pierreqi/r_code_50k · Datasets at Hugging Face

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gardasil.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/gardasil.R \docType{data} \name{gardasil} \alias{gardasil} \title{The Gardasil dataset in R data.frame format} \format{A data.frame with 1413 rows and 11 variables. \describe{ \item{Age}{Age in years} \item{AgeGroup}{Categorical age: 18-26 or 17-27} \item{Race}{white/black/hispanic/"other/unknown"} \item{Shots}{} \item{Completed}{yes/no} \item{InsuranceType}{medical assistance / private payer / hospital based / military} \item{MedAssist}{yes/no} \item{Location}{Four locations} \item{LocationType}{urban/suburban} \item{PracticeType}{pediatric / family practice / OB-GYN} \item{RaceSummary}{white/minority/"other/unknown"} }} \source{ Chou B, Krill LS, Horton BB, Barat CE, Trimble CL: Disparities in human papillomavirus vaccine completion among vaccine initiators. Obstet. Gynecol. 2011, 118:14–20. } \usage{ gardasil } \description{ The Gardasil dataset in R data.frame format } \details{ Note that a cleaned dataset like this is provided, it is recommended to place the code that produces it in data-raw. You can then use usethis::use_data_raw() to update the cleaned dataset in data/. See http://r-pkgs.had.co.nz/data.html. } \keyword{datasets}

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#' Startup script for PhyloProfile #' 1) install and load packages #' 2) start the PhyloProfile app source("R/functions.R") # List of dependent packages -------------------------------------------------- packages <- c("shiny", "shinyBS", "shinyjs", "colourpicker", "DT", "devtools", "ggplot2", "reshape2", "plyr", "dplyr", "tidyr", "scales", "grid", "gridExtra", "ape", "stringr", "gtable", "dendextend", "ggdendro", "gplots", "data.table", "taxize", "zoo", "RCurl", "energy", "RColorBrewer") # Set path for install packages while deploy into shiny server ---------------- # (from https://gist.github.com/wch/c3653fb39a00c63b33cf) # Find & install missing packages --------------------------------------------- installPackages(packages) # Load packages lapply(packages, library, character.only = TRUE) # Check version and install ggplot2 (require v >= 2.2.0) ---------------------- version_above <- function(pkg, than) { compareVersion(as.character(packageVersion(pkg)), than) } if ("ggplot2" %in% rownames(installed.packages())) { installPackages("ggplot2") library(ggplot2) } # Install packages from bioconductor ------------------------------------------ bioconductor_pkgs <- c("Biostrings", "bioDist") installPackagesBioconductor(bioconductor_pkgs) lapply(bioconductor_pkgs, library, character.only = TRUE) # Install OmaDB and its dependencies oma_pkgs <- c("GO.db", "GenomeInfoDbData") installPackagesBioconductor(oma_pkgs) lapply(oma_pkgs, library, character.only = TRUE) if (!("OmaDB" %in% rownames(installed.packages()))) { devtools::install_github("trvinh/OmaDB", force = TRUE) } library(OmaDB) # Install shinycssloaders from github ----------------------------------------- if (!("shinycssloaders" %in% rownames(installed.packages()))) { devtools::install_github("andrewsali/shinycssloaders", force = TRUE) library(shinycssloaders) }

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/alloy.R

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alloy.R

unique(x$alloy) length(unique(x$alloy)) # 60 # remove double spaces, commas, periods, caps # filter for unique sounds xx <- x %>% mutate(alloy = str_replace_all(alloy, '\\ ', '')) %>% mutate(alloy = str_replace_all(alloy, '\\,', '')) %>% mutate(alloy = str_replace_all(alloy, '\\.', '')) %>% mutate(alloy = str_to_lower( alloy)) # select(request, alloy) xx$alloy unique(xx$alloy) length(unique(xx$alloy)) # 47 # search aluminums y <- dplyr::filter(xx, grepl('al', alloy)) unique(y$alloy) # convert aluminums y <- xx %>% mutate(alloy.new = alloy) %>% mutate(alloy.new = ifelse(grepl('al',alloy), "aluminum", alloy.new)) # confirm unique(y$alloy.new) length(unique(y$alloy.new)) # 41 # search y <- dplyr::filter(xx, grepl('ductile', alloy)) unique(y$alloy) # convert y <- xx %>% mutate(alloy.new = alloy) %>% mutate(alloy.new = ifelse(grepl('al',alloy), "aluminum", alloy.new)) %>% mutate(alloy.new = ifelse(grepl('ductile',alloy), "ductile iron", alloy.new)) # confirm unique(y$alloy.new) length(unique(y$alloy.new)) # 36 # search y <- dplyr::filter(xx, grepl('gray', alloy)) unique(y$alloy) # convert y <- xx %>% mutate(alloy.new = alloy) %>% mutate(alloy.new = ifelse(grepl('al',alloy), "aluminum", alloy.new)) %>% mutate(alloy.new = ifelse(grepl('ductile',alloy), "ductile iron", alloy.new)) %>% mutate(alloy.new = ifelse(grepl('di',alloy), "ductile iron", alloy.new)) %>% mutate(alloy.new = ifelse(grepl('gray',alloy), "grey iron", alloy.new)) # confirm unique(y$alloy.new) length(unique(y$alloy.new)) # 29 # search y <- dplyr::filter(xx, grepl('cg', alloy)) unique(y$alloy) # convert y <- xx %>% mutate(alloy.new = alloy) %>% mutate(alloy.new = ifelse(grepl('al',alloy), "aluminum", alloy.new)) %>% mutate(alloy.new = ifelse(grepl('ductile',alloy), "ductile iron", alloy.new)) %>% mutate(alloy.new = ifelse(grepl('di',alloy), "ductile iron", alloy.new)) %>% mutate(alloy.new = ifelse(grepl('gray',alloy), "grey iron", alloy.new)) %>% mutate(alloy.new = ifelse(grepl('cg',alloy), "cgi", alloy.new)) # confirm unique(y$alloy.new) length(unique(y$alloy.new)) # 28 # search y <- dplyr::filter(xx, grepl("ss", alloy)) unique(y$alloy) # convert y <- xx %>% mutate(alloy.new = alloy) %>% mutate(alloy.new = str_replace_all(alloy.new, "[:punct:]","none")) %>% mutate(alloy.new = ifelse(grepl('al',alloy), "aluminum", alloy.new)) %>% mutate(alloy.new = ifelse(grepl('di',alloy), "ductile iron", alloy.new)) %>% mutate(alloy.new = ifelse(grepl('ductile',alloy), "ductile iron", alloy.new)) %>% mutate(alloy.new = ifelse(grepl('le iron',alloy), "ductile iron", alloy.new)) %>% mutate(alloy.new = ifelse(grepl('gray',alloy), "grey iron", alloy.new)) %>% mutate(alloy.new = ifelse(grepl('y iron',alloy), "grey iron", alloy.new)) %>% mutate(alloy.new = ifelse(grepl('cg',alloy), "cgi", alloy.new)) %>% mutate(alloy.new = ifelse(grepl('brass',alloy), "bras", alloy.new)) %>% mutate(alloy.new = ifelse(grepl('s steel',alloy), "stainless", alloy.new)) %>% mutate(alloy.new = ifelse(grepl('44',alloy), "stainless", alloy.new)) %>% mutate(alloy.new = ifelse(grepl('ss',alloy), "stainless", alloy.new)) %>% mutate(alloy.new = ifelse(grepl('teel',alloy), "lc steel", alloy.new)) %>% mutate(alloy.new = ifelse(grepl('bras',alloy), "brass", alloy.new)) %>% mutate(alloy.new = ifelse(alloy.new == "0" | alloy.new == "none" | alloy.new == "unknown", NA, alloy.new)) # confirm unique(y$alloy.new) length(unique(y$alloy.new)) # 11 # yy <- dplyr::filter(y, grepl("ss", alloy.new)) # unique(yy$alloy.new) yy <- y %>% filter(is.na(alloy.new)) yy <- y[2915:2920,]

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/poisson_models.R

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mikemiller442/TCP_Connection_Analysis

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poisson_models.R

library(pscl) library(tidyverse) tcp_data <- read.csv(file="TCP_dataset3.csv", header=TRUE, sep=",") nrow(tcp_data) tcp_data <- tcp_data %>% mutate(zero_RST_post_fin = post_fin_resets == 0) # makes an indicator variable of whether or not # there were zero TCP resets post FIN/ACK # The code below fits a logistic regression model. GLM stands for generalized linear model, so in # a nutshell this means that the log odds of zero TCP resets post FIN/ACK is a linear function of # these predictors. model1 <- glm(zero_RST_post_fin ~ avg_TCP_delta + avg_TCP_delta:avg_DupAcks + avg_TCP_delta:avg_KAs + avg_TCP_delta:avg_iRTT + avg_ack_RTT + avg_TCP_delta:avg_iRTT + avg_iRTT + avg_Retransmissions + avg_WindowUpdates + avg_ack_RTT + avg_KAs + avg_DupAcks, family = "binomial", data = tcp_data) print(summary(model1)) # The code below fits a zero inflated poisson model. There are two parts: the first part accounts # for the probability that there are zero TCP resets post FIN/ACK, and the second part predicts # the number of poisson counts of TCP resets post FIN/ACK after accounting for the probability # that there are zero. model2 <- zeroinfl(formula = post_fin_resets ~ avg_TCP_delta + avg_TCP_delta:avg_DupAcks + avg_TCP_delta:avg_KAs + avg_TCP_delta:avg_iRTT + avg_TCP_delta:avg_iRTT + avg_iRTT + avg_WindowUpdates + avg_ack_RTT + avg_KAs + avg_DupAcks | avg_TCP_delta + avg_TCP_delta:avg_DupAcks + avg_TCP_delta:avg_KAs + avg_TCP_delta:avg_iRTT + avg_ack_RTT + avg_TCP_delta:avg_iRTT + avg_iRTT + avg_Retransmissions + avg_WindowUpdates + avg_ack_RTT + avg_KAs + avg_DupAcks, data = tcp_data) print(summary(model2)) # The code below fits a zero inflated model exactly like the model above, but it uses a different # probability called the negative binomial distribution. This allows the variance of the response # to be larger than its mean, which would otherwise violate the assumptions of the poisson model. # Standard errors are higher, so the model must be pruned down to reflect that we probably overfit # before. model3 <- zeroinfl(formula = post_fin_resets ~ avg_TCP_delta + avg_TCP_delta:avg_iRTT + avg_iRTT + avg_WindowUpdates + avg_DupAcks + avg_Retransmissions + avg_ack_RTT | avg_TCP_delta + avg_ack_RTT + avg_ack_RTT + avg_KAs + avg_TCP_delta:avg_KAs + avg_DupAcks, dist = "negbin", data = tcp_data) print(summary(model3))

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/loadTheme.R

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loadTheme.R

# Define mytheme fontsize = 11 mytheme <- theme_bw() + theme(legend.position="none") + # Set information about ticks theme(axis.ticks=element_line(size=0.2358491)) + theme(axis.ticks.length=unit(0.05,"cm")) + # Remove all pre-defined lines theme(panel.grid.major=element_blank()) + theme(panel.grid.minor=element_blank()) + theme(panel.background=element_blank()) + theme(panel.border=element_blank()) + theme(plot.background=element_blank()) + # Determine style of box theme(axis.line = element_line(color= "black",size=0.2358491)) + #results in 0.5pt # Determine font size of axes theme(text = element_text(size=fontsize)) + theme(axis.title.y=element_text(vjust=0.3,size=fontsize)) + theme(axis.title.x=element_text(vjust=0.3,size=fontsize)) + theme(axis.text.x = element_text(size= fontsize)) + theme(axis.text.y = element_text(size= fontsize)) + theme(strip.text.x = element_text(size= fontsize)) + theme(strip.text.y = element_text(size= fontsize)) theme(strip.background=element_blank())

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server.R

library(shiny) shinyServer(function(input, output, session){ dat <- reactive({ switch(input$dataset, m = mtcars, p = pressure) }) output$table <- renderTable({ head(dat(), input$n) }) output$summary <- renderPrint({ summary(dat()) }) })

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robert19960424/IndGOterm

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revpairs.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/revpairs.R \name{revpairs} \alias{revpairs} \title{get reverse pairs from all the tumor samples} \usage{ revpairs( stable.pair, patients, threshold = 0.05, spairs_threshold = 0.99, threads = 1L, capacity = 300000L ) } \arguments{ \item{stable.pair}{a matrix or data.frame of stable pairs from normal samples with two columns, the expression of the genes in the first column is higher than that in the second column.} \item{patients}{a matrix of data.frame of tumor samples , the first column must be the geneID ,and tumor samples start with the second column.} \item{threshold}{a numeric value which is used to control false discovery rate under the p_value of the chip-square test or fisher's exact probability test , default is 0.05.} \item{spairs_threshold}{a threshold same with the "threshold" in function "spairs".} \item{threads}{an integer value to make sure how many threads you will use to complete the computation} \item{capacity}{an integer value to depict the computation capacity, ruling how many lines of stable pairs would be computed within one time.the default is 300000} } \value{ a matrix containing four columns respectively represent higher expression gene , lower expression gene , p_value under binomial distribution , false discovery rate under p.adjust } \description{ the function is used to get reverse gene pairs from a host of disease samples. this function support parallel computation . You need to set thread numbers to make sure how many threads do you need to use . } \examples{ stable.pair<-t(combn(sample(1:10,10),2)); geneid<-1:10; samples<-runif(100,min = 0,max = 50); patients<-matrix(c(geneid,samples),nrow = 10,byrow=F); reverse_pairs<-revpairs(stable.pair,patients,threshold=0.05,spairs_threshold=0.99,threads=1L,capacity=300000L) #compute with parallel reverse_pairs<-revpairs(stable.pair,patients,threshold=0.05,spairs_threshold=0.99,threads=10L,capacity=300000L) }

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esem_analysis_ver2.R

##--- Load packages ---## list.of.packages <- c("ggplot2", "lubridate", "dplyr", "corrplot", "tidyverse", "flipTime", "remotes") # list any missing packages new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[, "Package"])] # if packages missing --> install if (length(new.packages) > 0) { install.packages(new.packages, dependencies = TRUE) } remotes::install_github("Displayr/flipTime", force = T) # load all packages lapply(list.of.packages, require, character.only = TRUE) ##--- Load data ---## dataset<-read.csv("Data/repos_dep_0630.csv", header = T)[c(1:3419),] type<-read.csv("Data/repos_type_new.csv", header = T) ##--- Transform date columns ---## dataset$created_at<-as.character(dataset$created_at) dataset$created_at<-AsDate(dataset$created_at, us.format = T) dataset$updated_at<-as.character(dataset$updated_at) dataset$updated_at<-AsDate(dataset$updated_at, us.format = T) dataset$last_commit_date<-as.character(dataset$last_commit_date) dataset$last_commit_date<-AsDate(dataset$last_commit_date, us.format = T) ##--- Transform dependencies column ---## dataset$dependencies<-as.character(dataset$dependencies)

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Ford_Fukelson.R

#Tableau des sommets X=1:7 #Matrice d'adjacence du reseau avec ses capacitÚs A=rbind(c(0,5,8,0,0,0,0),c(0,0,0,4,2,0,0),c(0,0,0,0,5,2,0), c(0,0,0,0,0,0,7),c(0,0,0,0,0,0,3),c(0,0,0,0,0,0,3),c(0,0,0,0,0,0,0)) #Matrice des flots realisables P=matrix(0,nrow=length(X),ncol=length(X)) #Matrice de la marque m m= matrix(NA,nrow =length(X),ncol =3) #definition Flotmax Ford_Fukelson = function(X,A,P,m) { #definition du Flotmax Flotmax = 0 #definition de alphaj alphaj = 0 #definition de l'infinit inf = 50000 #L'ensemble S S = vector() s = 1 p = 7 m[s,] = c(NA,inf,1) S = append(S,s) #permet d'obtenir la position des arcs ou le flot est diff de la capacite de l'arc R1=A-P>0 #permet d'obtenir la position des arcs ou le flot de j a i est sup a zero R2=t(P)>0 #l'union de R1 et R2 C=R1|R2 #Permet d'avoir S barre Sb=setdiff(X,S) #renvoi la position des arcs respectant les conditions de la boucle Cnd=which(matrix(C[S,Sb]==TRUE,nrow=length(S),ncol=length(Sb)),arr.ind=TRUE) while(length(Cnd)>0) { x = S[Cnd[1,1]] y = Sb[Cnd[1,2]] if(R1[x,y]==TRUE) { V = A[x,y]-P[x,y] alphaj = min(c(m[x,2],V)) m[y,] = c(x,alphaj,1) } else if(R2[x,y]==TRUE){ V = P[y,x] alphaj = min(c(m[x,2],V)) m[y,] = c(x,alphaj,-1) } S = append(S,y) if(y == p){ Flotmax = Flotmax + alphaj break } Sb=setdiff(X,S) Cnd=which(matrix(C[S,Sb]==TRUE,nrow=length(S),ncol=length(Sb)),arr.ind=TRUE) } if(is.element(p,S)) { y = p x = m[y,1] while(y != s) { if(m[y,3]==1) { P[x,y] = P[x,y] + m[p,2] } else if(m[y,3]==-1) { P[x,y] = P[x,y] - m[p,2] } y = m[y,1] x = m[y,1] } Flotmax = Flotmax + Ford_Fukelson(X,A,P,m) } else { print(P) print(m) return(Flotmax) } } valflotMax = Ford_Fukelson(X,A,P,m) print(paste("Le flot de valeur max obtenu par l'algo de Ford Fukelson est : ",valflotMax))

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r

regressionMultivariateLassoByGroup.R

# Next steps: # consider refitting with lm() and examining slopes by event # consider automated way to keep track of slopes for individual events within group regressions # -maybe use median regression for this. # Heuristic overlap analysis: decision tree library(glmnet) library(dplyr) library(RColorBrewer) library(parallel) library(doParallel) #set up parallel cores for cv.glmnet # Calculate the number of cores no_cores <- detectCores() - 1 # Register parallel backend registerDoParallel(no_cores) #setwd("D:/SRCData/Git/GLPF") source("na.info.R") df.orig <- readRDS("./cached_data/8_process_new_categories/rds/summary_noWW_noQA.rds") #df.orig <- summaryDF df <- df.orig response <- c("lachno","bacHum") df <- df[-which(is.na(df$lachno)),] beginIV <- "Sag240_255" endIV <- "rBS44_S45_BF" begin <- which(names(df)==beginIV) end <- which(names(df)==endIV) IVs <- names(df)[begin:end] na.info.list <- na.info(df[,-dim(df)[2]],first.col = beginIV) rmRows <- unique(c(which(df$CAGRnumber %in% na.info.list$na.rows), na.info.list$nan.rows, na.info.list$inf.rows)) rmCols <- unique(which(names(df) %in% c(na.info.list$na.cols.partial, na.info.list$nan.cols, na.info.list$inf.cols))) dfrmCols <- df[,-rmCols] dfRmRows <- df[rmRows,] df <- df[,-rmCols] beginIV <- "Sag240_255" endIV <- "rBS44_S45_BF" begin <- which(names(df)==beginIV) end <- which(names(df)==endIV) IVs <- names(df)[begin:end] groupFreq <- table(df$eventGroup2) groups <- names(groupFreq)[which(groupFreq>21)] mg.List <- list() mg.cv.List <- list() lambdaType <- "lambda.min" filenm <- "MVLassoByGroupLminFull2.pdf" pdf(filenm) modelCoefList <- list() for(i in 1:length(groups)){ subdf <- df[which(df$eventGroup2==groups[i]),] IVs <- names(df)[begin:end] #subdf <- df foldID <- as.numeric(as.factor(subdf$eventNum)) events <- unique(subdf$eventNum) # # Add events as separate dichotomous IVs # if(length(events)>1){ # # eventDF <- data.frame(E1 = ifelse(subdf$eventNum == events[2],1,0)) # for(j in 2:length(events)){ # if(j==2)eventDF <- as.data.frame(ifelse(subdf$eventNum == events[j],1,0)) # else eventDF <- cbind(eventDF,ifelse(subdf$eventNum == events[j],1,0)) # } # names(eventDF) <- events[-1] # subdf <- cbind(subdf,eventDF) # IVs <- c(IVs,names(eventDF)) # } y <- log10(as.matrix(subdf[,response])) x <- as.matrix(subdf[IVs]) #If more than 2 events included in group, use event as fold ID, otherwise, use 5-fold XV if(length(unique(foldID))>2){ mg.cv <- cv.glmnet(x=x, y=y,family="mgaussian",alpha=1,foldid = foldID,parallel = TRUE) mg <- glmnet(x=x, y=y,family="mgaussian", alpha=1) }else{ mg.cv <- cv.glmnet(x=x, y=y,family="mgaussian",alpha=1,nfolds=5,parallel = TRUE) mg <- glmnet(x=x, y=y,family="mgaussian", alpha=1) } #Extract Coefficients from cv-determined model using lambda.1se if(lambdaType == "lambda.1se"){ Coefficients <- coef(mg, s = mg.cv$lambda.1se) Active.Index <- which(Coefficients[[1]] != 0) Active.Coefficients <- Coefficients[[1]][Active.Index];Active.Coefficients Active.Coef.names <- row.names(Coefficients[[1]])[Active.Index];Active.Coef.names } #Extract Coefficients from cv-determined model using lambda.min if(lambdaType == "lambda.min"){ Coefficients <- coef(mg, s = mg.cv$lambda.min) Active.Index <- which(Coefficients[[1]] != 0) Active.Coefficients <- Coefficients[[1]][Active.Index];Active.Coefficients Active.Coef.names <- row.names(Coefficients[[1]])[Active.Index];Active.Coef.names } modelCoefList[[i]] <- Active.Coef.names[-1] #Plot cross validated errors and other model results plot(mg.cv) predictions <- predict(mg.cv,newx=as.matrix(subdf[,IVs]),s=lambdaType,type = "response") plotpch <- 20 colorOptions <- brewer.pal(9, "Set1") plotCol <- colorOptions[1:length(events)] names(plotCol) <- events plotcolors <- plotCol[subdf$eventNum] par(mfcol=c(2,1),mar=c(3,4,3,1),oma=c(0,2,0,4)) #Plot Lachno plot(subdf[,response[1]],predictions[,1,1],col=plotcolors,pch=plotpch,log='x',xlab="",ylab="") mtext(response[1],line=1) mtext(paste(Active.Coef.names[-1],collapse=' + '),cex=0.7) mtext(groups[i],line=2,font=2) #Plot bacHum plot(subdf[,response[2]],predictions[,2,1],col=plotcolors,pch=plotpch,log='x',xlab="",ylab="") mtext("Predicted",side=2,line=-2,font=2,xpd=NA,outer=TRUE) mtext("Observed",side=1,line=2,font=2) mtext(response[2],line=1) legendNames <- names(plotCol) legend('bottomright',legend = legendNames,col=plotCol,pch=plotpch,inset = c(-0.15,0),bty = "n",xpd=NA) # calibrate Tobit regression and plot library(survival) IVs <- Active.Coef.names[-1] response <- response LOQ <- 225 ## Compute survival coefficients for Lachno regression ## if(length(IVs) > 0){ y <- Surv(log10(subdf[,response[1]]), subdf[,response[1]]>LOQ, type="left") #dfPredStd <- as.data.frame(scale(dfPred[,IVs])) form <- formula(paste('y ~',paste(IVs,collapse=' + '))) msurvStd <- survreg(form,data=subdf,dist='weibull') summary(msurvStd) predictions <- predict(msurvStd,newdata = subdf) par(mfcol=c(2,1),mar=c(3,4,3,1),oma=c(0,2,0,4)) #Plot Lachno plot(subdf[,response[1]],predictions,col=plotcolors,pch=plotpch,log='x',xlab="",ylab="") mtext(paste(response[1],"Survival"),line=1) mtext(paste(Active.Coef.names[-1],collapse=' + '),cex=0.7) mtext(groups[i],line=2,font=2) abline(h=4,v=10000,col="blue",lty=2) ## Compute survival coefficients for Lachno regression ## y <- Surv(log10(subdf[,response[2]]), subdf[,response[2]]>LOQ, type="left") #dfPredStd <- as.data.frame(scale(dfPred[,IVs])) form <- formula(paste('y ~',paste(IVs,collapse=' + '))) msurvStd <- survreg(form,data=subdf,dist='weibull') summary(msurvStd) predictions <- predict(msurvStd,newdata = subdf) #Plot BacHum plot(subdf[,response[2]],predictions,col=plotcolors,pch=plotpch,log='x',xlab="",ylab="") mtext(paste(response[2],"Survival"),line=1) mtext(groups[i],line=2,font=2) abline(h=4,v=10000,col="blue",lty=2) }else{ par(mfrow=c(1,1)) plot(1:10,1:10,xaxt="n",yaxt="n",ylab="",xlab="",pch="") text(5,5,"No Lasso Variables") } mg.List[[i]] <- mg mg.cv.List[[i]] <- mg.cv #---------------------------------------------------------------------------------- # calibrate model with all data from individual group model subdf <- df y <- log10(as.matrix(subdf[,response])) x <- as.matrix(subdf[Active.Coef.names[-1]]) foldIDs <- as.numeric(as.factor(subdf$eventNum)) which(table(foldIDs)<10) if(length(modelCoefList[[i]]) > 1) { mg.cv <- cv.glmnet(x=x, y=y,family="mgaussian",alpha=1,foldid = foldIDs,parallel = TRUE) mg <- glmnet(x=x, y=y,family="mgaussian", alpha=1) #Extract Coefficients from cv-determined model using lambda.1se if(lambdaType == "lambda.1se"){ Coefficients <- coef(mg, s = mg.cv$lambda.1se) Active.Index <- which(Coefficients[[1]] != 0) Active.Coefficients <- Coefficients[[1]][Active.Index];Active.Coefficients Active.Coef.names <- row.names(Coefficients[[1]])[Active.Index];Active.Coef.names } #Extract Coefficients from cv-determined model using lambda.min if(lambdaType == "lambda.min"){ Coefficients <- coef(mg, s = mg.cv$lambda.min) Active.Index <- which(Coefficients[[1]] != 0) Active.Coefficients <- Coefficients[[1]][Active.Index];Active.Coefficients Active.Coef.names <- row.names(Coefficients[[1]])[Active.Index];Active.Coef.names } #Plot cross validated errors and other model results plot(mg.cv) predictions <- predict(mg.cv,newx=x,s=lambdaType,type = "response") plotpch <- 20 colorOptions <- brewer.pal(9, "Set1") plotCol <- colorOptions[1:length(events)] names(plotCol) <- events plotcolors <- "grey" # plotCol[subdf$eventNum] par(mfcol=c(2,1),mar=c(3,4,3,1),oma=c(0,2,0,4)) #Plot Lachno plot(subdf[,response[1]],predictions[,1,1],col=plotcolors,pch=plotpch,log='x',xlab="",ylab="") mtext(response[1],line=1) mtext(paste(Active.Coef.names[-1],collapse=' + '),cex=0.7) mtext(groups[i],line=2,font=2) #Plot bacHum plot(subdf[,response[2]],predictions[,2,1],col=plotcolors,pch=plotpch,log='x',xlab="",ylab="") mtext("Predicted",side=2,line=-2,font=2,xpd=NA,outer=TRUE) mtext("Observed",side=1,line=2,font=2) mtext(response[2],line=1) legendNames <- names(plotCol) legend('bottomright',legend = legendNames,col=plotCol,pch=plotpch,inset = c(-0.15,0),bty = "n",xpd=NA) } } dev.off() shell.exec(filenm) ###------------------------------------------------------------------------ #Plot with all data by group and then by event plotAll <- TRUE lambdaType <- "lambda.min" if(plotAll) { filenm <- "GroupLassoByEvent.pdf" pdf(filenm) subdf <- df y <- log10(as.matrix(subdf[,response])) x <- as.matrix(subdf[,IVs]) events <- unique(subdf$eventNum) for(i in 1:length(groups)){ mg <- mg.List[[i]] mg.cv <- mg.cv.List[[i]] #Extract Coefficients from cv-determined model using lambda.1se if(lambdaType == "lambda.1se"){ Coefficients <- coef(mg, s = mg.cv$lambda.1se) Active.Index <- which(Coefficients[[1]] != 0) Active.Coefficients <- Coefficients[[1]][Active.Index];Active.Coefficients Active.Coef.names <- row.names(Coefficients[[1]])[Active.Index];Active.Coef.names } #Extract Coefficients from cv-determined model using lambda.min if(lambdaType == "lambda.min"){ Coefficients <- coef(mg, s = mg.cv$lambda.min) Active.Index <- which(Coefficients[[1]] != 0) Active.Coefficients <- Coefficients[[1]][Active.Index];Active.Coefficients Active.Coef.names <- row.names(Coefficients[[1]])[Active.Index];Active.Coef.names } predictions <- predict(mg.cv,newx=x,s=lambdaType,type = "response") for(j in 1:length(events)){ event <- events[j] plotpch <- 20 # colorOptions <- brewer.pal(9, "Set1") # # plotCol <- colorOptions[1:length(events)] # names(plotCol) <- events plotcolors <- "grey" eventcolor <- ifelse(subdf$eventNum==event,"blue",NA) # plotCol[subdf$eventNum] ylim <- range(predictions[,1,1]) ylim[1] <- ifelse(ylim[1] < 0,0,ylim[1]) ylim[2] <- ifelse(ylim[2] > 8,8,ylim[2]) par(mfcol=c(2,1),mar=c(3,4,3,1),oma=c(0,2,0,4)) #Plot Lachno plot(subdf[,response[1]],predictions[,1,1],col=plotcolors,pch=plotpch,log='x', xlab="",ylab="",ylim=ylim) points(subdf[,response[1]],predictions[,1,1],col=eventcolor,pch=plotpch) mtext(response[1],line=1) mtext(paste(Active.Coef.names[-1],collapse=' + '),cex=0.7) mtext(paste(groups[i],";",event),line=2,font=2) #Plot bacHum ylim <- range(predictions[,2,1]) ylim[1] <- ifelse(ylim[1] < 0,0,ylim[1]) ylim[2] <- ifelse(ylim[2] > 8,8,ylim[2]) plot(subdf[,response[2]],predictions[,2,1],col=plotcolors,pch=plotpch,log='x', xlab="",ylab="",ylim = ylim) points(subdf[,response[2]],predictions[,2,1],col=eventcolor,pch=plotpch) mtext("Predicted",side=2,line=-2,font=2,xpd=NA,outer=TRUE) mtext("Observed",side=1,line=2,font=2) mtext(response[2],line=1) legendNames <- names(plotCol) legend('bottomright',legend = legendNames,col=plotCol,pch=plotpch,inset = c(-0.15,0),bty = "n",xpd=NA) } } } dev.off() shell.exec(filenm)

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/analyses/Field_Monitoring/duplicates.R

