
if ( .var.arg ) {
dvarSD.deta <- dtheta.deta(ar.var, .lvar , earg = .evar )
} else {
dvarSD.deta <- dtheta.deta(ar.sdv, .lsdv , earg = .esdv )
}
myderiv <-
c(w) * cbind(if ( .nodrift ) NULL else dl.dsmn * dsmn.deta,
dl.dvarSD * dvarSD.deta,
dl.drho * drho.deta)
myderiv <- myderiv[, interleave.VGAM(M, M1 = M1)]
myderiv
}), list( .lsmn = lsmn, .lrho = lrho, .lsdv = lsdv, .lvar = lvar,
.esmn = esmn, .erho = erho, .esdv = esdv, .evar = evar,
.nodrift = nodrift , .var.arg = var.arg,
.type.likelihood = type.likelihood ))),
weight = eval(substitute(expression({
helpPor <- .poratM
### The EXACT EIMs
M.fin <- if ( .nodrift ) M1 + (M1 - 1) else
M1 + (M1 - 1) + (M1 - 2)
pre.wz <- array(NA_real_, dim = c(n, M.fin , NOS))
for (jj in 1:NOS) {
pre.wz[, , jj] <- AR1EIM.G2(y = y[, jj, drop = FALSE],
drift = ar.smn[, jj, drop = FALSE],
sdError = ar.sdv[, jj, drop = FALSE],
AR1coeff = ar.rho[, jj, drop = FALSE],
order = 1, nodrift = .nodrift ,
var.arg = .var.arg )
}
if ( !(.nodrift) ) {
dTHE.dETA <- cbind(dsmn.deta^2, dvarSD.deta^2, drho.deta^2,
matrix(0, nrow = n, ncol = NOS),
matrix(0, nrow = n, ncol = NOS),
dsmn.deta * drho.deta)
} else {
dTHE.dETA <- cbind(dvarSD.deta^2, drho.deta^2,
matrix(0, nrow = n, ncol = NOS))
}
wzExact <- arwzTS(wz = pre.wz, w = w,
M1 = M1, dTHE.dETA = dTHE.dETA)
if ( .print.EIM )
wzPrint1 <- arwzTS(wz = pre.wz, w = w, M1 = M1,
dTHE.dETA = dTHE.dETA, print.EIM = TRUE)
### Approximate EIMs
pre.wz <- matrix(0, nrow = n,
ncol = ifelse( .nodrift, 2 * M - 1, 3 * M - 3))
gamma0 <- ar.var / (1 - ar.rho^2)
ned2l.dsmn <- 1 / ar.var
ned2l.dvarSD <- if ( .var.arg ) 1 / (2 * ar.var^2) else 2 / ar.var
ned2l.drho <- gamma0 / ar.var
if (!( .nodrift ))
pre.wz[, M1*(1:NOS) - 2] <- ned2l.dsmn * dsmn.deta^2
pre.wz[, M1*(1:NOS) - 1] <- ned2l.dvarSD * dvarSD.deta^2
pre.wz[, M1*(1:NOS) ] <- ned2l.drho * drho.deta^2
wzApp <- w.wz.merge(w = w, wz = pre.wz, n = n,
M = ncol(pre.wz), ndepy = NOS)
wz <- if (helpPor) wzExact else wzApp
if ( .print.EIM ) {
wzEx1 <- matrix(NA_real_, nrow = n, ncol = NOS)
wzEx2 <- matrix(NA_real_, nrow = n, ncol = NOS)
wzApp <- wzApp[, 1:M, drop = FALSE]
wzApp <- array(wzApp, dim = c(n, M / NOS, NOS))
for (jj in 1:NOS) {
wzEx1[, jj] <- if (NOS == 1) rowSums(wzPrint1) else
rowSums(wzPrint1[, , jj, drop = FALSE])
wzEx2[, jj] <- rowSums(wzApp[, , jj, drop = FALSE])
}
print.Mat <- cbind(wzEx1, wzEx2)
colnames(print.Mat) <- c(paste("Exact", 1:NOS),
paste("Approximate", 1:NOS))
rownames(print.Mat) <- paste("", 1:n)
print(head(print.Mat))
} else {
rm(wzExact, wzApp)
}
wz
}), list( .var.arg = var.arg, .type.likelihood = type.likelihood,
.nodrift = nodrift, .poratM = poratM,
.print.EIM = print.EIM ))) )
}
|
f0e21d7ec0bc367df25d6e92655524b840093d80
|
57399b29b38f1d72bca228495f4da6d3dab0b0ae
|
/output/cluster/cluster_error.R
|
1c6d1f59fb6138831bc3492ce924df1822689280
|
[] |
no_license
|
zpb4/ms_project1
|
db1b25f6c09c0b3e7a627c8585168d42a85fc30c
|
4ebaad0da991a3e4fc0febbb546fc04d486a00b0
|
refs/heads/master
| 2020-06-22T19:01:52.968077
| 2019-08-01T16:56:38
| 2019-08-01T16:56:38
| 197,782,135
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,055
|
r
|
cluster_error.R
|
##ERROR for Clusters
rf_model<-'ncep_rf2'
rf_type<-'mean' #'cf' or 'mean'
obs<-'gpcc'
kmns<-4
fdays<-15
lat<-19:22 #box is [19:22,18:20]
lon<-18:20
obs_tp<-readRDS(paste('data/prcp/',obs,'_tp_wc_1984_2019.rds',sep=""))
rf_tp<-readRDS(paste('data/prcp/',rf_model,'_tp_',rf_type,'_6_mask.rds',sep=""))
clus_idx<-readRDS(paste('output/cluster/',rf_model,'_',obs,'_cluster_idx_',rf_type,'_man.rds',sep=""))
ev_index<-readRDS(paste('output/index/',obs,'_ev_index_1_full.rds',sep=""))
box_error<-array(NA, c(kmns,fdays))
for(i in 1:15){
for(k in 1:4){
forc<-rf_tp[lat,lon,i+1,(ev_index[which(clus_idx[,i]==k)]-i+1)]
forc_mn<-apply(forc,3,function(x){mean(x,na.rm=T)})
ob<-obs_tp[lat,lon,ev_index[which(clus_idx[,i]==k)]]
obs_mn<-apply(ob,3,function(x){mean(x,na.rm=T)})
err<-mean((forc_mn-obs_mn)/obs_mn) * 100
box_error[k,i]<-err
}
}
saveRDS(box_error,paste('output/cluster/',rf_model,'_',obs,'_box_error_',rf_type,'.rds',sep=""))
rm(list=ls())
######################################END#####################################
|
b30cb950cfc7485d3209b6654428cbfb6812b28e
|
61dea145f59499bf141b22462fd67d2bc8dad134
|
/R/barlab_order.R
|
562c7fe1caa5c67f8b084e6107169511cb643dd1
|
[] |
no_license
|
econDS/DEAR
|
9c88b69b2c7a1f8439aacd6139ebf0a23a389f8a
|
a2353380f90674de7c1c242b234097edd9432f8b
|
refs/heads/master
| 2022-03-27T14:05:04.896964
| 2022-02-17T02:08:13
| 2022-02-17T02:08:13
| 157,736,564
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,792
|
r
|
barlab_order.R
|
#' barplot with label and sorted frequency
#'
#' create barplot with label on top and sorted the categories by frequency
#' from highest to lowest.
#'
#' @param x a categorical variable.
#' @param percent a logical for display labels as percentage.
#' @param title a title of plot
#' @param unlimit a logical for setting plot more than 10 categories
#'
#' @return
#' @export
#'
#' @examples
#' a <- c("A", "A", "B", "B", "B", "C")
#' barlab_order(a)
#' barlab_order(a, percent = TRUE)
barlab_order <- function(x, percent=FALSE, title=deparse(substitute(x)),
unlimit=FALSE, decimal=0) {
freq <- as.matrix(table(x))
ind <- order(freq, decreasing = TRUE)
lbl <- attr(freq, "dimnames")[[1]][ind]
num_cat <- length(freq)
max_char_10 <- max(nchar(lbl[1:min(num_cat, 10)]))
rot_angle <- 90
if (max_char_10 > 43.352*num_cat^(-1.134)) {
rot_angle <- 65
}
if(unlimit){
p <- barplot(freq[ind], names.arg="", col=rainbow(10), yaxt="n",
ylim=c(0, max(freq)*1.2), main=title, ylab='count')
# x axis label
text(p[,1], -3, srt=rot_angle, adj=1, xpd=TRUE, labels=lbl[1:10], cex=1)
# data label
text(p, freq[ind]*1.05, label=freq[ind], pos=3, cex=0.8, srt=rot_angle, adj=-1)
# y axis label
aty <- seq(par("yaxp")[1], par("yaxp")[2], (par("yaxp")[2] - par("yaxp")[1])/par("yaxp")[3])
axis(2, at=aty, labels=format(aty, scientific=FALSE), hadj=0.9, cex.axis=0.8, las=2)
} else {
if(num_cat >= 10){
cat("There is", num_cat, "categories. Only first top 10th would plot.\n
Percent plot also not be available.")
op <- par(mar=c(min(max_char_10,25)*0.5,4,4,2))
p <- barplot(freq[ind][1:10], names.arg = "", col=rainbow(10), las=2,
ylim=c(0, max(freq)*1.11), main=title, ylab='count')
text(p[,1], -3, srt = rot_angle, adj= 1, xpd = TRUE, labels = lbl[1:10] , cex=1.2)
text(p, freq[ind][1:10], label = freq[ind][1:10], pos = 3, cex = 0.8)
rm(op)
} else {
if(percent){
pct <- round(table(x)*100/sum(table(x)), decimal)
pct_lb <- paste(pct,"%",sep="")
p <- barplot(pct[ind], names.arg = lbl, main=title,
col=rainbow(10), ylim=c(0, max(pct)*1.199), ylab='percent')
text(p, pct[ind], label = pct_lb[ind], pos = 3, cex = 0.8)
} else {
p <- barplot(freq[ind], names.arg = lbl, main=title, yaxt="n",
col=rainbow(10), ylim=c(0, max(freq)*1.2), ylab='count')
text(p, freq[ind], label = freq[ind], pos = 3, cex = 0.8)
# y axis label
aty <- seq(par("yaxp")[1], par("yaxp")[2], (par("yaxp")[2] - par("yaxp")[1])/par("yaxp")[3])
axis(2, at=aty, labels=format(aty, scientific=FALSE), hadj=0.9, cex.axis=0.8, las=2)
}
}
}
}
|
026576a68bcaee535b5cb19abe6ee57ee68d4939
|
69ddcaa08f6f0e1837bdc215fe54f2df45909bb0
|
/ui.R
|
9c98536e9a2164360ac943481e731a1e140243a1
|
[] |
no_license
|
ats2/DataProducts
|
ba64a5ba5fae0ca7f5067ee8fee17a50728a7bf3
|
528562b632295c6c7c2e391a2ef842a302ed580b
|
refs/heads/master
| 2021-01-10T13:57:24.085092
| 2015-10-23T02:23:21
| 2015-10-23T02:23:21
| 44,737,833
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 734
|
r
|
ui.R
|
shinyUI(
fluidPage(
# Application title
titlePanel("Newsletter Subscriptions"),
sidebarLayout(
# Sidebar with a slider and selection inputs
sidebarPanel(
selectInput("selection", "Choose a brand:", choices = brands),
checkboxInput("trend", "Show Trend Line"),
numericInput('forecast', 'Add Forecast Weeks (0 to 8)', 0, min = 0, max = 8, step = 1),
sliderInput("date","Date Range:",
min=min(data$WK_DATE),max=max(data$WK_DATE),value=c(min(data$WK_DATE),max(data$WK_DATE)))
),
# Show trend
mainPanel(
#plotOutput("plot")
tabsetPanel(
tabPanel("App",plotOutput("plot")),
tabPanel("Documentation",includeMarkdown("Documentation.md"))
)
)
)
))
|
470a92bc450f9c7fb701392752acd99b967b163c
|
67c2a90c7edfac3cfd891cb332c45e71cf4a6ad1
|
/R/cdm_penalty_values_tlp.R
|
6198be8b531e03625d7ba0fc733cf00cb5d94546
|
[] |
no_license
|
alexanderrobitzsch/CDM
|
48316397029327f213967dd6370a709dd1bd2e0a
|
7fde48c9fe331b020ad9c7d8b0ec776acbff6a52
|
refs/heads/master
| 2022-09-28T18:09:22.491208
| 2022-08-26T11:36:31
| 2022-08-26T11:36:31
| 95,295,826
| 21
| 11
| null | 2019-06-19T09:40:01
| 2017-06-24T12:19:45
|
R
|
UTF-8
|
R
| false
| false
| 177
|
r
|
cdm_penalty_values_tlp.R
|
## File Name: cdm_penalty_values_tlp.R
## File Version: 0.04
cdm_penalty_values_tlp <- function(x, tau )
{
y <- abs(x) / tau
y <- ifelse( y > 1, 1, y )
return(y)
}
|
46e0a45998192bc6ca786ce9b84ca4ebbd107221
|
0d7e791ea12733367b56cf6338e137accd3cf9fe
|
/meta17network/man/set_dirs.Rd
|
b3e29d38d414f83640c5ee20822550280d7e08cd
|
[] |
no_license
|
aminorberg/meta17network-pkg
|
aefb010f8eb7a711049695bc30b01bee71e24050
|
34526c168603cf7d6b45ad59b9cec15d7c10d993
|
refs/heads/master
| 2023-04-18T00:08:35.833429
| 2023-02-05T11:23:50
| 2023-02-05T11:23:50
| 282,894,553
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 580
|
rd
|
set_dirs.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/set_dirs.r
\name{set_dirs}
\alias{set_dirs}
\title{Defining directories}
\usage{
set_dirs(working_dir, fit_fold = "fits")
}
\arguments{
\item{working_dir}{Full path to the working directory, under which everything else will be created.}
\item{fit_fold}{Folder where the model fits will be saved (defaults to "fits")}
}
\value{
List of directories needed for the modelling pipeline, automatically created as subdirectories under the working_dir
}
\description{
Defining directories/paths to be used
}
|
2145a9483ce7d5ef55703ca21e79bbb6d7900eeb
|
434104ea9a2d952caf418189c413eaae20b90e41
|
/man/chroma_random.Rd
|
a0a0301f42be15b99c78cb8763a38a4703cd758b
|
[] |
no_license
|
R-forks-to-learn/colorscale
|
281f855240224e5fcf89b6f4edac2d4ca00b028e
|
8a6426d3aa2887b2e69aabcf70b7cb1cfe9a488a
|
refs/heads/master
| 2022-04-14T14:16:18.823725
| 2020-04-16T15:11:35
| 2020-04-16T15:11:35
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 414
|
rd
|
chroma_random.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/chroma-utils.R
\name{chroma_random}
\alias{chroma_random}
\title{Random colors}
\usage{
chroma_random(n = 1L)
}
\arguments{
\item{n}{Number of colors desired.}
}
\value{
A vector of hexadecimal string(s).
}
\description{
Creates a random color by generating a random hexadecimal string.
}
\examples{
chroma_random()
chroma_random(10)
}
|
089a0762c33b0825eac3fd0a398c412ffc1b54aa
|
a9a713bd5993bd5d18dcb73f51f477a4d56e2a21
|
/entry/markups/r-files/visits_area_special.R
|
753de5fdaa90d753d0c6b497d585b45a7987bc38
|
[] |
no_license
|
d-sedov/location-configurations
|
95865f1fc07fda6f3a52922f2654837887af7c08
|
4f476d4c385c0cba435e78984f56b8ec46c6a957
|
refs/heads/master
| 2022-12-20T00:28:56.985992
| 2020-10-14T12:42:18
| 2020-10-14T12:42:18
| 304,011,398
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 9,200
|
r
|
visits_area_special.R
|
###############################################################################
#
# FILE: visits_area_special.R
#
# BY: Dmitry Sedov
#
# DATE: Sun May 10 2020
#
# DESC: This code contains the code to estimate the extra visits from an
# an increase in area specifically for a large CBSA
# (in order to parallelize).
#
# IN:
# 0. CBSA - the identifier of the urban area.
# 1. Rho - the disutility of distance.
# 2. c_a - the coefficient on the area.
# 3. remainder - the remainder of row number division by 100
# to select a subset ofrestaurants.
#
###############################################################################
################################ Libraries ####################################
suppressPackageStartupMessages(library(readr))
suppressPackageStartupMessages(library(dplyr))
suppressPackageStartupMessages(library(ggplot2))
suppressPackageStartupMessages(library(gsubfn))
suppressPackageStartupMessages(library(data.table))
suppressPackageStartupMessages(library(stargazer))
###############################################################################
################################ Constants ####################################
days = 31
input_folder = '/home/quser/project_dir/urban/data/output/spatial-demand/main_demand'
population_folder = '/home/quser/project_dir/urban/data/output/descriptive'
output_folder = '/home/quser/project_dir/urban/data/output/entry/markups'
###############################################################################
################################# Functions ###################################
PrepareData <- function(cbsa) {
# Function to import and prepare single-CBSA dataset for estimation of extra
# visits from extra area.
#
# IN:
# cbsa - string with the cbsa number
# OUT:
# pairs, deltas
# Set the cbsa folder name
cbsa_folder_name <- paste0('cbsa', cbsa)
# Import the cbg-restaurant pairs
pairs_file_name <- paste0('pairs', cbsa, '.csv')
pairs_file_path <- file.path(input_folder, cbsa_folder_name, pairs_file_name)
pairs <- read_csv(pairs_file_path, col_types = cols(cbg = col_character()))
# Import the deltas
deltas_file_name <- paste0('deltas_optimized', cbsa, '.csv')
deltas_file_path <- file.path(input_folder, cbsa_folder_name, deltas_file_name)
suppressMessages(deltas <- read_csv(deltas_file_path))
# Import the total population count
population_file_name <- 'cbg_population.csv'
population_file_path <- file.path(population_folder, population_file_name)
suppressMessages(population <- read_csv(population_file_path,
col_types = cols(home_cbg = col_character())))
# Clean the pairs data
pairs <- pairs %>%
rename(home_cbg = cbg) %>%
select(-r_cbsa)
if ('cbg_cbsa_x' %in% colnames(pairs)) {
pairs <- pairs %>%
rename(cbg_cbsa = cbg_cbsa_x) %>%
select(-cbg_cbsa_y)
}
pairs <- pairs %>% mutate(distance_km = distance / 1000) %>%
mutate(distance_km_2 = distance_km ^ 2)
pairs <- pairs %>% filter(!is.na(number_devices_residing))
pairs <- pairs %>% left_join(population)
# Conversion to data tables
pairs <- as.data.table(pairs)
deltas <- as.data.table(deltas)
setkey(pairs, sname_place_id)
setkey(deltas, sname_place_id)
# Conversion to data tables and set the variables in the outer scope
value <- list(pairs = pairs,
deltas = deltas)
return(value)