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raubreywhite/trial_dofiles

e06a5b3b39e9195eda79dd33856d67c918ec4053

eface3b83b107cf7e621b3c654e65b5cbd45b711

refs/heads/master

2022-06-14T03:26:17.492945

2022-06-02T07:27:04

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duplicates.R

###### SETUP STARTS ###### setwd("C:/data processing/trial_dofiles") fileSources = file.path("r_code", list.files("r_code", pattern = "*.[rR]$")) fileSources=file.path(getwd(),fileSources) sapply(fileSources, debugSource) Setup(IS_GAZA=FALSE) ###### SETUP ENDS ###### ###### LOAD DATA ###### d <- LoadDataFileFromNetwork() # duplicate bookings or files # time difference between bookdates for multiple bookings to see differences setorder(d,booknum) d[,bookdateprevious:=shift(bookdate,n=1L),by=motheridno] d[,diftimebook:=difftime(bookdate, bookdateprevious, units="days")] xtabs(~d[ident_dhis2_booking==T]$diftimebook, addNA=T) xtabs(~d[booknum==1]$diftimebook, addNA=T) d[,fullname:=stringr::str_c(firstname, ' ', fathersname)] d[,fullname:=stringr::str_c(fullname, ' ', middlename)] d[,fullname:=stringr::str_c(fullname, ' ', familyname1)] d[,fullname:=stringr::str_c(fullname, ' ', familyname2)] d[, fullname:=stringr::str_replace(fullname," *"," ")] duplicates <- d[,c("motheridno", "fullname", "familyname1", "familyname2", "ident_dhis2_booking", "ident_dhis2_ppc", "bookdate", "booknum", "diftimebook", "bookorgname", "bookorgdistrict")] xtabs(~duplicates$diftimebook) setorder(duplicates, bookorgdistrict,bookorgname,fullname, motheridno) duplicates[,fullnamenum:=1:.N, by=fullname] xtabs(~duplicates$fullnamenum) xtabs(~duplicates$booknum, addNA=TRUE) # duplicates[,halfnamenum:=1:.N, by=halfname] # xtabs(~duplicates$halfnamenum) # xtabs(~duplicates$halfnamenum, addNA=TRUE) openxlsx::write.xlsx(unique(duplicates),file.path(FOLDER_DATA_RESULTS, "quality_control", sprintf("duplicates_%s.xlsx", lubridate::today()))) ################ # demographics # ################ # # demo <- fread(fs::path(FOLDER_DATA_RAW, # "e.reg-intervention", # "2021-08-12", # "Clinical Demographics.csv")) # # demo <- fread("C:/data processing/data_raw/e.reg-intervention/2021-08-12/Clinical Demographics.csv", encoding="UTF-8") nrow(demo) for (i in names(demo)) setnames(demo, i, ExtractOnlyEnglishLettersAndNumbers(i)[[1]]) setnames(demo,"instance","uniqueid") setnames(demo,"created","datecreated") setnames(demo,"lastupdated","dateupdated") setnames(demo,"organisationunit","demoorgunit") setnames(demo,"organisationunitname","demoorgname") # if(badname %in% names(d)) setnames(demo, badname, goodname) if("trackedentitytype" %in% names(d)) setnames(demo, "trackedentitytype", "trackedentity") # if(!goodname %in% names(d)) ERROR!! if(!"trackedentity" %in% names(demo)) stop("cant find trackedentity") setnames(demo,"inactive","dummy") setnames(demo,"identificationdocumenttype","idtype") if(!"identificationdocumentnumber" %in% names(demo)){ warning("no identification document number -- we create one") demo[,identificationdocumentnumber:=1:.N] } setnames(demo,"identificationdocumentnumber","demoidnumber") #setnames(demo,"areyouwillingtoreceivesmstextmessagesandremindersaboutyourvisits", "doyouwanttoreceivesms") xtabs(~demo$identificationdocumentnumber) demo[,datecreated:=stringr::str_extract(datecreated, "^.{10}")] xtabs(~demo$datecreated) demo[, datecreated:=as.Date(datecreated)] xtabs(~demo$datecreated) xtabs(~demo$datecreated) str(demo$datecreated) demo[,idnonum:=.N, by=demoidnumber] xtabs(~demo[idnonum>1]$demoidnumber, addNA=T) # number of digits (9, 8, 10) demo[,idnumdigits:=nchar(as.integer(demoidnumber))] xtabs(~demo$idnumdigits, addNA=T) demo[,yearcreated:=lubridate::year(datecreated)] xtabs(~demo$yearcreated) demo[,demoorgname:=ExtractOnlyEnglishLetters(demoorgname)] xtabs(~demo$demoorgname) demo <- demo[!demoorgname %in% c("test", "testfacility")] ############# # full name # ############# demo[,fullname:=stringr::str_c(firstname, ' ', fathersname)] demo[,fullname:=stringr::str_c(fullname, ' ', middlename)] demo[,fullname:=stringr::str_c(fullname, ' ', womanfamilyname)] demo[,fullname:=stringr::str_c(fullname, ' ', husbandsfamilyname)] demo[, fullname:=stringr::str_replace(fullname," *"," ")] ############# # half name # ############# demo[,halfname:=stringr::str_c(firstname, ' ', fathersname)] demo[,halfname:=stringr::str_c(halfname, ' ', middlename)] demo[, halfname:=stringr::str_replace(halfname," *"," ")] demo[,numhalfname:=.N, by=c("halfname","demoorgname")] demo[numhalfname==7, c("fullname","datecreated")] # merge bookordistrict stuff # sData <- as.data.table(readxl::read_excel("../data_raw/structural_data/bookorgname.xlsx")) setnames(demo, "demoorgname","bookorgname") dData <- merge( demo, sData, by="bookorgname", all.x=T) # names in english and numbers dData[, numinname:=FALSE] dData[stringr::str_detect(fullname,"[0-9]"), numinname:=TRUE] xtabs(~dData$numinname, addNA=T) iddata <- dData[,c("bookorgname", "yearcreated", "idnumdigits", "numinname", "idnonum")] iddata[,denom:=.N, by=c("bookorgname","yearcreated")] ag <- iddata[,.(N=.N, Num_digits_less_than_9=sum(idnumdigits<9, na.rm = T), Num_id_digits_9=sum(idnumdigits==9, na.rm=T), Num_id_digits_more_than_9=sum(idnumdigits>9, na.rm=T), Numinname=sum(numinname==T, na.rm=T), Numinnameandmultiple=sum(numinname==T & idnonum>1, na.rm=T)), keyby=.(yearcreated,bookorgname)] ag[,prop_less_than_9:=round(Num_digits_less_than_9/N, digits=3)] ag[,prop_id_digits_9:=round(Num_id_digits_9/N, digits=3)] ag[,prop_id_digits_more_than_9:=round(Num_id_digits_more_than_9/N, digits=5)] ag[,prop_numinname:=round(Numinname/N, digits=5)] ag[,prop_numinnamemultipleidno:=round(Numinnameandmultiple/N, digits=5)] tokeep <- ag[prop_id_digits_9<1.0] openxlsx::write.xlsx(ag,file.path(FOLDER_DATA_RESULTS, "quality_control", "duplicates", sprintf("id_quality_digits_%s.xlsx", lubridate::today()))) toanalyze <- dData[numhalfname>1,c("uniqueid", "bookorgname", "demoorgunit", "datecreated", "dateupdated", "idtype", "demoidnumber", "firstname", "fathersname", "middlename", "womanfamilyname", "husbandsfamilyname", "dateofbirth", "ageatmarriage", "ageatfirstpregnancy", "consanguinity", "educationinyears", "yearcreated", "idnonum", "idnumdigits", "fullname", "halfname", "numhalfname")] setorder(toanalyze, halfname, yearcreated, demoorgunit) openxlsx::write.xlsx(toanalyze, file.path(FOLDER_DATA_RESULTS, "quality_control", "duplicates", sprintf("halfnamedups_%s.xlsx",CLINIC_INTERVENTION_DATE))) # either they have the same id number or they have a previous half name and name repeated but need to include # even if prevfile name is missing ############# # analyses # ############# # shift last names toanalyze[,womanfamilynameprev:=shift(womanfamilyname,n=1L),by=c("halfname","bookorgname")] toanalyze[,husbandsfamilynameprev:=shift(husbandsfamilyname,n=1L),by=c("halfname","bookorgname")] toanalyze[,newhalfname:=halfname] toanalyze[,halfnameprev:=shift(newhalfname,n=1L),by=c("halfname","bookorgname")] toanalyze[,newhalfname:=NULL] toanalyze[halfnameprev==halfname,prevfilesame:=1] xtabs(~toanalyze$prevfilesame, addNA=T) toanalyze[halfnameprev==halfname & womanfamilynameprev==womanfamilynameprev,prevfilesame:=2] xtabs(~toanalyze$prevfilesame, addNA=T) toanalyze[halfnameprev==halfname & husbandsfamilynameprev==husbandsfamilyname,prevfilesame:=3] xtabs(~toanalyze$prevfilesame, addNA=T) toanalyze[halfnameprev==halfname & womanfamilynameprev==womanfamilynameprev & husbandsfamilynameprev==husbandsfamilyname,prevfilesame:=4] xtabs(~toanalyze$prevfilesame, addNA=T) # differnces in id numbers for these cases setorder(toanalyze,bookorgname,halfname,demoorgname,yearcreated) toanalyze[!is.na(prevfilesame),yearcreatedbefore:=shift(yearcreated,n=1L),by=c("halfname","bookorgname")] # shift year created to get difference toanalyze[,yearDif:=as.numeric(yearcreated-yearcreatedbefore)] xtabs(~toanalyze$yearDif, addNA=T) # possible multiple names # ################## # multiple fields # ################## setorder(demo,demoorgname, halfname, womanfamilyname,husbandsfamilyname,demoidnumber, yearcreated) demo[,numhalfname:=.N, by=c("halfname","demoorgname")] xtabs(~demo$numhalfname)

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/R/print.R

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cran/ExhaustiveSearch

55dfc7c515a98079400fabfcd79e1937717103c8

0215018c8769856fd9fb6aa4e30716f026bf7622

refs/heads/master

2023-02-24T13:47:10.245395

2021-01-18T16:00:11

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print.R

formatSecTime = function(sec) { days = sec / 60 / 60 / 24 hour = (sec / 60 / 60) %% 24 min = (sec / 60) %% 60 osec = sec %% 60 paste(paste0(sprintf("%02d", floor(c(days, hour, min, osec))), c("d ", "h ", "m ", "s")), collapse = "") } #' Print ExhaustiveSearch #' #' Prints a compact summary of the results of an ExhaustiveSearch object. #' #' @param x Object of class 'ExhaustiveSearch'. #' @param ... Further arguments passed to or from other methods. #' #' @return No return value. The function is only called to print results to the #' console. #' #' @author Rudolf Jagdhuber #' #' @seealso [ExhaustiveSearch()] #' #' @importFrom utils capture.output #' @export print.ExhaustiveSearch = function(x, ...) { evalOn = ifelse(x$setup$nTest == 0, paste0("training set (n = ", format(x$setup$nTrain, big.mark = ","), ")\n"), paste0("test set (n = ", format(x$setup$nTest, big.mark = ","), ")\n")) cat("\n+-------------------------------------------------+") cat("\n| Exhaustive Search Results |") cat("\n+-------------------------------------------------+\n") cat("Model family: ", x$setup$family, "\n") cat("Intercept: ", x$setup$intercept, "\n") cat("Performance measure: ", x$setup$performanceMeasure, "\n") cat("Models fitted on: ", " training set (n = ", x$setup$nTrain, ")\n", sep = "") cat("Models evaluated on: ", evalOn) cat("Models evaluated: ", format(x$nModels, big.mark = ","), ifelse(x$evaluatedModels != x$nModels, " (Incomplete!)", ""), "\n") cat("Models saved: ", format(x$setup$nResults, big.mark = ","), "\n") cat("Total runtime: ", formatSecTime(x$runtimeSec), "\n") cat("Number of threads: ", x$batchInfo$nBatches, "\n") cat("\n+-------------------------------------------------+") cat("\n| Top Feature Sets |") cat("\n+-------------------------------------------------+\n") cat(paste(capture.output(resultTable(x, 5, " ")), collapse = "\n"), "\n\n") }

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/R/TwilightFree.R

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no_license

ABindoff/TwilightFree

3c168f099b8119cb05aafcf63ce7820339951415

d65928df5367077c4abbb89f37f10c950f8d9bc0

refs/heads/master

2021-05-15T06:33:46.454401

2021-02-24T10:26:09

113,830,244

9

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2021-01-14T23:57:10

2017-12-11T08:05:52

HTML

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R

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r

TwilightFree.R

require(SGAT) require(raster) #' Specify a model for forwards-backwards estimation #' #' @param df data.frame containing `Light`, `Date`, and optionally `Temp` data #' @param alpha hyperparameters for the noise (shading) assumption #' @param beta hyperparameters for the movement assumption #' @param dt optional parameter specifying the number of seconds in a segment (day) #' @param threshold tag-specific value for luminance at twilight (obtained by calibration) #' @param zenith solar zenith angle at twilight #' @param deployed.at deployment location c(lon, lat) for first day of observation #' @param retrieved.at retrieval location c(lon, lat) for last day of observation #' @param fixd optional data.frame of fixed (known) locations containing `Date`, `Lon`, `Lat` (will overwrite deployed.at and retrieved.at locations if != NULL) #' @param sst raster of SST data from NOAA OI SST #' @importFrom stats dgamma dnorm median #' @importFrom raster extract getZ #' @export #' @return a TwilightFree model object which can be fitted using SGAT::essie() TwilightFree <- function(df, alpha = c(1, 1/10), beta = c(1, 1/4), dt = NULL, threshold = 5, zenith = 96, deployed.at = F, retrieved.at = F, fixd = NULL, sst = NULL){ if(is.null(df$Temp)){ ## fix bug, needs a $Temp column even if it's NA df$Temp <- NA } # Define segment by date seg <- floor((as.numeric(df$Date)- as.numeric(min(df$Date)))/(24*60*60)) # Split into `slices` slices <- split(df,seg) slices <- slices[-c(1,length(slices))] # find min date in each slice dmin <- c() for (i in 1:length(slices[])) { dmin[i] <- min(slices[[i]]$Date) } dmin <- strptime(as.POSIXct(dmin, "GMT", origin = "1970-01-01"), "%Y-%m-%d", "GMT") ## sst raster from ncdf file at # https://www.esrl.noaa.gov/psd/repository/ # (NOAA OI SST -> Weekly and Monthly -> sst.wkmean.*) indices <- NA if(!is.null(sst)){ indices <<- .bincode(as.POSIXct(dmin), as.POSIXct(strptime(raster::getZ(sst), "%Y-%m-%d", "GMT"), "GMT"), right = FALSE) } # fixed locations, if retrieved.at and deployed.at are supplied it these will be used unless fixd != NULL x0 <- matrix(0, length(slices), 2) x0[1,] <- deployed.at x0[length(slices),] <- retrieved.at fixed <- rep_len(c(as.logical(deployed.at[1L]), logical(length(slices)-2), as.logical(retrieved.at[1L])), length.out = length(slices)) # if a data.frame containing `Date` in %Y-%m-%d format, `Lon` and `Lat` is supplied these will be utilised here if(!is.null(fixd)) { slice_date <- lapply(slices, function(x) min(x$Date)) slice_date <- as.vector(unlist(lapply(slice_date, function(x) as.character(strptime(x, format = "%Y-%m-%d"))))) indx <- which(slice_date %in% fixd$Date) locs <- matrix(0, length(slices), 3) for (i in seq_along(indx)) { locs[indx[i], ] <- c(fixd$Lon[i], fixd$Lat[i], 1) } x0 <- locs[, 1:2] fixed <- as.logical(locs[, 3]) } ## Times (hours) between observations time <- .POSIXct(sapply(slices, function(d) mean(d$Date)), "GMT") if (is.null(dt)) dt <- diff(as.numeric(time) / 3600) ## Contribution to log posterior from each x location logpk <- function(k, x) { n <- nrow(x) logl <- double(n) ss <- SGAT::solar(slices[[k]]$Date) obsDay <- (slices[[k]]$Light) >= threshold ## Loop over location for (i in seq_len(n)) { ## Compute for each x the time series of zeniths expDay <- SGAT::zenith(ss, x[i, 1], x[i, 2]) <= zenith ## comparison to the observed light -> is L=0 (ie logl=-Inf) if (any(obsDay & !expDay)) { logl[i] <- -Inf } else { count <- sum(expDay & !obsDay) logl[i] <- dgamma(count, alpha[1], alpha[2], log = TRUE) } } ## Return sum of likelihood + prior logl + logp0(k, x, slices) } ## Behavioural (movement) contribution to the log posterior logbk <- function(k, x1, x2) { spd <- pmax.int(SGAT::gcDist(x1, x2), 1e-06) / dt[k] dgamma(spd, beta[1L], beta[2L], log = TRUE) } list( logpk = logpk, logbk = logbk, fixed = fixed, x0 = x0, time = time, alpha = alpha, beta = beta, sst = sst ) } #' calculate SST component of log-posterior in TwilightFree model logp0 <- function(k, x, slices) { x[, 1] <- x[, 1] %% 360 tt <- median(slices[[k]]$Temp, na.rm = TRUE) if (is.na(tt)) { 0 } else { dnorm(tt, raster::extract(sst[[indices[k]]], x), 2, log = T) } }

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/Black Friday Sales/Bivariate.R

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Bivariate.R

frow_f <- fluidRow( box( title = "Bivariate Chart" ,status = "primary" ,solidHeader = TRUE,background = 'purple' ,collapsible = TRUE ,plotOutput("bar1", height = "500px",width = "500px") ), box(title = "Variables in the data",status = "primary" ,solidHeader = TRUE,background = 'aqua' ,collapsible = TRUE,selectInput("x_axis","Select the value for x-axis",colnames(data),selected = ""), selectInput("yaxis","Select the value for y-axis",colnames(data),selected = "Purchase") )) ui<-shinyUI( dashboardPage( dashboardHeader(title = "BLACK FRIDAY SALES",titleWidth = 300), dashboardSidebar( sidebarMenu(id = 'sidebarmenu', menuItem("Bivariate Plots", icon = icon('bar-chart'), tabName = 'chart1' ))), dashboardBody( tabItems( tabItem("chart1",frow_f) ) ),skin = 'red' ) ) server <- function(input, output,session){ output$bar1<-renderPlot({ bar1<-tapply(data[,input$yaxis], list(data[,input$x_axis]), mean) barplot(bar1,col = 'red',ylab = "Purchase Amount") }) } shinyApp(ui, server)

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AdarshMundra/R-Programing-Udemy

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2020-01-19T07:29:36

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qplot.R

#code with Adarsh Mundra a<-read.csv(file.choose()) a getwd() rm(a) a<-read.csv("DemographicData.csv") #-------------------------------------------------------------# a nrow(a)# number of rows ncol(a)# number of col head(a)# head file head(a, n=10)# head file of 10 dataset tail(a)# tail file tail(a,n=10)# tail file of 10 dataset str(a) #structrue of dataset summary(a) #summary of a #------------------------------------------------------ Using the $ sign a[3,3] a[3,"Birth.rate"] a$Internet.users #extract whole column a$Internet.users[2]*a$Birth.rate[2] a$Birth.rate[2]#extract specific cell in col #=====================QPLOT library("ggplot2") qplot(data = a,x=Internet.users) qplot(data = a,x=Income.Group,y=Birth.rate) qplot(data = a,x=Income.Group,y=Birth.rate,size =10) qplot(data = a,x=Income.Group,y=Birth.rate,size=I(10)) qplot(data = a,x=Income.Group,y=Birth.rate,size= I(3) ,colour =I("RED ")) qplot(data = a,x=Income.Group,y=Birth.rate,geom = "boxplot") qplot(data = a,x=Income.Group,y=Birth.rate) qplot(data = a,x=Internet.users,y=Birth.rate,size=I(4)) qplot(data = a,y=Internet.users,x=Birth.rate,size=I(6),colour=Income.Group) #-------------------------------------Create DataFrame mydf<- data.frame(Countries_2012_Dataset,Codes_2012_Dataset,Regions_2012_Dataset) mydf head(mydf) colnames(mydf)<-c("Country","Code","Region") #-------------------------------------Mergging DataFrame head(a) merg<- merge(a,mydf,by.x = "Country.Name",by.y = "Country") head(merg) merg<- merge(a,mydf,by.x = "Country.Code",by.y = "Code") merg$Country<-NULL head(merg) #-------------------------------------QQPLOT qplot(data = merg,x=Internet.users,y=Birth.rate) qplot(data = merg,x=Internet.users,y=Birth.rate,colour=Region) #-------------------------------------Shape #Shape qplot(data = merg,x=Internet.users,y=Birth.rate,colour=Region, shape = I(18),size=I(6)) #TRanperemcy qplot(data = merg,x=Internet.users,y=Birth.rate,colour=Region,size=I(5) , shape = I(18),alpha=.7) #TITTLe qplot(data = merg,x=Internet.users,y=Birth.rate,colour=Region,size=I(5) , shape = I(18),alpha=.06,main="BirthRate vs InternetUser")

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/R Scripts/Scripts Hisam and I Worked on/EWMA Volatility.R

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arkagogoldey/Finance_R_Files

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EWMA Volatility.R

#EWMA (Exponentially Weighted Moving Average Vol) library(quantmod) getSymbols("UNP", from='2010-01-01') head(x) x<-UNP$UNP.Adjusted EWMA<-function(x,lambda) { returns<-Delt(x,type="log") return_sq<-returns^2 y<-as.matrix(x) n=(1:nrow(y)-1) z<-as.matrix(n) weights<-(1-lambda)*lambda^z weights<-sort(weights,decreasing=FALSE) product<-weights*return_sq product<-as.matrix(product) product<-na.omit(product) Variance<-colSums(product) Volatility<-sqrt(Variance) final<-cbind(Variance,Volatility) } a<-EWMA(x,.94) a

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/Ad-hocs/wordcloud_comparison.R

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mark-me/lyric_mining

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refs/heads/master

2020-05-21T05:43:27.929771

2019-05-20T14:11:54

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wordcloud_comparison.R

df_artist_lyrics <- df_lyrics %>% filter(!is.na(lyric)) %>% group_by(artist) %>% summarise(lyric = paste0(lyric, collapse = " ")) %>% ungroup() %>% mutate(doc_id = row_number()) %>% dplyr::select(doc_id, text = lyric, everything()) # Make factor of artists for modelling levels_artists <- unique(df_artist_lyrics$artist) df_artist_lyrics$artist <- factor(df_artist_lyrics$artist, levels = levels_artists) # Convert df_source to a corpus: df_corpus corpus_artist_lyrics <- Corpus(VectorSource(df_artist_lyrics$text)) # Clean corpus corpus_artist_lyrics %<>% tm_map(removeWords, c("-", "—","“", "‘","…", "NA", "character")) %>% tm_map(content_transformer(tolower)) %>% tm_map(content_transformer(removeNumbers)) %>% tm_map(content_transformer(removePunctuation)) %>% tm_map(removeWords, stopwords("english")) %>% tm_map(content_transformer(stripWhitespace)) tdm_lyrics <- TermDocumentMatrix(corpus_lyrics) tdm_lyrics = as.matrix(tdm_lyrics) colnames(tdm_lyrics) <- levels_artists dev.new(width = 1000, height = 1000, unit = "px") comparison.cloud(tdm_lyrics, random.order=FALSE, colors = c("aquamarine","darkgoldenrod","tomato", "aquamarine","darkgoldenrod","tomato"), title.colors = c("aquamarine","darkgoldenrod","tomato", "aquamarine","darkgoldenrod","tomato"), title.size=1, max.words=300)

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/man/MLeffort.Rd

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quang-huynh/MLZ

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refs/heads/master

2022-05-07T23:03:56.165755

2022-03-31T06:15:26

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MLeffort.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/estimation.R \name{MLeffort} \alias{MLeffort} \title{Mean length with effort mortality estimator} \usage{ MLeffort(MLZ_data, start, n_age, estimate.M = TRUE, log.par = FALSE, eff_init = 0, n_season = 1L, obs_season = 1L, timing = 0, figure = TRUE) } \arguments{ \item{MLZ_data}{An object of class \code{\linkS4class{MLZ_data}} containing mean lengths and life history data of stock.} \item{start}{A list of starting values. Names of start list must contain \code{q} and \code{M}.} \item{n_age}{The number of ages above age tc in the model.} \item{estimate.M}{If \code{TRUE}, natural mortality (M) will be estimated. Otherwise, the value of M is obtained from slot \code{MLZ_data@M}.} \item{log.par}{Whether parameters are estimated in logspace (\code{TRUE}) or untransformed space (\code{FALSE}).} \item{eff_init}{The assumed equilibrium effort prior to the first year of the model (0 = virgin conditions).} \item{n_season}{The number of seasons modeled in a year.} \item{obs_season}{The season corresponding to the observed mean lengths.} \item{timing}{The fraction of time (i.e., between 0 - 1) within \code{obs_season} that mean lengths are observed.} \item{figure}{If \code{TRUE}, a call to \code{plot} of observed and predicted mean lengths will be produced.} } \value{ An object of class \code{\linkS4class{MLZ_model}}. } \description{ Estimator of fishing and natural mortality from a time series of mean length and effort data. } \examples{ \dontrun{ data(Nephrops) Nephrops <- calc_ML(Nephrops, sample.size = FALSE) res <- MLeffort(Nephrops, start = list(q = 0.1, M = 0.2), n_age = 24, eff_init = Nephrops@Effort[1]) } } \references{ Then, A.Y, Hoenig, J.M, and Huynh, Q.C. In revision. Estimating fishing and natural mortality rates, and catchability coefficient, from a series of observations on mean length and fishing effort. ICES Journal of Marine Science. }

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/man/new_report.Rd

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riverlee/reports

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new_report.Rd

\name{new_report} \alias{new_report} \title{Report Template} \usage{ new_report(report = "report", template = getOption("temp.reports"), bib.loc = getOption("bib.loc"), name = getOption("name.reports"), github.user = getOption("github.user"), sources = getOption("sources.reports"), path = getwd(), AN.xlsx = TRUE, slidify = getOption("slidify.template"), open = FALSE, ...) } \arguments{ \item{report}{A character vector of length two or one: (1) the main directory name and (2) sub directory names (i.e., all the file contents will be imprinted with this name). If the length of \code{report} is one this name will be used as the main directory name and all sub directories and files.} \item{template}{A character string of the internal reports template or an external path to a template in the reports package style. This argument allows the user to change the contents of the report directory that is generated. See \code{templates} for more.} \item{bib.loc}{Optional path to a .bib resource.} \item{path}{The path to where the project should be created. Default is the current working directory.} \item{name}{A character string of the user's name to be used on the report.} \item{github.user}{GitHub user name (character string).} \item{sources}{A vector of path(s) to other scripts to be sourced in the report project upon startup (adds this location to the report project's \code{.Rprofile}).} \item{AN.xlsx}{logical. If \code{TRUE} the article notes (AN) will be in .xlsx format. If \code{FALSE} the document will be a .csv file.} \item{slidify}{The template to be used in the PRESENTATION .Rmd. This can be one of the types from \code{slidify_templates} or a path to an .Rmd file. This argument will be overrode if a custom reports template is supplied with an .Rmd file in the inst directory named slidify.Rmd (\code{/inst/slidify.Rmd}).} \item{open}{logical. If \code{TRUE} the project will be opened in RStudio.} \item{\ldots}{Other arguments passed to \code{\link[slidify]{author}}.} } \value{ Creates a report template. } \description{ Generate a report/paper template to increase efficiency. } \section{Suggestion}{ The user may want to set \code{\link[base]{options}} for \code{bib.loc}, \code{github.user}, \code{name.reports} \code{sources.reports},\code{slidify.template} and \code{reveraljs.loc} in the user's primary \code{.Rprofile}: \enumerate{ \item{\bold{bib.loc} - The path to the users primary bibliography} \item{\bold{name.reports} - The name to use on reports} \item{\bold{temp.reports} - The primary template to use to generate reports (see \code{template})} \item{\bold{github.user} - GitHub user name} \item{\bold{speed.temp} - A speed dial like interface that allows the template argument to take a numeric arguement. Setting this option takes the form of:\cr \code{options(speed.temp=list(`1`="wordpress_rmd", `2`="basic_rmd"))}} \item{\bold{sources.reports} - Path(s) to additional files/scripts that should be included to be sourced in the project startup} \item{\bold{slidify.template} - Path to, or defualt, .Rmd file tempalte for use in as the .Rmd used in the slidify presentations (see \code{slidify_templates} for possible non-path arguments)} } } \section{Additional Guide}{ Introductory video \url{http://www.youtube.com/watch?v=ArHQjQyIS70} } \examples{ ## new_report() } \references{ \href{https://github.com/ramnathv/slidifyExamples/tree/gh-pages/examples}{slidify examples} } \seealso{ \code{\link[reports]{doc_temp}}, \code{\link[reports]{presentation}}, \code{\link[reports]{templates}}, \code{\link[reports]{slidify_templates}}, \code{\link[slidify]{author}} \href{https://github.com/hakimel/reveal.js/}{Installation section of reveal.js GitHub} }

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/conditions.R

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juhnowski/r_lesson

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conditions.R

var1 = 5 var2 = 35 if((var1+var2)>100){ print(">100") } else if ((var1+var2)>75){ print(">75") } else if ((var1+var2)>50){ print(">50") } else { print("less") } switch(1, "1" = print("one"), "2" = print("two") ) switch("%", "1" = print("one"), "2" = print("two"), print("default") )

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empdist.R

library(mTDR) context("empdist") test_that("empdist() returns cumulative density.", { data("chipseq") u <- empdist(chipseq$R1, chipseq$R2) expect_is(u, "matrix") expect_equal(length(pd), nrow(chipseq)) expect_equal(nrow(u), nrow(chipseq)) expect_equal(ncol(u), 2) expect_true(all(u>=0&u<=1)) })

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BusulfanPackages.R

require(GenSA) #library for simualted annealing require(foreach) require(xlsx)#for exporting require(iterators) require(parallel) require(doParallel) require(ggplot2) require(gridExtra) require(gdata)

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config.R

print("Run config.R") #install relied packages source("global.R") #init input and library rm(list=ls()) library(shiny) library(dplyr) library(data.table) library(DT) library(readr) #change if necessary dataDir="./InData/" dataSuffix=".tsv" #global variables if any #maxRect=2500

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/Spatial Interaction Models /Spatial Interaction Modelling for Dummies.R

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YX577/Urban-Simulation

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2022-11-20T01:36:37.629944

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Spatial Interaction Modelling for Dummies.R