}
visits_if_area_increased <- function(sg_id, area_coef) {
# Function to compute the number of visits for a given restaurant with
# an increase area.
# IN:
# 1. sg_id - restaurant identifier.
# 2. area_coef - the coefficient on the area.
# OUT:
# 1. vector: c(sg_id, counterfactual_visits)
#
# Copy the pairs - deltas joined dataframe
pairs_deltas <- copy(pairs_deltas_iteration)
pairs_deltas[.(sg_id), delta := delta + area_coef]
pairs_deltas[,
`:=`(
choice_utility = exp(delta + rho1 * distance_km + rho2 * distance_km_2)
)
]
pairs_deltas[,
`:=`(
total_utility = 1 + sum(choice_utility)),
by = .(home_cbg)
]
pairs_deltas[,
choices_made := total_pop * days * (choice_utility / total_utility)
]
pairs_deltas <- pairs_deltas[,
.(predicted_choices = sum(choices_made)),
by = .(sname_place_id)]
value <- pairs_deltas[sname_place_id == sg_id, predicted_choices]
no_change <- visits[sname_place_id == sg_id, visits]
marginal_visits <- value - no_change
# Save summary of the results
one_line <- paste(sg_id, as.character(no_change),
as.character(value),
as.character(marginal_visits),
sep = ',')
write(one_line, file = online_results_file_path, append = TRUE)
return(list(sname_place_id = sg_id, altered_visits = value))
}
###############################################################################
################################## Main code ##################################
args <- commandArgs(trailingOnly = TRUE)
cbsa <- as.character(args[1])
rho1 <- as.numeric(args[2])
rho2 <- 0
c_a <- as.numeric(args[3])
remainder <- as.numeric(args[4])
cbsa_folder_name <- paste0('cbsa', cbsa)
cat('cbsa: ', cbsa, 'remainder: ', remainder, '\n')
# Create directory if it doesn't exist yet
# dir.create(file.path(output_folder, cbsa_folder_name), showWarnings = FALSE)
# dir.create(file.path(output_folder, cbsa_folder_name, 'parts'), showWarnings = FALSE)
# Set 'online-results' path
online_results_file_name <- paste0('online_visits_altered', cbsa,
'_remainder', as.character(remainder),
'.csv')
online_results_file_path <- file.path(output_folder, cbsa_folder_name, 'parts', online_results_file_name)
# Prepare data
list[pairs, deltas] <- PrepareData(cbsa)
# Compute the predicted number of visits
pairs_deltas_main <- merge(pairs,
deltas,
by = 'sname_place_id',
all.x = T)
pairs_deltas_main[,
`:=`(
choice_utility = exp(delta + rho1 * distance_km + rho2 * distance_km_2)
)
]
pairs_deltas_main[,
`:=`(
total_utility = 1 + sum(choice_utility)),
by = .(home_cbg)
]
pairs_deltas_main[,
choices_made := total_pop * days * (choice_utility / total_utility)
]
visits <- pairs_deltas_main[,
.(visits = sum(choices_made)),
by = .(sname_place_id)]
# Join pairs and deltas for iteration over sname id, changing area
pairs_deltas_iteration <- merge(pairs,
deltas,
by = 'sname_place_id',
all.x = T)
# Import the already-computed sname_place_ids:
already_completed_file_path <- file.path(output_folder,
paste0('cbsa', cbsa),
paste0('online_visits_altered', cbsa, '.csv'))
already_completed <- read_csv(already_completed_file_path,
col_names = FALSE)
already_completed <- already_completed %>% rename(sname_place_id = X1)
not_completed <- deltas %>%
anti_join(already_completed) %>%
arrange(sname_place_id) %>%
mutate(id = row_number()) %>%
filter((id %% 100) == remainder)
# Compute visits when extra area unit is added / subtracted
altered_visits_p1 <- lapply(not_completed$sname_place_id,
function(x) {visits_if_area_increased(x, c_a)})
# altered_visits_m1 <- lapply(deltas$sname_place_id,
# function(x) {visits_if_area_increased(x, -c_a)})
# Convert the list to dataframe
altered_visits_p1 <- rbindlist(lapply(altered_visits_p1, as.data.frame, stringsAsFactors = FALSE))
# altered_visits_m1 <- rbindlist(lapply(altered_visits_m1, as.data.frame, stringsAsFactors = FALSE))
# Rename columns
altered_visits_p1 <- altered_visits_p1 %>% rename(visits_p1 = altered_visits)
# altered_visits_m1 <- altered_visits_m1 %>% rename(visits_m1 = altered_visits)
visits <- altered_visits_p1 %>% left_join(visits) # %>% left_join(altered_visits_m1)
visits <- visits %>% mutate(diff_p1 = visits_p1 - visits) # %>% mutate(diff_m1 = visits - visits_m1)
# Export the altered visits
visits_altered_file_name <- paste0('all_visits_altered', cbsa,
'_remainder', as.character(remainder),
'.csv')
visits_altered_file_path <- file.path(output_folder,
cbsa_folder_name,
'parts',
visits_altered_file_name)
write_csv(visits, visits_altered_file_path)
###############################################################################
|
a0ddc799ef57680f8e15dde06b77b9f36b7c7034
|
688185e8e8df9b6e3c4a31fc2d43064f460665f1
|
/man/plafit.Rd
|
09d2bc6c2c64bfe13ab9b870668d04f75f654c6c
|
[] |
no_license
|
IPS-LMU/emuR
|
4b084971c56e4fed9032e40999eeeacfeb4896e8
|
eb703f23c8295c76952aa786d149c67a7b2df9b2
|
refs/heads/master
| 2023-06-09T03:51:37.328416
| 2023-05-26T11:17:13
| 2023-05-26T11:17:13
| 21,941,175
| 17
| 22
| null | 2023-05-29T12:35:55
| 2014-07-17T12:32:58
|
R
|
UTF-8
|
R
| false
| true
| 1,702
|
rd
|
plafit.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plafit.R
\name{plafit}
\alias{plafit}
\title{Calculate the coefficients of a parabola}
\usage{
plafit(wav, fit = FALSE, n = 101)
}
\arguments{
\item{wav}{a vector or single column matrix of numeric values to which the
2nd order polynomial is to be fitted.}
\item{fit}{if FALSE, return the coefficients of the polynomial; if TRUE, the
values of the polynomial are returned to the same length as the vector wav.}
\item{n}{in fitting the polynomial, linear time normalisation is first
applied to the input vector wav to 101 points. The polynomial is fitted
under the assumption that these points extend linearly in time between t =
-1 and t = 1 with t = 0 occurring at the temporal midpoint.}
}
\value{
The function returns the coefficients of c0, c1, c2 in the parabola
y = c0 + c1t + c2t\eqn{\mbox{\textasciicircum}}{^}2 where t extends between
-1 and 1. The function can also be used to derive the values of the
parabola as a function of time from the coefficients.
}
\description{
Fit a second ordered polynomial to a vector of values
}
\details{
The function fits a parabola (2nd order polynomial) following the method of
van Bergem, Speech Communication, 14, 1994, 143-162. The algorithm fixes
the parabola at the onset, midpoint, and offset of the vector i.e. such
htat the fitted parabola and original vector have the same values at these
points.
}
\examples{
# fit a polynomial to a segment of fundamental frequency data
plafit(vowlax.fund[1,]$data)
# return the fitted values of the polynomial
plafit(vowlax.fund[1,]$data, fit=TRUE)
}
\seealso{
\code{\link{dct}}
}
\author{
Jonathan Harrington
}
\keyword{math}
|
7bab8cb6845dfc10de79683229d2c5320f06e5b9
|
20d793950af5e0c63f2a55eabbd1987d2fac517f
|
/man/howell.Rd
|
2285465fb6e850baf5cf5edc21d77a063affbb91
|
[] |
no_license
|
RemkoDuursma/lgrdata
|
d708ad4af6dc586aa7ffbdc4ccf9bbb551da23bf
|
40f47e6588c056dfc9c909951e718ce4775bda3e
|
refs/heads/master
| 2020-04-18T15:36:00.468086
| 2019-06-19T09:45:57
| 2019-06-19T09:45:57
| 167,615,274
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 823
|
rd
|
howell.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/zzz.R
\docType{data}
\name{howell}
\alias{howell}
\title{Howell height, age and weight data}
\format{A data frame with 783 rows and 4 variables:
\describe{
\item{\code{sex}}{factor male or female}
\item{\code{age}}{double Age (years) }
\item{\code{weight}}{double Body weight (kg)}
\item{\code{height}}{double Total height (cm)}
}}
\source{
<https://tspace.library.utoronto.ca/handle/1807/17996>, subsetted for
non-missing data and one outlier removed.
}
\usage{
howell
}
\description{
These data were also used by McElreath (2016, "Statistical Rethinking",
CRC Press). Data include measurements of height, age and weight on Khosan people.
}
\examples{
data(howell)
with(howell, plot(age, height, pch=19, col=sex))
}
\keyword{datasets}
|
e669445adbe1db377accf4f43574147996b69e15
|
1d7683d7394100ff40ffca9a906987f553058e46
|
/MCM/美赛/PRESS分析.R
|
705ee765f2585d1d7975a0f3ca9f6cf665f5d8fc
|
[] |
no_license
|
lvksh/2018MCM
|
49353b5c5b3e0e222fcdb4042a508e1e51d857fd
|
a3a73e62f3032cc60d455025742ea0a7958da0bd
|
refs/heads/master
| 2020-07-25T12:29:09.134851
| 2019-09-13T15:24:54
| 2019-09-13T15:24:54
| 208,289,750
| 2
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 4,946
|
r
|
PRESS分析.R
|
# 灵敏度分析
# load data_2 & analysi
#####
ProblemCData_CRTCB <- read_excel("ProblemCData_CRTCB.xlsx")
Y <- filter(ProblemCData_CRTCB,
MSN == 'TPOPP' | # population
MSN == 'TETCB' | # total energy consumption
MSN == 'CRTCB') # renewable + nuclear total consumption
X <- filter(ProblemCData_CRTCB,
# consumption for energies and sectors
# NG
MSN == 'NNACB' | # NG tran
MSN == 'NNCCB' | # NG comercial
MSN == 'NNICB' | # NG industry
MSN == 'NNRCB' | # NG residential
# petro
MSN == 'PAACB' |
MSN == 'PACCB' |
MSN == 'PAICB' |
MSN == 'PARCB' |
# expen
# NG
MSN == 'NGACV' |
MSN == 'NGCCV' |
MSN == 'NGICV' |
MSN == 'NGRCV' |
# petro
MSN == 'PAACV' |
MSN == 'PACCV' |
MSN == 'PAICV' |
MSN == 'PARCV' )
Y <- filter(Y,Year >= 1970)
X <- filter(X,Year >= 1970)
Y_distinct <- function(statecode) {
YY <- sapply(1:n_distinct(Y$MSN),function(e){
temp <- filter(eval(parse(text = paste0("Y_",statecode))),MSN == unique(Y$MSN)[e])
temp <- temp[,'Data']
temp
})
YY
}
X_distinct <- function(statecode) {
XX <- sapply(1:n_distinct(X$MSN),function(e){
temp <- filter(eval(parse(text = paste0("X_",statecode))),MSN == unique(X$MSN)[e])
temp <- temp[,'Data']
temp
})
XX
}
for (statecode in c("AZ","CA","NM","TX")) {
eval(parse(text = paste0('X_',statecode,' <- filter(X,StateCode == ',paste0('"',statecode,'"'),')')))
eval(parse(text = paste0('Y_',statecode,' <- filter(Y,StateCode == ',paste0('"',statecode,'"'),')')))
}
for (statecode in c("AZ","CA","NM","TX")) {
eval(parse(text = paste0('X_',statecode,'_name <- X_distinct(',paste0('"',statecode,'"'),')')))
eval(parse(text = paste0('Y_',statecode,'_name <- Y_distinct(',paste0('"',statecode,'"'),')')))
}
for (statecode in c("AZ","CA","NM","TX")) {
eval(parse(text = paste0('X_',statecode,'_name <- data.frame(X_',statecode,'_name)')))
eval(parse(text = paste0('Y_',statecode,'_name <- data.frame(Y_',statecode,'_name)')))
}
for (statecode in c("AZ","CA","NM","TX")) {
eval(parse(text = paste0('colnames(X_',statecode,'_name) <- unique(X$MSN)')))
eval(parse(text = paste0('colnames(Y_',statecode,'_name) <- unique(Y$MSN)')))
}
# for (statecode in c("AZ","CA","NM","TX")) {
# eval(parse(text = paste0('write.table(X_',statecode,'_name',",'","X_",statecode,'_modelbuilding.csv',"',",'sep="',",","\")")))
# eval(parse(text = paste0('write.table(Y_',statecode,'_name',",'","Y_",statecode,'_modelbuilding.csv',"',",'sep="',",","\")")))
# }
#####
# AZ
# Y1 : PRESS : 27527580451 SSE : 24208194049
# Y2 : PRESS : 225347199092 SSE: 195387643205
Y <- Y_AZ_name[,-2]
X <- X_AZ_name
Y1 <- Y[,1]
Y2 <- Y[,2]
X9 <- X[,9]
X16 <- X[,16]
X13 <- X[,13]
p <- numeric(40)
for (i in 1:40){
mod <- lm(Y1[-i]~X9[-i]+X16[-i])
co <- mod$coefficients
Yh <- co[1]*1+co[2]*X9[i]+co[3]*X16[i]
p[i] <- Y1[i]-Yh
}
sum(p^2)
p <- numeric(40)
for (i in 1:40){
mod <- lm(Y2[-i]~X9[-i]+X13[-i])
co <- mod$coefficients
Yh <- co[1]*1+co[2]*X9[i]+co[3]*X13[i]
p[i] <- Y2[i]-Yh
}
sum(p^2)
# CA
# Y1 : PRESS : 9.78881890542e+11 SSE : 8.94406597838e+11
# Y2 : PRESS : 5.15478e+11 SSE: 9.1466691981e+10
Y <- Y_CA_name[,-2]
X <- X_CA_name
Y1 <- Y[,1]
Y2 <- Y[,2]
X9 <- X[,9]
# JKTCB
X3 <- X[,3]
p <- numeric(40)
for (i in 1:40){
mod <- lm(Y1[-i]~X9[-i])
co <- mod$coefficients
Yh <- co[1]*1+co[2]*X9[i]
p[i] <- Y1[i]-Yh
}
sum(p^2)
p <- numeric(40)
for (i in 1:40){
mod <- lm(Y2[-i]~JKTCB[-i]+X3[-i])
co <- mod$coefficients
Yh <- co[1]*1+co[2]*JKTCB[i]+co[3]*X3[i]
p[i] <- Y2[i]-Yh
}
sum(p^2)
# NM
# Y1 : PRESS : 61755441738 SSE : 56942150649
# Y2 : PRESS : 480666650 SSE: 388610135
Y <- Y_NM_name[,-2]
X <- X_NM_name
Y1 <- Y[,1]
Y2 <- Y[,2]
X9 <- X[,9]
X10 <- X[,10]
X16 <- X[,16]
p <- numeric(40)
for (i in 1:40){
mod <- lm(Y1[-i]~X9[-i])
co <- mod$coefficients
Yh <- co[1]*1+co[2]*X9[i]
p[i] <- Y1[i]-Yh
}
sum(p^2)
p <- numeric(40)
for (i in 1:40){
mod <- lm(Y2[-i]~X10[-i]+X16[-i])
co <- mod$coefficients
Yh <- co[1]*1+co[2]*X10[i]+co[3]*X16[i]
p[i] <- Y2[i]-Yh
}
sum(p^2)
# TX
# Y1 : PRESS : 2.250407e+14 SSE : 2.906097e+12
# Y2 : PRESS : 724282174263 SSE: 616417874230
Y <- Y_TX_name[,-2]
X <- X_TX_name
Y1 <- Y[,1]
Y2 <- Y[,2]
X9 <- X[,9]
X4 <- X[,4]
X3 <- X[,3]
X13 <- X[,13]
p <- numeric(40)
for (i in 1:40){
mod <- lm(Y1[-i]~X4[-i]+X9[-i]+X13[-i])
co <- mod$coefficients
Yh <- co[1]*1+co[2]*X4[i]+co[3]*X9[-i]+co[4]*X13[i]
p[i] <- Y1[i]-Yh
}
sum(p^2)
p <- numeric(40)
for (i in 1:40){
mod <- lm(Y2[-i]~X4[-i]+X3[-i])
co <- mod$coefficients
Yh <- co[1]*1+co[2]*X4[i]+co[3]*X3[i]
p[i] <- Y2[i]-Yh
}
sum(p^2)