# http://rpubs.com/adam_dennett/257231 library(sp) library(MASS) library(reshape2) library(geojsonio) library(rgdal) library(downloader) library(maptools) library(dplyr) library(broom) library(stplanr) library(ggplot2) library(leaflet) #Fetch a GeoJson of some district-level boundaries from the ONS Geoportal. First add the URL to an object EW <- geojson_read("http://geoportal.statistics.gov.uk/datasets/8edafbe3276d4b56aec60991cbddda50_2.geojson", what = "sp") #have a quick look at the top of the data file head(EW@data) #pull out london using grep and the regex wildcard for'start of the string' (^) to to look for the bit of #the district code that relates to London (E09) from the 'lad15cd' column in the data slot of our spatial polygons dataframe London <- EW[grep("^E09",EW@data$lad15cd),] #plot it plot(London) #and have a look under the bonnet summary(London) #CALCULATING A DISTANCE MATRIX #boundaries we have are not in British National Grid - the bit that says proj4string tells me #that we are in WGS84 or using latitude and longitude coordinates. We need to change this to #British National Grid so our distances are in metres and not decimal degrees, then do everything #we need to do to generate a distance matrix. #first transfrom to BNG - this will be important for calculating distances using spTransform BNG = "+init=epsg:27700" LondonBNG <- spTransform(London, BNG) #now, order by borough code - *this step will be imporant later on* LondonBNG <- LondonBNG[order(LondonBNG$lad15cd),] #now use spDists to generate a big distance matrix of all distances between boroughs in London dist <- spDists(LondonBNG) #melt this matrix into a list of origin/destination pairs using melt. Melt in in the reshape2 package. Reshape2, dplyr and ggplot, together, are some of the best packages in R, so if you are not familiar with them, get googling and your life will be much better! distPair <- melt(dist) # FLOW DATA #read in your London Commuting Data cdata <- read.csv("https://www.dropbox.com/s/7c1fi1txbvhdqby/LondonCommuting2001.csv?raw=1") #read in a lookup table for translating between old borough codes and new borough codes CodeLookup <- read.csv("https://www.dropbox.com/s/h8mpvnepdkwa1ac/CodeLookup.csv?raw=1") #read in some population and income data popincome <- read.csv("https://www.dropbox.com/s/84z22a4wo3x2p86/popincome.csv?raw=1") #now merge these supplimentary data into your flow data dataframe cdata$OrigCodeNew <- CodeLookup$NewCode[match(cdata$OrigCode, CodeLookup$OldCode)] cdata$DestCodeNew <- CodeLookup$NewCode[match(cdata$DestCode, CodeLookup$OldCode)] cdata$vi1_origpop <- popincome$pop[match(cdata$OrigCodeNew, popincome$code)] cdata$vi2_origsal <- popincome$med_income[match(cdata$OrigCodeNew, popincome$code)] cdata$wj1_destpop <- popincome$pop[match(cdata$DestCodeNew, popincome$code)] cdata$wj2_destsal <- popincome$med_income[match(cdata$DestCodeNew, popincome$code)] #Data needs to be ordered by borough code, if it's not, we will run into problems when #we try to merge our distance data back in later, so to make sure, we can arrange by orign #and then destination using dplyr's 'arrange' function cdata <- arrange(cdata, OrigCodeNew, DestCodeNew) #First create a new total column which excludes intra-borough flow totals (well sets them to a very very small number for reasons you will see later...) cdata$TotalNoIntra <- ifelse(cdata$OrigCode == cdata$DestCode,0,cdata$Total) cdata$offset <- ifelse(cdata$OrigCode == cdata$DestCode,0.0000000001,1) # now add the distance column into the dataframe cdata$dist <- distPair$value head(cdata) # to make this demonstration even easier, let’s just select a small subset of these # flows (we can come back to the whole dataset later on #We'll just use the first 7 boroughs by code, so first, create a vector of these 7 to match with our data toMatch<-c("00AA", "00AB", "00AC", "00AD", "00AE", "00AF", "00AG") #subset the data by the 7 sample boroughs #first the origins cdatasub <- cdata[grep(paste(toMatch,collapse = "|"), cdata$OrigCode),] #then the destinations cdatasub <- cdatasub[grep(paste(toMatch,collapse = "|"), cdata$DestCode),] #now chop out the intra-borough flows cdatasub <- cdatasub[cdatasub$OrigCode!=cdatasub$DestCode,] #now unfortunately if you look at the file, for some reason the grep process has left a lot of empty data cells in the dataframe, so let's just chop out everything after the 7*7 - 7 (42) pairs we are interested in... cdatasub <- cdatasub[1:42,] #now re-order so that OrigCodeNew, DestCodeNew and TotalNoIntra are the first three columns *note that you have to be explicit about the select function in the dplyr package as MASS also has a 'select' function and R will try and use this by default. We can be explict by using the syntax package::function cdatasub <- dplyr::select(cdatasub, OrigCodeNew, DestCodeNew, Total, everything()) # re-order so that 'lad15cd' is the first column in LondonBNG # HUSSEIN library(sf) LondonBNG_sf <- st_as_sf(LondonBNG) # re-order so that 'lad15cd' is the first column in LondonBNG - OTHERWISE od2line WON'T WORK LondonBNG_sf <- dplyr::select(LondonBNG_sf, lad15cd, everything()) # convert back to sp LondonBNG <- as(LondonBNG_sf, 'Spatial') # End HUSSEIN #use the od2line function from Robin Lovelace's excellent stplanr package travel_network <- od2line(flow = cdatasub, zones = LondonBNG) #and set the line widths to some sensible value according to the flow w <- cdatasub$Total / max(cdatasub$Total) * 10 #now plot it... plot(travel_network, lwd = w) plot(LondonBNG, add=T) # leaflet map #transform to wgs84 travel_networkwgs <- spTransform(travel_network, "+init=epsg:4326") #plot in leaflet leaflet() %>% addTiles() %>% addPolylines(data = travel_networkwgs) #now we can create pivot table to turn paired list into matrix (and compute the margins as well) cdatasubmat <- dcast(cdatasub, Orig ~ Dest, sum, value.var = "Total", margins=c("Orig", "Dest")) cdatasubmat # MODELLING #First plot the commuter flows against distance and then fit a model line with a ^-2 parameter # -2 is the parameter used for Newton's Gravity Model. We are just using it as a starting point plot1 <- qplot(cdata$dist, cdata$Total) #and now the model fit... plot1 + stat_function(fun=function(x)x^-2, geom="line", aes(colour="^-2")) #now, what about the origin and destination data... plot2 <- qplot(cdata$vi1_origpop, cdata$Total) plot2 + stat_function(fun=function(x)x^1, geom="line", aes(colour="^1")) plot3 <- qplot(cdata$wj2_destsal, cdata$Total) plot3 + stat_function(fun=function(x)x^1, geom="line", aes(colour="^1")) #OK, so it looks like we’re not far off (well, destination salary doesn’t look too promising as a predictor, #but we’ll see how we get on…), so let’s see what flow estimates with these starting parameters look like. #set up some variables to hold our parameter values in: mu <- 1 alpha <- 1 beta <- -2 k <- 1 T2 <- sum(cdatasub$Total) #Now let’s create some flow estimates using Equation 2 above… Begin by applying the parameters to the variables: vi1_mu <- cdatasub$vi1_origpop^mu wj2_alpha <- cdatasub$wj2_destsal^alpha dist_beta <- cdatasub$dist^beta T1 <- vi1_mu*wj2_alpha*dist_beta k <- T2/sum(T1) #balancing parameter, ensures total flow matches reality #run the model and store all of the new flow estimates in a new column in the dataframe cdatasub$unconstrainedEst1 <- round(k*vi1_mu*wj2_alpha*dist_beta,0) #check that the sum of these estimates makes sense sum(cdatasub$unconstrainedEst1) #turn it into a little matrix and have a look at your handy work cdatasubmat1 <- dcast(cdatasub, Orig ~ Dest, sum, value.var = "unconstrainedEst1", margins=c("Orig", "Dest")) cdatasubmat1 # How do the flow estimates compare with the actual flows? Eyeballing works, but we need something more mathy # TESTING THE “GOODNESS-OF-FIT”. # METHOD 1: R-Squared CalcRSquared <- function(observed,estimated){ r <- cor(observed,estimated) R2 <- r^2 R2 } CalcRSquared(cdatasub$Total,cdatasub$unconstrainedEst1) # our model accounts for about 51% of the variation of flows in the system. Not bad, but not brilliant either. # METHOD 2: RMSE CalcRMSE <- function(observed,estimated){ res <- (observed - estimated)^2 RMSE <- round(sqrt(mean(res)),3) RMSE } CalcRMSE(cdatasub$Total,cdatasub$unconstrainedEst1) # The closer to 0 the RMSE value, the better the model. (Now it is 2503...let's try to improve it) # POISSON REGRESSION - To Calibrate # Flow distribution qplot(cdata$Total) + geom_histogram() # If it looks like Poisson, and it quacks like Poisson... qplot(log(dist), log(Total), data=cdata) + geom_smooth(method = lm) #run the unconstrained model uncosim <- glm(Total ~ log(vi1_origpop)+log(wj2_destsal)+log(dist), na.action = na.exclude, family = poisson(link = "log"), data = cdatasub) summary(uncosim) # Calibrated values # k (intercept) = -15.631802 # μ = 1.747997 # α = 1.642331 # β = -1.411889 # Calculate Flow Estimates Using Calibrated Coefficients #first asign the parameter values from the model to the appropriate variables k <- uncosim$coefficients[1] mu <- uncosim$coefficients[2] alpha <- uncosim$coefficients[3] beta <- -uncosim$coefficients[4] #now plug everything back into the Equation 6 model... (be careful with the positive and negative signing of the parameters as the beta parameter may not have been saved as negative so will need to force negative) cdatasub$unconstrainedEst2 <- exp(k+(mu*log(cdatasub$vi1_origpop))+(alpha*log(cdatasub$wj2_destsal))-(beta*log(cdatasub$dist))) #which is exactly the same as this... cdatasub$unconstrainedEst2 <- (exp(k)*exp(mu*log(cdatasub$vi1_origpop))*exp(alpha*log(cdatasub$wj2_destsal))*exp(-beta*log(cdatasub$dist))) #and of course, being R, there is an even easier way of doing this... cdatasub$fitted <- fitted(uncosim) #run the model and store all of the new flow estimates in a new column in the dataframe cdatasub$unconstrainedEst2 <- round(cdatasub$unconstrainedEst2,0) sum(cdatasub$unconstrainedEst2) #turn it into a little matrix and have a look at your handy work cdatasubmat2 <- dcast(cdatasub, Orig ~ Dest, sum, value.var = "unconstrainedEst2", margins=c("Orig", "Dest")) cdatasubmat2 # And the $1,000,000 question - has calibrating the parameters improved the model…? CalcRSquared(cdatasub$Total,cdatasub$unconstrainedEst2) CalcRMSE(cdatasub$Total,cdatasub$unconstrainedEst2)

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library('quantmod') library('corrplot') getSymbols(c("AMTD"), from="2015-01-19", to="2020-01-17", src="yahoo", periodicity = 'weekly') data = read.csv('AMTD Trend.csv', header = T) AMTDT = data[,c("Week", "TDAmeritrade")] ##Earnings AMTD[53] AMTDT[223,] x = c(2, 15, 28, 41, 54, 67, 80, 93, 106, 119, 132, 145, 158, 171, 184, 197, 210, 223, 236, 249, 261) AMTDC = AMTD$AMTD.Close[x] ##Quarterly Dates AMTDO = AMTD$AMTD.Open[x] AMTDD = (AMTDC - AMTDO)/(AMTDO) AMTDD #Move one week after earnings result AMTDTE = c() #Trend Sums AMTDTE[1] = sum(AMTDT$TDAmeritrade[1:x[1]]) AMTDTE[2] = sum(AMTDT$TDAmeritrade[x[1]:x[2]]) AMTDTE[3] = sum(AMTDT$TDAmeritrade[x[2]:x[3]]) AMTDTE[4] = sum(AMTDT$TDAmeritrade[x[3]:x[4]]) AMTDTE[5] = sum(AMTDT$TDAmeritrade[x[4]:x[5]]) AMTDTE[6] = sum(AMTDT$TDAmeritrade[x[5]:x[6]]) AMTDTE[7] = sum(AMTDT$TDAmeritrade[x[6]:x[7]]) AMTDTE[8] = sum(AMTDT$TDAmeritrade[x[7]:x[8]]) AMTDTE[9] = sum(AMTDT$TDAmeritrade[x[8]:x[9]]) AMTDTE[10] = sum(AMTDT$TDAmeritrade[x[9]:x[10]]) AMTDTE[11] = sum(AMTDT$TDAmeritrade[x[10]:x[11]]) AMTDTE[12] = sum(AMTDT$TDAmeritrade[x[11]:x[12]]) AMTDTE[13] = sum(AMTDT$TDAmeritrade[x[12]:x[13]]) AMTDTE[14] = sum(AMTDT$TDAmeritrade[x[13]:x[14]]) AMTDTE[15] = sum(AMTDT$TDAmeritrade[x[14]:x[15]]) AMTDTE[16] = sum(AMTDT$TDAmeritrade[x[15]:x[16]]) AMTDTE[17] = sum(AMTDT$TDAmeritrade[x[16]:x[17]]) AMTDTE[18] = sum(AMTDT$TDAmeritrade[x[17]:x[18]]) AMTDTE[19] = sum(AMTDT$TDAmeritrade[x[18]:x[19]]) AMTDTE[20] = sum(AMTDT$TDAmeritrade[x[19]:x[20]]) AMTDTE[21] = sum(AMTDT$TDAmeritrade[x[20]:x[21]]) ##Breaking down by quarter Q1 y = c(1, 5, 9, 13, 17, 21) AMTDQ1D = AMTDD[y] AMTDTQ1 = AMTDTE[y] AMTDTQ1 AMTDQ1D2 = AMTDQ1D[2:5] AMTDTQ10.log = diff(as.vector(log(AMTDTQ1))) length(AMTDTQ10.log) AMTDTQ1.log = AMTDTQ10.log[1:4] AMTDQ1D2[1] ##price change on 1st event, AMTDTQ1.log[1] ##Trends change for second event - from 1st event fitQ1L <- lm(AMTDQ1D2 ~ AMTDTQ1.log) summary(fitQ1L) ratesALQ1 <- data.frame(AMTDQ1D2, AMTDTQ1.log) corrplot.mixed(cor(ratesALQ1), upper = "ellipse") fitQ1L Q1FuncL <- function(x){ y = -0.04138*x - 0.02034 y } AMTDTE[21] AMTDTE[17] AMTDTEV = c(AMTDTE[21], AMTDTE[17]) AMTDTENOW = diff(as.vector(log(AMTDTEV))) AMTDTENOW val = AMTDTENOW val Q1FuncL(val) AMTDQ1D2 AMTDTQ10.log ##Breaking down by quarter Q2 y = c(4, 8, 12, 16, 20) AMTDQ1D = AMTDD[y] AMTDTQ2 = AMTDTE[y] AMTDQ2D2 = AMTDQ1D[2:5] AMTDTQ2.log = diff(as.vector(log(AMTDTQ1))) fitQ2L <- lm(AMTDQ2D2 ~ AMTDTQ2.log) summary(fitQ2L) ratesALQ2 <- data.frame(AMTDQ2D2, AMTDTQ2.log) corrplot.mixed(cor(ratesALQ2), upper = "ellipse") fitQ2L Q2FuncL <- function(x){ y = -0.003427*x + 0.049751 y } AMTDTE[20] AMTDTE[16] AMTDTEV = c(AMTDTE[20], AMTDTE[16]) AMTDTENOW = diff(as.vector(log(AMTDTEV))) AMTDTENOW Q2FuncL(AMTDTENOW) AMTDQ2D2[4] ##2014 - 2019 Q1 Analysis Data Monthly ##0 = earnings 1/21/2014 ## sum = 1, 2, 3, 4 months ##1 = earnings 4/23/2014 ## sum = 4,5,6,7 months getSymbols(c("AMTD"), from="2014-01-19", to="2019-01-18", src="yahoo", periodicity = 'weekly') data2 = read.csv('AMTD Trends 2014-2019.csv', header = T) AMTDT2 = data2[,c("Week", "TDAmeritrade")] length(AMTDT2$Week) AMTDT2$Week[41] x = c(1, 14, 27, 40, 53, 66, 79, 92, 105, 118, 131, 144, 157, 170, 183, 196, 209, 222, 235, 248, 260) ## 13 length(x) xE = c(1, 14, 27, 41, 53, 66, 79, 93, 105, 118, 131, 145, 157, 170, 183, 197, 210, 223, 236, 249, 261) trendSum = c() trendSum[1] = sum(AMTDT2$TDAmeritrade[x[1]:x[2]]) trendSum[2] = sum(AMTDT2$TDAmeritrade[x[2]:x[3]]) trendSum[3] = sum(AMTDT2$TDAmeritrade[x[3]:x[4]]) trendSum[4] = sum(AMTDT2$TDAmeritrade[x[4]:x[5]]) trendSum[5] = sum(AMTDT2$TDAmeritrade[x[5]:x[6]]) trendSum[6] = sum(AMTDT2$TDAmeritrade[x[6]:x[7]]) trendSum[7] = sum(AMTDT2$TDAmeritrade[x[7]:x[8]]) trendSum[8] = sum(AMTDT2$TDAmeritrade[x[8]:x[9]]) trendSum[9] = sum(AMTDT2$TDAmeritrade[x[9]:x[10]]) trendSum[10] = sum(AMTDT2$TDAmeritrade[x[10]:x[11]]) trendSum[11] = sum(AMTDT2$TDAmeritrade[x[11]:x[12]]) trendSum[12] = sum(AMTDT2$TDAmeritrade[x[12]:x[13]]) trendSum[13] = sum(AMTDT2$TDAmeritrade[x[13]:x[14]]) trendSum[14] = sum(AMTDT2$TDAmeritrade[x[14]:x[15]]) trendSum[15] = sum(AMTDT2$TDAmeritrade[x[15]:x[16]]) trendSum[16] = sum(AMTDT2$TDAmeritrade[x[16]:x[17]]) trendSum[17] = sum(AMTDT2$TDAmeritrade[x[17]:x[18]]) trendSum[18] = sum(AMTDT2$TDAmeritrade[x[18]:x[19]]) trendSum[19] = sum(AMTDT2$TDAmeritrade[x[19]:x[20]]) trendSum[20] = sum(AMTDT2$TDAmeritrade[x[20]:x[21]]) AMTDC2 = AMTD$AMTD.Close[xE] ##Quarterly Dates AMTDO2 = AMTD$AMTD.Open[xE] AMTDD2 = (AMTDC2 - AMTDO2)/(AMTDO2) ##Breaking down by quarter Q1 y = c(4,8,12,16,20) yE = c(5,9,13,17,21) AMTDQ1D2 = AMTDD2[yE] AMTDTrendQ1 = trendSum[y] AMTDTrendQ1 AMTDQ1D2 = AMTDQ1D2[2:4] AMTDQ1D2 AMTDTQ10.log = diff(as.vector(log(AMTDTrendQ1))) AMTDTQ10.log AMTDTQ10.log = AMTDTQ10.log[1:3] fitQ1LY <- lm(AMTDQ1D2$AMTD.Close ~ AMTDTQ10.log) summary(fitQ1LY) ratesALQ2 <- data.frame(AMTDQ1D2, AMTDTQ10.log) corrplot.mixed(cor(ratesALQ2), upper = "ellipse") ##lfit=loess(AMTDQ1D2$AMTD.Close~AMTDTQ10.log + AMTDTQ10.log, control = loess.control(surface = "direct")) ##Yh = predict(lfit, 0.08712293) fitQ1LY Q1FuncL <- function(x){ y = 0.02612*x -0.01037 y } Q1FuncL(0.08712293) Q1FuncL(0.297) ## Breaking Down by Q2 y = c(1,5,9,13,17) yE = c(2,6,10,14,18) AMTDQ2D2 = AMTDD2[yE] AMTDTrendQ2 = trendSum[y] AMTDTrendQ2 AMTDQ2D2 = AMTDQ2D[2:4] AMTDQ2D2 AMTDTQ10.log = diff(as.vector(log(AMTDTrendQ1))) AMTDTQ10.log AMTDTQ10.log = AMTDTQ10.log[1:3] fitQ2L <- lm(AMTDQ1D2$AMTD.Close ~ AMTDTQ10.log) summary(fitQ2L) ratesALQ2 <- data.frame(AMTDQ1D2, AMTDTQ10.log) corrplot.mixed(cor(ratesALQ2), upper = "ellipse") fitQ1LY Q1FuncL <- function(x){ y = 0.0678*x -0.01183 y } Q1FuncL(0.08712293) Q1FuncPoly <- function(x){ y = -0.1442*x^2 + 0.0382*x - 0.0037 y } Q1FuncPoly(0.08712293)

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/approx.R \name{approx_dt} \alias{approx_dt} \title{Approximate missing values in a data.table.} \usage{ approx_dt( dt, xdata, xcol, ycol, idxcols = NULL, keepna = FALSE, extrapolate = FALSE ) } \arguments{ \item{dt}{a data.table.} \item{xdata}{the range to interpolate to. This is the range the result will have along the dimension `xcol`.} \item{xcol}{name of the column for interpolation.} \item{ycol}{name of the column that contains the value to be interpolated.} \item{idxcols}{columns that identify a row (besides xcol), i.e., the remaining index dimensions.} \item{keepna}{keep NA values for rows that can not be interpolated (since they are outside of [min(xcol), max(xcol)]), default is FALSE.} \item{extrapolate}{use the closest values to fill `ycol` outside of the interpolation domain, default is FALSE. This will also work if there is only one value along `ycol`, i.e., no interpolation is taking place.} } \value{ a data.table with the range given by `xdata` along `xcol`. Columns not given in `idxcols` will be kept but NAs will appear on extrapolated and interpolated rows. } \description{ Similar to, but not quite like, `stats::approx`. Does only support constant extrapolation and linear interpolation. The resulting `data.table` only contains the range provided by `xdata` along `xcol`. Without extrapolation, `xcol` in the resulting `data.table` may not cover the range given by `xdata`. } \examples{ dt <- as.data.table(ChickWeight) ## delete all values but 1 dt[Chick == 1 & Time > 0, weight := NA] ## delete all values but 2 dt[Chick == 2 & Time > 2, weight := NA] ## extrapolation from 1 value approx_dt(dt, 0:21, "Time", "weight", idxcols=c("Chick", "Diet"), extrapolate = TRUE)[Chick == 1] ## extrapolation and interpolation approx_dt(dt, 0:21, "Time", "weight", idxcols=c("Chick", "Diet"), extrapolate = TRUE)[Chick == 2] ## column not in idxcols approx_dt(dt, 0:21, "Time", "weight", idxcols="Chick", extrapolate = TRUE)[Chick == 2] dt <- as.data.table(ChickWeight) ## interpolation only approx_dt(dt, 0:21, "Time", "weight", idxcols=c("Chick", "Diet"))[Chick == 2] }

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#' drf_base #' @export drf_base drf_base = function(name = 'Dockerfile', version='r-base:4.0.2', workdir = '/') { base_version <- glue('{version}') return(list(base = base_version, name = name, workdir = workdir)) } #' drf_apt_get #' @export drf_apt_get drf_apt_get = function(apt_get='everything', packages = NULL) { if (apt_get == 'everything' & is.null(packages)) { packages <- c( "sudo", "gdebi-core", "pandoc", "pandoc-citeproc", "libcurl4-gnutls-dev", "libcairo2-dev", "libxt-dev", "xtail", "wget", "libssl-dev", "libxml2-dev", "python3-venv", "libpq-dev", "libsodium-dev", "libudunits2-dev", "libgdal-dev", "systemctl", "git", "libssh2-1", "libssh2-1-dev", "unzip", "curl" ) } return(list(apt_get = packages)) } #' drf_packages #' @export drf_packages drf_packages <- function(renv_location = NULL, packages = 'devtools') { if (!is.null(renv_location)) { message('In renv_location') } list(packages = packages, renv = renv_location) } #' drf_copy #' @export drf_copy drf_copy <- function(localpath_vector = NULL) { return(localpath_vector) } #' build_container #' @export build_container build_container <- function(dockerflow_path = '.dockerflow.DockerPlumber.json', build = FALSE) { config_file <- read_json(dockerflow_path) toJSON(config_file, pretty = TRUE) meta_data <- map(config_file[[1]], ~ unlist(.)) apt_get <- map(config_file[[2]], ~ unlist(.)) r_packages <- map(config_file[[3]], ~ unlist(.)) copy_in <- map(config_file[[4]], ~ unlist(.)) title <- glue('# {meta_data$name}') base <- glue('FROM {meta_data$base}') workdir <- glue('WORKDIR {meta_data$workdir}') base_apt <- 'RUN apt-get update --allow-releaseinfo-change -qq && apt-get install -y ' apt_get_query <- paste(c(base_apt, apt_get$apt_get), collapse = ' \\\n\t') # install_renv <- 'RUN R -e "install.packages(\'renv\');renv::consent(provided=TRUE);renv::init()"' # install_renv <- 'RUN R -e "install.packages(\'renv\')"' preferred_packages <- install_packages <- map_chr( r_packages$packages, function(pkg, dependencies = TRUE) { glue('RUN R -e "install.packages(\'{pkg}\', dependencies = {dependencies})"') } ) copy_in <- map_chr(copy_in, function(file) { glue('COPY ./{file} ./{file}') }) dockername <- meta_data$name if(file_exists(dockername)) file_delete(dockername) walk( list(title, base, workdir, apt_get_query, # install_renv, preferred_packages, copy_in), function(file_line) { file_line <- unlist(file_line) file_line <- paste(file_line, '\n') map(file_line, message) map(file_line, ~ write_file(x = ., path = dockername, append = TRUE)) } ) # write_file(x = title, path = dockername, append = TRUE) # docker build -t productor_api --file ./DockerfileApi . command_to_run <- glue('docker build -t {tolower(dockername)} --file ./{dockername} . >> .dockerfiles.txt') message('please run tail -f .dockerfiles.txt to follow the installation') message(command_to_run) if (build) { system(command_to_run) } } #' build_me_docker #' @export build_me_docker build_me_docker <- function(name = 'DockerPlumber', version = 'rocker/shiny:4.0.1', packages = c('shiny'), localpath_vector = 'app.R') { container <- list( drf_base(name = name, version = version ), drf_apt_get(), drf_packages(packages = packages), drf_copy(localpath_vector = localpath_vector) ) meta <- container[[1]] json_path <- glue('.dockerflow.{meta$name}.json') container <- prettify(toJSON(container)) write_file(x = container, path = json_path) list(json_path = json_path, container = container) }

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/EJ6b.Convergence.R

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EJ6b.Convergence.R

# # Illustrating the Inverse Transform method. # # 1) Simulate Uniforms uu<-runif(10000) # 2) Compute F-1(U) for all these uniforms xx<-qnorm(uu) hist(xx,nclass=100,prob=T) #points(xx,dnorm(xx),col="red") lines(xx,dnorm(xx),col="red",lwd=2) # # Some Commands to illustrate convergence of Estimators # CLT with various Student-t Data # Convergence of other estimators (stdev, kurtosis) # to a normal distribution? par(mfrow=c(2,1)) ################################################### # Simulating the sample mean # Is is asymptotically normal as per the CLT ? # nobs<-2000; nsimul<-5000 returns<-matrix(rt(nsimul*nobs,df=2),nrow=nobs) qqnorm(returns[1,],main="The data"); qqline(returns[1,]) means<-apply(returns,2,mean) qqnorm(means,main="The sample means"); qqline(means) #################################################### # Simulating the sample standard deviation # In small sample: # 1) Is it biased ? # 2) Is its Std.Dev. equal to the theoretical value (sig/sqrt(2T)) # 3) Is it normally distributed? nobs<-1000; nsimul<-10000 sdtrue<-1 rets<-matrix(rnorm(nsimul*nobs,sd=sdtrue),ncol=nsimul) dof<-30 rets<-matrix(rt(nsimul*nobs,df=dof),ncol=nsimul) sdtrue<-sqrt(dof/(dof-2)) stds <-apply(rets,2,sd) mean(stds); sdtrue sd(stds); sdtrue/sqrt(2*nobs) theory<-qnorm(c(0.025,0.975),1,sdtrue/sqrt(2*nobs)) actual<-quantile(stds,c(0.025,0.975)) round(matrix(c(theory,actual),ncol=2,byrow=T, dimnames=list(c("theory","actual"),c("2.5%","97.5%"))) ,3) qqnorm(stds);qqline(stds) hist(stds,nclass=100,prob=T);box() abline(v=theory) abline(v=actual,col="red") # Is the asymptotic Confidence Intervals "correct"? # Do we reject 5% of the time if we use them? round( sum(stds>theory[1]&stds<theory[2])/nsimul ,3) ######################################################## # Estimator of Skewness # Is it consistent? # Does its variance equal the asymptotic approximation? # Is it normal? # Do we reject 5% of the time under H0? library(moments) nobs<-4000; nsimul<-10000; dof<-1000 returns<-matrix(rt(nsimul*nobs,dof),ncol=nsimul) skews <-apply(returns,2,skewness) mean(skews) sd(skews); sqrt(6/nobs) hist(skews,prob=T,nclass=100) qqnorm(skews);qqline(skews) theo<-qnorm(c(0.025,0.975),0,sqrt(6/nobs)) actu<-quantile(skews,c(0.025,0.975)) round(matrix(c(theo,actu),ncol=2,byrow=T, dimnames=list(c("theory","actual"),c("2.5%","97.5%"))),3) # Should reject 5% of the time under the null sum(skews>theo[1]&skews<theo[2])/nsimul ######################################################## # Estimator of the kurtosis # Is it consistent? Is it normal? # # Do we reject 5% of the time under H0? library(moments) nobs<-1000; nsimul<-10000; dof<-60 #returns<-matrix(rnorm(nsimul*nobs),ncol=nsimul) returns<-matrix(rt(nsimul*nobs,dof),ncol=nsimul) kurts <-apply(returns,2,kurtosis) mean(kurts) sd(kurts); sqrt(24/nobs) hist(kurts,prob=T,nclass=50) qqnorm(kurts);qqline(kurts) theo<-qnorm(0.95,3,sqrt(24/nobs)) actu<-quantile(kurts,0.95) round(c(theo,actu),3) sum(kurts>theo)/nsimul # Should reject 5% of the time # under the null

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graph_data.Rd

\name{graph_data} \alias{a} \alias{b} \docType{data} \title{ Two randomly shuffled isomorphic graphs } \description{ A and B are randomly generated adjacency matrices as shown in the example of run_graph_match function. a and b are adjacency matrices from the test_simple example in graphm library. } \format{ \describe{ \item{\code{a}}{an 10x10 adjacency matrix representing graph G to be matched} \item{\code{b}}{an 10x10 adjacency matrix representing graph H to be matched} } } \examples{ print (a) print (b) ## maybe str(graph_data) ; plot(graph_data) ... } \keyword{datasets}

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/Code/PLSC503-2021-WeekTwelve.R

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PLSC503-2021-WeekTwelve.R

########################################## # Code for PLSC 503 - Spring 2021 # # Regression models for ordinal- and # nominal-level outcomes... # ########################################## # Packages, etc.: require(RCurl) require(MASS) require(mlogit) require(VGAM) require(aod) require(car) # Options: options(scipen = 6) # bias against scientific notation options(digits = 3) # show fewer decimal places # setwd(): # # setwd("~/Dropbox (Personal)/PLSC 503/Notes") ##################################################### # Multinomial logit, etc. temp<-getURL("https://raw.githubusercontent.com/PrisonRodeo/PLSC503-2021-git/master/Data/Election1992small.csv") nes92<-read.csv(text=temp, header=TRUE) rm(temp) summary(nes92) nes92.mlogit<-vglm(presvote~partyid, multinomial, nes92) summary(nes92.mlogit) Bush.nes92.mlogit<-vglm(formula=presvote~partyid, family=multinomial(refLevel=1),data=nes92) summary(Bush.nes92.mlogit) Clinton.nes92.mlogit<-vglm(formula=presvote~partyid, family=multinomial(refLevel=2),data=nes92) summary(Clinton.nes92.mlogit) # Conditional logit... colnames(nes92)<-c("caseid","presvote","partyid","FT.Bush","FT.Clinton","FT.Perot") nes92$PVote<-factor(nes92$presvote,labels=c("Bush","Clinton","Perot")) head(nes92) nes92CL<-mlogit.data(nes92,shape="wide",choice="PVote",varying=4:6) head(nes92CL,6) # Conditional logistic regression: nes92.clogit<-mlogit(PVote~FT|partyid,data=nes92CL) summary(nes92.clogit) # Interpretation: temp<-getURL("https://raw.githubusercontent.com/PrisonRodeo/PLSC503-2021-git/master/Data/Election1992.csv") BigNES92<-read.csv(text=temp, header=TRUE) rm(temp) NES.MNL<-vglm(presvote~partyid+age+white+female,data=BigNES92, multinomial(refLevel=1)) summaryvglm(NES.MNL) wald.test(b=c(t(coef(NES.MNL))),Sigma=vcov(NES.MNL),Terms=c(5,6)) wald.test(b=c(t(coef(NES.MNL))),Sigma=vcov(NES.MNL),Terms=c(1,3,5,7,9)) # Hats, yo: PickBush<-ifelse(fitted.values(NES.MNL)[,1]>fitted.values(NES.MNL)[,2] & fitted.values(NES.MNL)[,1]>fitted.values(NES.MNL)[,3], 1,0) PickWJC<-ifelse(fitted.values(NES.MNL)[,2]>fitted.values(NES.MNL)[,1] & fitted.values(NES.MNL)[,2]>fitted.values(NES.MNL)[,3], 2, 0) PickHRP<-ifelse(fitted.values(NES.MNL)[,3]>fitted.values(NES.MNL)[,1] & fitted.values(NES.MNL)[,3]>fitted.values(NES.MNL)[,2], 3, 0) OutHat<-PickBush+PickWJC+PickHRP table(BigNES92$presvote,OutHat) # Odds ratios: mnl.or <- function(model) { coeffs <- c(t(coef(NES.MNL))) lci <- exp(coeffs - 1.96 * diag(vcov(NES.MNL))^0.5) or <- exp(coeffs) uci <- exp(coeffs + 1.96* diag(vcov(NES.MNL))^0.5) lreg.or <- cbind(lci, or, uci) lreg.or } mnl.or(NES.MNL) # In-sample predictions: hats<-as.data.frame(fitted.values(NES.MNL)) names(hats)<-c("Bush","Clinton","Perot") attach(hats) pdf("InSampleRScatterplotMatrix.pdf",8,7) spm(~Bush+Clinton+Perot,pch=20,plot.points=TRUE, diagonal="histogram",col=c("black","grey")) dev.off() pdf("InSampleMNLPredProbsR.pdf",8,6) par(mfrow=c(1,3)) plot(BigNES92$partyid,Bush,xlab="Party ID") plot(BigNES92$partyid,Clinton,xlab="Party ID") plot(BigNES92$partyid,Perot,xlab="Party ID") par(mfrow=c(1,1)) dev.off() # Conditional logit example: nes92.clogit<-mlogit(PVote~FT|partyid,data=nes92CL) summary(nes92.clogit) # In-sample predictions: CLhats<-predict(nes92.clogit,nes92CL) pdf("InSampleCLHatsR.pdf",7,6) plot(nes92$FT.Bush,CLhats[,1],pch=19, col=rgb(100,0,0,100,maxColorValue=255), xlab="Feeling Thermometer", ylab="Predicted Probability") points(nes92$FT.Clinton+runif(nrow(CLhats),-1,1), CLhats[,2],pch=4,col=rgb(0,0,100,100,maxColorValue=255)) points(nes92$FT.Perot+runif(nrow(CLhats),-1,1), CLhats[,3],pch=17,col=rgb(0,100,0,50,maxColorValue=255)) lines(lowess(nes92$FT.Bush,CLhats[,1]),lwd=2,col="red") lines(lowess(nes92$FT.Clinton,CLhats[,2]),lwd=2,col="blue") lines(lowess(nes92$FT.Perot,CLhats[,3]),lwd=2,col="darkgreen") legend("topleft",bty="n",c("Bush","Clinton","Perot"), col=c("red","blue","darkgreen"),pch=c(19,4,17)) dev.off() #################################################### # Now, ordinal-response models... # # GOP Thermometer score plot: temp<-getURL("https://raw.githubusercontent.com/PrisonRodeo/PLSC503-2021-git/master/Data/ANES-pilot-2016.csv") ANES<-read.csv(text=temp) rm(temp) ANES$ftjeb<-ifelse(ANES$ftjeb==998,NA,ANES$ftjeb) pdf("Notes/ANES-FT-Jeb-2016.pdf",7,6) par(mar=c(4,4,2,2)) hist(ANES$ftjeb,breaks=seq(0,100,by=1),main="", xlab="Feeling Thermometer Score for Jeb!") dev.off() ################################## # Ordered simulation: set.seed(7222009) X<-runif(1000,0,10) Ystar<-0 + 1*X + rnorm(1000) Y1<-Ystar Y1[Ystar<2.5]<-1 Y1[Ystar>=2.5 & Ystar<5]<-2 Y1[Ystar>=5 & Ystar<7.5]<-3 Y1[Ystar>=7.5]<-4 table(Y1) summary(lm(Ystar~X)) summary(lm(Y1~X)) pdf("OrdinalOneR.pdf",7,5) par(mar=c(4,4,2,2)) par(mfrow=c(1,2)) plot(X,Ystar,pch=20,xlab="X",ylab="Y*") abline(lm(Ystar~X),lwd=3,col="red") abline(h=c(2.5,5,7.5),lty=2) plot(X,Y1,pch=20,xlab="X",ylab="Y1") abline(lm(Y1~X),lwd=3,col="red") dev.off() Y2<-Ystar Y2[Ystar<2]<-1 Y2[Ystar>=2 & Ystar<8]<-2 Y2[Ystar>=8 & Ystar<9]<-3 Y2[Ystar>9]<-4 table(Y2) summary(lm(Y2~X)) pdf("OrdinalTwoR.pdf",7,5) par(mar=c(4,4,2,2)) par(mfrow=c(1,2)) plot(X,Ystar,pch=20,xlab="X",ylab="Y*") abline(lm(Ystar~X),lwd=3,col="red") abline(h=c(2,8,9),lty=2) plot(X,Y2,pch=20,xlab="X",ylab="Y2") abline(lm(Y2~X),lwd=3,col="red") dev.off() # Best Example Ever... temp<-getURL("https://raw.githubusercontent.com/PrisonRodeo/PLSC503-2021-git/master/Data/Beer.csv") beer<-read.csv(text=temp, header=TRUE) rm(temp) summary(beer) beer.logit<-polr(as.factor(quality)~price+calories+craftbeer +bitter+malty,data=beer) summary(beer.logit) beer.probit<-polr(as.factor(quality)~price+calories+craftbeer+ bitter+malty,data=beer,method="probit") summary(beer.probit) # Odds Ratios olreg.or <- function(model) { coeffs <- coef(summary(beer.logit)) lci <- exp(coeffs[ ,1] - 1.96 * coeffs[ ,2]) or <- exp(coeffs[ ,1]) uci <- exp(coeffs[ ,1] + 1.96 * coeffs[ ,2]) lreg.or <- cbind(lci, or, uci) lreg.or } olreg.or(beer.logit) # Predicted probs calories<-seq(60,200,1) price<-mean(beer$price) craftbeer<-median(beer$craftbeer) bitter<-mean(beer$bitter) malty<-mean(beer$malty) beersim<-cbind(calories,price,craftbeer,bitter,malty) beer.hat<-predict(beer.logit,beersim,type='probs') pdf("ROrdinalProbs.pdf",6,5) par(mar=c(4,4,2,2)) plot(c(60,200), c(0,1), type='n', xlab="Calories", ylab='Fitted Probability') lines(60:200, beer.hat[1:141, 1], lty=1, lwd=3) lines(60:200, beer.hat[1:141, 2], lty=2, lwd=3) lines(60:200, beer.hat[1:141, 3], lty=3, lwd=3) lines(60:200, beer.hat[1:141, 4], lty=4, lwd=3) dev.off() # Cumulative probs: xaxis<-c(60,60:200,200) yaxis1<-c(0,beer.hat[,1],0) yaxis2<-c(0,beer.hat[,2]+beer.hat[,1],0) yaxis3<-c(0,beer.hat[,3]+beer.hat[,2]+beer.hat[,1],0) yaxis4<-c(0,beer.hat[,4]+beer.hat[,3]+beer.hat[,2]+beer.hat[,1],0) pdf("ROrdinalCumProbs.pdf",6,5) par(mar=c(4,4,2,2)) plot(c(60,200), c(0,1), type='n', xlab="Calories", ylab="Cumulative Probability") polygon(xaxis,yaxis4,col="white") polygon(xaxis,yaxis3,col="grey80") polygon(xaxis,yaxis2,col="grey50") polygon(xaxis,yaxis1,col="grey10") dev.off() # fin