|
e4d683d6ee5688088295e194267c819df70b9fb8
|
1a6bd47467a151a6c59dac7948df7a6bf1d7694a
|
/Coherence.R
|
51d15b4ba0a3f0544000172697038eb08956e6fd
|
[] |
no_license
|
gopalam/R_Matlab_Scripts
|
1c5f64fe4e279044ff5dd37fe6845de609b60a30
|
b1ecd55008bb7a7d485a5609968b75e1b01ab5f3
|
refs/heads/main
| 2023-07-22T13:40:20.623552
| 2021-09-11T20:06:25
| 2021-09-11T20:06:25
| 405,449,726
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,212
|
r
|
Coherence.R
|
Coherence <- function(signal1,signal2,Dt) {
# input signal1 , signal1, vectors to analyze for coherence.
# Dt, sampling interval (in time units of time, e.g 15 min sampling interval, Dt=1/96.)
#Frequency-dependent coherence on a pair of variables
# Dt= sampling interval in units of days
# coherence can be determined for pairs of tims series variables
# output= coherence (c) and frequency (f) vectors
#
# written as described in Menke, W., and J. Menke (2009), Environmental Data Analysis with
# MATLAB, 288 pp., Elsevier, New York.
N=length(signal1);
# round off to even number of points
N=floor(N/2)*2;
signal1=signal1[1:N];
signal2=signal2[1:N];
N=2*floor(N/2);
Nf = N/2+1;
fny = 1/(2*Dt);
Df = fny/(N/2);
f1 = Df
f2=0:(Nf-1);
f=f1*f2;
# bandwidth factors
lowside = 0.75;
highside =1.25;
# initialize matrix
C = matrix( 0 ,nrow=Nf, ncol=1)
# compute cross spectral density and power spectral density
# no need to normalize, since all normalizations cancel
u = fft( signal1[1:N] ); # fft
v = fft( signal2[1:N] ); # fft
u = u[1:Nf]; # delete negative frequencies
v = v[1:Nf];
usv = Conj(u) * v; # cross spectral density of u and v
usu = Conj(u) * u; # power spectral density of u
vsv = Conj(v) * v; # power spectral density of v
# average over band
usva=matrix( 0 ,nrow=Nf, ncol=1)
usua=matrix( 0 ,nrow=Nf, ncol=1)
vsva=matrix( 0 ,nrow=Nf, ncol=1)
for (i in 1:Nf) {
fi = Df*(i-1); # center frequency
flow = lowside*fi;
fhigh = highside*fi;
ilow = floor(flow/Df)+1;
ihigh = floor(fhigh/Df)+1;
if (ilow < 1){
ilow=1;
}
else {
if (ilow > Nf) {
ilow=Nf;
}
}
if (ihigh < 1) {
ihigh=1;
}
else {
if (ihigh > Nf) {
ihigh=Nf;
}
}
# integral over frequency range
usva = mean( usv[ilow:ihigh] );
usua = mean( usu[ilow:ihigh] );
vsva = mean( vsv[ilow:ihigh] );
C[i] = (Conj(usva)*usva) / (usua * vsva) ;
C=Re(C);
}
Ch=cbind(f,C)
return(Ch)
}
|
9097eb3ea8521d3a70bc9b5112a7b756bbd25697
|
61836adc436f52ad22dc9546c4fc435c1d581234
|
/cachematrix.R
|
ac7001db2a3954cff9006c8bf26da7d8e165f46a
|
[] |
no_license
|
sonkrishna/ProgrammingAssignment2
|
e806c637f7158295e08d9a760c1db93bcaa99189
|
28944627867098abf01ca4369c335840d1683e21
|
refs/heads/master
| 2020-12-29T18:52:31.021546
| 2014-10-26T12:35:24
| 2014-10-26T12:35:24
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,899
|
r
|
cachematrix.R
|
## This file has 2 functions.
## makeCacheMatrix - creates a wrapper around a matrix,
#helps cache matrix inverse
## cacheSolve - Computes matrix inversion if not already computed before.
#stores inverted matrix in its environment, that can be retrieved when needed
#next time
## This function,
#1. Takes in a matrix (invertible).
#2. If not given when called, matrix can be set by set function exposed
#3. set, get, setInverse, getInverse functions are returned
#4. get function gives the matrix stored in this environment
#5. setInverse function stores inverse of matrix into im
#6. getInverse returns inverse of the matrix stored in im.
makeCacheMatrix <- function(x = matrix()) {
im <- NULL
set <- function(mat) {
x <<- mat
im <<- NULL
}
get <- function() x
setInverse <- function(inv) im <<- inv
getInverse <- function() im
list(set = set, get = get,
setInverse = setInverse,
getInverse = getInverse)
}
## This function,
#1. Takes output of makeCacheMatrix as input
#2. Return of makeCacheMatrix is its environment (im and x),
#list(set, get, setInverse, getInverse)
#3. Sees if the input has inverse computed, else reads matix,
#computes inverse, and stores back in environment
#4. Returns inverse of matrix
cacheSolve <- function(x, ...) {
mi <- x$getInverse()
if(!is.null(mi)) {
message("Getting inverse")
return(mi)
}
data <- x$get()
mi <- solve(data, ...)
x$setInverse(mi)
mi
}
## Usage,
#1. m <- matrix(c(1, 0, 5, 2, 1, 6, 3, 4, 0), 3, 3) #Makes matrix
#2. setwd("D:/Krishna/Personal/Tutorial/Data Science JHU/R/Assignments
#/02/code/ProgrammingAssignment2/") #Sets working directory for codefile
#3. source("cachematrix.R") #Loads makeCacheMatrix, cacheSolve into memory
#4. auxMatrix <- makeCacheMatrix(m) # creates cache matrix functions
#5. cacheSolve(auxMatrix) #computes n stores inverse
|
6aa38a4ca594644ff0e8d629c0b5d66a415dc38f
|
55663fab76aba92730ab3b91e12b079e7d8b3466
|
/EB.R
|
d5aba94692475e656206b3e5b9b28af1ab068bbc
|
[] |
no_license
|
BimoUnderWater/Image-Processing
|
97fec43f038997499172ccdd7dbf05c5db075733
|
c48065d938d7f708a915c74250b53c6046c92f8f
|
refs/heads/main
| 2023-03-06T19:38:16.549330
| 2021-02-22T14:37:44
| 2021-02-22T14:37:44
| null | 0
| 0
| null | null | null | null |
SHIFT_JIS
|
R
| false
| false
| 10,712
|
r
|
EB.R
|
##########################################################
# 魚解析用スクリプト 2021/01/09 山中改良
# 魚解析用スクリプト 2019/04/18 山中作成
# マガン解析用スクリプト 2018/03/15 横山作成を基に作成
# できること:画像解析，暗さを指定して処理の自動停止，結果をcsv形式で自動保存，
# 解析日の自動入力，画像・マスク・結果の保存先フォルダの指定,グラフ作成,総個体数の表示
# 画像解析手法:差分→マスク処理→2値化→オブジェクトサイズ分類→ラベリング
# # ：オプション機能．使用したいときに#を消去する．
# ## ：処理過程の説明
# ### :入力が必要な部分
# 使用するためにはEBImageパッケージのインストールが必要
##########################################################
## 処理時間の計測開始
ts<-proc.time()
## EBImage（画像処理パッケージ）の起動
library(EBImage)
library(tcltk)
## 撮影情報の入力
place <- "izunuma" ### 撮影場所を入力
time <- " 04:11" ### 撮影開始時刻を入力
today <- Sys.Date() ##今日(解析を実施した日)の日付を表示
todayc <- format(today, "%Y-%m-%d")
#todayc <- "2018-07-27" ### 撮影日と解析日が異なる場合は手入力
now <- Sys.time() ##解析時時刻取得
todayc <- format(now, "-%Y%m%d-%H%M%S") ##解析時時刻文字列
todayc
## 解析に使用するパラメータを設定
th <- 0.1 ### 2値化閾値を入力
ob <- 2000 ### オブジェクトサイズを入力
stop <- 0.43 ### 解析を停止する暗さ(輝度値)を入力(izunuma171116は0.19)
erth <- 3
dith <- 81
## マスク画像を保存した作業ディレクトリ(ファイルパス)の設定
## 解析に使用する画像を保存した作業ディレクトリ(ファイルパス)の設定
file.dir <- "C:/test/1029"
### " "に指定したいフォルダの１つ前までのパスを入力しておく.パスは/で区切ること.撮影日と解析日が異なる場合は下行で手入力する
#file.dir <- "result0.10-1.csv" ### " "にフォルダのパスを入力する.パスは/で区切ること
## 解析結果と処理時間のファイルを出力したい作業ディレクトリ(ファイルパス)の設定
result.dir <- "C:/result" ### " "にフォルダのパスを入力する.パスは/で区切ること
## 解析結果の出力ファイル名を指定する
result.name <- paste0("result", todayc,th,ob,stop,erth,dith, ".csv") ### 撮影日と解析日が異なる場合は下行で手入力する
#result.name <- "result0.10-1.csv" ### " "に結果のファイル名を入力.拡張子はcsvにする
## グラフの出力ファイル名を指定する
#graph.name <- paste0("graph", todayc, ".pdf") ### 撮影日と解析日が異なる場合は下行で手入力する
#graph.name <- "graph.10-1.csv" ### " "にグラフのファイル名を入力.拡張子はjpgにする
## 処理時間の出力ファイル名を指定する
#time.name <- paste0("time", todayc, ".csv") ### 撮影日と解析日が異なる場合は下行で手入力する
#time.name <- "time0.10-1.csv" ### ""に処理時間のファイル名を入力.拡張子はcsvにする
## 解析準備
## マスク画像を保存した作業ディレクトリ(ファイルパス)を指定
#setwd(mask.dir)