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isRemoveAllVars = # #` @title Determines which expressions are of the form rm(list = ls()) # function(e, remove = FALSE, asIndex = TRUE) { w = sapply(e, function(x) is.call(x) && as.character(x[[1]]) %in% c("remove", "rm")) if(!any(w)) return(if(remove) e else if(asIndex) integer() else list()) w2 = sapply(e[w], isRemoveAllCall) i = which(w)[w2] if(remove) e[-i] else if(asIndex) i else e[i] } isRemoveAllCall = function(x) { # Currently checks only for list = ls(...)) where we could have anything in ... !is.na(i <- match("list", names(x))) && is.call(x[[i]]) && as.character(x[[i]][[1]]) %in% c("ls", "objects") && # Check if using a different environment (is.na( i <- match("envir", names(x))) || (is.call(x[[i]]) && as.character(x[[i]]) == "globalenv")) }

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iris-ex.R

#' --- #' title: "Iris data examples for EqCov paper" #' author: "Michael Friendly and Matthew Sigal" #' date: "21 Jun 2016" #' --- #' This script reproduces all of the analysis and graphs for the MANOVA of the `Iris` data #' in the paper and also includes other analyses not described there. It is set up as an #' R script that can be "compiled" to HTML, Word, or PDF using `knitr::knit()`. This is most #' convenient within R Studio via the `File -> Compile Notebook` option. #+ echo=FALSE knitr::opts_chunk$set(warning=FALSE, message=FALSE, R.options=list(digits=4)) #' ## Load packages and the data library(heplots) library(car) # actually, loaded by heplots data(iris) #' ## Initial scatterplots and data ellipses op <- par(mfcol=c(1,2), mar=c(5,4,1,1)+.1) scatterplot(Sepal.Width ~ Sepal.Length | Species, data=iris, ellipse=TRUE, levels=0.68, smoother=NULL, reg.line=FALSE, grid=FALSE, legend.coords=list(x=7, y=4.4), col=c("red", "blue", "black")) scatterplot(Sepal.Width ~ Sepal.Length | Species, data=iris, ellipse=TRUE, levels=0.68, smoother=NULL, grid=FALSE, reg.line=FALSE, cex=0, legend.plot=FALSE, col=c("red", "blue", "black")) par(op) #' ## Using the covEllipse function #' Uncentered and centered, first two variables covEllipses(iris[,1:4], iris$Species, fill=c(rep(FALSE,3), TRUE)) covEllipses(iris[,1:4], iris$Species, center=TRUE, fill=c(rep(FALSE,3), TRUE), fill.alpha=.1, label.pos=c(1:3,0)) #' All pairs when more than two are specified covEllipses(iris[,1:4], iris$Species, fill=c(rep(FALSE,3), TRUE), variables=1:4, fill.alpha=.1) covEllipses(iris[,1:4], iris$Species, center=TRUE, fill=c(rep(FALSE,3), TRUE), variables=1:4, label.pos=c(1:3,0), fill.alpha=.1) #' ## view in PCA space #' NB: scale.=FALSE by default iris.pca <- prcomp(iris[,1:4]) summary(iris.pca) op <- par(mfcol=c(1,2), mar=c(5,4,1,1)+.1) covEllipses(iris.pca$x, iris$Species, fill=c(rep(FALSE,3), TRUE), label.pos=1:4, fill.alpha=.1, asp=1) covEllipses(iris.pca$x, iris$Species, fill=c(rep(FALSE,3), TRUE), center=TRUE, label.pos=1:4, fill.alpha=.1, asp=1) par(op) # all variables covEllipses(iris.pca$x, iris$Species, fill=c(rep(FALSE,3), TRUE), variables=1:4, label.pos=1:4, fill.alpha=.1) covEllipses(iris.pca$x, iris$Species, center=TRUE, fill=c(rep(FALSE,3), TRUE), variables=1:4, label.pos=1:4, fill.alpha=.1) # Plot the last two, PC 3,4 covEllipses(iris.pca$x, iris$Species, fill=c(rep(FALSE,3), TRUE), variables=3:4, label.pos=c(1:3,0), fill.alpha=.1, asp=1) covEllipses(iris.pca$x, iris$Species, fill=c(rep(FALSE,3), TRUE), center=TRUE, variables=3:4, label.pos=c(1:3,0), fill.alpha=.1, asp=1) #' ## compare classical and robust covariance estimates covEllipses(iris[,1:4], iris$Species) covEllipses(iris[,1:4], iris$Species, fill=TRUE, method="mve", add=TRUE, labels="") #' Box's M test iris.boxm <- boxM(iris[, 1:4], iris[, "Species"]) iris.boxm #' covEllipses has a method for `"boxm"` objects covEllipses(iris.boxm, fill=c(rep(FALSE,3), TRUE) ) covEllipses(iris.boxm, fill=c(rep(FALSE,3), TRUE), center=TRUE, label.pos=1:4 ) #' Boxplots of means, using `car::Boxplot` op <- par(mfrow=c(1, 4), mar=c(5,4,1,1)) for (response in names(iris)[1:4]){ Boxplot(iris[, response] ~ Species, data=iris, ylab=response, axes=FALSE, col=c("red", "blue", "gray")) box() axis(2) axis(1, at=1:3, labels=c("Setosa", "Vers.", "Virginca")) } par(op) #' ## models & plots iris.mod <- lm(as.matrix(iris[, 1:4]) ~ Species, data=iris) Anova(iris.mod) iris.boxm <- boxM(iris.mod) iris.boxm #' ## canonical view of MANOVA test library(candisc) iris.can <- candisc(iris.mod) op <- par(mar=c(5,4,1,1)+.1) plot(iris.can, ellipse=TRUE) par(op) #' ## multivariate Levene test irisdev <- abs( colDevs(iris[,1:4], iris$Species, median) ) irisdev.mod <- lm( irisdev ~ iris$Species) Anova(irisdev.mod) #' ## robust MLM irisdev.rmod <- robmlm( irisdev ~ iris$Species) Anova(irisdev.rmod) pairs(irisdev.rmod, variables=1:4, fill=TRUE, fill.alpha=.1) #' ## covariance ellipses of absolute deviations covEllipses(irisdev, group=iris$Species, variables=1:4, fill=c(rep(FALSE,3), TRUE), fill.alpha=0.1, label.pos=c(1:3,0)) pairs(irisdev.mod, variables=1:4, fill=TRUE, fill.alpha=.1) #' Canonical views for Levene's test library(candisc) irisdev.can <- candisc(irisdev.mod) irisdev.can plot(irisdev.can, which=1) plot(irisdev.can, ellipse=TRUE)

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/cachematrix.R

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cachematrix.R

##This function is going to make the cache matrix ##Two variables called x and s, x is the matrix requiring processing, ##and s is the result after inversing makeCacheMatrix <- function(x = numeric()){ s <- NULL set<-function(y){ x<<-y s<<-NULL } get <- function() x setinverse<-function(solve) s<<-solve getinverse<-function() s list(set = set, get = get, setinverse = setinverse, getinverse = getinverse) } ##This function is going to cache the inverse matrix cacheSolve <- function(x){ s <- x$getinverse() if(!is.null(s)){ message("Getting cached data") return(s) } data <- x$get() s <- solve(data) x$setinverse(s) print(s) }

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/man/sixCycleStat.Rd

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uvacorpnet/rem

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sixCycleStat.Rd

\name{sixCycleStat} \alias{sixCycleStat} \alias{sixCycle} \title{Calculate six cycle statistics} % \description{Calculate the endogenous network statistic \code{sixCycle} that measures the tendency for events to close four cycles in two-mode event sequences.} \usage{ sixCycleStat(data, time, sender, target, halflife, weight = NULL, eventtypevar = NULL, eventtypevalue = 'standard', eventfiltervar = NULL, eventfilterAB = NULL, eventfilterAJ = NULL, eventfilterIB = NULL, eventfilterIJ = NULL, eventvar = NULL, variablename = 'fourCycle', returnData = FALSE, dataPastEvents = NULL, showprogressbar = FALSE, inParallel = FALSE, cluster = NULL ) } \arguments{ \item{data}{ A data frame containing all the variables.} \item{time}{ Numeric variable that represents the event sequence. The variable has to be sorted in ascending order.} \item{sender}{ A string (or factor or numeric) variable that represents the sender of the event.} \item{target}{ A string (or factor or numeric) variable that represents the target of the event.} \item{halflife}{ A numeric value that is used in the decay function. The vector of past events is weighted by an exponential decay function using the specified halflife. The halflife parameter determins after how long a period the event weight should be halved. E.g. if \code{halflife = 5}, the weight of an event that occured 5 units in the past is halved. Smaller halflife values give more importance to more recent events, while larger halflife values should be used if time does not affect the sequence of events that much.} \item{weight}{ An optional numeric variable that represents the weight of each event. If \code{weight = NULL} each event is given an event weight of \code{1}. } \item{eventtypevar}{ An optional variable that represents the type of the event. Use \code{eventtypevalue} to specify how the \code{eventtypevar} should be used to filter past events. } \item{eventtypevalue}{ An optional value (or set of values) used to specify how paste events should be filtered depending on their type. \code{'standard'} is implemented.} \item{eventfiltervar}{ An optinoal variable that allows filtering of past events using an event attribute; not implemented.} \item{eventfilterAB}{ An optional value used to specify how paste events should be filtered depending on their attribute; not implemented.} \item{eventfilterAJ}{ see \code{eventfilterAB}.} \item{eventfilterIB}{see \code{eventfilterAB}.} \item{eventfilterIJ}{see \code{eventfilterAB}.} \item{eventvar}{ An optional dummy variable with 0 values for null-events and 1 values for true events. If the \code{data} is in the form of counting process data, use the \code{eventvar}-option to specify which variable contains the 0/1-dummy for event occurrence. If this variable is not specified, all events in the past will be considered for the calulation of the four cycle statistic, regardless if they occurred or not (= are null-events). Misspecification could result in grievous errors in the calculation of the network statistic.} \item{variablename}{ An optional value (or values) with the name the four cycle statistic variable should be given. To be used if \code{returnData = TRUE}.} \item{returnData}{ \code{TRUE/FALSE}. Set to \code{FALSE} by default. The new variable(s) are bound directly to the \code{data.frame} provided and the data frame is returned in full.} \item{dataPastEvents}{ An optional \code{data.frame} with the following variables: column 1 = time variable, column 2 = sender variable, column 3 = target on other variable (or all "1"), column 4 = weight variable (or all "1"), column 5 = event type variable (or all "1"), column 6 = event filter variable (or all "1"). Make sure that the data frame does not contain null events. Filter it out for true events only.} \item{showprogressbar}{\code{TRUE/FALSE}. To be implemented.} \item{inParallel}{ \code{TRUE/FALSE}. An optional boolean to specify if the loop should be run in parallel.} \item{cluster}{ An optional numeric or character value that defines the cluster. By specifying a single number, the cluster option uses the provided number of nodes to parallellize. By specifying a cluster using the \code{makeCluster}-command in the \code{doParallel}-package, the loop can be run on multiple nodes/cores. E.g., \code{cluster = makeCluster(12, type="FORK")}.} } \details{ The \code{sixCycleStat()}-function calculates an endogenous statistic that measures whether events have a tendency to form six cycles. The effect is further described in the following paper: D. Valeeva, F.W. Takes and E.M. Heemskerk, The duality of firms and directors in board interlock networks: A relational event modeling approach, Social Networks 62: 68-79, Elsevier, 2020. } % \value{ % % } % \references{ % % } % \note{ % % } \author{ Diliara Valeeva, Frank Takes and Eelke Heemskerk of the University of Amsterdam's CORPNET group \email{corpnet@uva.nl} } \seealso{ \link{rem-package} } \examples{See fourCycleStat() examples. } %\keyword{key}

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/R/blob_client_funcs.R

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blob_client_funcs.R

#' Operations on a blob endpoint #' #' Get, list, create, or delete blob containers. #' #' @param endpoint Either a blob endpoint object as created by [storage_endpoint], or a character string giving the URL of the endpoint. #' @param key,token,sas If an endpoint object is not supplied, authentication credentials: either an access key, an Azure Active Directory (AAD) token, or a SAS, in that order of priority. If no authentication credentials are provided, only public (anonymous) access to the share is possible. #' @param api_version If an endpoint object is not supplied, the storage API version to use when interacting with the host. Currently defaults to `"2019-07-07"`. #' @param name The name of the blob container to get, create, or delete. #' @param confirm For deleting a container, whether to ask for confirmation. #' @param lease For deleting a leased container, the lease ID. #' @param public_access For creating a container, the level of public access to allow. #' @param x For the print method, a blob container object. #' @param ... Further arguments passed to lower-level functions. #' #' @details #' You can call these functions in a couple of ways: by passing the full URL of the share, or by passing the endpoint object and the name of the container as a string. #' #' If authenticating via AAD, you can supply the token either as a string, or as an object of class AzureToken, created via [AzureRMR::get_azure_token]. The latter is the recommended way of doing it, as it allows for automatic refreshing of expired tokens. #' #' @return #' For `blob_container` and `create_blob_container`, an S3 object representing an existing or created container respectively. #' #' For `list_blob_containers`, a list of such objects. #' #' @seealso #' [storage_endpoint], [az_storage], [storage_container] #' #' @examples #' \dontrun{ #' #' endp <- blob_endpoint("https://mystorage.blob.core.windows.net/", key="access_key") #' #' # list containers #' list_blob_containers(endp) #' #' # get, create, and delete a container #' blob_container(endp, "mycontainer") #' create_blob_container(endp, "newcontainer") #' delete_blob_container(endp, "newcontainer") #' #' # alternative way to do the same #' blob_container("https://mystorage.blob.core.windows.net/mycontainer", key="access_key") #' create_blob_container("https://mystorage.blob.core.windows.net/newcontainer", key="access_key") #' delete_blob_container("https://mystorage.blob.core.windows.net/newcontainer", key="access_key") #' #' # authenticating via AAD #' token <- AzureRMR::get_azure_token(resource="https://storage.azure.com/", #' tenant="myaadtenant", #' app="myappid", #' password="mypassword") #' blob_container("https://mystorage.blob.core.windows.net/mycontainer", token=token) #' #' } #' @rdname blob_container #' @export blob_container <- function(endpoint, ...) { UseMethod("blob_container") } #' @rdname blob_container #' @export blob_container.character <- function(endpoint, key=NULL, token=NULL, sas=NULL, api_version=getOption("azure_storage_api_version"), ...) { do.call(blob_container, generate_endpoint_container(endpoint, key, token, sas, api_version)) } #' @rdname blob_container #' @export blob_container.blob_endpoint <- function(endpoint, name, ...) { obj <- list(name=name, endpoint=endpoint) class(obj) <- c("blob_container", "storage_container") obj } #' @rdname blob_container #' @export print.blob_container <- function(x, ...) { cat("Azure blob container '", x$name, "'\n", sep="") url <- httr::parse_url(x$endpoint$url) url$path <- x$name cat(sprintf("URL: %s\n", httr::build_url(url))) if(!is_empty(x$endpoint$key)) cat("Access key: <hidden>\n") else cat("Access key: <none supplied>\n") if(!is_empty(x$endpoint$token)) { cat("Azure Active Directory access token:\n") print(x$endpoint$token) } else cat("Azure Active Directory access token: <none supplied>\n") if(!is_empty(x$endpoint$sas)) cat("Account shared access signature: <hidden>\n") else cat("Account shared access signature: <none supplied>\n") cat(sprintf("Storage API version: %s\n", x$endpoint$api_version)) invisible(x) } #' @rdname blob_container #' @export list_blob_containers <- function(endpoint, ...) { UseMethod("list_blob_containers") } #' @rdname blob_container #' @export list_blob_containers.character <- function(endpoint, key=NULL, token=NULL, sas=NULL, api_version=getOption("azure_storage_api_version"), ...) { do.call(list_blob_containers, generate_endpoint_container(endpoint, key, token, sas, api_version)) } #' @rdname blob_container #' @export list_blob_containers.blob_endpoint <- function(endpoint, ...) { res <- call_storage_endpoint(endpoint, "/", options=list(comp="list")) lst <- lapply(res$Containers, function(cont) blob_container(endpoint, cont$Name[[1]])) while(length(res$NextMarker) > 0) { res <- call_storage_endpoint(endpoint, "/", options=list(comp="list", marker=res$NextMarker[[1]])) lst <- c(lst, lapply(res$Containers, function(cont) blob_container(endpoint, cont$Name[[1]]))) } named_list(lst) } #' @rdname blob_container #' @export create_blob_container <- function(endpoint, ...) { UseMethod("create_blob_container") } #' @rdname blob_container #' @export create_blob_container.character <- function(endpoint, key=NULL, token=NULL, sas=NULL, api_version=getOption("azure_storage_api_version"), ...) { endp <- generate_endpoint_container(endpoint, key, token, sas, api_version) create_blob_container(endp$endpoint, endp$name, ...) } #' @rdname blob_container #' @export create_blob_container.blob_container <- function(endpoint, ...) { create_blob_container(endpoint$endpoint, endpoint$name) } #' @rdname blob_container #' @export create_blob_container.blob_endpoint <- function(endpoint, name, public_access=c("none", "blob", "container"), ...) { public_access <- match.arg(public_access) headers <- if(public_access != "none") modifyList(list(...), list("x-ms-blob-public-access"=public_access)) else list(...) obj <- blob_container(endpoint, name) do_container_op(obj, options=list(restype="container"), headers=headers, http_verb="PUT") obj } #' @rdname blob_container #' @export delete_blob_container <- function(endpoint, ...) { UseMethod("delete_blob_container") } #' @rdname blob_container #' @export delete_blob_container.character <- function(endpoint, key=NULL, token=NULL, sas=NULL, api_version=getOption("azure_storage_api_version"), ...) { endp <- generate_endpoint_container(endpoint, key, token, sas, api_version) delete_blob_container(endp$endpoint, endp$name, ...) } #' @rdname blob_container #' @export delete_blob_container.blob_container <- function(endpoint, ...) { delete_blob_container(endpoint$endpoint, endpoint$name, ...) } #' @rdname blob_container #' @export delete_blob_container.blob_endpoint <- function(endpoint, name, confirm=TRUE, lease=NULL, ...) { if(!delete_confirmed(confirm, paste0(endpoint$url, name), "container")) return(invisible(NULL)) headers <- if(!is_empty(lease)) list("x-ms-lease-id"=lease) else list() obj <- blob_container(endpoint, name) invisible(do_container_op(obj, options=list(restype="container"), headers=headers, http_verb="DELETE")) } #' Operations on a blob container or blob #' #' Upload, download, or delete a blob; list blobs in a container; create or delete directories; check blob availability. #' #' @param container A blob container object. #' @param blob A string naming a blob. #' @param dir For `list_blobs`, a string naming the directory. Note that blob storage does not support real directories; this argument simply filters the result to return only blobs whose names start with the given value. #' @param src,dest The source and destination files for uploading and downloading. See 'Details' below. #' @param info For `list_blobs`, level of detail about each blob to return: a vector of names only; the name, size, blob type, and whether this blob represents a directory; or all information. #' @param confirm Whether to ask for confirmation on deleting a blob. #' @param blocksize The number of bytes to upload/download per HTTP(S) request. #' @param lease The lease for a blob, if present. #' @param type When uploading, the type of blob to create. Currently only block and append blobs are supported. #' @param append When uploading, whether to append the uploaded data to the destination blob. Only has an effect if `type="AppendBlob"`. If this is FALSE (the default) and the destination append blob exists, it is overwritten. If this is TRUE and the destination does not exist or is not an append blob, an error is thrown. #' @param overwrite When downloading, whether to overwrite an existing destination file. #' @param use_azcopy Whether to use the AzCopy utility from Microsoft to do the transfer, rather than doing it in R. #' @param max_concurrent_transfers For `multiupload_blob` and `multidownload_blob`, the maximum number of concurrent file transfers. Each concurrent file transfer requires a separate R process, so limit this if you are low on memory. #' @param prefix For `list_blobs`, an alternative way to specify the directory. #' @param recursive For the multiupload/download functions, whether to recursively transfer files in subdirectories. For `list_blobs`, whether to include the contents of any subdirectories in the listing. For `delete_blob_dir`, whether to recursively delete subdirectory contents as well. #' @param put_md5 For uploading, whether to compute the MD5 hash of the blob(s). This will be stored as part of the blob's properties. Only used for block blobs. #' @param check_md5 For downloading, whether to verify the MD5 hash of the downloaded blob(s). This requires that the blob's `Content-MD5` property is set. If this is TRUE and the `Content-MD5` property is missing, a warning is generated. #' @param snapshot,version For `download_blob`, optional snapshot and version identifiers. These should be datetime strings, in the format "yyyy-mm-ddTHH:MM:SS.SSSSSSSZ". If omitted, download the base blob. #' #' @details #' `upload_blob` and `download_blob` are the workhorse file transfer functions for blobs. They each take as inputs a _single_ filename as the source for uploading/downloading, and a single filename as the destination. Alternatively, for uploading, `src` can be a [textConnection] or [rawConnection] object; and for downloading, `dest` can be NULL or a `rawConnection` object. If `dest` is NULL, the downloaded data is returned as a raw vector, and if a raw connection, it will be placed into the connection. See the examples below. #' #' `multiupload_blob` and `multidownload_blob` are functions for uploading and downloading _multiple_ files at once. They parallelise file transfers by using the background process pool provided by AzureRMR, which can lead to significant efficiency gains when transferring many small files. There are two ways to specify the source and destination for these functions: #' - Both `src` and `dest` can be vectors naming the individual source and destination pathnames. #' - The `src` argument can be a wildcard pattern expanding to one or more files, with `dest` naming a destination directory. In this case, if `recursive` is true, the file transfer will replicate the source directory structure at the destination. #' #' `upload_blob` and `download_blob` can display a progress bar to track the file transfer. You can control whether to display this with `options(azure_storage_progress_bar=TRUE|FALSE)`; the default is TRUE. #' #' `multiupload_blob` can upload files either as all block blobs or all append blobs, but not a mix of both. #' #' `blob_exists` and `blob_dir_exists` test for the existence of a blob and directory, respectively. #' #' `delete_blob` deletes a blob, and `delete_blob_dir` deletes all blobs in a directory (possibly recursively). This will also delete any snapshots for the blob(s) involved. #' #' ## AzCopy #' `upload_blob` and `download_blob` have the ability to use the AzCopy commandline utility to transfer files, instead of native R code. This can be useful if you want to take advantage of AzCopy's logging and recovery features; it may also be faster in the case of transferring a very large number of small files. To enable this, set the `use_azcopy` argument to TRUE. #' #' The following points should be noted about AzCopy: #' - It only supports SAS and AAD (OAuth) token as authentication methods. AzCopy also expects a single filename or wildcard spec as its source/destination argument, not a vector of filenames or a connection. #' - Currently, it does _not_ support appending data to existing blobs. #' #' ## Directories #' Blob storage does not have true directories, instead using filenames containing a separator character (typically '/') to mimic a directory structure. This has some consequences: #' #' - The `isdir` column in the data frame output of `list_blobs` is a best guess as to whether an object represents a file or directory, and may not always be correct. Currently, `list_blobs` assumes that any object with a file size of zero is a directory. #' - Zero-length files can cause problems for the blob storage service as a whole (not just AzureStor). Try to avoid uploading such files. #' - `create_blob_dir` and `delete_blob_dir` are guaranteed to function as expected only for accounts with hierarchical namespaces enabled. When this feature is disabled, directories do not exist as objects in their own right: to create a directory, simply upload a blob to that directory. To delete a directory, delete all the blobs within it; as far as the blob storage service is concerned, the directory then no longer exists. #' - Similarly, the output of `list_blobs(recursive=TRUE)` can vary based on whether the storage account has hierarchical namespaces enabled. #' - `blob_exists` will return FALSE for a directory when the storage account does not have hierarchical namespaces enabled. #' #' @return #' For `list_blobs`, details on the blobs in the container. For `download_blob`, if `dest=NULL`, the contents of the downloaded blob as a raw vector. For `blob_exists` a flag whether the blob exists. #' #' @seealso #' [blob_container], [az_storage], [storage_download], [call_azcopy], [list_blob_snapshots], [list_blob_versions] #' #' [AzCopy version 10 on GitHub](https://github.com/Azure/azure-storage-azcopy) #' [Guide to the different blob types](https://docs.microsoft.com/en-us/rest/api/storageservices/understanding-block-blobs--append-blobs--and-page-blobs) #' #' @examples #' \dontrun{ #' #' cont <- blob_container("https://mystorage.blob.core.windows.net/mycontainer", key="access_key") #' #' list_blobs(cont) #' #' upload_blob(cont, "~/bigfile.zip", dest="bigfile.zip") #' download_blob(cont, "bigfile.zip", dest="~/bigfile_downloaded.zip") #' #' delete_blob(cont, "bigfile.zip") #' #' # uploading/downloading multiple files at once #' multiupload_blob(cont, "/data/logfiles/*.zip", "/uploaded_data") #' multiupload_blob(cont, "myproj/*") # no dest directory uploads to root #' multidownload_blob(cont, "jan*.*", "/data/january") #' #' # append blob: concatenating multiple files into one #' upload_blob(cont, "logfile1", "logfile", type="AppendBlob", append=FALSE) #' upload_blob(cont, "logfile2", "logfile", type="AppendBlob", append=TRUE) #' upload_blob(cont, "logfile3", "logfile", type="AppendBlob", append=TRUE) #' #' # you can also pass a vector of file/pathnames as the source and destination #' src <- c("file1.csv", "file2.csv", "file3.csv") #' dest <- paste0("uploaded_", src) #' multiupload_blob(cont, src, dest) #' #' # uploading serialized R objects via connections #' json <- jsonlite::toJSON(iris, pretty=TRUE, auto_unbox=TRUE) #' con <- textConnection(json) #' upload_blob(cont, con, "iris.json") #' #' rds <- serialize(iris, NULL) #' con <- rawConnection(rds) #' upload_blob(cont, con, "iris.rds") #' #' # downloading files into memory: as a raw vector, and via a connection #' rawvec <- download_blob(cont, "iris.json", NULL) #' rawToChar(rawvec) #' #' con <- rawConnection(raw(0), "r+") #' download_blob(cont, "iris.rds", con) #' unserialize(con) #' #' # copy from a public URL: Iris data from UCI machine learning repository #' copy_url_to_blob(cont, #' "https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data", #' "iris.csv") #' #' } #' @rdname blob #' @export list_blobs <- function(container, dir="/", info=c("partial", "name", "all"), prefix=NULL, recursive=TRUE) { info <- match.arg(info) opts <- list(comp="list", restype="container") # ensure last char is always '/', to get list of blobs in a subdir if(dir != "/") { if(!grepl("/$", dir)) dir <- paste0(dir, "/") prefix <- dir } if(!is_empty(prefix)) opts <- c(opts, prefix=as.character(prefix)) if(!recursive) opts <- c(opts, delimiter="/") res <- do_container_op(container, options=opts) lst <- res$Blobs while(length(res$NextMarker) > 0) { opts$marker <- res$NextMarker[[1]] res <- do_container_op(container, options=opts) lst <- c(lst, res$Blobs) } if(info != "name") { prefixes <- lst[names(lst) == "BlobPrefix"] blobs <- lst[names(lst) == "Blob"] prefix_rows <- lapply(prefixes, function(prefix) { structure(list(Type="BlobPrefix", Name=unlist(prefix$Name), `Content-Length`=NA, BlobType=NA), class="data.frame", row.names=c(NA_integer_, -1L)) }) blob_rows <- lapply(blobs, function(blob) { structure(c(Type="Blob", Name=blob$Name, unlist(blob$Properties)), class="data.frame", row.names=c(NA_integer_, -1L)) }) df_prefixes <- do.call(vctrs::vec_rbind, prefix_rows) df_blobs <- do.call(vctrs::vec_rbind, blob_rows) no_prefixes <- nrow(df_prefixes) == 0 no_blobs <- nrow(df_blobs) == 0 if(no_prefixes && no_blobs) return(data.frame()) else if(no_prefixes) df <- df_blobs else if(no_blobs) df <- df_prefixes else df <- vctrs::vec_rbind(df_prefixes, df_blobs) if(length(df) > 0) { # reorder and rename first 2 columns for consistency with ADLS, file ndf <- names(df) namecol <- which(ndf == "Name") sizecol <- which(ndf == "Content-Length") typecol <- which(names(df) == "BlobType") names(df)[c(namecol, sizecol, typecol)] <- c("name", "size", "blobtype") df$size <- if(!is.null(df$size)) as.numeric(df$size) else NA df$size[df$size == 0] <- NA df$isdir <- is.na(df$size) dircol <- which(names(df) == "isdir") if(info == "all") { if(!is.null(df$`Last-Modified`)) df$`Last-Modified` <- as_datetime(df$`Last-Modified`) if(!is.null(df$`Creation-Time`)) df$`Creation-Time` <- as_datetime(df$`Creation-Time`) vctrs::vec_cbind(df[c(namecol, sizecol, dircol, typecol)], df[-c(namecol, sizecol, dircol, typecol)]) } else df[c(namecol, sizecol, dircol, typecol)] } else data.frame() } else unname(vapply(lst, function(b) b$Name[[1]], FUN.VALUE=character(1))) } #' @rdname blob #' @export upload_blob <- function(container, src, dest=basename(src), type=c("BlockBlob", "AppendBlob"), blocksize=if(type == "BlockBlob") 2^24 else 2^22, lease=NULL, put_md5=FALSE, append=FALSE, use_azcopy=FALSE) { type <- match.arg(type) if(use_azcopy) azcopy_upload(container, src, dest, type=type, blocksize=blocksize, lease=lease, put_md5=put_md5) else upload_blob_internal(container, src, dest, type=type, blocksize=blocksize, lease=lease, put_md5=put_md5, append=append) } #' @rdname blob #' @export multiupload_blob <- function(container, src, dest, recursive=FALSE, type=c("BlockBlob", "AppendBlob"), blocksize=if(type == "BlockBlob") 2^24 else 2^22, lease=NULL, put_md5=FALSE, append=FALSE, use_azcopy=FALSE, max_concurrent_transfers=10) { type <- match.arg(type) if(use_azcopy) return(azcopy_upload(container, src, dest, type=type, blocksize=blocksize, lease=lease, put_md5=put_md5, recursive=recursive)) multiupload_internal(container, src, dest, recursive=recursive, type=type, blocksize=blocksize, lease=lease, put_md5=put_md5, append=append, max_concurrent_transfers=max_concurrent_transfers) } #' @rdname blob #' @export download_blob <- function(container, src, dest=basename(src), blocksize=2^24, overwrite=FALSE, lease=NULL, check_md5=FALSE, use_azcopy=FALSE, snapshot=NULL, version=NULL) { if(use_azcopy) azcopy_download(container, src, dest, overwrite=overwrite, lease=lease, check_md5=check_md5) else download_blob_internal(container, src, dest, blocksize=blocksize, overwrite=overwrite, lease=lease, check_md5=check_md5, snapshot=snapshot, version=version) } #' @rdname blob #' @export multidownload_blob <- function(container, src, dest, recursive=FALSE, blocksize=2^24, overwrite=FALSE, lease=NULL, check_md5=FALSE, use_azcopy=FALSE, max_concurrent_transfers=10) { if(use_azcopy) return(azcopy_download(container, src, dest, overwrite=overwrite, lease=lease, recursive=recursive, check_md5=check_md5)) multidownload_internal(container, src, dest, recursive=recursive, blocksize=blocksize, overwrite=overwrite, lease=lease, check_md5=check_md5, max_concurrent_transfers=max_concurrent_transfers) } #' @rdname blob #' @export delete_blob <- function(container, blob, confirm=TRUE) { if(!delete_confirmed(confirm, paste0(container$endpoint$url, container$name, "/", blob), "blob")) return(invisible(NULL)) # deleting zero-length blobs (directories) will fail if the x-ms-delete-snapshots header is present # and this is a HNS-enabled account: # since there is no way to detect whether the account is HNS, and getting the blob size requires # an extra API call, we try deleting with and without the header present hdrs <- list(`x-ms-delete-snapshots`="include") res <- try(do_container_op(container, blob, headers=hdrs, http_verb="DELETE"), silent=TRUE) if(inherits(res, "try-error")) res <- do_container_op(container, blob, headers=NULL, http_verb="DELETE") invisible(res) } #' @rdname blob #' @export create_blob_dir <- function(container, dir) { # workaround: upload a zero-length file to the desired dir, then delete the file destfile <- file.path(dir, basename(tempfile())) opts <- options(azure_storage_progress_bar=FALSE) on.exit(options(opts)) upload_blob(container, rawConnection(raw(0)), destfile) delete_blob(container, destfile, confirm=FALSE) invisible(NULL) } #' @rdname blob #' @export delete_blob_dir <- function(container, dir, recursive=FALSE, confirm=TRUE) { if(dir %in% c("/", ".") && !recursive) return(invisible(NULL)) if(!delete_confirmed(confirm, paste0(container$endpoint$url, container$name, "/", dir), "directory")) return(invisible(NULL)) if(recursive) { # delete everything under this directory conts <- list_blobs(container, dir, recursive=TRUE, info="name") for(n in rev(conts)) delete_blob(container, n, confirm=FALSE) } if(dir != "/" && blob_exists(container, dir)) delete_blob(container, dir, confirm=FALSE) } #' @rdname blob #' @export blob_exists <- function(container, blob) { res <- do_container_op(container, blob, headers = list(), http_verb = "HEAD", http_status_handler = "pass") if(httr::status_code(res) == 404L) return(FALSE) httr::stop_for_status(res, storage_error_message(res)) return(TRUE) } #' @rdname blob #' @export blob_dir_exists <- function(container, dir) { if(dir == "/") return(TRUE) # multiple steps required to handle HNS-enabled and disabled accounts: # 1. get blob properties # - if no error, return (size == 0) # - error can be because dir does not exist, OR HNS disabled # 2. get dir listing # - call API directly to avoid retrieving entire list # - return (list is not empty) props <- try(get_storage_properties(container, dir), silent=TRUE) if(!inherits(props, "try-error")) return(props[["content-length"]] == 0) # ensure last char is always '/', to get list of blobs in a subdir if(substr(dir, nchar(dir), nchar(dir)) != "/") dir <- paste0(dir, "/") opts <- list(comp="list", restype="container", maxresults=1, delimiter="/", prefix=dir) res <- do_container_op(container, options=opts) !is_empty(res$Blobs) }