## マスク画像の読み込み
#mask <- readImage(mask.file)
## 処理する画像を保存した作業ディレクトリ(ファイルパス)を指定
setwd(file.dir)
## ディレクトリ内のファイル一覧を作成
files <- list.files()
files
## 「JPG」拡張子を持つファイルをリストアップ
JPG.files <- grep("\\.jpg$", files)
JPG.files
## 処理結果を行列として定義
result <- matrix(",", nrow = length(JPG.files), ncol = 8)
## 総個体数nを定義
n <- 0
n1 <- (length(JPG.files)/3)
pb <- txtProgressBar(min=1, max=n1, style=3)
## 以下,画像処理ディレクトリ内の画像の枚数分の処理を繰り返す
for (i in 1:(length(JPG.files)/3)) {
## 画像解析
## 解析に使用する画像を読み込む.img2を解析対象の画像とし,前後の画像を使用
img1 <- readImage(files[JPG.files[3*i-2]])
img2 <- readImage(files[JPG.files[3*i-1]])
img3 <- readImage(files[JPG.files[3*i]])
#display(img2) ## 解析対象の画像を表示
#img1 <- img1*mask ##水面に反射している場合オンに
#img2 <- img2*mask
#img3 <- img3*mask
## RGBのうちRのみを抽出
imgb1 <- channel(img1,"red")
imgb2 <- channel(img2,"red")
imgb3 <- channel(img3,"red")
#display(imgb2) ## B画像の表示
#writeImage(imgb2,"imgb2.jpg") ## B画像の保存
## 全ての輝度値の平均を算出
mean <- mean(imgb2[,])
## 画像が明るくなりすぎたら解析を停止
if(mean >= stop) {
## 全ての輝度値の中央値を算出
#median1 <- median(imgb1[,])
#median2 <- median(imgb2[,])
#median3 <- median(imgb3[,])
#median <- abs(median1-median2)
##全部で動かす
#if(0.005 <= median) {th <- (median*4)+0.01}
##明るさが変わったら止める
#if(median <= 0.001) {
## 背景差分
## 前後の写真との差分により背景及び雲を除去する．雲は数秒で動き難い．
## img2を減ずるのはの画像を白の値にするため．
## 前後平均画像との背景差分
imgd <- imgb1-imgb2
imgd3 <- imgb3-imgb2
imgd2 <- abs(imgd)
imgd4 <- abs(imgd3)
## 単純2値化
#hist(imgm) ## 輝度のヒストグラムの表示
### ヒストグラムグラム等により閾値を設定.小さい値ほど残りやすく,大きい値ほど消えやすい
imgt <- imgd2 >th
imgt3 <- imgd4 >th
#display(imgt) ## 2値化画像の表示
#writeImage(imgt,"imgt.jpg") ## 2値化画像の 保存
kern <- makeBrush(erth, shape="disc")## フィルタの作成
imge1<-erode(imgt,kern)## 明るい部分を減らす
imge3<-erode(imgt3,kern)## 明るい部分を減らす
kern2 <- makeBrush(dith, shape="disc")## フィルタの作成
imgm1<-dilate(imge1,kern2)## 明るい部分を増やす
imgm3<-dilate(imge3,kern2)## 明るい部分を増やす
imgm<-fillHull(imgm1) ##穴の開いた魚を無くす
imgm2<-fillHull(imgm3) ##穴の開いた魚を無くす
#display(imgd) ## 差分画像の表示
#writeImage(imgd,"imgd.jpg") ## 差分画像の保存
## マスク処理
## 画像にマスクをかける
#imgm <- imgm*mask ## 差分画像とマスク範囲が重なる部分（=魚(ノイズを含む)）のみを残す
#display(imgm) ## マスク画像の表示
#writeImage(imgm,"imgm.jpg") ## マスク画像の保存
## ノイズ除去
## オブジェクトサイズ処理によるノイズ除去
## オブジェクトのサイズをリストアップ
imgt <- bwlabel(imgm)
imgt3 <- bwlabel(imgm2)
sizelist <- computeFeatures.shape(imgt)[,1]
sizelist3 <- computeFeatures.shape(imgt3)[,1]
## 対象が多すぎると時間がかかる．閾値とのバランス
#hist(sizelist) ## オブジェクトサイズのヒストグラムを表示
## オブジェクトサイズがより小さい対象をノイズとして除去
## 除去するオブジェクトサイズを設定.ヒストグラムと実際の魚のオブジェクトサイズから判断(600m→2.2px,1km→1.2px)
objn <- which(sizelist >= ob) ## 設定したサイズ以上のオブジェクトを魚として抽出
objn3 <- which(sizelist3 >= ob)
a <- 1:length(sizelist)
a3 <- 1:length(sizelist3)
if(length(objn) == 0){
b <- a
}else{
b <- a[-objn]
}
if(length(objn3) == 0){
b3 <- a3
}else{
b3 <- a3[-objn3]
}
imgn <- rmObjects(imgt, b) ## 小さいオブジェクト（＝ノイズ）を消去
imgn3 <- rmObjects(imgt3, b3) ## 小さいオブジェクト（＝ノイズ）を消去
#display(imgn) ## ノイズ除去画像の表示
#s <- as.character(i)
#s1 <- paste("imgn",s,".jpg", sep="")
#writeImage(imgn,s1)
##ラベリング（オブジェクトに通し番号をつける）
imgl <- bwlabel(imgn)
imgl3 <- bwlabel(imgn3)
#}else{imgl <- 0}
}else{
imgl <- 0
imgl3 <- 0
}
## カウント結果
## 処理結果にファイル名,カウント数,輝度値の平均を入れる．
result[3*i-2,1] <- 3*i-2 ### グラフの横軸に使用する.撮影間隔に従い式を入力する
result[3*i-2,3] <- files[JPG.files[3*i-2]] ##写真のファイル名
result[3*i-2,5] <- max(imgl) ## ラベリングの最大値（＝オブジェクトの個数）
result[3*i-2,7] <- mean ## 輝度値の平均
result[3*i-1,1] <- 3*i-1 ### グラフの横軸に使用する.撮影間隔に従い式を入力する
result[3*i-1,3] <- files[JPG.files[3*i-1]] ##写真のファイル名
result[3*i-1,5] <- max(imgl3) ## ラベリングの最大値（＝オブジェクトの個数）
result[3*i-1,7] <- mean ## 輝度値の平均
result[3*i,1] <- 3*i ### グラフの横軸に使用する.撮影間隔に従い式を入力する
result[3*i,3] <- files[JPG.files[3*i]] ##写真のファイル名
result[3*i,5] <- 0 ## ラベリングの最大値（＝オブジェクトの個数）
result[3*i,7] <- 0 ## 輝度値の平均
setTxtProgressBar(pb, i)
## 処理結果を出力保存
setwd(result.dir)
write(t(result), file=result.name, ncolumns=8)
## 処理結果のグラフを作成・出力保存
#x <- result[,1]
#y <- result[,5]
#n <- n+max(imgl)
### 軸の最大・最小、ラベル名、プロットの色や形等を任意で指定する
#plot(x, y, type = "n", ylim=c(0,80), xlab = "photo [枚]", ylab = "fish count [匹]")
#points(x, y , col = "red", pch = 16)
#lines(x, y, col = "red")
## グラフに撮影情報を表示
#date <- paste0(todayc, time)
#n.picture <- paste0("Number of pictures: ", length(JPG.files))
#n.fish <- paste0("Total count of fishes: ", n)
#mtext(place,side=3,line=3,adj=0,cex=1)
#mtext(date,side=3,line=2,adj=0,cex=1)
#mtext(n.picture,side=3,line=1,adj=0,cex=1)
#mtext(n.fish,side=3,line=0,adj=0,cex=1)
#dev.copy(device=pdf, file=graph.name, family="Japan1GothicBBB")
#dev.off()
## 処理時間の計測終了
te <- proc.time()-ts ## 処理時間＝終了時間-開始時間
## 処理時間を出力保存
#write(te, file=time.name)
## 次の解析のため,作業ディレクトリを画像が保存されたフォルダに戻す
setwd(file.dir)
}
result
te
|
65f7e9c54ce4dcb74e5b82fa6b7999cd14b82d71
|
a113344ac3a2bd80b0528079183ce6c43e4b65de
|
/man/dot-get_H.Rd
|
65f5cb12237a66d085d0c0778a15cb96d70e5798
|
[
"MIT"
] |
permissive
|
cdmuir/tealeaves
|
2ae939d9a2f2560f2b5af38c1bdbae9ae053b119
|
f4495e2403107d7b549165ac5f8c3a48bbac086a
|
refs/heads/master
| 2022-08-11T23:27:56.357289
| 2022-07-20T02:31:39
| 2022-07-20T02:31:39
| 156,582,831
| 7
| 1
| null | null | null | null |
UTF-8
|
R
| false
| true
| 1,648
|
rd
|
dot-get_H.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/tleaves.R
\name{.get_H}
\alias{.get_H}
\title{H: sensible heat flux density (W / m^2)}
\usage{
.get_H(T_leaf, pars, unitless)
}
\arguments{
\item{T_leaf}{Leaf temperature in Kelvin}
\item{pars}{Concatenated parameters (\code{leaf_par}, \code{enviro_par}, and \code{constants})}
\item{unitless}{Logical. Should function use parameters with \code{units}? The function is faster when FALSE, but input must be in correct units or else results will be incorrect without any warning.}
}
\value{
Value in W / m\eqn{^2} of class \code{units}
}
\description{
H: sensible heat flux density (W / m^2)
}
\details{
\deqn{H = P_\mathrm{a} c_p g_\mathrm{h} (T_\mathrm{leaf} - T_\mathrm{air})}{H = P_a c_p g_h * (T_leaf - T_air)}
\tabular{lllll}{
\emph{Symbol} \tab \emph{R} \tab \emph{Description} \tab \emph{Units} \tab \emph{Default}\cr
\eqn{c_p} \tab \code{c_p} \tab heat capacity of air \tab J / (g K) \tab 1.01\cr
\eqn{g_\mathrm{h}}{g_h} \tab \code{g_h} \tab boundary layer conductance to heat \tab m / s \tab \link[=.get_gh]{calculated}\cr
\eqn{P_\mathrm{a}}{P_a} \tab \code{P_a} \tab density of dry air \tab g / m^3 \tab \link[=.get_Pa]{calculated}\cr
\eqn{T_\mathrm{air}}{T_air} \tab \code{T_air} \tab air temperature \tab K \tab 298.15\cr
\eqn{T_\mathrm{leaf}}{T_leaf} \tab \code{T_leaf} \tab leaf temperature \tab K \tab input
}
}
\examples{
library(tealeaves)
cs <- make_constants()
ep <- make_enviropar()
lp <- make_leafpar()
T_leaf <- set_units(298.15, K)
tealeaves:::.get_H(T_leaf, c(cs, ep, lp), FALSE)
}
\seealso{
\code{\link{.get_gh}}, \code{\link{.get_Pa}}
}
|
62891260bee7e1b59f5fd1881c36e3c215b25884
|
6cc899d63c4bab1e667cc0f1c1e6286fbb8596fc
|
/man/dunn_index.Rd
|
e089c236cd5d413931b126f5d3dfe646ed7f25dd
|
[] |
no_license