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/BOXCOX-in-R/BOX_COX_Main.r

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no_license

coderwithpurpose/ML

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56a1cdaa1b16034f3a78f8c83fd8c638c6af7e98

refs/heads/master

2020-04-12T12:36:44.351026

2019-10-29T03:12:06

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BOX_COX_Main.r

library(MASS) rm(list=ls()) housing_data = scan('https://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data') housing_df = as.data.frame(matrix(housing_data, ncol=14, byrow=TRUE), stringsAsFactors=FALSE) housing_prices = housing_df[, c(14)] features = housing_df[, c(1:13)] housing_reg = lm(housing_prices~as.matrix(features)) par(mfrow=c(2,2)) plot(housing_reg) orig_fitted_values = fitted(housing_reg) par(mfrow=c(1,1)) orig_std_res = stdres(housing_reg) plot(orig_fitted_values, orig_std_res, main="Fitted Values vs Standardized Residuals", xlab="Fitted Values" ,ylab="Standardized Residuals") outlier_list_extreme_stand_res = c(369, 372, 373) features_v2 = features[-outlier_list_extreme_stand_res, ] housing_prices_v2 = housing_prices[-outlier_list_extreme_stand_res] housing_reg_v2 = lm(housing_prices_v2~as.matrix(features_v2)) par(mfrow=c(2,2)) plot(housing_reg_v2) second_outlier_list = c(369, 368, 366) features_v3 = features_v2[-second_outlier_list, ] housing_prices_v3 = housing_prices_v2[-second_outlier_list] housing_reg_v3 = lm(housing_prices_v3~as.matrix(features_v3)) par(mfrow=c(2,2)) plot(housing_reg_v3) # third_outlier_list = c(407) third_outlier_list = c(375) features_v4 = features_v3[-third_outlier_list, ] housing_prices_v4 = housing_prices_v3[-third_outlier_list] housing_reg_v4 = lm(housing_prices_v4~as.matrix(features_v4)) par(mfrow=c(2,2)) plot(housing_reg_v4) final_outlier_list = c(406) features_v5 = features_v4[-final_outlier_list, ] housing_prices_v5 = housing_prices_v4[-final_outlier_list] housing_reg_v5 = lm(housing_prices_v5~as.matrix(features_v5)) par(mfrow=c(2,2)) plot(housing_reg_v5) library(prodlim) par(mfrow=c(1,1)) matching_rows = row.match(features, features_v5) outlier_indices = which(is.na(matching_rows)) print(outlier_indices) # run the box-cox transformation bc <- boxcox(housing_prices_v5~as.matrix(features_v5)) # find the best parameter (lambda <- bc$x[which(bc$y==max(bc$y))]) # transforamting the dependant variable new_dep_var = ((((housing_prices_v5)^lambda)-1)/lambda) # now apply regression model again afterboxcox <- lm(new_dep_var ~ (as.matrix(features_v5))) par(mfrow=c(2,2)) plot(afterboxcox) # plotting the fitted house price against the true price par(mfrow=c(1,1)) # plot(new_dep_var, housing_prices_v4) ## TODO is to get the predicted value using the last model .. stand_red_after_box_cox = stdres(afterboxcox) fitted_box_cox_vals = predict(afterboxcox) # reverted_fitted_box_cox_vals = 10^(log10(fitted_box_cox_vals*lambda + 1)/lambda) reverted_fitted_box_cox_vals = (fitted_box_cox_vals*lambda + 1)^(1/lambda) plot(reverted_fitted_box_cox_vals, housing_prices_v5, main="Fitted Values vs Actual housing prices", xlab="Fitted Values", ylab="Housing Prices") plot(reverted_fitted_box_cox_vals, stand_red_after_box_cox, main="Fitted values vs Standardized residuals", xlab="Fitted Values", ylab="Standardized Residuals")

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/R/gogr.R

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ajschumacher/gogr

dfc15170c1de917847a052485447d4b7ee6ea8ba

f4f3d477853cbb446e8c4b18dda82d47098550a3

refs/heads/master

2020-04-25T23:32:03.348179

2015-01-01T18:16:07

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gogr.R

#' Use gog visualization system from R #' #' Communicate with a \href{https://github.com/ajschumacher/gog}{gog} #' server for data visualization independent of R. #' #' @docType package #' @name gogr #' @import jsonlite #' @import httr NULL #' Send data to a gog server #' #' This function takes takes a data frame and sends it to a gog server. #' The gog server is responsible for passing the data to a gog frontend #' for visualization. #' #' @param x a data frame #' @param url the gog /data endpoint to send to #' #' @export #' @examples #' \dontrun{ #' gog(iris) #' } gog <- function(x, url="http://localhost:4808/data") { text <- toJSON(x) response <- POST(url, body=text) invisible(response) }

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/plot2.R

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no_license

DJ-L/repo160720

69b2fc3ecb793d65b312b270ae5f7ee76ff29a39

26d737a81dd2456d01bd896ec87f3923acc47345

refs/heads/master

2021-01-16T21:36:39.727409

2016-07-20T09:26:40

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plot2.R

setwd("C:\\Users\\Daniel\\Documents\\Coursera\\Exploring Data\\Week 1\\exdata-data-household_power_consumption") #Loading data full_data<-read.csv("household_power_consumption.txt",stringsAsFactors=FALSE,header = TRUE,sep = ";") full_data$Date = strptime(full_data$Date,"%d/%m/%Y") feb_data<-full_data["2007-02-01"<=full_data$Date & full_data$Date<="2007-02-02",] feb_data$sec <- as.numeric(feb_data$Date)-as.numeric(strptime("01/02/2007","%d/%m/%Y"))+as.numeric(substr(feb_data$Time,1,2))*3600+as.numeric(substr(feb_data$Time,4,5))*60+as.numeric(substr(feb_data$Time,7,8)) #Check missing result <- feb_data$Global_active_power=="?" table(result)#No missing during selected days #Create the plot par(mfrow=c(1,1)) Thu<-0 Fri<-as.numeric(strptime("02/02/2007","%d/%m/%Y"))-as.numeric(strptime("01/02/2007","%d/%m/%Y")) Sat<-as.numeric(strptime("03/02/2007","%d/%m/%Y"))-as.numeric(strptime("01/02/2007","%d/%m/%Y")) par(pch=0, col="black") plot(feb_data$sec,as.numeric(feb_data$Global_active_power),xaxt = "n", type = "l",xlab="", ylab="Global active power (kilowatts)") axis(1,c(Thu,Fri,Sat),c("Thu","Fri","Sat")) dev.copy(png,'plot2.png') dev.off()

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/R/options.R

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no_license

nemochina2008/jasptools

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635f3f135a43455370e5f00f908c3de7449606f8

refs/heads/master

2020-03-28T15:07:01.728286

2017-12-15T13:41:47

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options.R

#' Obtain options to run JASP analyses with. #' #' \code{analysisOptions} provides an easy way to create analysis options. You #' may use the json from the Qt terminal or from the json files found in #' resources. The former you have to provide yourself, for the latter you only #' have to specify the name of the analysis. #' #' #' @param source String containing valid json, or the name of a JASP analysis. #' If you provide json, be sure to use single quotes. #' @param hint Boolean. Should additional hints be placed in the output so you #' know how to give values to differents types of options? Only works if #' \code{source} is set to the name of an analysis. #' @return A list containing options you can supply to \code{jasptools::run}. #' If \code{source} is an analysis name then all default options have been #' filled in and booleans set to FALSE. The options that have no default are #' left empty. If \code{hint} is set to TRUE then hints are set for these empty #' options; they are placed between \%'s. #' @examples #' #' options <- jasptools::analysisOptions("BinomialTest") #' options[["variables"]] <- "contBinom" #' #' # Above and below are identical (below is taken from the Qt terminal) #' #' options <- jasptools::analysisOptions('{ #' "id" : 0, #' "name" : "BinomialTest", #' "options" : { #' "VovkSellkeMPR" : false, #' "confidenceInterval" : false, #' "confidenceIntervalInterval" : 0.950, #' "descriptivesPlots" : false, #' "descriptivesPlotsConfidenceInterval" : 0.950, #' "hypothesis" : "notEqualToTestValue", #' "plotHeight" : 300, #' "plotWidth" : 160, #' "testValue" : 0.50, #' "variables" : [ "contBinom" ] #' }, #' "perform" : "run", #' "revision" : 0, #' "settings" : { #' "ppi" : 192 #' } #' }') #' #' @export analysisOptions analysisOptions <- function(source, hint = FALSE) { if (! is.character(source) || length(source) > 1) { stop("Expecting a character input of length 1 as source, either a json string or analysis name.") } type <- "file" if (jsonlite::validate(source) == TRUE) { type <- "qt" } options <- NULL if (type == "qt") { options <- .analysisOptionsFromQt(source) } else { rawOptions <- .analysisOptionsFromFile(source) options <- .fillOptions(rawOptions, hint) } return(options) } .analysisOptionsFromFile <- function(analysis) { file <- file.path(.getPkgOption("json.dir"), paste0(analysis, ".json")) analysisOpts <- try(jsonlite::read_json(file), silent = TRUE) if (inherits(analysisOpts, "try-error")) { stop("The JSON file for the analysis you supplied could not be found. Please ensure that (1) its name matches the main R function and (2) your working directory is set properly.") } if ("options" %in% names(analysisOpts)) { return(analysisOpts[["options"]]) } else if ("options" %in% names(analysisOpts[["input"]])) { return(analysisOpts[["input"]][["options"]]) } else { stop("The JSON file was found, but it appears to be invalid") } } .analysisOptionsFromQt <- function(x) { json <- try(jsonlite::fromJSON(x, simplifyVector=FALSE), silent = TRUE) if (inherits(json, "try-error")) { stop("Your json is invalid, please copy the entire message including the outer braces { } that was send to R in the Qt terminal. Remember to use single quotes around the message.") } if ("options" %in% names(json)) { return(json[["options"]]) } else { stop("The JSON file appears to be invalid") } } .fillOptions <- function(options, hint = FALSE) { output <- list() for (i in 1:length(options)) { option <- options[[i]] if ("default" %in% names(option)) { output[[option[["name"]]]] <- option[["default"]] } else { if (option[["type"]] == "Table" && hint) { template <- option[["template"]] output[[option[["name"]]]] <- list(list()) for (j in 1:length(template)) { name <- template[[j]][["name"]] value <- .optionTypeToValue(template[[j]], hint) output[[option[["name"]]]][[1]][[name]] <- value } } else { output[[option[["name"]]]] <- .optionTypeToValue(option, hint) } } } return(output) } .optionTypeToValue <- function(option, hint = FALSE) { switch(option[["type"]], Boolean = FALSE, Integer = if (hint) { "%420%" } else { "" }, IntegerArray = if (hint) { c("%25%", "%95%") } else { list() }, List = option[["options"]][[1]], Number = option[["value"]], Table = list(), String = if (hint) { "%SomeString%" } else { "" }, Term = if (hint) { "%variable1%" } else { "" }, Terms = if (hint) { list(c("%variable1%"), c("%variable2%"), c("%variable1%", "%variable3%")) } else { list() }, Variable = if (hint) { "%variable1%" } else { "" }, Variables = if (hint) { c("%variable1%", "%variable2%") } else { list() }, VariablesGroups = if (hint) { list(c("%variable1%", "%variable2%"), c("%variable3%", "%variable4%")) } else { list() }, NULL ) }

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/time_series/残差检验.R

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no_license

hallo128/R

f9aec7535397f22906f142ad4b77e1011cb565d9

cabb57f9d953433b87e6340869f06b54a326f82c

refs/heads/master

2021-01-17T18:56:57.361377

2016-06-25T16:00:10

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残差检验.R

setwd("d:/") ###读入数据（只能一条数据，可以事前处理，也可以事后处理） data=read.csv("hj.csv",header=F)[,1] #改变为时间序列数据 hj=ts(data,start = c(1980,1),frequency = 12) ####################1求长期趋势Tt library(TSA) #与season()函数有关 #季节平均模型(计算长期趋势) time=time(hj) #提取模型的时间，要求数据位序列类型数据 model=lm(hj~time) #plot(hj,type='o') #画出趋势图 #abline(model,col='blue') #长期趋势 Tt=ts(fitted(model),start = c(1980,1),freq=12) ####################2求季节趋势St Month=season(hj) #提取季节因素 ####加法模型 model1=lm(residuals(model)~Month-1) #-1不再有截距项 St=ts(fitted(model1),start = c(1980,1),freq=12) Tas=Tt+St #残差 St_res1=model1$residuals #平稳性检验 plot(St_res1,type='o') #正态分布 #1QQ图 qqnorm(St_res1) qqline(St_res1) #2(H0：正态) shapiro.test(St_res1) #3(H0：正态) jarque.bera.test(St_res1) #4直方图 hist(St_res1) #5箱型图 boxplot(St_res1) #########独立性检验 ###游程检验(变量必须为因子)————随机性检验 #H0:独立 runs.test(factor(sign(St_res1))) ###########相关性检验 ###1样本自相关函数（H0:rho(k)=0） acf(St_res1) #接受域，k阶无相关性。拒绝域，k阶有相关性 ###2相关性(H0:不相关) Box.test(St_res1,lag=3,type='Ljung-Box')#前3个残差 Box.test(St_res1,lag=3,type='Box-Pierce')#前3个残差 ####################2求季节趋势St #乘法模型 newhj=hj/fitted(model) model2=lm(newhj~Month-1) #-1不再有截距项 St1=ts(fitted(model2),start = c(1980,1),freq=12) Tas1=Tt*St1 #残差 St_res2=model2$residuals #全部 plot(hj,type='o') #画出真实趋势图 s1=summary(model) s1$coef[,1] s2=summary(model1) s2$coef[,1] s3=summary(model2) s3$coef[,1]

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save fit objects.R

save_fit <- function(name, caret) { if(caret) { named <- paste0("fit_", name, "_d") namer <- paste0("fit_", name, "_r") d <- get(named) r <- get(namer) save(list = c(named, namer), file = paste0("experiments/results/5cv/", name, ".RData")) } else { save(list = name, file = paste0("experiments/results/5cv/", name, ".RData")) } } lapply(trained, save_fit, caret = TRUE)

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# %++% operator test_that("Concat operators is cool", { expect_equal( "yo" %++% "da", "yoda" ) expect_identical( 1:10 %++% NULL, as.character(1:10) ) expect_length( letters %++% LETTERS, 26 ) }) # %||% op test_that("OR operator fits its purpose", { expect_equal( NULL %||% 42, 42 ) }) # %ni% op test_that("Not in operator", { expect_equal( c(1, 42) %ni% 1:10, c(FALSE, TRUE) ) }) # ‰..% op test_that("Sample operator works the way it should", { pool <- 1:100 # Normal behaviour expect_length( pool %..% 5, 5 ) # Handling errors expect_error( pool %..% "five" ) }) # %<>% test_that("Diff operator is neat", { df_diff <- iris %<>% data.frame(x = iris$Petal.Length) expect_identical( names(df_diff), c("Sepal.Length", "Sepal.Width", "Petal.Width", "Species") ) })

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#This is a test script for using github data(cars) head(cars) library(ggplot2) data(mpg) head(mpg) p <- ggplot(mpg, aes(class, hwy)) p + geom_boxplot(aes(colour = drv))

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Power.R

# Set working directory and load data ............................... #setwd("C:/Users/Violeta/Dropbox/Ubiqum/3_Deep.Analytics") #power <- data.table::fread("Energy/household_power_consumption.txt", sep = ";", na.strings = '?') # Libraries ......................................................... #pacman::p_load(dplyr, tidyr) # Create Working Data ..................................................... #power <- power %>% # unite(DateTime, Date, Time , sep = " ", remove = F) %>% # rename(Global = Global_active_power, Reactive = Global_reactive_power, Intensity = Global_intensity, # Kitchen = Sub_metering_1, Laundry = Sub_metering_2, WHAC = Sub_metering_3) %>% # mutate(Global = Global/60, Kitchen = Kitchen/1000, Laundry = Laundry/1000, WHAC = WHAC/1000) %>% # mutate(TotalSub = Kitchen + Laundry + WHAC, # Unregistered = Global - TotalSub, # Efficiency = Global/(Voltage*Intensity)) #write.csv(power, "Energy/power_data.csv", row.names = F) # ........................................................................................... pacman::p_load(dplyr, tidyr, lubridate, zoo, forecast, ggplot2, htmlwidgets, dygraphs, xts, seasonal) setwd("C:/Users/Violeta/Dropbox/Ubiqum/3_Deep.Analytics/Energy") power <- data.table::fread("power_data.csv") # Set Time Format power$DateTime <- parse_date_time(power$DateTime, orders = "dmy HMS") power <- power %>% mutate(Date = dmy(Date)) # Define Total Daily demand (kW) ....................................................... power$day <- floor_date(power$Date, "day") power_day <- power %>% group_by(D = day) %>% summarise(GP = sum(Global, na.rm = T), React = sum(Reactive, na.rm = T), Kitchen = sum(Kitchen, na.rm = T), Laundry = sum(Laundry, na.rm = T), WHAC = sum(WHAC, na.rm = T), T.Sub = sum(TotalSub, na.rm = T), Unregistered = sum(Unregistered, na.rm = T), Efficiency = sum(Efficiency, na.rm = T)) # what method to choose to plot the line of best fit? testing geom_line vs geom_smooth and its methods p.auto <- ggplot(power_day, aes(D, GP)) + geom_line() + geom_smooth(method = "auto") + xlab("") + theme_minimal() p.loess <- ggplot(power_day, aes(D, GP)) + geom_line() + geom_smooth(method = "loess") + xlab("") + theme_minimal() gridExtra::grid.arrange(p.auto, p.loess, top = "Fitting line of best fit: gam vs loess") # the GAM method fits the data better. # Plotting geom_smooth to fit a line of best fit (leave as 'auto' instead of typing 'gam') ggplot(power_day) + geom_smooth(aes(D, GP), method = "auto", color = "blue", alpha = 0.7) + geom_smooth(aes(D, Kitchen), method = "auto", color = "tomato", alpha = 0.7) + geom_smooth(aes(D, Laundry), method = "auto", color = "seagreen", alpha = 0.7) + geom_smooth(aes(D, WHAC), method = "auto", color = "black", alpha = 0.7) + ggtitle("Daily Total Energy Consumption", subtitle = "Consumption over 47 months") + ylab("Total Use kWh") + xlab("") + theme_minimal() # Define Total Week demand(kW) ........................................................ power$weekly <- floor_date(power$Date, "week") power_week <- power %>% group_by(W = weekly) %>% summarise(GP = sum(Global, na.rm = T), React = sum(Reactive, na.rm = T), Kitchen = sum(Kitchen, na.rm = T), Laundry = sum(Laundry, na.rm = T), WHAC = sum(WHAC, na.rm = T), T.Sub = sum(TotalSub, na.rm = T), Unregistered = sum(Unregistered, na.rm = T), Efficiency = sum(Efficiency, na.rm = T)) ggplot(power_week, aes(W, GP)) + geom_line(color = "tomato", lwd = 1) + ggtitle("Weekly Total Energy Consumption", subtitle = "Consumption over 47 months") + ylab("Total Use kWh") + xlab("") + theme_minimal() # Line of best fit to the data: ggplot(power_week, aes(W, GP)) + geom_line() + geom_smooth(method = "auto") + xlab("") + theme_minimal() # returns loess method # Define Total Monthly demand (kW) ..................................................... power$month <- floor_date(power$Date, "month") power_month <- power %>% group_by(M = month) %>% summarise(GP = sum(Global, na.rm = T), React = sum(Reactive, na.rm = T), Kitchen = sum(Kitchen, na.rm = T), Laundry = sum(Laundry, na.rm = T), WHAC = sum(WHAC, na.rm = T), T.Sub = sum(TotalSub, na.rm = T), Unregistered = sum(Unregistered, na.rm = T), Efficiency = sum(Efficiency, na.rm = T)) ggplot(power_month) + geom_line(aes(M, GP), color = "blue", lwd = 1) + geom_line(aes(M, T.Sub), color = "deeppink3", lwd = 1) + ggtitle("Daily Total Energy Consumption", subtitle = "Consumption over 47 months") + ylab("Total Use kWh") + xlab("") + theme_minimal() #Line of best fit: ggplot(power_month, aes(M,GP)) + geom_line() + geom_smooth(method = "auto") + theme_minimal() + xlab("") # returns loess method # Subset, average Energy Cosumption month_av <- power %>% group_by(M = month) %>% summarise(GP = mean(Global, na.rm = T), React = mean(Reactive, na.rm = T), Kitchen = mean(Kitchen, na.rm = T), Laundry = mean(Laundry, na.rm = T), WHAC = mean(WHAC, na.rm = T), T.Sub = mean(TotalSub, na.rm = T), Unregistered = mean(Unregistered, na.rm = T), Efficiency = mean(Efficiency, na.rm = T)) ggplot(month_av, aes(M, T.Sub)) + geom_area(fill = "blue", lwd = 1) + ggtitle("Monthly Average Energy Consumption", subtitle = "Consumption over 47 months") + ylab("Average Use Wh") + xlab("") + theme_minimal() #Line of Best Fit: ggplot(month_av, aes(M, GP)) + geom_line() + geom_smooth() + xlab("") + theme_minimal() # returns loess method # Define Total Yearly demand (kW) ....................................................... power$year <- floor_date(power$Date, "year") power_year <- power %>% group_by(Y = year) %>% summarise(GP = sum(Global, na.rm = T), React = sum(Reactive, na.rm = T), Kitchen = sum(Kitchen, na.rm = T), Laundry = sum(Laundry, na.rm = T), WHAC = sum(WHAC, na.rm = T), T.Sub = sum(TotalSub, na.rm = T), Unregistered = sum(Unregistered, na.rm = T), Efficiency = sum(Efficiency, na.rm = T)) power_year <- power_year[-1,] #remove 2006 since not even a complete month ggplot(power_year, aes(Y, GP)) + geom_line(color = "gold", lwd = 1) + ggtitle("Yearly Total Energy Consumption", subtitle = "Consumption over 47 months") + ylab("Total Use kWh") + xlab("") + theme_minimal() + ylim(c(7500, 10000)) # Line of Best Fit: ggplot(power_year, aes(Y, GP)) + geom_line() + geom_smooth() + xlab("") + theme_minimal() # returns loess method #(gam method was detected by auto for only daily energy consumption. Loess has been detected for weekly, monthly, yearly patterns) # Adding perspective to Consumption ...................................................... ggplot() + geom_line(data = power_day, aes(D, GP), color = "orange") + geom_line(data = power_week, aes(W, GP), color = "tomato") + geom_line(data = power_month, aes(M, GP), color = "blue") + ggtitle("Total Energy Consumption", subtitle = "Daily, Weekly, Monthly") + ylab("Total Use kWh") + xlab("") + theme_minimal() # create time series object for the dygraph ................................................ dayly <- xts(power_day$GP, power_day$D) weekly <- xts(power_week$GP, power_week$W) monthly <- xts(power_month$GP, power_month$M) data_ts <- cbind(Daily = dayly, Weekly = weekly, Montly = monthly) (consumption <- dygraph(data_ts, main = "Total Energy Consimption", ylab = "Total kWh") %>% dyRangeSelector() %>% dyRoller(rollPeriod = 48)) # roll period set by monthly obs # Pie Chart for room comaprison: # Time Series ............................................................................ day_ts <- ts(power_day$GP, frequency = 356, start = c(2007,1), end = c(2010,356)) week_ts <- ts(power_week$GP, frequency = 53, start = c(2007, 1), end = c(2010, 50)) month_ts <- ts(power_month$GP, frequency = 12, start = c(2007,1), end = c(2010, 11)) year_ts <- ts(power_year$GP, frequency = 1, start = c(2007), end = c(2010)) # Bars and moving averages ............................................................... autoplot(month_ts) + theme_minimal() ggseasonplot(month_ts, year.labels = T) + theme_minimal() # plotting seasons ggsubseriesplot(month_ts) + theme_minimal() # seasonal changes over time gglagplot(month_ts, do.lines = F) # Better correlation with lag12 ggAcf(month_ts) + theme_minimal() # r12 and r1 are best fit ggtsdisplay(month_ts, plot.type = "histogram") # in the monthly gathered data, r1 and r12 are highest peaks. And lowest at r7 # Defining Moving Average for Monthly Consumption autoplot(month_ts, series = "GP") + autolayer(ma(month_ts),series = "6-MA") + theme_minimal() # Decompose based on additive: month_ts %>% decompose(type = "additive") %>% autoplot() + theme_minimal() # Decompose with X11: fit <- month_ts %>% seas(x11 = "") autoplot(fit) + theme_minimal() + ggtitle("Monthy Time Series") # Seasonally adjusted for improved predictions: autoplot(month_ts, series = "GP") + autolayer(trendcycle(fit), series = "Trend") + autolayer(seasadj(fit), series = "Seasonally Adjusted") + theme_minimal() + ggtitle("Seasonaly Adjusted Month Time Series") # Decompose Seasonal Extraction in ARIMA Time Series: month_ts %>% decompose() %>% autoplot() + theme_minimal() + ggtitle("Seasonal Exraction in ARIMA Decomposition, Time Series") # STL Decomposition "Seasonal and Trend decomposition using Loess": # The default fits a parabola to the points, odd number for t.window: fit_stl <- month_ts %>% stl(t.window = 11, s.window = "periodic", robust = T) autoplot(fit_stl) + theme_minimal() + ggtitle("STL Decomposition, Month Time Series") # Na´ve Forecast: fit_stl %>% seasadj() %>% naive() %>% autoplot() + theme_minimal() + ggtitle("Seasonally Adjusted Na´ve Forecast", subtitle = "High Granurality for Monthly Time Series") # Na´ve Forecast, not adjusted: fit_stl %>% forecast(method = "naive") %>% autoplot() + theme_minimal() + ggtitle("Na´ve Forecast, Not-Seasonally Adjusted", subtitle = "Monthly Time Series") fit_day <- day_ts %>% stl(t.window = 360, s.window = "periodic", robust = T) fit_day %>% forecast(method = "naive") %>% autoplot() + theme_minimal() + ggtitle("Forecasting Daily Consumption", subtitle = "STL + Random Walk") # Detecting Anomaly ........................................................................ #install.packages("anomalize") require(anomalize) month_noNA <- na.omit(power_month) # have to omit NA to use Anomalize: # We use STL which uses seasonal decomposition. The alternative methos is "twitter" which uses # trend to remove the trend. (Results slightly differ). # Method "gesd" detects outliers better than the alternative "iqr". # Unregistered Areas:9 anomalies detected month_noNA %>% time_decompose(Unregistered, method = "stl") %>% anomalize(remainder, method = "gesd") %>% time_recompose() %>% plot_anomaly_decomposition(alpha_dots = 1, size_circles = 6, color_yes = "red") + ggtitle("Energy Consumption in Unregistered Areas", subtitle = "9 Anomalies in the Remainder calculated with GESD across 47 months") # T.Submeters: 5 anomalies detected month_noNA %>% time_decompose(T.Sub, method = "stl") %>% anomalize(remainder, method = "gesd") %>% time_recompose() %>% plot_anomaly_decomposition(alpha_dots = 0.5, size_circles = 6, color_yes = "deeppink") + ggtitle("Energy Consumption for all Three Submeters", subtitle = "5 Anomalies in the Remainder calculated with GESD across 47 months") # Entire House: 2 anomalies detected month_noNA %>% time_decompose(GP, method = "stl") %>% anomalize(remainder, method = "gesd") %>% time_recompose() %>% plot_anomaly_decomposition(alpha_dots = 0.5, size_circles = 6, color_yes = "black") + ggtitle("Energy Consumption registered for the Entire Household", subtitle = "2 Anomalies in the Remainder calculated with GESD across 47 months")

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/multilevel_kernel.R \name{multilevel_kernel} \alias{multilevel_kernel} \title{Runs a multilevel Markov kernel} \usage{ multilevel_kernel( model, theta, discretization, observations, nparticles, resampling_threshold, coupled_resampling, ref_trajectory_coarse = NULL, ref_trajectory_fine = NULL, algorithm = "CPF", treestorage = FALSE ) } \arguments{ \item{model}{a list representing a hidden Markov model, e.g. \code{\link{hmm_ornstein_uhlenbeck}}} \item{theta}{a vector of parameters as input to model functions} \item{discretization}{lists containing stepsize, nsteps, statelength, obstimes for fine and coarse levels, and coarsetimes of length statelength_fine indexing time steps of coarse level} \item{observations}{a matrix of observations, of size nobservations x ydimension} \item{nparticles}{number of particles} \item{resampling_threshold}{ESS proportion below which resampling is triggered (always resample at observation times by default)} \item{coupled_resampling}{a 2-way coupled resampling scheme, such as \code{\link{coupled2_maximal_independent_residuals}}} \item{ref_trajectory_coarse}{a matrix of reference trajectory for coarser discretization level, of size xdimension x statelength_coarse} \item{ref_trajectory_fine}{a matrix of reference trajectory for finer discretization level, of size xdimension x statelength_fine} \item{algorithm}{character specifying type of algorithm desired, i.e. \code{\link{CPF}} for conditional particle filter, \code{\link{CASPF}} for conditional ancestor sampling particle filter, \code{\link{CBSPF}} for conditional backward sampling particle filter} \item{treestorage}{logical specifying tree storage of Jacob, Murray and Rubenthaler (2013); if missing, this function store all states and ancestors} } \value{ two new trajectories stored as matrices of size xdimension x statelength_coarse/fine. } \description{ Runs two coupled kernels that leaves the corresponding smoothing distribution (at each discretization level) invariant. }

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################### ### Vizualizare ### ################### ### A.) Interfete Interactive # TODO: urmatoarele ore; # A.1.) Shiny app: # - links & gallery: # https://shiny.rstudio.com/ # https://shiny.rstudio.com/gallery/ # A.2.) Dashboards: # - de evaluat: shinydashboard, flexdashboard; library("shiny") library("shinyjs") library("shinyBS") # Buttons & Components library("shinydashboard") # diverse dashboard-uri library("flexdashboard") ########### ### Colours ### Function colors(): # - displays the names of all available colors; # - find.col(): helper function (see below); ### Function heat.colors(): #################### ### Helper Functions find.col = function(name="red", start=1, max=30, bottom.mrg=8, ...) { is.col = grepl(name, colors()); n.max = min(sum(is.col), start + max - 1); id = seq(start, n.max); name.col = colors()[is.col][id] x = rep(1, length(id)); names(x) = name.col; # set bottom margin old.par = par(mar=c(bottom.mrg,1,2,1) + 0.1) barplot(x, col=name.col, las=3, ...) par(old.par) invisible(name.col) } plot.col = function(col, bottom.mrg=8, ...) { x = rep(1, length(col)); names(x) = names(col); # set bottom margin old.par = par(mar=c(bottom.mrg,1,2,1) + 0.1) barplot(x, col=col, las=3, ...) par(old.par) invisible() } ###################### ### Examples find.col() find.col("green") find.col("pale") plot.col(heat.colors(30))

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## ## Test previous errors on kselection ## ## Created by Daniel Rodriguez Perez on 10/10/2014. ## ## Copyright (c) 2014 Daniel Rodriguez Perez. ## ## This program is free software: you can redistribute it and/or modify ## it under the terms of the GNU General Public License as published by ## the Free Software Foundation, either version 3 of the License, or ## (at your option) any later version. ## ## This program is distributed in the hope that it will be useful, ## but WITHOUT ANY WARRANTY; without even the implied warranty of ## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the ## GNU General Public License for more details. ## ## You should have received a copy of the GNU General Public License ## along with this program. If not, see <http://www.gnu.org/licenses/> ## context("Tests previous errors in kselection") test_that("evaluate data.frames with low rows", { x <- matrix(c(rnorm(5, 2, .1), rnorm(5, 3, .1), rnorm(5, -2, .1), rnorm(5, -3, .1)), 10, 2) obj <- kselection(x, max_centers = 9) expect_that(class(obj), equals("Kselection")) expect_warning(kselection(x), "The maximum number of clusters has been reduced from 15 to 9") })

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library(ggplot2) require(gridExtra) records <- read.delim('coverage.dat', header=TRUE) # # Recovery all lines # p1 <- ggplot(records, aes(x=PPercent, y=OPercent, color=TotalInventory) ) p1 <- p1 + geom_line(aes(group=Language)) p1 <- p1 + scale_color_gradient("Inventory Size", trans="log", high="orange", low="blue") p1 <- p1 + xlab('Transcript Percentage') + ylab("Percentage of Observed Phonemes") p1 <- p1 + theme_classic() p1 <- p1 + guides(color="none") pdf("recovery_rate.pdf") print(p1) x <- dev.off() # # Recovery - smoothed # p <- ggplot(records, aes(x=PPercent, y=OPercent)) p <- p + geom_smooth() p <- p + xlab('Transcript Percentage') + ylab("Percentage of Observed Phonemes") p <- p + xlim(0, 100) + ylim(0, 100) p <- p + theme_classic() pdf("recovery_rate_combined.pdf") print(p) x <- dev.off() # # Recovery -- blocked into 10s # records$Block <- as.factor(round(records$TotalInventory, -1)) p <- ggplot(records, aes(x=PPercent, y=OPercent, group=Block, fill=Block, color=Block )) p <- p + geom_smooth() p <- p + scale_color_brewer(palette="Set1") p <- p + scale_fill_brewer(palette="Set1") p <- p + xlab('Transcript Percentage') + ylab("Percentage of Observed Phonemes") p <- p + xlim(0, 100) + ylim(0, 100) p <- p + theme_classic() pdf("recovery_rate_blocked.pdf") print(p) x <- dev.off() p2 <- ggplot(records, aes(x=TranscriptLength, y=OPercent, color=TotalInventory) ) p2 <- p2 + geom_line(aes(group=Language)) p2 <- p2 + scale_color_gradient("Inventory Size", trans="log", high="orange", low="blue") p2 <- p2 + xlab('Transcript Length (Phonemes)') p2 <- p2 + ylab("Percentage of Observed Phonemes") p2 <- p2 + theme_classic() p2 <- p2 + guides(color="none") pdf("recovery_rate_vs_transcript_length.pdf") print(p2) x <- dev.off() p1 <- p1 + ggtitle('a. Recovery Rate (Transcript Percentage)') + theme(plot.title=element_text(hjust=0)) p2 <- p2 + ggtitle('b. Recovery Rate (Transcript Length)') + theme(plot.title=element_text(hjust=0)) p1 <- p1 + geom_smooth(colour="#333333", method="loess") p2 <- p2 + geom_smooth(colour="#333333", method="loess") # plot 3 p3 <- p2 + scale_x_log10() # the above will generate ggplot2 warnings from the counts at point zero: # Transformation introduced infinite values in continuous x-axis p3 <- p3 + ggtitle('c. Recovery Rate (Log Transformed Transcript Length)') + theme(plot.title=element_text(hjust=0)) # force the same scale p1 <- p1 + ylim(0, 100) p2 <- p2 + ylim(0, 100) p3 <- p3 + ylim(0, 100) ggsave("combined.pdf", grid.arrange(p1, p2, p3, ncol=1))

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/newyorktimes.R \name{rss_rank} \alias{rss_rank} \title{Section Rank.} \usage{ rss_rank(search_section = "Arts") } \arguments{ \item{search_section}{A Character.(the character should choose from: Africa, Americas, ArtandDesign, Arts, AsiaPacific, Automobile, Baseball, Books, Business, Climate, CollegeBasketball, CollegeFootball, Dance, Dealbook, DiningandWine, Economy, Education, EnergyEnvironment, Europe, FashionandStyle, Golf, Health, Hockey, HomePage, Jobs, Lens, MediaandAdvertising, MiddleEast, MostEmailed, MostShared, MostViewed, Movies, Music, NYRegion, Obituaries, PersonalTech, Politics, ProBasketball, ProFootball, RealEstate, Science, SmallBusiness, Soccer, Space, Sports, SundayBookReview, Sunday-Review, Technology, Television, Tennis, Theater, TMagazine, Travel, Upshot, US, Weddings, Well, YourMoney).} } \value{ A dataframe inclde article's title, link, description, published date, and their rank based on \code{search_section}. } \description{ Get recent ranked articles' information in specific section. } \examples{ rss_rank('Arts') }

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library(sqldf) filename<-"household_power_consumption.txt" # Uses sql query to choose target dates DF <- read.csv.sql(filename, sep=";", sql = 'select * from file where Date = "1/2/2007" OR Date = "2/2/2007"') # Creates dest file png(filename="plot4.png",width=480,height=480,units="px") # Sets 2*2 form par(mfrow=c(2,2)) # Draws top-left plot(time,DF$Global_active_power,type="l",ylab="Global Acvtive Power", xlab = "") # Draws top-right plot(time,DF$Voltage,type="l",ylab="Voltage", xlab = "datetime") # Draws bottom-left plot(time, DF$Sub_metering_1, type="l", col="black", xlab="", ylab="Energy sub metering") lines(time, DF$Sub_metering_2, col="red") lines(time, DF$Sub_metering_3, col="blue") legend("topright", col=c("black", "red", "blue"),lty=1,legend=c("Sub_metering_1","Sub_metering_2","Sub_metering_3"),bty="n",cex = 0.8) # Draws bottom-right plot(time,DF$Global_reactive_power,type="l",ylab="Global_reAcvtive_power", xlab = "datetime") # Closes the dev dev.off()

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#fig 3 #library(hadleyverse) #library(stargazer) #setwd("C:/Users/Darin/Documents/populism/descriptive") #load("C:/Users/Darin/Documents/populism/descriptive/Joined.Rdta") #dc <- as.data.frame(dc) stargazer(dc, title = "Sample Summary Statistics", covariate.labels = c("Vote Share" ,"Populism", "PSI", "Party Strength"), label = "descriptive") d1 <- dc %>% select(region, elec_result_major, score, PSI, PI_7) %>% group_by(region) %>% summarize_each(funs(median)) %>% reshape2::melt() %>% reshape2::dcast(variable ~ region, value.var = "value") %>% mutate(Variable = c("Vote Share", "Populism", "PSI", "Party Strength")) %>% select(Variable, Americas, Europe) stargazer(d1, summary = F, title = "Breakdown of Populism and Party System Attributes by Region", rownames = F) t1 <- t.test(dc$score, dc$PSI) t1t <- tidy(t1) %>% mutate(Variable = "PSI") %>% select(Variable, estimate, tstat = statistic, p.value) t2 <- t.test(dc$score, dc$PI_7) t2t <- tidy(t2) %>% mutate(Variable = "Party Strength") %>% select(Variable, estimate, tstat = statistic, p.value) ts <- rbind(t1t, t2t) %>% mutate(Estimate = round(estimate, digits = 2), TStat = round(tstat, digits = 2), PValue = round(p.value, digits = 4)) %>% select(Variable, Estimate, TStat, PValue) stargazer(ts, summary = F, rownames = F, title = "Difference of Means - Populism Score")

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kernThomas.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/rPSNC.R \name{kernThomas} \alias{kernThomas} \title{Gaussian kernel with bandwidth omega} \usage{ kernThomas(r, omega, ...) } \description{ Gaussian kernel with bandwidth omega }

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/sms.R \name{find_best_selection_SA} \alias{find_best_selection_SA} \title{find_best_selection_SA} \usage{ find_best_selection_SA(area_census, insms, inseed = -1) } \arguments{ \item{area_census}{A census dataset consisting of various areas rows.} \item{insms}{A microsimulation object which holds the data and details of the simulation such as iterations, lexicon.} \item{inseed}{A number to be used for random seed.} } \value{ msm_results An object with the results of the simulation, of this area. } \description{ Run a simulation in parallel mode with Simulated Annealing } \examples{ library(sms) data(survey) data(census) in.lexicon=createLexicon() in.lexicon=addDataAssociation(in.lexicon, c("he","he")) in.lexicon=addDataAssociation(in.lexicon, c("females","female")) this_area=as.data.frame(census[1,]) #Select the first area from the census table insms= new("microsimulation",census=census, panel=survey, lexicon=in.lexicon, iterations=5) myselection= find_best_selection_SA( this_area, insms, inseed=1900) print(myselection) } \author{ Dimitris Kavroudakis \email{dimitris123@gmail.com} }

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varImpACC.R

#' varImpACC #' #' Computes the variable importance regarding the accuracy (ACC). #' #' @param object An object as returned by cforest. #' @param mincriterion The value of the test statistic or 1 - p-value that must be exceeded in order to include a #' split in the computation of the importance. The default mincriterion = 0 guarantees that all splits are included. #' @param conditional The value of the test statistic or 1 - p-value that must be exceeded in order to include a split #' in the computation of the importance. The default mincriterion = 0 guarantees that all splits are included. #' @param threshold The threshold value for (1 - p-value) of the association between the variable of interest and a #' covariate, which must be exceeded inorder to include the covariate in the conditioning scheme for the variable of #' interest (only relevant if conditional = TRUE). A threshold value of zero includes all covariates. #' @param nperm The number of permutations performed. #' @param OOB A logical determining whether the importance is computed from the out-of-bag sample or the learning #' sample (not suggested). #' @param pre1.0_0 Prior to party version 1.0-0, the actual data values were permuted according to the original #' permutation importance suggested by Breiman (2001). Now the assignments to child nodes of splits in the variable #' of interest are permuted as described by Hapfelmeier et al. (2012), which allows for missing values in the #' explanatory variables and is more efficient wrt memory consumption and computing time. This method does not #' apply to conditional variable importances. #' #' @return Vector with computed permutation importance for each variable #' @export #' #' @examples #' data(iris) #' iris2 = iris #' iris2$Species = factor(iris$Species == "versicolor") #' iris.cf = cforest(Species ~ ., data = iris2,control = cforest_unbiased(mtry = 2, ntree = 50)) #' set.seed(123) #' a = varImpACC(object = iris.cf) #' varImpACC = function (object, mincriterion = 0, conditional = FALSE, threshold = 0.2, nperm = 1, OOB = TRUE, pre1.0_0 = conditional) { return(varImp(object, mincriterion = mincriterion, conditional = conditional, threshold = threshold, nperm = nperm, OOB = OOB, pre1.0_0 = pre1.0_0, measure = "ACC")) }

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angle_logi=function(x,y,weight,nlambda,lambda.min,lambda,standardize,epsilon) { if (is.null(lambda)) {z = logiway1(x, y, weight, nlambda, lambda.min, standardize, epsilon)} if (!is.null(lambda)) {z = logiway2(x, y, weight, lambda, standardize, epsilon)} return(z) }

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make_lsd_date_file <- function(phylodata, outfile = 'outfile.date'){ if(class(phylodata) == 'DNAbin'){ taxa_names <- rownames(phylodata) }else if(class(phylodata) == 'phylo'){ taxa_names <- phylodata$tip.label } dates <- sapply(taxa_names, function(x) gsub('.+_', '', x), USE.NAMES = F) lines <- paste0(taxa_names, ' ', dates, collapse = '\n') cat(length(taxa_names), '\n', file = outfile) cat(lines, file = outfile, append = T) print(paste('Dates file saved in ', outfile)) }

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cr$pushGroup() cr$setSource(fill_pattern) cr$fillPreserve() cr$setSource(stroke_pattern) cr$stroke() cr$popGroupToSource(cr) cr$paintWithAlpha(alpha)

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best <- function(stateChr, outcomeChr) { # Function to find the best hospital in a state # Read file data file_data <- read.csv("outcome-of-care-measures.csv",colClasses = "character") # Convert data type from char to numeric and suppress warning suppressWarnings(file_data[, 11] <- as.numeric(file_data[, 11])) suppressWarnings(file_data[, 17] <- as.numeric(file_data[, 17])) suppressWarnings(file_data[, 23] <- as.numeric(file_data[, 23])) #Merge data set hospital_data <- file_data[,c(2,7,11,17,23)] #provide proper column names colnames(hospital_data) <- c("hospital","state","heart attack","heart failure","pneumonia") # Check for valid input argument if ( stateChr %in% hospital_data$state == FALSE) stop("invalid state") if (outcomeChr %in% c("heart attack","heart failure","pneumonia") == FALSE) stop("invalid outcome") # Eliminate NA Values mydata <- na.omit(hospital_data[which(hospital_data$state==stateChr),]) #Find row number for min value by outcome if (outcomeChr == "heart attack") {rownum<- which(mydata$"heart attack" == min(mydata[,3]))} if (outcomeChr == "heart failure") {rownum<- which(mydata$"heart failure" == min(mydata[,4]))} if (outcomeChr == "pneumonia") {rownum<- which(mydata$"pneumonia" == min(mydata[,5]))} #Return Hospital name for identified row mydata[rownum,1] } # best("TX", "heart failure") # best("MD", "heart attack") # best("MD", "pneumonia") # best("BB", "heart attack") # best("NY", "hert attack")

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#script loads all functions used in project source('R/00_utils.R') #script loads all libraries used in project source('R/01_load-libs.R') #script cleans raw data source('R/02_clean-data.R')

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#Functions used in S3a_esrd_claims.R to create the pre-esrd claims tables. #The schema for the tables changes from year to year. For example, there is no #cdtype field prior to 2014, since all diagnosis codes were ICD9 prior to 2014. #The script handles these year-to-year changes in schema. create_claim_table <- function( data_dir, con, filenames, fieldnames, column_type, column_type_2015, table_name_pt) { # send information to insert each year of claims data into the same postgres table fieldnames = tolower(fieldnames) for (filename in filenames) { incident_year = substr(filename, str_length(filename) - 3, str_length(filename)) if (incident_year < 2015) { # claims prior to 2015 are all icd9, so we set cdtype to I for those years csvfile = read_csv(file.path(data_dir, str_glue("{filename}.csv")), col_types = column_type_2015) csvfile = csvfile %>% mutate(cdtype = "I") } else { csvfile = read_csv(file.path(data_dir, str_glue("{filename}.csv")), col_types = column_type) } tblname = str_remove(filename, incident_year) names(csvfile) = tolower(names(csvfile)) fields = names(csvfile) patients = dbGetQuery( con, str_glue( "SELECT usrds_id FROM {table_name_pt}") ) df = patients %>% inner_join( csvfile, by = "usrds_id") %>% mutate( incident_year = incident_year) df$pdgns_cd = df$pdgns_cd %>% trimws() %>% str_pad(., width = 7, side = "right", pad = "0") if (grepl('_ip_', tblname)){ df = createIP_CLM(df, incident_year) } else { df <- df %>% filter(!is.na(masked_clm_from) & (masked_clm_from != "")) } rm(csvfile) # Append every set, except '2012' which will be the first table to import. # this is b/c 2012 has the format that we want to use to create the table # and append the other years since the format changes between 2011 and 2012-2017 if (incident_year==2012){ drop_table_function(con, tblname) print(str_glue("creating {tblname} claims using {incident_year}={nrow(df)} patients={nrow(df %>% distinct(usrds_id, keep_all=FALSE))}")) dbWriteTable( con, tblname, df[, fieldnames], append = FALSE, row.names = FALSE) } else { print(str_glue("adding {incident_year} to {tblname}={nrow(df)} patients={nrow(df %>% distinct(usrds_id, keep_all=FALSE))}")) dbWriteTable( con, tblname, df[, fieldnames], append = TRUE, row.names = FALSE) } } } createIP_CLM = function(df, incident_year) { # filtering for table named "preesrd5y_ip_clm" print(str_glue("filtering IP claims {incident_year}")) df = df %>% filter( !is.na(masked_clm_from) & (masked_clm_from != "") & !is.na(drg_cd) & (drg_cd != "") ) return(df) }

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decomposition.Rd

\name{decomposition} \alias{decomposition} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Decompose an asymmetric matrix } \description{ The decomposition of an asymmetric matrix into a symmetric matrix and a skew-symmetric matrix is an elementary result from mathematics that is the cornerstone of this package. The decomposition into a skew-symmetric and a symmetric component is written as: \eqn{Q=S+A}, where \eqn{Q} is an asymmetric matrix, \eqn{S} is a symmetric matrix, and \eqn{A} is a skew-symmetric matrix. } \usage{ decomposition(X) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{X}{ An asymmetric matrix } } \value{ \item{S }{The symmetric part of the matrix} \item{A }{The skew-symmetric part of the matrix} } \author{ Berrie Zielman } \examples{ data("Englishtowns") Q <- decomposition(Englishtowns) # the skew-symmetric part Q$A }

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/easy_plot_ly.R \name{easy_plot_ly} \alias{easy_plot_ly} \title{Easy Plotting with Plotly} \usage{ ## x, y, z, and color variables need to be in a vectorized form like x$data or y$data. ## If you are using the surface plot type, a z variable matrix is all that should be used, ## if non matrix data is put into the funciton for x and y they will be converted to a z matrix ## Line plots should use quantitative color variables. 3D density plots require only an x and y variable ## and will create a z matrix for you easy_plot_ly(x, y, z, color, type, data) } \arguments{ \item{x}{The x-axis variable} \item{y}{The y-axis variable} \item{z}{The z-axis variable} \item{color}{A cateogrical or quantitative variable to subset the data} \item{type}{The type of plot you would like to generate: "scatter", "line", "surface", "3d_density", "mesh", or "auto" to have plotly generate one for you} \item{data}{The datset that will be used} } \value{ A 2D or 3D plot based on the package plotly } \description{ This function provides a simple way to generate 2D and 3D plots using the R package plotly. } \examples{ Scatter Plot: easy_plot_ly(x = iris$Sepal.Length, y = iris$Sepal.Width, z = iris$Petal.Length, color = iris$Species, type = 'scatter', data = iris) Line Plot: df1 <- data.frame(x = sin(1:1000), y = cos(1:1000), z = 1:1000) easy_plot_ly(x = df1$x, y = df1$y, z = df1$z, color = df1$x, type = 'line', data = df1) Surface Plot: easy_plot_ly(z = volcano, type = 'surface', data = volcano) 3D Density Plot: easy_plot_ly(x = iris$Sepal.Length, y = iris$Sepal.Width, type = '3d_density', data = iris) Mesh Plot: easy_plot_ly(x = iris$Sepal.Length, y = iris$Sepal.Width, z = iris$Petal.Length, type = 'mesh', data = iris) }

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/R/pullback_vdp.R

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mcrucifix/iceages_scripts

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pullback_vdp.R

# Copyright (c) 2012 Michel Crucifix <michel.crucifix@uclouvain.be> # Permission is hereby granted, free of charge, to any person obtaining # a copy of this software and associated documentation files (the # "Software"), to deal in the Software without restriction, including # without limitation the rights to use, copy, modify, merge, publish, # distribute, sublicense, and/or sell copies of the Software, and to # permit persons to whom the Software is furnished to do so, subject # the following conditions: # The above copyright notice and this permission notice shall be # incluuded in all copies or substantial portions of the Software. # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, # EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF # MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND INFRINGEMENT # IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR # CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, # TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE # SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # ------------------------------------------------------------------ # R version 2.15.2 (2012-10-26) -- "Trick or Treat" # ------------------------------------------------------------------ require(iceages) # pullback attractors of vanderpol oscillator # driven by periodic ersatz of precession and obliquity # with standard parameters Astro <- read_astro(1,1) times=seq(0,200,0.5) # determinstic parameter set parvdp = c(alpha=30.0, beta=0.7, gammapre=0.6, gammaobl=0.6, omega=4.10, asym=0) # stochastic parameter set parvdps = c(alpha=30.0, beta=0.7, gammapre=0.6, gammaobl=0.6, omega=4.10, sigma=0.5) # initial conditions positioned on the pullback attractor at time 0 # we do this rather than using the convenient 'pullaback_d' because # we want to be able to restart from the same IC with the stochastic model init <- basin(models$vdp_d, par=parvdp, -700., 0, Astro=Astro)$clusters p41 <- list() for (i in seq(nrow(init))) { p41[[i]] <- propagate_d(models$vdp_d, times, init=init[i,], par=parvdp, Astro=Astro) } # tweaks the paramer parvdp40 = parvdp parvdp40['omega'] = 4.0 # generates the 'stochastic' and 'deterministic' tweaked attractors s41 <- propagate_s(models$vdp_s, init=init[1,], par=parvdps, times, Astro=Astro, seed=95) # we selected here the 'fourth' pullback attractor # chosen after some trial and error to be representative # of the phenomenon we want to illustrate p40 <- pullback_d(models$vdp_d, times=times, par=parvdp40, Astro=Astro)$S[[4]] # ... and save ! save(file='../RData/pullback_11.RData', p41, s41, p40,times)

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MercenaryGhost/Time-Series-models-to-estimate-Bitcoin-Prices.

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#ARIMA Model library(readxl) Bitcoin_dataset_updated <- read_excel("E:/sem 3-2/Applied econometrics/Assignment 3/Bitcoin_dataset_updated.xlsx", sheet = "Sheet1") library(dplyr) library(lubridate) Bitcoin_dataset_updated$week <- floor_date(Bitcoin_dataset_updated$Date, "week") x<-ddply(Bitcoin_dataset_updated, .(week), function(z) mean(z$price)) #to convert the data into weekly averages. View(x) write.csv(x, "E:/sem 3-2/Applied econometrics/Assignment 3/weekly.csv",row.names = FALSE) write.csv(Bitcoin_dataset_updated, "E:/sem 3-2/Applied econometrics/Assignment 3/bitcoin.csv",row.names = FALSE) weekly <- read_excel("E:/sem 3-2/Applied econometrics/Assignment 3/weekly.xlsx") View(weekly) rm(x) Bitcoin.ts <- ts(weekly$`avg price`, frequency = 52, start = c(2015,1)) # considered values only from 2015 first week above values are deleted in excel manually before loading the weekly df. Bitcoin.ts plot.ts(Bitcoin.ts) plot.ts(log(Bitcoin.ts)) Bitcoin.tsdiff1 = diff(Bitcoin.ts, differences = 1) plot.ts(Bitcoin.tsdiff1) Bitcoin.tsdiff2 = diff(Bitcoin.tsdiff1, differences = 1) plot.ts(Bitcoin.tsdiff2) library(tseries) library(aTSA) adf.test(Bitcoin.ts, alternative="stationary") adf.test(Bitcoin.tsdiff1, alternative="stationary") adf.test(Bitcoin.tsdiff2, alternative="stationary") stationary.test(Bitcoin.ts) stationary.test(Bitcoin.ts, method = "pp") stationary.test(Bitcoin.ts, method = "kpss") stationary.test(Bitcoin.tsdiff1) stationary.test(Bitcoin.tsdiff1, method = "pp") stationary.test(Bitcoin.tsdiff2) acf(Bitcoin.tsdiff1, lag.max = 50) pacf(Bitcoin.tsdiff1, lag.max = 50) library(forecast) library(urca) auto.arima(Bitcoin.tsdiff1) auto.arima(Bitcoin.ts) bitcoin.tsarima <- arima(Bitcoin.ts, order = c(1,1,0)) View(bitcoin.tsarima) summary(bitcoin.tsarima) Bitcoin.tsforecasts <- forecast(bitcoin.tsarima, h = 10, level = c(95)) Bitcoin.tsforecasts plot(Bitcoin.tsforecasts) acf(Bitcoin.tsforecasts$residuals, lag.max=50) Box.test(Bitcoin.tsforecasts$residuals, lag=50, type="Ljung-Box") Box.test(Bitcoin.tsforecasts$residuals, lag=50, type="Box-Pierce") plot.ts(Bitcoin.tsforecasts$residuals) hist(Bitcoin.tsforecasts$residuals, breaks = 50) #ARDL Model weekly2 <- read_excel("E:/sem 3-2/Applied econometrics/Assignment 3/weekly2.xlsx") View(weekly2) Bitcoin2.ts <- ts(weekly2, frequency = 52, start = c(2015,1)) plot.ts(Bitcoin2.ts[,1]) plot.ts(Bitcoin2.ts[,2]) plot.ts(Bitcoin2.ts[,3]) plot.ts(Bitcoin2.ts[,4]) x = diff(Bitcoin2.ts[,3], differences = 1) y = diff(Bitcoin2.ts[,4], differences = 1) plot.ts(x) plot.ts(y) stationary.test(Bitcoin2.ts[,2]) stationary.test(Bitcoin2.ts[,3]) stationary.test(Bitcoin2.ts[,4]) stationary.test(x) stationary.test(y) Bitcoin2.ts.tab <- cbind(Bitcoin2.ts,diff(Bitcoin2.ts[,2]),diff(Bitcoin2.ts[,3]),diff(Bitcoin2.ts[,4])) View(Bitcoin2.ts.tab) library(dynlm) library(knitr) library(broom) Bitcoin2.tsdyn1 <- dynlm(d(price)~L(d(price),1)+d(transactions)+d(SP),data = Bitcoin2.ts) Bitcoin2.tsdyn2 <- dynlm(L(price,1)~price+L(price,-1)+L(d(transactions),1)+L(d(SP),1),data = Bitcoin2.ts) Bitcoin2.tsdyn3 <- dynlm(d(price)~L(d(price),1)+L(d(transactions),0:1)+L(d(SP),0),data = Bitcoin2.ts) Bitcoin2.tsdyn4 <- dynlm(d(price)~L(d(price),1)+L(d(transactions),0:2)+L(d(SP),0),data = Bitcoin2.ts) Bitcoin2.tsdyn5 <- dynlm(d(price)~L(d(price),1)+L(d(transactions),0:2)+L(d(SP),0:2),data = Bitcoin2.ts) Bitcoin2.tsdyn6 <- dynlm(d(price)~L(d(price),1)+L(d(transactions),0:3)+L(d(SP),0:3),data = Bitcoin2.ts) kable(tidy(summary(Bitcoin2.tsdyn1)), digits=4, caption="The Bitcoin auto regressive distributed lag model1") kable(tidy(summary(Bitcoin2.tsdyn2)), digits=4, caption="The Bitcoin auto regressive distributed lag model2") kable(tidy(summary(Bitcoin2.tsdyn3)), digits=4, caption="The Bitcoin auto regressive distributed lag model3") kable(tidy(summary(Bitcoin2.tsdyn4)), digits=4, caption="The Bitcoin auto regressive distributed lag model4") kable(tidy(summary(Bitcoin2.tsdyn5)), digits=4, caption="The Bitcoin auto regressive distributed lag model5") kable(tidy(summary(Bitcoin2.tsdyn6)), digits=4, caption="The Bitcoin auto regressive distributed lag model6") glL1 <- glance(Bitcoin2.tsdyn1)[c("r.squared","statistic","AIC","BIC")] glL3 <- glance(Bitcoin2.tsdyn3)[c("r.squared","statistic","AIC","BIC")] glL4 <- glance(Bitcoin2.tsdyn4)[c("r.squared","statistic","AIC","BIC")] tabl <- rbind(glL1, as.numeric(glL3), as.numeric(glL4)) kable(tabl, caption="Goodness-of-fit statistics for Bitcoin-ARDL models") ehat <- resid(Bitcoin2.tsdyn4) acf(ehat,lag.max = 50) res1 <- resid(Bitcoin2.tsdyn4) res2 <- lag(resid(Bitcoin2.tsdyn4),-1) plot(res1,res2) abline(v=mean(res1, na.rm = TRUE), lty=2) abline(h=mean(res2, na.rm = TRUE), lty=2) res3 <- lag(resid(Bitcoin2.tsdyn4),-2) plot(res1,res3) abline(v=mean(res1, na.rm = TRUE), lty=2) abline(h=mean(res3, na.rm = TRUE), lty=2) library(lmtest) a <- bgtest(Bitcoin2.tsdyn4, order=1, type="F", fill=0) b <- bgtest(Bitcoin2.tsdyn4, order=1, type="F", fill=NA) c <- bgtest(Bitcoin2.tsdyn4, order=4, type="Chisq", fill=0) d <- bgtest(Bitcoin2.tsdyn4, order=4, type="Chisq", fill=NA) dfr <- data.frame(rbind(a[c(1,2,4)], b[c(1,2,4)], c[c(1,2,4)], d[c(1,2,4)])) dfr <- cbind(c("1, F, 0", "1, F, NA", "4, Chisq, 0", "4, Chisq, NA"), dfr) names(dfr)<-c("Method", "Statistic", "Parameters", "p-Value") kable(dfr, caption="Breusch-Godfrey test for the Bitcoin-ARDL model no 4") library(dLagM) auto.arima(weekly2$transactions) transactions.ts <- ts(weekly2$transactions) transactions.arima <- arima(transactions.ts,order = c(0,1,2)) transactions_forecast <- forecast::forecast(transactions.arima, h=5, level = c(95)) transactions_forecast auto.arima(weekly2$SP) SP.ts <- ts(weekly2$SP) SP.arima <- arima(SP.ts,order = c(0,1,1)) SP_forecast <- forecast::forecast(SP.arima, h=5, level = c(95)) SP_forecast #ARDL Forecast using dLagM package #we cannot use this for a dynlm object, so I have re-estimated the same model using ardlDlm(), this is supported by the dLagM::forecast() function. diff.ts <- cbind(diff(weekly2$price,1),diff(weekly2$transactions,1),diff(weekly2$SP)) #diff of all three variables. View(diff.ts) rem.p = list(X2 = c(3) , X3 = c(1,2,3)) # p denotes the x lags(for all x variables) and q denotes the AR part, so to remove some select lags we can use this remove parameter. rem.q = c(2) # I have given some buffer values to avoid dY.t-1 getting removed and similar for x variables as well. remove = list(p = rem.p , q = rem.q) view(remove) model.ardlDlm = ardlDlm(formula = X1 ~ X2 + X3, data = data.frame(diff.ts) , #data should be a dataframe not anyother vector type p = 3 , q = 2 , remove = remove) #as there is no parameter to directly calculate differences I have given the differences data itself as the variables. x.new = matrix(c(-3866.7, 32.7, -1505.9, 0,0,0), ncol = 3, nrow = 2) #these are the differences of predicted values from ARIMA. dLagM::forecast(model = model.ardlDlm , x = x.new , h = 4,interval = TRUE, nSim = 100)