|
mobius-eng/ravcutils
|
1b26a513b8b6eecb4d71ed26f775d462c1870287
|
7e3138c0b8ac1df8799bde50b391de2205f98d92
|
refs/heads/master
| 2023-08-13T11:32:27.675698
| 2021-10-13T10:48:09
| 2021-10-13T10:48:09
| 373,436,701
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 587
|
rd
|
dunn_index.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ravcutils.R
\name{dunn_index}
\alias{dunn_index}
\title{Dunn index to select cluster numbers}
\usage{
dunn_index(data, classification, centroids, stdev)
}
\arguments{
\item{data}{matrix of observations}
\item{classification}{vector of classiffication labels for each observation}
\item{centroids}{Matrix whose each row is the centroid of a cluster}
\item{stdev}{vector of standard deviations for each variable}
}
\description{
Optimal selection is done for the number of clusters that maximize the index
}
|
26d905c831600213e5c1790bb189eaab74227c2f
|
ef8bf01e222787092e3c62dadf19c573558b68b5
|
/man/TF_EAE_LAND_OCCUPTN_1991.Rd
|
efa2ec3f2d240749b161175a909116d287eebd1b
|
[
"CC-BY-2.0"
] |
permissive
|
weRbelgium/BelgiumStatistics
|
fdd8998e52ae43bf1564a61cba6a676edacd8dd4
|
37758b79164412322db35e9bb172d0ea5f3b192b
|
refs/heads/master
| 2021-01-21T05:59:22.308770
| 2015-11-19T22:59:05
| 2015-11-19T22:59:05
| 44,752,835
| 17
| 2
| null | null | null | null |
UTF-8
|
R
| false
| false
| 651
|
rd
|
TF_EAE_LAND_OCCUPTN_1991.Rd
|
% Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/datasets.R
\docType{data}
\name{TF_EAE_LAND_OCCUPTN_1991}
\alias{TF_EAE_LAND_OCCUPTN_1991}
\title{TF_EAE_LAND_OCCUPTN_1991}
\description{
leefmilieu: TF_EAE_LAND_OCCUPTN_1991. More information about this data can be found in the inst/docs folder and at \url{http://statbel.fgov.be/nl/statistieken/opendata/datasets/leefmilieu}
}
\examples{
\dontrun{
data(TF_EAE_LAND_OCCUPTN_1991)
str(TF_EAE_LAND_OCCUPTN_1991)
}
}
\references{
\url{http://statbel.fgov.be/nl/statistieken/opendata/home}, \url{http://statbel.fgov.be/nl/statistieken/opendata/datasets/leefmilieu}
}
|
9b03d9f8e06a7b42b959725a4f2ee1894a4ba66a
|
10955e82b2b8e760bb5aed755dcc4555a6ee7ae4
|
/R/script_ProATPsyn.R
|
3be946eb45e933aef12939667f8975708f09ff8e
|
[] |
no_license
|
wangjs/BioCycTU
|
b0dbd7aa94b4c8a92af45779946fed5664ebf1f0
|
d278b20f301ef30267f8b47495b6b21e0df4b08b
|
refs/heads/master
| 2021-05-06T10:32:04.256753
| 2014-06-03T19:22:13
| 2014-06-03T19:22:13
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 11,894
|
r
|
script_ProATPsyn.R
|
#! /usr/bin/Rscript --vanilla
## subunit KOID
## beta K02112
## alpha K02111
## gamma K02115
## delta K02113
## epsilon K02114
## c K02110
## a K02108
## b K02109
CutSeq <- function(cutSeq){
# USE: cut a vector based on the 'cutSeq'. This function is used to cut "full" seq. It means the sum of 'cutSeq' should be equal to the length of vector, which is used to cut.
# INPUT: 'cutSeq' a sequence used to cut the vector.
# OUTPUT: the index matrix
# remove 0, because we cannot cut a sequence by the internal of 0.
cutSeq <- cutSeq[cutSeq != 0]
vecCutseq <- length(cutSeq)
if (vecCutseq == 1) {
headCut <- 1
endCut <- cutSeq
} else {
# loopCutSeq is the circle of vecCutseq
loopCutSeq <- list()
for(i in 1:vecCutseq) {
loopCutSeq[[i]] <- cutSeq[1:i]
}
loopSumCutSeq <- sapply(loopCutSeq, sum)
# the head and tail sequence
headCut <- c(1,loopSumCutSeq[1:(vecCutseq-1)]+1)
endCut <- loopSumCutSeq
}
cutMat <- matrix(c(headCut, endCut), 2, byrow=TRUE)
return(cutMat)
}
CutSeqEqu <- function(vecLen, equNum){
# USE: to cut a vector with equal internal.
# INPUT: 'vecLen' the length of vector used to cut. 'equNum' the equal internal
# OUTPUT: the index matrix.
if (equNum > vecLen){
# the internal is bigger than the length of vecLen. So we use the full vecLen.
cutMat <- matrix(c(1, vecLen))
} else {
timeNum <- vecLen %/% equNum
remainer <- vecLen %% equNum
cutSeq <- c(rep(equNum, timeNum), remainer)
cutMat <- CutSeq(cutSeq)
}
return(cutMat)
}
# load KEGG and BioCyc database
load('biocycSpe.RData')
load('bioKEGGSpe.RData')
load('KEGGCycAPI.RData')
library(foreach)
library(doMC)
library(XML)
registerDoMC(4)
# the common species
commSpe <- merge(biocycSpe, bioKEGGSpe, by.x = 'TaxonomyID', by.y = 'TaxonomyID', sort = FALSE)
commProSpe <- commSpe[grepl('Prokaryotes', commSpe[, 8]), ]
# remove duplicate BioCyc speID according to KEGG speID
commKEGGVec <- commProSpe[duplicated(commProSpe[, 6]), 6]
commKEGGMat <- commProSpe[commProSpe[, 6] %in% commKEGGVec, ]
delRowName <- c('553', '839', '1057', '1056', '1066', '1346', '1267', '1481', '1480', '1482', '1582', '1648', '1645', '1647', '1723', '1681', '1666', '1902', '2107', '1918', '2299', '2473')
commProSpe <- commProSpe[!(rownames(commProSpe) %in% delRowName), ]
# some species names changed
commProSpe[commProSpe[, 2] %in% 'BANT198094-WGS', 2] <- 'ANTHRA'
## # **select species used in phylogenetic profiling**
## phyloProSpe <- read.csv('wholeListFile.csv', row.names = 1)
## commProSpe <- commProSpe[commProSpe[, 6] %in% phyloProSpe[, 2], ]
# get KO list
KOVec <- c('K02111', 'K02112', 'K02115', 'K02113', 'K02114', 'K02110', 'K02108', 'K02109')
names(KOVec) <- c('alpha', 'beta', 'gamma', 'delta', 'epsilon', 'c', 'a', 'b')
KOList <- vector('list', 8)
names(KOList) <- paste(names(KOVec), 'KO', sep = '')
for (i in 1:8) {
KOMat <- getKEGGKO(KOVec[i])
KOMat <- KOMat[KOMat[, 1] %in% commProSpe[, 6], ]
KOList[[i]] <- KOMat
}
# whole species vector
wholeSpe = vector()
for (i in 1:8) {
wholeSpe = union(wholeSpe, KOList[[i]][, 1])
}
# merge
ATPKO <- vector('list', length(wholeSpe))
names(ATPKO) <- wholeSpe
for (i in 1:length(wholeSpe)) {
eachSpe <- lapply(KOList, function(x) {
eachSpeKO <- x[x[, 1] %in% wholeSpe[i], ,drop = FALSE]
# some species may lack certain subuints
speKONum <- nrow(eachSpeKO)
if (speKONum == 0) {
uniKO <- NA
} else {
uniKO <- eachSpeKO[, 2]
}
return(unname(uniKO))
})
ATPKO[[i]] <- unlist(eachSpe)
}
# transfer KEGG ID to BioCyc ID
# BioCyc speID
uniSpe <- commProSpe[commProSpe[, 6] %in% wholeSpe, ]
uniSpe <- uniSpe[order(as.character(uniSpe[, 6])), ]
uniSpe <- uniSpe[rank(wholeSpe), ]
ATPKOKEGGSpe <- names(ATPKO)
names(ATPKO) <- as.character(uniSpe[, 2])
tmp1 <- ATPKO
# some may contain ''', like 'atpF'', and could not be identified by R ramote server
for (i in 1:length(ATPKO)) {
print(paste('It is running ', i, '.', sep = ''))
x <- ATPKO[[i]]
KEGGIDVec <- paste(ATPKOKEGGSpe[i], x, sep = ':')
KEGGIDVec <- paste(KEGGIDVec, collapse='+')
KEGGsymTable <- webTable(paste('http://rest.kegg.jp/list/', KEGGIDVec, sep = ''), n = 2)
KEGGsym <- KEGGsymTable[, 2]
KEGGsym <- sapply(strsplit(KEGGsym, split = ';', fixed = TRUE), '[', 1)
y <- character(length = length(x))
y[which(is.na(x))] <- NA
y[which(!is.na(x))] <- KEGGsym
hasSym <- which(!grepl(' ', y))
x[hasSym] <- y[hasSym]
ATPKO[[i]] <- x
}
identical(sapply(tmp1, length), sapply(ATPKO, length))
identical(sapply(tmp1, is.na), sapply(tmp1, is.na))
# sperate ATPKO
hasDot <- sapply(ATPKO, function(x) {
hasDotEach <- grepl('\'', x)
if (sum(hasDotEach) > 0){
return(TRUE)
} else {
return(FALSE)
}
})
save(ATPKO, ATPKOKEGGSpe, file = 'ATPKO.RData')
# ========================== script =======================
ATPKODot <- ATPKO[hasDot]
ATPKOKEGGSpeDOT <- ATPKOKEGGSpe[hasDot]
save(ATPKODot, ATPKOKEGGSpeDOT, file = 'ATPKODOT.RData')
ATPKO <- ATPKO[!hasDot]
ATPKOKEGGSpe <-ATPKOKEGGSpe[!hasDot]
save(ATPKO, ATPKOKEGGSpe, file = 'ATPKONODOT.RData')
# =========================================================
# BioCyc geneID. Cut the whole length with internal 4
cutMat <- CutSeqEqu(length(ATPKODot), 4)
for (j in 1:ncol(cutMat)) {
ATPKOCycPart <- foreach (i = cutMat[1, j]:cutMat[2, j]) %dopar% {
# may have NA
print(paste('It is running ', i, ' with the name of ', names(ATPKODot)[i], '.', sep = ''))
iniVal <- ATPKODot[[i]]
iniVal[!is.na(iniVal)] <- sapply(iniVal[!is.na(iniVal)], KEGGID2CycID, speKEGGID = ATPKOKEGGSpeDOT[i], speCycID = names(ATPKODot)[i])
return(iniVal)
}
names(ATPKOCycPart) <- names(ATPKODot)[cutMat[1, j]:cutMat[2, j]]
save(ATPKOCycPart, file = paste('ATPKOCycPartDOT', cutMat[1, j], '_', cutMat[2, j], '.RData', sep = ''))
Sys.sleep(30)
}
# BioCyc geneID. Cut the whole length with internal 4
cutMat <- CutSeqEqu(length(ATPKO), 4)
for (j in 1:ncol(cutMat)) {
ATPKOCycPart <- foreach (i = cutMat[1, j]:cutMat[2, j]) %dopar% {
# may have NA
print(paste('It is running ', i, ' with the name of ', names(ATPKO)[i], '.', sep = ''))
iniVal <- ATPKO[[i]]
iniVal[!is.na(iniVal)] <- sapply(iniVal[!is.na(iniVal)], KEGGID2CycID, speKEGGID = ATPKOKEGGSpe[i], speCycID = names(ATPKO)[i])
return(iniVal)
}
names(ATPKOCycPart) <- names(ATPKO)[cutMat[1, j]:cutMat[2, j]]
save(ATPKOCycPart, file = paste('ATPKOCycNODOTPart', cutMat[1, j], '_', cutMat[2, j], '.RData', sep = ''))
Sys.sleep(60)
}
list2list <- function(lsInput){
# not support NA
## $alphaKO
## [1] "TU0-6636" "TU0-6635" "TU00243"
## $betaKO
## [1] "TU0-6636" "TU0-6635" "TU00243"
## $gammaKO
## [1] "TU0-6636" "TU0-6635" "TU00243"
## $detaKO
## [1] "TU0-6636" "TU0-6635" "TU00243"
## $epsilonKO
## [1] "TU0-42328" "TU0-6636" "TU0-6635" "TU00243"
## $cKO
## [1] "TU0-6636" "TU0-6635" "TU00243"
## $aKO
## [1] "TU0-6636" "TU0-6635" "TU00243"
## $bKO
## [1] "TU0-6636" "TU0-6635" "TU00243"
uniEle <- unique(unlist(lsInput))
uniList <- vector('list', length(uniEle))
names(uniList) <- uniEle
for (i in 1:length(uniEle)) {
uniList[[i]] <- names(lsInput)[sapply(lsInput, function(x){uniEle[i] %in% x})]
}
return(uniList)
}
# transcription unit
ATPTU <- vector('list', length(ATPKO))
names(ATPTU) <- names(ATPKO)
for (i in 1:length(ATPKO)) {
# get TU name
eachTU <- lapply(ATPKO[[i]], function(x) {
if (is.na(x)) {
TU <- NA
} else {
eachGeneInfo <- getCycTUfGene(x, speID = names(ATPKO)[i])
TU <- eachGeneInfo
if (is.null(TU)) {
TU <- NA
} else {}
}
return(TU)
})
# range the list
# select non NA
hasNA <- sapply(eachTU, function(x) {
if (sum(is.na(x)) > 0) {
return(TRUE)
} else {
return(FALSE)
}
})
TUnona <- eachTU[which(!hasNA)]
TUList <- list2list(TUnona)
# select NA
TUna <- eachTU[which(hasNA)]
if (length(TUna) != 0) {
TUList$noTU <- names(TUna)
} else {}
ATPTU[[i]] <- TUList
}
save(ATPTU, file = 'ATPCycPhyloTU.RData')
#################### process the repeat data ################
load('wholeTUList.RData')
delNum <- 284
nonDelNum <- which(!((1:length(wholeTUList)) %in% delNum))
wholeTUList <- wholeTUList[nonDelNum]
duNames <- names(wholeTUList)[which(duplicated(names(wholeTUList)))]
which(names(wholeTUList) %in% duNames)
wholeTUList[which(names(wholeTUList) %in% duNames)]
load('ATPKOCyc.RData')
delNum <- c(1240, 1293, 1428, 1857)
nonDelNum <- which(!((1:length(ATPKOCyc)) %in% delNum))
ATPKOCyc <- ATPKOCyc[nonDelNum]
duNames <- names(ATPKOCyc)[which(duplicated(names(ATPKOCyc)))]
which(names(ATPKOCyc) %in% duNames)
ATPKOCyc[which(names(ATPKOCyc) %in% duNames)]
############## calculate the loss/repeat ATP ############
ATPSubMat <- matrix(ncol = 8, nrow = length(ATPKO))
colnames(ATPSubMat) <- names(KOVec)
rownames(ATPSubMat) <- names(ATPKO)
for (i in 1:length(ATPKO)) {
subGene <- ATPKO[[i]]
subName <- names(subGene)
ATPsubName <- names(KOVec)
subNamePat <- paste('^', ATPsubName, 'KO', sep = '')
subNum <- integer(8)
for (j in 1:8) {
sub <- subGene[grep(subNamePat[j], subName)]
if (sum(is.na(sub)) > 0) {
num <- 0
} else {
num <- length(sub)
}
subNum[j] <- num
}
ATPSubMat[i, ] <- subNum
}
write.csv(ATPSubMat, 'ATPSubMat.csv')
ATPSubMat2 <- apply(ATPSubMat, 1:2, function(x) {
if (x > 0) {
x <- 1
} else {
x <- 0
}
return(x)
})
require(pheatmap)
subComplexCol <- data.frame(Subunit = factor(c(rep(1, 5), rep(2, 3)), labels = c('F1', 'Fo')))
rownames(subComplexCol) <- names(KOVec)
pheatmap(as.matrix(dist(t(ATPSubMat), diag = TRUE, upper = TRUE)), annotation = subComplexCol, clustering_method = "average", cellwidth = 15, cellheight = 12)
########################## test code ########################
## test2 <- ATPKO[1:2]
## for (i in 1:length(test2)){
## may have NA
## iniVal <- test2[[i]]
## iniVal[!is.na(iniVal)] <- names(KEGGID2CycID(iniVal[!is.na(iniVal)], names(test2)[i], n = 4))
## test2[[i]] <- iniVal
## }
## test1 <- vector('list', 3)
## names(test1) <- c('ECOLI', 'RRUB269796', 'MTUB1304279-WGS')
## ecoKO <- c('EG10098', 'EG10101', 'EG10104', 'EG10105', 'EG10100', 'EG10102', 'EG10099', 'EG10103')
## names(ecoKO) <- names(ATPKO$ECOLI)
## rruKO <- c('GCN1-1247', 'GCN1-1249', 'GCN1-1248', 'GCN1-1246', 'GCN1-1250', 'GCN1-3300', 'GCN1-3301', 'GCN1-3298', 'GCN1-3299')
## names(rruKO) <- names(ATPKO$RRUB269796)
## mtuhKO <- c(NA, NA, 'GSRF-1250', NA, 'GSRF-1251', 'GSRF-1247', 'GSRF-1246', 'GSRF-1248')
## names(mtuhKO) <- names(ATPKO$`MTUB1304279-WGS`)
## test1[[1]] <- ecoKO
## test1[[2]] <- rruKO
## test1[[3]] <- mtuhKO
## eachTU <- lapply(test1[[2]], function(x) {
## if (is.na(x)) {
## TU <- NA
## } else {
## eachGeneInfo <- getCycTUfGene(x, speID = 'RRUB269796')
## TU <- eachGeneInfo
## if (is.null(TU)) {
## TU <- NA
## } else {}
## }
## return(TU)
## })
## ATPTU <- vector('list', length(test1))
## names(ATPTU) <- names(test1)
## for (i in 1:length(test1)) {
## get TU name
## eachTU <- lapply(test1[[i]], function(x) {
## if (is.na(x)) {
## TU <- NA
## } else {
## eachGeneInfo <- getCycTUfGene(x, speID = names(test1)[i])
## TU <- eachGeneInfo
## if (is.null(TU)) {
## TU <- NA
## } else {}
## }
## return(TU)
## })
## range the list
## select non NA
## hasNA <- sapply(eachTU, function(x) {
## if (sum(is.na(x)) > 0) {
## return(TRUE)
## } else {
## return(FALSE)
## }
## })
## TUnona <- eachTU[which(!hasNA)]
## TUList <- list2list(TUnona)
## select NA
## TUna <- eachTU[which(hasNA)]
## if (length(TUna) != 0) {
## TUList$noTU <- names(TUna)
## } else {}
## ATPTU[[i]] <- TUList
## }
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.