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/R/compare_index.R

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BerSerK/FenJi-A-Pricing

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compare_index.R

index1 <- read.csv('~/Downloads/index.csv') for ( i in 1:length(index1$BargainDate)) { index1$Date[i] = as.Date(index1$BargainDate[i], '%m/%d/%Y') } plot(index1$Date, index1$index, type='l')

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/R_exam_project_canis_lupus_italicus.r

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Enricalo/Monitoring_2021

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R_exam_project_canis_lupus_italicus.r

library(raster) # library(readr) # library(ncdf4) # setwd("C:/LAB_2020_2021/") #recall geographic data from raster package #altitude data, crop map on Italy IT<-getData('alt', country='IT', mask=TRUE, col=cl) #alternative map #library(maptools) #data("wrld_simpl") #plot(wrld_simpl) #plot(wrld_simpl, xlim=c(10,15), ylim=c(36,48), axes=T) #map not detailed enough #first graph wolf distribution 2010-2015 wolf1012 <- read_delim("canislupus1012.csv", "\t", escape_double = FALSE, trim_ws = TRUE) #change colors to look like a geographical map on a book cl1 <- colorRampPalette(c('darkolivegreen2','darkolivegreen4', 'chocolate', 'chocolate4', 'coral4', 'brown4', 'grey34', 'grey58', 'grey', 'white'))(100) # #plot new colors and name plot(IT, col=cl1, , main="wolf_2010_2012") #plot points about wolfs occurence data in Italy from 2010 to 2012 wolf_2010_2012<-points(wolf1012$decimalLongitude, wolf1012$decimalLatitude, col="turquoise1", pch=19, cex = 0.7) #second graph wolf distribution 2018-2020 wolf1820 <- read_delim("canislupus1820.csv", "\t", escape_double = FALSE, trim_ws = TRUE) #recall geographic data from raster package #altitude data, crop map on Italy #IT<-getData('alt', country='IT', mask=TRUE, col=cl) #change colors to look like a geographical map on a book #cl1 <- colorRampPalette(c('darkolivegreen2','darkolivegreen4','gold4','sienna1', 'chocolate4', 'brown', 'brown4', 'orangered4','grey58', 'white'))(100) # #plot new colors plot(IT, col=cl1, main="wolf_2018_2020") #plot points about wolfs occurence data in Italy from 2010 to 2015 points(wolf1820$decimalLongitude, wolf1820$decimalLatitude, col="black", pch=19, cex = 0.7) #compare par(mfrow=c(1,2)) plot(IT, col=cl1, , main="wolf_2010_2012") points(wolf1012$decimalLongitude, wolf1012$decimalLatitude, col="turquoise1", pch=19, cex = 0.7, main="wolf_2010_2012") plot(IT, col=cl1, main="wolf_2018_2020") points(wolf1820$decimalLongitude, wolf1820$decimalLatitude, col="black", pch=19, cex = 0.7, main="wolf_2018_2020") #save picture png("wolf_occurences_2010_2020.png") par(mfrow=c(1,2)) plot(IT, col=cl1, , main="wolf_2010_2012") points(wolf1012$decimalLongitude, wolf1012$decimalLatitude, col="turquoise1", pch=19, cex = 0.7, main="wolf_2010_2012") plot(IT, col=cl1, main="wolf_2018_2020") points(wolf1820$decimalLongitude, wolf1820$decimalLatitude, col="black", pch=19, cex = 0.7, main="wolf_2018_2020") dev.off() #clear the screen dev.off() ### #plot preys during the years #plot wild boars 2010 2015 boar1012 <- read_delim("susscrofa1012.csv", "\t", escape_double = FALSE, trim_ws = TRUE) #recall geographic data from raster package #altitude data, crop map on Italy plot(IT, col=cl1, main="wild_boar_prey_2010_2012") #plot points about boars occurence data in Italy from 2010 to 2012 points(boar1012$decimalLongitude, boar1012$decimalLatitude, col="turquoise1", pch=17, cex = 0.7, main="boar_2010_2012") #plot wild boars 2018 2020 boar1820 <- read_delim("susscrofa1820.csv", "\t", escape_double = FALSE, trim_ws = TRUE) #recall geographic data from raster package #altitude data, crop map on Italy plot(IT, col=cl1, main="wild_boar_prey_2018_2020") #plot points about boars occurence data in Italy from 2010 to 2015 points(boar1820$decimalLongitude, boar1820$decimalLatitude, col="black", pch=17, cex = 0.7, main="boar_2018_2020") #compare par(mfrow=c(1,2)) plot(IT, col=cl1, main="wild_boar_prey_2010_2012") points(boar1012$decimalLongitude, boar1012$decimalLatitude, col="turquoise1", pch=17, cex = 0.7, main="boar_2010_2012") plot(IT, col=cl1, main="wild_boar_prey_2018_2020") points(boar1820$decimalLongitude, boar1820$decimalLatitude, col="black", pch=17, cex = 0.7, main="boar_2018_2020") # save picture png("boar_occurences_2010_2020.png") par(mfrow=c(1,2)) plot(IT, col=cl1, main="wild_boar_prey_2010_2012") points(boar1012$decimalLongitude, boar1012$decimalLatitude, col="turquoise1", pch=17, cex = 0.7, main="boar_2010_2012") plot(IT, col=cl1, main="wild_boar_prey_2018_2020") points(boar1820$decimalLongitude, boar1820$decimalLatitude, col="black", pch=17, cex = 0.7, main="boar_2018_2020") dev.off() #clear the screen dev.off() #plot deers 2010 2015 deer1012 <- read_delim("cervelaph1015.csv", "\t", escape_double = FALSE, trim_ws = TRUE) #recall geographic data from raster package #altitude data, crop map on Italy plot(IT, col=cl1, main="deer_prey_2010_2015") #plot points about deers occurence data in Italy from 2010 to 2015 points(deer1012$decimalLongitude, deer1012$decimalLatitude, col="turquoise1", pch=17, cex = 0.7, main="deer_2010_2012") #plot deers 2018 2020 deer1820 <- read_delim("cervelaph1820.csv", "\t", escape_double = FALSE, trim_ws = TRUE) #recall geographic data from raster package #altitude data, crop map on Italy plot(IT, col=cl1, main="deer_prey_2018_2020") #plot points about deers occurence data in Italy from 2010 to 2015 points(deer1820$decimalLongitude, deer1820$decimalLatitude, col="black", pch=17, cex = 0.7, main="deer_2018_2020") #compare par(mfrow=c(1,2)) plot(IT, col=cl1, main="deer_prey_2010_2012") points(deer1012$decimalLongitude, deer1012$decimalLatitude, col="turquoise1", pch=17, cex = 0.7, main="deer_2010_2012") plot(IT, col=cl1, main="deer_prey_2018_2020") points(deer1820$decimalLongitude, deer1820$decimalLatitude, col="black", pch=17, cex = 0.7, main="deer_2018_2020") #save picture png("deer_occurences_2010_2020.png") par(mfrow=c(1,2)) plot(IT, col=cl1, main="deer_prey_2010_2012") points(deer1012$decimalLongitude, deer1012$decimalLatitude, col="turquoise1", pch=17, cex = 0.7, main="deer_2010_2012") plot(IT, col=cl1, main="deer_prey_2018_2020") points(deer1820$decimalLongitude, deer1820$decimalLatitude, col="black", pch=17, cex = 0.7, main="deer_2018_2020") dev.off() #clear the screen dev.off () #### #plot the spatial extent of vegetation cover #download copernicus data FCOVER May 2010 and May 2020 #plot the data FCOVER2010 <- raster("c_gls_FCOVER_201005240000_GLOBE_VGT_V1.4.1.nc") cl2 <- colorRampPalette(c('tan2','tan4','sienna','darkolivegreen2','darkolivegreen4','darkgreen'))(100) # plot(FCOVER2010, col=cl2, main="VegetationIndex_2010") ext <- c(0,20,35,55) # xmin xmax ymin ymax FCOVER2010_Italy <- crop(FCOVER2010, ext) plot(FCOVER2010_Italy, col=cl2, , main="VegetationIndex_2010") #save a picture png("FCOVER2010.png") plot(FCOVER2010_Italy, col=cl2, main="VegetationIndex_2010") dev.off() FCOVER2020 <- raster("c_gls_FCOVER_202005240000_GLOBE_PROBAV_V1.5.1.nc") cl2 <- colorRampPalette(c('tan2','tan4','sienna','darkolivegreen2','darkolivegreen4','darkgreen'))(100) # plot(FCOVER2020, col=cl2, main="VegetationIndex_2020") ext <- c(0,20,35,55) # xmin xmax ymin ymax FCOVER2020_Italy <- crop(FCOVER2020, ext) plot(FCOVER2020_Italy, col=cl2, , main="VegetationIndex_2020") #save a picture png("FCOVER2020.png") plot(FCOVER2020_Italy, col=cl2, main="VegetationIndex_2020") dev.off() #differences between the two images #Was there any increase in vegetation cover? cldif<-colorRampPalette(c('grey58', 'sienna4', 'green4', 'orange', 'gold', 'yellow'))(100) # difV<- FCOVER2010_Italy - FCOVER2020_Italy plot(difV, col=cldif, main="VegetationIndex_changes_10years") #save a picture png("difference_FCOVER.png") plot(difV, col=cldif, main="VegetationIndex_changes_10years") dev.off() #compare par(mfrow=c(2,4)) #wolf plot(IT, col=cl1, , main="wolf_2010_2012") points(wolf1012$decimalLongitude, wolf1012$decimalLatitude, col="yellow1", pch=19, cex = 0.7, main="wolf_2010_2012") #boar plot(IT, col=cl1, main="wild_boar_prey_2010_2012") points(boar1012$decimalLongitude, boar1012$decimalLatitude, col="turquoise1", pch=17, cex = 0.7, main="boar_2010_2012") #deer plot(IT, col=cl1, main="deer_prey_2010_2012") points(deer1012$decimalLongitude, deer1012$decimalLatitude, col="turquoise1", pch=17, cex = 0.7, main="deer_2010_2012") #vegetation plot(FCOVER2010_Italy, col=cl2, main="VegetationIndex_2010") ## #second row 2020 #wolf plot(IT, col=cl1, main="wolf_2018_2020") points(wolf1820$decimalLongitude, wolf1820$decimalLatitude, col="yellow1", pch=19, cex = 0.7, main="wolf_2018_2020") #boar plot(IT, col=cl1, main="wild_boar_prey_2018_2020") points(boar1820$decimalLongitude, boar1820$decimalLatitude, col="black", pch=17, cex = 0.7, main="boar_2018_2020") #deer plot(IT, col=cl1, main="deer_prey_2018_2020") points(deer1820$decimalLongitude, deer1820$decimalLatitude, col="black", pch=17, cex = 0.7, main="deer_2018_2020") #vegetation plot(FCOVER2020_Italy, col=cl2, main="VegetationIndex_2020") #save picture png("final_considerations_2010_2020.png") par(mfrow=c(2,4)) plot(IT, col=cl1, , main="wolf_2010_2012") points(wolf1012$decimalLongitude, wolf1012$decimalLatitude, col="yellow1", pch=19, cex = 0.7, main="wolf_2010_2012") plot(IT, col=cl1, main="wild_boar_prey_2010_2015") points(boar1012$decimalLongitude, boar10125$decimalLatitude, col="turquoise1", pch=17, cex = 0.7, main="boar_2010_2012") plot(IT, col=cl1, main="deer_prey_2010_2015") points(deer1012$decimalLongitude, deer1012$decimalLatitude, col="turquoise1", pch=17, cex = 0.7, main="deer_2010_2020") plot(FCOVER2010_Italy, col=cl2, main="VegetationIndex_2010") plot(IT, col=cl1, main="wolf_2018_2020") points(wolf1820$decimalLongitude, wolf1820$decimalLatitude, col="yellow1", pch=19, cex = 0.7, main="wolf_2018_2020") plot(IT, col=cl1, main="wild_boar_prey_2018_2020") points(boar1820$decimalLongitude, boar1820$decimalLatitude, col="black", pch=17, cex = 0.7, main="boar_2018_2020") plot(IT, col=cl1, main="deer_prey_2018_2020") points(deer1820$decimalLongitude, deer1820$decimalLatitude, col="black", pch=17, cex = 0.7, main="deer_2018_2020") plot(FCOVER2020_Italy, col=cl2, main="VegetationIndex_2020") dev.off() #the end

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/tests/testthat/test-unnest.R

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test-unnest.R

test_that("can keep empty rows", { df <- tibble(x = 1:3, y = list(NULL, tibble(), tibble(a = 1))) out1 <- df %>% unnest(y) expect_equal(nrow(out1), 1) out2 <- df %>% unnest(y, keep_empty = TRUE) expect_equal(nrow(out2), 3) expect_equal(out2$a, c(NA, NA, 1)) }) test_that("empty rows still affect output type", { df <- tibble( x = 1:2, data = list( tibble(y = character(0)), tibble(z = integer(0)) ) ) out <- unnest(df, data) expect_equal(out, tibble(x = integer(), y = character(), z = integer())) }) test_that("bad inputs generate errors", { df <- tibble(x = 1, y = list(mean)) expect_snapshot((expect_error(unnest(df, y)))) }) test_that("unesting combines augmented vectors", { df <- tibble(x = as.list(as.factor(letters[1:3]))) expect_equal(unnest(df, x)$x, factor(letters[1:3])) }) test_that("vector unnest preserves names", { df <- tibble(x = list(1, 2:3), y = list("a", c("b", "c"))) out <- unnest(df, x) expect_named(out, c("x", "y")) }) test_that("rows and cols of nested-dfs are expanded", { df <- tibble(x = 1:2, y = list(tibble(a = 1), tibble(b = 1:2))) out <- df %>% unnest(y) expect_named(out, c("x", "a", "b")) expect_equal(nrow(out), 3) }) test_that("can unnest nested lists", { df <- tibble( x = 1:2, y = list(list("a"), list("b")) ) rs <- unnest(df, y) expect_identical(rs, tibble(x = 1:2, y = list("a", "b"))) }) test_that("can unnest mixture of name and unnamed lists of same length", { df <- tibble( x = c("a"), y = list(y = 1:2), z = list(1:2) ) expect_identical( unnest(df, c(y, z)), tibble(x = c("a", "a"), y = c(1:2), z = c(1:2)) ) }) test_that("can unnest list_of", { df <- tibble( x = 1:2, y = vctrs::list_of(1:3, 4:9) ) expect_equal( unnest(df, y), tibble(x = rep(1:2, c(3, 6)), y = 1:9) ) }) test_that("can combine NULL with vectors or data frames", { df1 <- tibble(x = 1:2, y = list(NULL, tibble(z = 1))) out <- unnest(df1, y) expect_named(out, c("x", "z")) expect_equal(out$z, 1) df2 <- tibble(x = 1:2, y = list(NULL, 1)) out <- unnest(df2, y) expect_named(out, c("x", "y")) expect_equal(out$y, 1) }) test_that("vectors become columns", { df <- tibble(x = 1:2, y = list(1, 1:2)) out <- unnest(df, y) expect_equal(out$y, c(1L, 1:2)) }) test_that("multiple columns must be same length", { df <- tibble(x = list(1:2), y = list(1:3)) expect_snapshot((expect_error(unnest(df, c(x, y))))) df <- tibble(x = list(1:2), y = list(tibble(y = 1:3))) expect_snapshot((expect_error(unnest(df, c(x, y))))) }) test_that("can use non-syntactic names", { out <- tibble("foo bar" = list(1:2, 3)) %>% unnest(`foo bar`) expect_named(out, "foo bar") }) test_that("unpacks df-cols (#1112)", { df <- tibble(x = 1, y = tibble(a = 1, b = 2)) expect_identical(unnest(df, y), tibble(x = 1, a = 1, b = 2)) }) test_that("unnesting column of mixed vector / data frame input is an error", { df <- tibble(x = list(1, tibble(a = 1))) expect_snapshot((expect_error(unnest(df, x)))) }) test_that("unnest() advises on outer / inner name duplication", { df <- tibble(x = 1, y = list(tibble(x = 2))) expect_snapshot(error = TRUE, { unnest(df, y) }) }) test_that("unnest() advises on inner / inner name duplication", { df <- tibble( x = list(tibble(a = 1)), y = list(tibble(a = 2)) ) expect_snapshot(error = TRUE, { unnest(df, c(x, y)) }) }) test_that("unnest() disallows renaming", { df <- tibble(x = list(tibble(a = 1))) expect_snapshot(error = TRUE, { unnest(df, c(y = x)) }) }) test_that("unnest() works on foreign list types recognized by `vec_is_list()` (#1327)", { new_foo <- function(...) { structure(list(...), class = c("foo", "list")) } df <- tibble(x = new_foo(tibble(a = 1L), tibble(a = 2:3))) expect_identical(unnest(df, x), tibble(a = 1:3)) # With empty list df <- tibble(x = new_foo()) expect_identical(unnest(df, x), tibble(x = unspecified())) # With empty types df <- tibble(x = new_foo(tibble(a = 1L), tibble(a = integer()))) expect_identical(unnest(df, x), tibble(a = 1L)) expect_identical(unnest(df, x, keep_empty = TRUE), tibble(a = c(1L, NA))) # With `NULL`s df <- tibble(x = new_foo(tibble(a = 1L), NULL)) expect_identical(unnest(df, x), tibble(a = 1L)) expect_identical(unnest(df, x, keep_empty = TRUE), tibble(a = c(1L, NA))) }) # other methods ----------------------------------------------------------------- test_that("rowwise_df becomes grouped_df", { skip_if_not_installed("dplyr", "0.8.99") df <- tibble(g = 1, x = list(1:3)) %>% dplyr::rowwise(g) rs <- df %>% unnest(x) expect_s3_class(rs, "grouped_df") expect_equal(dplyr::group_vars(rs), "g") }) test_that("grouping is preserved", { df <- tibble(g = 1, x = list(1:3)) %>% dplyr::group_by(g) rs <- df %>% unnest(x) expect_s3_class(rs, "grouped_df") expect_equal(dplyr::group_vars(rs), "g") }) # Empty inputs ------------------------------------------------------------ test_that("can unnest empty data frame", { df <- tibble(x = integer(), y = list()) out <- unnest(df, y) expect_equal(out, tibble(x = integer(), y = unspecified())) }) test_that("unnesting bare lists of NULLs is equivalent to unnesting empty lists", { df <- tibble(x = 1L, y = list(NULL)) out <- unnest(df, y) expect_identical(out, tibble(x = integer(), y = unspecified())) }) test_that("unnest() preserves ptype", { tbl <- tibble(x = integer(), y = list_of(ptype = tibble(a = integer()))) res <- unnest(tbl, y) expect_equal(res, tibble(x = integer(), a = integer())) }) test_that("unnesting typed lists of NULLs retains ptype", { df <- tibble(x = 1L, y = list_of(NULL, .ptype = tibble(a = integer()))) out <- unnest(df, y) expect_identical(out, tibble(x = integer(), a = integer())) }) test_that("ptype can be overriden manually (#1158)", { df <- tibble( a = list("a", c("b", "c")), b = list(1, c(2, 3)), ) ptype <- list(b = integer()) out <- unnest(df, c(a, b), ptype = ptype) expect_type(out$b, "integer") expect_identical(out$b, c(1L, 2L, 3L)) }) test_that("ptype works with nested data frames", { df <- tibble( a = list("a", "b"), b = list(tibble(x = 1, y = 2L), tibble(x = 2, y = 3L)), ) # x: double -> integer ptype <- list(b = tibble(x = integer(), y = integer())) out <- unnest(df, c(a, b), ptype = ptype) expect_identical(out$x, c(1L, 2L)) expect_identical(out$y, c(2L, 3L)) }) test_that("skips over vector columns", { df <- tibble(x = integer(), y = list()) expect_identical(unnest(df, x), df) }) test_that("unnest keeps list cols", { df <- tibble(x = 1:2, y = list(3, 4), z = list(5, 6:7)) out <- df %>% unnest(y) expect_equal(names(out), c("x", "y", "z")) }) # Deprecated behaviours --------------------------------------------------- test_that("cols must go in cols", { df <- tibble(x = list(3, 4), y = list("a", "b")) expect_snapshot(unnest(df, x, y)) }) test_that("need supply column names", { df <- tibble(x = 1:2, y = list("a", "b")) expect_snapshot(unnest(df)) }) test_that("sep combines column names", { local_options(lifecycle_verbosity = "warning") df <- tibble(x = list(tibble(x = 1)), y = list(tibble(x = 1))) expect_snapshot(out <- df %>% unnest(c(x, y), .sep = "_")) expect_named(out, c("x_x", "y_x")) }) test_that("unnest has mutate semantics", { df <- tibble(x = 1:3, y = list(1, 2:3, 4)) expect_snapshot(out <- df %>% unnest(z = map(y, `+`, 1))) expect_equal(out$z, 2:5) }) test_that(".drop and .preserve are deprecated", { local_options(lifecycle_verbosity = "warning") df <- tibble(x = list(3, 4), y = list("a", "b")) expect_snapshot(df %>% unnest(x, .preserve = y)) df <- tibble(x = list(3, 4), y = list("a", "b")) expect_snapshot(df %>% unnest(x, .drop = FALSE)) }) test_that(".id creates vector of names for vector unnest", { local_options(lifecycle_verbosity = "warning") df <- tibble(x = 1:2, y = list(a = 1, b = 1:2)) expect_snapshot(out <- unnest(df, y, .id = "name")) expect_equal(out$name, c("a", "b", "b")) })

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/3-revision_entrega/scripts/2-crear_datos_revision.R

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2-crear_datos_revision.R

# se crean datos para revisiar la muestra y para estimar library(sf) library(raster) library(dplyr) library(tidyverse) library(srvyr) source("R/funciones.R") ### entrega BITS bits_2018 <- read_sf("datos_entrada/datos_bits/PUNTOS DE VALIDACION-2018/Puntos_de_Validacion_2018_Revisados.shp") bits_2018_points <- bits_2018 %>% st_cast("POINT") # detectamos puntos repetidos bits_2018_points$equals <- st_equals(bits_2018_points) %>% map_int(first) # seleccionamos únicamente un punto cuando hay repetidos bits_2018_unique <- bits_2018_points %>% group_by(equals) %>% top_n(n = 1, identifier) %>% ungroup() # agregamos variable de mapa MADMEX para comparar y determinar coincidencia # última versión MADMEX 2018: raster_31 <- raster("datos_entrada/madmex_sentinel2_2018_31.tif") # Reproyectamos raster y extraemos la variable para agregarla a sf de BITS # agregamos también columnas para el valor de intérpretes en 17 clases # (solo estaba en 31 clases). crs_lcc <- crs(raster_31) %>% as.character() bits_2018_lcc <- st_transform(bits_2018_unique, crs = crs_lcc) bits_raster <- raster::extract(raster_31, y = bits_2018_lcc) bits_2018_lcc <- bits_2018_lcc %>% add_column(raster = bits_raster) %>% mutate_at(vars(raster, interp1, interp2, interp3), .funs = list(c31 = identity, c17 = clasifica_31_17)) %>% select(-raster, -interp1, -interp2, -interp3) # determinamos coincidencia con la primera etiqueta de los revisores (top) y # con alguna de las 3 asignadas bits_2018_lcc_row <- bits_2018_lcc %>% rowwise() %>% mutate( correcto_31_top = raster_c31 == interp1_c31, correcto_31_top3 = raster_c31 %in% c(interp1_c31, interp2_c31, interp3_c31), correcto_17_top = raster_c17 == interp1_c17, correcto_17_top3 = raster_c17 %in% c(interp1_c17, interp2_c17, interp3_c17) ) %>% ungroup() %>% select(OBJECTID, identifier, correcto_31_top:correcto_17_top3) bits_2018_lcc <- bits_2018_lcc %>% left_join(bits_2018_lcc_row) write_rds(bits_2018_lcc, "datos_salida/bits_2018_lcc.rdata") # para la estimación se requiere la información del marco y diseño # leemos tamaños de diseño de muestreo marco <- read_csv("datos_salida/tamanos_2.csv") %>% dplyr::select(-p, -n_0) %>% rename(classid = clase, n_planned = n) # leemos muestra entregada a BITS y revisamos repetidos muestra_pais <- read_rds(path = "datos_salida/muestra_pais.rds") muestra_pais <- muestra_pais %>% mutate(id_muestra = 1:n()) muestra_pais$equals <- st_equals(muestra_pais) %>% map_int(first) # puntos con repetición (195) muestra_eq <- muestra_pais %>% group_by(equals) %>% mutate(n = n()) %>% filter(n > 1) st_write(muestra_eq, "datos_salida/muestra_pais_reps.shp") # sin repetición muestra_pais_unique <- muestra_pais %>% group_by(equals) %>% top_n(n = 1, id_muestra) %>% ungroup() %>% dplyr::select(-equals) # unimos puntos únicos con puntos BITS para tener variables de estratificación: # edo y classid de datos originales bits_2018_edo <- select(bits_2018_lcc, -equals) %>% st_join(muestra_pais_unique, join = st_is_within_distance, dist = 0.02, left = TRUE) # para los ponderadores necesitamos saber el tamaño por estrato bits_2018_w <- bits_2018_edo %>% left_join(marco, by = c("classid", "edo")) %>% group_by(classid, edo) %>% mutate(n_obs = n()) %>% ungroup() %>% mutate(estrato = paste0(classid, "-", edo)) # revisamos planeados vs observados bits_2018_w %>% st_drop_geometry() %>% dplyr::select(classid, edo, n_planned, n_obs) %>% dplyr::distinct() %>% mutate(diff = n_planned - n_obs) %>% filter(diff > 0) %>% arrange(-diff) glimpse(bits_2018_w) write_rds(bits_2018_w, path = "datos_salida/bits_2018_weights.rdata") # creamos datos con diseño para estimación con survey y srvyr library(srvyr) bits_design <- bits_2018_w %>% as_survey_design(ids = identifier, strata = estrato, fpc = N) write_rds(bits_design, path = "datos_salida/bits_2018_design.rdata") # entrega Pedro # dos etiquetas: etiqueta pixel y etiqueta hectárea, resultan de evaluar # únicamente el pixel seleccionado y de evaluar una hectárea alrededor pedro <- read_sf("datos_salida/muestras_pedro_etiquetada/muestra300_etiq_pedro.shp") bits_2018_lcc_df <- st_drop_geometry(bits_2018_lcc) # agregamos clasificación 17 clases y etiquetas de BITS, determinamos # coincidencias bits_pedro <- pedro %>% st_drop_geometry() %>% mutate( pedro_c17 = clasifica_31_17(pedro31cl), pedro1ha_c17 = clasifica_31_17(pedro1ha), pedro_c31 = pedro31cl, pedro1ha_c31 = pedro1ha ) %>% dplyr::select(-interp1, -interp2, -interp3, -pedro31cl, -pedro1ha) %>% left_join(bits_2018_lcc_df, by = "identifier") %>% rowwise() %>% mutate( p_correcto_31_pix = (pedro_c31 == raster_c31), p_correcto_31_ha = (pedro1ha_c31 == raster_c31), p_correcto_31 = p_correcto_31_pix | p_correcto_31_ha, p_bits_correcto_31_pix = pedro_c31 %in% c(interp1_c31, interp2_c31, interp3_c31), p_bits_correcto_31_1ha = pedro1ha_c31 %in% c(interp1_c31, interp2_c31, interp3_c31), p_bits_correcto_31 = p_bits_correcto_31_pix | p_bits_correcto_31_1ha, p_correcto_17_pix = (pedro_c17 == raster_c17), p_correcto_17_ha = (pedro1ha_c17 == raster_c17), p_correcto_17 = p_correcto_17_pix | p_correcto_17_ha, p_bits_correcto_17_pix = pedro_c17 %in% c(interp1_c17, interp2_c17, interp3_c17), p_bits_correcto_17_1ha = pedro1ha_c17 %in% c(interp1_c17, interp2_c17, interp3_c17), p_bits_correcto_17 = p_bits_correcto_17_pix | p_bits_correcto_17_1ha ) %>% ungroup() write_rds(bits_pedro, path = "datos_salida/bits_pedro.rds") # usando diseño de muestreo (pob. es muestra BITS) # datos con los que se creó la muestra Pedro (no finales) bits_2018_sample <- read_sf("datos_entrada/datos_bits/Validacion_Final_Mapa-2018_BITS_190211/validacion_final_2018.shp") bits_2018_sample_n <- bits_2018_sample %>% st_drop_geometry() %>% group_by(raster_m18) %>% summarise(N = n()) bits_pedro_w <- bits_pedro %>% left_join(bits_2018_sample_n, by = "raster_m18") bits_pedro_design <- bits_pedro_w %>% as_survey_design(ids = identifier, strata = raster_m18, fpc = N) write_rds(bits_pedro_design, path = "datos_salida/bits_pedro_design.rds")

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/run_analysis.R

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jolenechen83/CleaningDataAssignment

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run_analysis.R

# Data(zip file) were downloaded and extracted into working directory. All data # were stored in the data folder. # 1. Merges the training and the test sets to create one data set. path_rf <- "./data" #Read all the files testActivity <- read.table(file.path(path_rf, "test" , "Y_test.txt" ),header = FALSE) trainActivity <- read.table(file.path(path_rf, "train", "Y_train.txt"),header = FALSE) testSubject <- read.table(file.path(path_rf, "test" , "subject_test.txt"),header = FALSE) trainSubject <- read.table(file.path(path_rf, "train", "subject_train.txt"),header = FALSE) testFeatures <- read.table(file.path(path_rf, "test" , "X_test.txt" ),header = FALSE) trainFeatures <- read.table(file.path(path_rf, "train", "X_train.txt"),header = FALSE) # merge the dataset dSubject <- rbind(trainSubject, testSubject) dActivity<- rbind(trainActivity, testActivity) dFeatures<- rbind(trainFeatures, testFeatures) names(dSubject)<-c("Subject") names(dActivity)<- c("Activity") FeaturesNames <- read.table(file.path(path_rf, "features.txt"),head=FALSE) names(dFeatures)<- FeaturesNames$V2 # Merge columns to get the data frame Data for all data dCombine <- cbind(dSubject, dActivity) Data <- cbind(dFeatures, dCombine) # 2. Extracts only the measurements on the mean and standard deviation # for each measurement. FeaturesNames2<-FeaturesNames$V2[grep("mean\$\$|std\$\$", FeaturesNames$V2)] selected<-c(as.character(FeaturesNames2), "Subject", "Activity" ) Data<-subset(Data,select=selected) write.csv(Data,file=paste( "subsetData.csv", sep="")) # 3. Uses descriptive activity names to name the activities in the data set labels <- read.table(file.path(path_rf, "activity_labels.txt"),header = FALSE) # 4. Appropriately labels the data set with descriptive variable names. # prefix t is replaced by time # prefix f is replaced by frequency # Acc is replaced by Accelerometer # Gyro is replaced by Gyroscope # Mag is replaced by Magnitude # BodyBody is replaced by Body names(Data)<-gsub("^t", "time", names(Data)) names(Data)<-gsub("^f", "frequency", names(Data)) names(Data)<-gsub("Acc", "Accelerometer", names(Data)) names(Data)<-gsub("Gyro", "Gyroscope", names(Data)) names(Data)<-gsub("Mag", "Magnitude", names(Data)) names(Data)<-gsub("BodyBody", "Body", names(Data)) # 5.From the data set in step 4, creates a second, independent # tidy data set with the average of each variable for each activity # and each subject. library(plyr) Data2<-aggregate(. ~Subject + Activity, Data, mean) Data2<-Data2[order(Data2$Subject,Data2$Activity),] write.table(Data2, file = "tidydataset.txt",row.name=FALSE)

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/01_90_expand_grid.R

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01_90_expand_grid.R

# combn produces a matrix of all combinations photos.combn <- combn(c("Bride", "Groom", "Bride's Parents", "Groom's Parents", "Bride's Siblings", "Groom's Siblings"), 3) photos.combn <- t(photos.combn) # expand.grid produces a dataframe of vector A against vector B photos.expand.grid <- expand.grid(c("Bride", "Bride's Parents", "Bride's Siblings"), c("Groom", "Groom's Parents", "Groom's Siblings")) photos.expand.grid <- expand.grid(c("Bride", "Groom"), c("Bride's Parents", "Groom's Parents"), c("Bride's Siblings", "Groom's Siblings"))

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/attic/simulation_math_util_fn_old.R

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adalisan/JOFC-MatchDetect

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simulation_math_util_fn_old.R

## functions run.mc.replicate<-function(model,p, r, q, c.val, d = p-1, pprime1 = ifelse(model=="gaussian",p+q,p+q+2), # cca arguments , signal+noise dimension pprime2 = ifelse(model=="gaussian",p+q,p+q+2), # cca arguments, signal+noise dimension Wchoice = "avg", #How to impute L pre.scaling = TRUE, #Make the measurement spaces have the same scale oos = TRUE, #embed test observations by Out-of-sampling ? alpha = NULL, n = 100, m = 100, #Number of training and test observations sim.grass=FALSE, eigen.spectrum=FALSE, old.gauss.model.param=FALSE, separability.entries.w, compare.pom.cca=TRUE, # Run PoM and CCA to compare with JOFC? oos.use.imputed, level.mcnemar=0.01, #At what alpha, should unweighted(w=0.5) and optimal w^* be compared def.w=0.5, #The null hypothesis is that power(def.w) >= power(rival.w) (by default ,def.w is the w for the unweighted case which equals 0.5) rival.w=NULL, proc.dilation=FALSE, #when investigating convergence of JOFC to PoM, should Procrustes analysis of configurations include the dilation component? assume.matched.for.oos, w.vals, #w values to use for JOFC wt.equalize, verbose=FALSE, power.comparison.test=TRUE){ print(paste("random ",runif(1))) print("run.mc.replicate") # # The followin if statement is Not really necessary, unless we change our mind about rival.w being the best in every MC replicate # and want to make rival.w a constant (preferably the best overall) if (is.null(rival.w)){ if (0.95 %in% w.vals){ rival.w=0.95 } else if (0.9 %in% w.vals){ rival.w=0.9 } else if (0.99 %in% w.vals){ rival.w=0.99 } } w.max.index <- length(w.vals) size <- seq(0, 1, 0.01) len <- length(size) power.w.star <- 0 power.mc= array(0,dim=c(w.max.index,len)) #power values for JOFC in this MC replicate power.cca.mc = array(0,dim=c(len)) #power values for CCA in this MC replicate power.pom.mc = array(0,dim=c(len)) #power values for PoM in this MC replicate power.cca.reg.mc = array(0,dim=c(len)) #power values for reg CCA in this MC replicate config.mismatch <- list(frob.norm=array(0,dim=c(w.max.index))) #Frob. norm of configuration difference #between PoM and JOFC with smallest w min.stress.for.w.val = array(0,dim=c(w.max.index)) #minimum stress value for smacof algorithm pom.stress <- 0 T0.cca.reg <- array(0,dim=c(m)) #Test statistics for regularized CCA under null TA.cca.reg <- array(0,dim=c(m)) #Test statistics for regularized CCA under alternative T0.cca <- array(0,dim=c(m)) #Test statistics for CCA under null TA.cca <- array(0,dim=c(m)) #Test statistics for CCA under alternative T0.pom <- array(0,dim=c(m)) #Test statistics for PoM under null TA.pom <- array(0,dim=c(m)) #Test statistics for JOFC under alternative T0 <- matrix(0,w.max.index,m) #Test statistics for JOFC under null TA <- matrix(0,w.max.index,m) #Test statistics for JOFC under alternative T0.best.w <- matrix(0,2,m) #Test statistics for JOFC (comparison of w=0.5 with optimal w* TA.best.w <- matrix(0,2,m) cont.table <- matrix(0,2,2) Fid.Err.Term.1 <- array(0,dim=c(w.max.index)) Fid.Err.Term.2 <- array(0,dim=c(w.max.index)) Comm.Err.Term <- array(0,dim=c(w.max.index)) sigma <- matrix(0,p,p) means <- array(0 , dim=c(w.max.index,2*d)) if (is.null(alpha)) { if (model=="gaussian"){ sigma<- diag(p) if (old.gauss.model.param) sigma <-Posdef(p,r) alpha.mc <- mvrnorm(n+(2*m), rep(0,p),sigma) } else if (model=="dirichlet"){ alpha.mc <- rdirichlet(n+2*m, rep(1,p+1)) } else stop("unknown model") } else { alpha.mc <- alpha[[mc]] } ## optimal power optim.power<- c() if (model=="gaussian"){ for (aleph in size){ crit.val.1<-qgamma(aleph,(p)/2,scale=2/r,lower.tail=FALSE) crit.val.2<-crit.val.1 type.2.err<-pgamma(crit.val.2,shape=(p)/2,scale=2*(1+1/r)) beta<- 1-type.2.err optim.power<- c(optim.power,beta) } } ## n pairs of matched points if (model=="gaussian"){ xlist <- matched_rnorm(n, p, q, c.val, r, alpha=alpha.mc[1:n, ],sigma.alpha=sigma,old.gauss.model.param=old.gauss.model.param) } else{ xlist <- matched_rdirichlet(n, p, r, q, c.val, alpha.mc[1:n, ]) } X1 <- xlist$X1 X2 <- xlist$X2 if (model=="gaussian") sigma.mc<-xlist$sigma.beta D1 <- dist(X1) D2 <- dist(X2) if (verbose) print("random matched pairs generated\n") #prescaling if (pre.scaling) { s <- lm(as.vector(D1) ~ as.vector(D2) + 0)$coefficients } else { s <- 1 } #m pairs of unmatched points if (model=="gaussian"){ ## test observations -- m pairs of matched and m pairs of unmatched ylist <- matched_rnorm(m, p, q, c.val, r, alpha=alpha.mc[(n+1):(n+m), ], sigma.alpha=sigma,old.gauss.model.param=old.gauss.model.param, sigma.beta=sigma.mc) Y2A <- matched_rnorm(m, p, q, c.val, r, alpha=alpha.mc[(n+m+1):(n+m+m), ], sigma.alpha=sigma,old.gauss.model.param=old.gauss.model.param, sigma.beta=sigma.mc)$X2 } else{ ylist <- matched_rdirichlet(m, p, r, q, c.val, alpha.mc[(n+1):(n+m), ]) Y2A <- matched_rdirichlet(m, p, r, q, c.val, alpha.mc[(n+m+1):(n+m+m), ])$X2 } Y1 <- ylist$X1 Y20 <- ylist$X2 # Dissimilarity matrices for in-sample +out-of-sample D10A <- as.matrix(dist(rbind(X1, Y1))) D20 <- as.matrix(dist(rbind(X2, Y20))) * s D2A <- as.matrix(dist(rbind(X2, Y2A))) * s D1<-as.matrix(D1) D2<-as.matrix(D2) pom.config<-c() cca.config<-c() D2<-D2*s if (verbose) print("PoM and CCA embedding\n") if (compare.pom.cca) { ## ==== cca ==== #embed in-sample measurements if (oos == TRUE) { if (c.val==0){ if (model=="gaussian"){ X1t <- smacofM(D1,ndim = p,verbose=FALSE) X2t <- smacofM(D2,ndim = p,verbose=FALSE) } else{ X1t <- smacofM(D1,ndim = p+1,verbose=TRUE) X2t <- smacofM(D2,ndim = p+1,verbose=FALSE) } } else{ X1t <- smacofM(D=D1,ndim= pprime1,verbose=FALSE) X2t <- smacofM(D=D2,ndim= pprime2,verbose=FALSE) } xcca <- cancor(X1t, X2t) #project using projection vectors computed by CCA #if (profile.mode) Rprof("profile-oosMDS.out",append=TRUE) Y1t <- (oosMDS(D10A, X1t) %*% xcca$xcoef)[, 1:d] Y20t <- (oosMDS(D20, X2t) %*% xcca$ycoef)[, 1:d] Y2At <- (oosMDS(D2A, X2t) %*% xcca$ycoef)[, 1:d] #if (profile.mode) Rprof(NULL) #cca.config<-rbind(X1t,X2t) } else { if (c.val==0){ if (model=="gaussian"){ X1t <- smacofM(D10A, ndim=p,verbose=FALSE) D20A <-dist(rbind(X2, Y20, Y2A)) X2t <- smacofM(D20A, ndim=p,verbose=FALSE) } else{ X1t <- smacofM(D10A, ndim=p+1,verbose=FALSE) D20A <-dist(rbind(X2, Y20, Y2A)) X2t <- smacofM(D20A, ndim=p+1,verbose=FALSE) } } else{ if (model=="gaussian"){ pprime1 <- p+q pprime2 <- p+q } else{ pprime1 <- p+q+2 pprime2 <- p+q+2 } X1t <- smacofM(D10A, ndim=pprime1,verbose=FALSE,init=cmdscale(D10A,pprime1)) D20A <-dist(rbind(X2, Y20, Y2A)) X2t <- smacofM(D20A, ndim=pprime2,verbose=FALSE,init=cmdscale(D20A,pprime2)) } if (verbose) print("CCA embedding complete\n") center1 <- colMeans(X1t[1:n, ]) # column means of training obs center2 <- colMeans(X2t[1:n, ]) X1t <- X1t - matrix(center1, n+m, pprime1, byrow=TRUE) # column-center training only X2t <- X2t - matrix(center2, n+2*m, pprime2, byrow=TRUE) cca <- cancor(X1t[1:n, ], X2t[1:n, ]) Y1t <- (X1t[(n+1):(n+m), ] %*% cca$xcoef )[, 1:d] Y20t <- (X2t[(n+1):(n+m), ] %*% cca$ycoef)[, 1:d] Y2At <- (X2t[(n+m+1):(n+2*m), ] %*% cca$ycoef)[, 1:d] } T0.cca <- rowSums((Y1t - Y20t)^2) TA.cca <- rowSums((Y1t - Y2At)^2) power.cca.mc <- get_power(T0.cca, TA.cca, size) if (verbose) print("CCA test statistic complete\n") ##low-dimensional (regularized) CCA ## ==== cca ==== #embed in-sample measurements if (oos == TRUE) { if (c.val==0){ if (model=="gaussian"){ X1t <- smacofM(D1,ndim = floor((d+p)/2),verbose=FALSE) X2t <- smacofM(D2,ndim = floor((d+p)/2),verbose=FALSE) } else{ X1t <- smacofM(D1,ndim = floor((d+p)/2)+1,verbose=TRUE) X2t <- smacofM(D2,ndim = floor((d+p)/2)+1,verbose=FALSE) } } else{ X1t <- smacofM(D=D1,ndim= floor((d+p)/2)+1,verbose=FALSE) X2t <- smacofM(D=D2,ndim= floor((d+p)/2)+1,verbose=FALSE) } xcca <- cancor(X1t, X2t) #project using projection vectors computed by CCA #if (profile.mode) Rprof("profile-oosMDS.out",append=TRUE) Y1t <- (oosMDS(D10A, X1t) %*% xcca$xcoef)[, 1:d] Y20t <- (oosMDS(D20, X2t) %*% xcca$ycoef)[, 1:d] Y2At <- (oosMDS(D2A, X2t) %*% xcca$ycoef)[, 1:d] #if (profile.mode) Rprof(NULL) #cca.config<-rbind(X1t,X2t) } else { if (c.val==0){ if (model=="gaussian"){ X1t <- smacofM(D10A, ndim=floor((d+p)/2),verbose=FALSE) D20A <-dist(rbind(X2, Y20, Y2A)) X2t <- smacofM(D20A, ndim=floor((d+p)/2),verbose=FALSE) } else{ X1t <- smacofM(D10A, ndim= floor((d+p)/2)+1,verbose=FALSE) D20A <-dist(rbind(X2, Y20, Y2A)) X2t <- smacofM(D20A, ndim= floor((d+p)/2)+1,verbose=FALSE) } } else{ if (model=="gaussian"){ pprime1 <- p+q pprime2 <- p+q } else{ pprime1 <- p+q+2 pprime2 <- p+q+2 } X1t <- smacofM(D10A, ndim=pprime1,verbose=FALSE,init=cmdscale(D10A,pprime1)) D20A <-dist(rbind(X2, Y20, Y2A)) X2t <- smacofM(D20A, ndim=pprime2,verbose=FALSE,init=cmdscale(D20A,pprime2)) } if (verbose) print("CCA embedding complete\n") center1 <- colMeans(X1t[1:n, ]) # column means of training obs center2 <- colMeans(X2t[1:n, ]) X1t <- X1t - matrix(center1, n+m, pprime1, byrow=TRUE) # column-center training only X2t <- X2t - matrix(center2, n+2*m, pprime2, byrow=TRUE) cca <- cancor(X1t[1:n, ], X2t[1:n, ]) Y1t <- (X1t[(n+1):(n+m), ] %*% cca$xcoef )[, 1:d] Y20t <- (X2t[(n+1):(n+m), ] %*% cca$ycoef)[, 1:d] Y2At <- (X2t[(n+m+1):(n+2*m), ] %*% cca$ycoef)[, 1:d] } T0.cca.reg <- rowSums((Y1t - Y20t)^2) TA.cca.reg <- rowSums((Y1t - Y2At)^2) power.cca.reg.mc <- get_power(T0.cca.reg, TA.cca.reg, size) ## ==== pom = procrustes o mds ==== if (oos == TRUE) { #Embed in-sample X1t <- smacofM(D1, ndim=d,verbose=FALSE) X2t <- smacofM(D2, ndim=d,verbose=FALSE) if (verbose) print (colMeans(X1t)) if (verbose) print (colMeans(X2t)) # Compute Proc from in-sample embeddings proc <- MCMCpack::procrustes(X2t, X1t, dilation=proc.dilation) # Out-of sample embed and Proc Transform dissimilarities #if (profile.mode) Rprof("profile-oosMDS.out",append=TRUE) Y1t <- oosMDS(D10A, X1t) Y20t <- oosMDS(D20, X2t) %*% proc$R * proc$s Y2At <- oosMDS(D2A, X2t) %*% proc$R * proc$s #if (profile.mode) Rprof(NULL) X2tp<-X2t %*% proc$R * proc$s pom.config<-rbind(X1t,X2tp) pom.stress<- sum((as.dist(D1) - dist(X1t))^2) pom.stress<- pom.stress+ sum((as.dist(D2) - dist(X2tp))^2) if (verbose) print("PoM embedding complete\n") } else { X1t <- smacofM(D10A,ndim= d,verbose=FALSE,init=cmdscale(D10A,d)) D20A <-dist(rbind(X2, Y20, Y2A)) X2t <- smacofM(D20A,ndim= d,verbose=FALSE,init=cmdscale(D20A,d)) center1 <- colMeans(X1t[1:n, ]) center2 <- colMeans(X2t[1:n, ]) X1t <- X1t - matrix(center1, n+m, d, byrow=TRUE) # column-center training only X2t <- X2t - matrix(center2, n+2*m, d, byrow=TRUE) proc <- MCMCpack::procrustes(X2t[1:n, ], X1t[1:n, ], dilation=proc.dilation) Y1t <- X1t[(n+1):(n+m), ] Y20t <- X2t[(n+1):(n+m), ] %*% proc$R * proc$s Y2At <- X2t[(n+m+1):(n+2*m), ] %*% proc$R * proc$s } T0.pom <- rowSums((Y1t - Y20t)^2) TA.pom <- rowSums((Y1t - Y2At)^2) power.pom.mc <- get_power(T0.pom, TA.pom, size) if (verbose) print("PoM test statistic complete \n") } ## ==== jofc ==== # Impute "between-condition" dissimilarities from different objects if (Wchoice == "avg") { L <- (D1 + D2)/2 } else if (Wchoice == "sqrt") { L <- sqrt((D1^2 + D2^2)/2) } else if (Wchoice == "NA+diag(0)") { L <- matrix(NA,n,n) diag(L)<- 0 } if (oos == TRUE) { #In sample embedding # Form omnibus dissimilarity matrix M <- omnibusM(D1, D2, L) init.conf<-NULL if (compare.pom.cca) init.conf<- pom.config # Embed in-sample using different weight matrices (differentw values) X.embeds<-JOFC.Fid.Commens.Tradeoff(M,d,w.vals,separability.entries.w,init.conf=init.conf,wt.equalize=wt.equalize) Fid.Err.Term.1 <- X.embeds[[w.max.index+2]] Fid.Err.Term.2 <- X.embeds[[w.max.index+3]] Comm.Err.Term <- X.embeds[[w.max.index+4]] Fid.Err.Sum.Term.1 <- X.embeds[[w.max.index+5]] Fid.Err.Sum.Term.2 <- X.embeds[[w.max.index+6]] Comm.Err.Sum.Term <- X.embeds[[w.max.index+7]] FC.ratio <- X.embeds[[w.max.index+8]] FC.ratio.2 <- X.embeds[[w.max.index+9]] FC.ratio.3 <- X.embeds[[w.max.index+10]] print("Fid.Err.Term.1" ) print(Fid.Err.Term.1 ) print("Comm.Err.Term ") print(Comm.Err.Term ) min.stress.for.w.val <- X.embeds[[w.max.index+1]] if (verbose) print("JOFC embeddings complete\n") # # OOS Dissimilarity matrices # D.oos.1<-dist(Y1) D.oos.2.null <- dist(Y20) D.oos.2.alt <- dist(Y2A) #Imputing dissimilarity entries for OOS if (Wchoice == "avg") { L.tilde.null <- (D.oos.1 + D.oos.2.null)/2 L.tilde.alt <- (D.oos.1 + D.oos.2.alt)/2 } else if (Wchoice == "sqrt") { L.tilde.null <- sqrt((D.oos.1^2 + D.oos.2.null^2)/2) L.tilde.alt <- sqrt((D.oos.1^2 + D.oos.2.alt^2)/2) } else if (Wchoice == "NA+diag(0)") { L.tilde.null <- matrix(NA,m,m) L.tilde.alt <- matrix(NA,m,m) diag(L.tilde.null)<- 0 diag(L.tilde.alt)<- 0 } #Form OOS omnibus matrices M.oos.0<- omnibusM(D.oos.1,D.oos.2.null, L.tilde.null) M.oos.A<- omnibusM(D.oos.1,D.oos.2.alt, L.tilde.alt) for (l in 1:w.max.index){ if (verbose) print("OOS embedding for JOFC for w= \n") if (verbose) print(w.vals[l]) w.val.l <- w.vals[l] X <- X.embeds[[l]] oos.obs.flag<- c(rep(1,2*n),rep(0,2*m)) #Compute Weight matrix corresponding in-sample entries oos.Weight.mat.1<-w.val.to.W.mat(w.val.l,(2*n),separability.entries.w,wt.equalize) #Compute Weight matrix corresponding OOS entries oos.Weight.mat.2<-w.val.to.W.mat(w.val.l,(2*m),separability.entries.w,wt.equalize) # If assume.matched.for.oos is true, we assume OOS dissimilarities are matched(in reality, # they are matched for the matched pairs, but unmatched for the unmatched pairs) # If assume.matched.for.oos is true, we ignore the dissimilarities between matched/unmatched # pairs if (!assume.matched.for.oos){ oos.Weight.mat.2[1:m,m+(1:m)]<-0 oos.Weight.mat.2[m+(1:m),(1:m)]<-0 } # if (oos.use.imputed is true) we treat the dissimiilarities between in-sample and out-of-sample measurements # from different conditions like fidelity terms # otherwise they are ignored if (oos.use.imputed){ oos.Weight.mat.w <- matrix(1-w.val.l,2*n,2*m) } else{ oos.Weight.mat.w <- rbind(cbind(matrix(1-w.val.l,n,m), matrix(0,n,m) ), cbind(matrix(0,n,m),matrix(1-w.val.l,n,m)) ) } oos.Weight.mat<-omnibusM(oos.Weight.mat.1,oos.Weight.mat.2,oos.Weight.mat.w) # Since we are going to oos-embedding, set the weights of in-sample embedding of stress # We are using previous in-sample embeddings, anyway oos.Weight.mat[1:(2*n),1:(2*n)]<-0 if (verbose) print("dim(M.oos.0)") if (verbose) print(dim(M.oos.0)) if (verbose) print("dim(M.oos.A)") if (verbose) print(dim(M.oos.A)) if (verbose) print("dim(oos.Weight.mat)") if (verbose) print(dim(oos.Weight.mat)) if (verbose) print("dim(X)") if (verbose) print(dim(X)) #if (verbose) {print("oos.obs.flag") ideal.omnibus.0 <- as.matrix(dist(rbind(X1,X2,Y1,Y20))) ideal.omnibus.A <- as.matrix(dist(rbind(X1,X2,Y1,Y2A))) omnibus.oos.D.0 <- omnibusM(M,M.oos.0,ideal.omnibus.0[1:(2*n),(2*n)+(1:(2*m))]) omnibus.oos.D.A <- omnibusM(M,M.oos.A, ideal.omnibus.A[1:(2*n),(2*n)+(1:(2*m))]) oos.Weight.mat[is.na(omnibus.oos.D.0)]<-0 omnibus.oos.D.0[is.na(omnibus.oos.D.0)]<-1 omnibus.oos.D.A[is.na(omnibus.oos.D.A)]<-1 if (verbose) print("JOFC null omnibus OOS embedding \n") #if (profile.mode) Rprof("profile-oosIM.out",append=TRUE) Y.0t<-oosIM(D=omnibus.oos.D.0, X=X, init = "random", verbose = FALSE, itmax = 1000, eps = 1e-8, W = oos.Weight.mat, isWithin = oos.obs.flag, bwOos = TRUE) if (verbose) print("JOFC alternative omnibus OOS embedding \n") Y.At<-oosIM(D=omnibus.oos.D.A, X=X, init = "random", verbose = FALSE, itmax = 1000, eps = 1e-8, W = oos.Weight.mat, isWithin = oos.obs.flag, bwOos = TRUE) #if (profile.mode) Rprof(NULL) Y1t<-Y.0t[1:m,] Y2t<-Y.0t[m+(1:m),] Y1t.A<-Y.At[1:m,] Y2At<-Y.At[m+(1:m),] X2tp<-pom.config[n+(1:n),] X1t<-pom.config[(1:n),] X.0<-rbind(X1t,X2tp) X.a<-X[1:n,] X.b<-X[n+(1:n),] mean.a <- colMeans(X.a) mean.b <- colMeans(X.b) # means[l,]<- c(mean.a,mean.b) proc.pom2JOFC <- MCMCpack::procrustes(X,X.0,dilation=FALSE,translation=TRUE) #proc.pom2JOFC.a <- MCMCpack::procrustes(X.a,X1t,dilation=FALSE,translation=TRUE) #proc.pom2JOFC.b <- MCMCpack::procrustes(X.b,X2tp,dilation=FALSE,translation=TRUE) #X.c<-rbind(X.a-mean.a,X.b-mean.b) #proc.pom2JOFC.a <- MCMCpack::procrustes(X.c,X.0,dilation=FALSE,translation=TRUE) config.mismatch$frob.norm[l] <- norm (proc.pom2JOFC$X.new-X.0,'F') #config.mismatch[l,2] <- norm (proc.pom2JOFC.a$X.new-X.0,'F') #config.mismatch[l,3] <- norm (proc.pom2JOFC.b$X.new-X2tp,'F') # if (verbose) print(means[l,]) T0[l,] <- rowSums((Y1t - Y2t)^2) TA[l,] <- rowSums((Y1t.A - Y2At)^2) if (verbose) print("JOFC test statistic complete \n") power.mcnemar.l <- get_power(T0[l,],TA[l,],level.mcnemar) if (power.mcnemar.l>power.w.star){ rival.w <- w.vals[l] power.w.star <- power.mcnemar.l w.val.rival.idx <- l } } } else { M0 <- omnibusM(D10A, D20, W=(D10A+D20)/2) MA <- omnibusM(D10A, D2A, W=(D10A+D2A)/2) X0.embeds<-JOFC.Fid.Commens.Tradeoff(M0,d,w.vals,separability.entries.w,wt.equalize=wt.equalize) XA.embeds<-JOFC.Fid.Commens.Tradeoff(MA,d,w.vals,separability.entries.w,wt.equalize=wt.equalize) for (l in 1:w.max.index){ X0 <- X0.embeds[[l]] XA <- XA.embeds[[l]] T0[l,] <- rowSums((X0[(n+1):(n+m), ] - X0[(n+m+n+1):(n+m+n+m), ])^2) TA[l,] <- rowSums((XA[(n+1):(n+m), ] - XA[(n+m+n+1):(n+m+n+m), ])^2) #Not done yet #if (compare.pom.cca){ #X.0<-rbind(X1t,X2t %*% proc$R * proc$s) #proc.pom2JOFC <- MCMCpack::procrustes(X,X.0,dilation=FALSE) #config.mismatch[l] <- norm (proc.pom2JOFC$X.new-X.0,'F') #} } } # Power comparison test # In order to compare the best w^* vs w=0.5 in an unbiased way # re-run the simulation only for w= w^* and w=0.5 # compute the contingency table using those results #if (power.comparison.test){ ## n pairs of matched points if (model=="gaussian"){ xlist <- matched_rnorm(n, p, q, c.val, r, alpha=alpha.mc[1:n, ],sigma.alpha=sigma, old.gauss.model.param=old.gauss.model.param, sigma.beta=sigma.mc) } else{ xlist <- matched_rdirichlet(n, p, r, q, c.val, alpha.mc[1:n, ]) } X1 <- xlist$X1 X2 <- xlist$X2 D1 <- dist(X1) D2 <- dist(X2) if (verbose) print("random matched pairs generated\n") if (pre.scaling) { s <- lm(as.vector(D1) ~ as.vector(D2) + 0)$coefficients } else { s <- 1 } if (model=="gaussian"){ ## test observations -- m pairs of matched and m pairs of unmatched ylist <- matched_rnorm(m, p, q, c.val, r, alpha=alpha.mc[(n+1):(n+m), ],sigma.alpha=sigma, old.gauss.model.param=old.gauss.model.param,sigma.beta=sigma.mc) Y2A <- matched_rnorm(m, p, q, c.val, r, alpha=alpha.mc[(n+m+1):(n+m+m), ],sigma.alpha=sigma, old.gauss.model.param=old.gauss.model.param,sigma.beta=sigma.mc)$X2 } else{ ylist <- matched_rdirichlet(m, p, r, q, c.val, alpha.mc[(n+1):(n+m), ]) Y2A <- matched_rdirichlet(m, p, r, q, c.val, alpha.mc[(n+m+1):(n+m+m), ])$X2 } Y1 <- ylist$X1 Y20 <- ylist$X2 D10A <- as.matrix(dist(rbind(X1, Y1))) D20 <- as.matrix(dist(rbind(X2, Y20))) * s D2A <- as.matrix(dist(rbind(X2, Y2A))) * s D1<-as.matrix(D1) D2<-as.matrix(D2) D2<-D2*s ## ==== jofc ==== if (Wchoice == "avg") { L <- (D1 + D2)/2 } else if (Wchoice == "sqrt") { L <- sqrt((D1^2 + D2^2)/2) } else if (Wchoice == "NA+diag(0)") { L <- matrix(NA,n,n) diag(L)<- 0 } if (oos == TRUE) { #In sample embedding M <- omnibusM(D1, D2, L) init.conf<-NULL if (compare.pom.cca) init.conf<- pom.config # # Use only def.w=0.5 and rival.w for w.vals X.embeds.compare<-JOFC.Fid.Commens.Tradeoff(M,d,c(def.w,rival.w),separability.entries.w,init.conf=init.conf,wt.equalize=wt.equalize) if (verbose) print("JOFC embeddings complete\n") # # OOS Dissimilarity matrices # D.oos.1<-dist(Y1) D.oos.2.null <- dist(Y20) *s D.oos.2.alt <- dist(Y2A) *s if (Wchoice == "avg") { L.tilde.null <- (D.oos.1 + D.oos.2.null)/2 L.tilde.alt <- (D.oos.1 + D.oos.2.alt)/2 } else if (Wchoice == "sqrt") { L.tilde.null <- sqrt((D.oos.1^2 + D.oos.2.null^2)/2) L.tilde.alt <- sqrt((D.oos.1^2 + D.oos.2.alt^2)/2) } else if (Wchoice == "NA+diag(0)") { L.tilde.null <- matrix(NA,m,m) L.tilde.alt <- matrix(NA,m,m) diag(L.tilde.null)<- 0 diag(L.tilde.alt)<- 0 } M.oos.0<- omnibusM(D.oos.1,D.oos.2.null, L.tilde.null) M.oos.A<- omnibusM(D.oos.1,D.oos.2.alt, L.tilde.alt) for (l in 1:2){ if (verbose) print(paste(rival.w)) if (l==1){ w.val.l <- def.w } else { w.val.l <- rival.w } X<-X.embeds.compare[[l]] oos.obs.flag<- c(rep(1,2*n),rep(0,2*m)) oos.Weight.mat.1<-w.val.to.W.mat(w.val.l,(2*n),separability.entries.w,wt.equalize) oos.Weight.mat.2<-w.val.to.W.mat(w.val.l,(2*m),separability.entries.w,wt.equalize) if (!assume.matched.for.oos){ oos.Weight.mat.2[1:m,m+(1:m)]<-0 oos.Weight.mat.2[m+(1:m),(1:m)]<-0 } if (oos.use.imputed){ oos.Weight.mat.w <- matrix(1-w.val.l,2*n,2*m) } else{ oos.Weight.mat.w <- rbind(cbind(matrix(1-w.val.l,n,m), matrix(0,n,m) ), cbind(matrix(0,n,m),matrix(1-w.val.l,n,m)) ) } oos.Weight.mat<-omnibusM(oos.Weight.mat.1,oos.Weight.mat.2,oos.Weight.mat.w) oos.Weight.mat[1:(2*n),1:(2*n)]<-0 ideal.omnibus.0 <- as.matrix(dist(rbind(X1,X2,Y1,Y20))) ideal.omnibus.A <- as.matrix(dist(rbind(X1,X2,Y1,Y2A))) omnibus.oos.D.0 <- omnibusM(M,M.oos.0, ideal.omnibus.0[1:(2*n),(2*n)+(1:(2*m))]) omnibus.oos.D.A <- omnibusM(M,M.oos.A, ideal.omnibus.A[1:(2*n),(2*n)+(1:(2*m))]) oos.Weight.mat[is.na(omnibus.oos.D.0)]<-0 omnibus.oos.D.0[is.na(omnibus.oos.D.0)]<-1 omnibus.oos.D.A[is.na(omnibus.oos.D.A)]<-1 if (verbose) print("JOFC null omnibus OOS embedding \n") # if (profile.mode) Rprof("profile-oosIM.out",append=TRUE) Y.0t<-oosIM(D=omnibus.oos.D.0, X=X, init = "random", verbose = FALSE, itmax = 1000, eps = 1e-8, W = oos.Weight.mat, isWithin = oos.obs.flag, bwOos = TRUE) if (verbose) print("JOFC alternative omnibus OOS embedding \n") Y.At<-oosIM(D=omnibus.oos.D.A, X=X, init = "random", verbose = FALSE, itmax = 1000, eps = 1e-8, W = oos.Weight.mat, isWithin = oos.obs.flag, bwOos = TRUE) #if (profile.mode) Rprof(NULL) Y1t<-Y.0t[1:m,] Y2t<-Y.0t[m+(1:m),] Y1t.A<-Y.At[1:m,] Y2At<-Y.At[m+(1:m),] T0.best.w[l,] <- rowSums((Y1t - Y2t)^2) TA.best.w[l,] <- rowSums((Y1t.A - Y2At)^2) if (verbose) print("JOFC test statistic complete \n") } } w.val.def.idx <- which(w.vals==def.w) w.val.rival.idx<- which(w.vals==rival.w) crit.value<-get_crit_val(T0.best.w[1,],level.mcnemar) crit.value.2<-get_crit_val(T0.best.w[2,],level.mcnemar) if (verbose){ print("crit.values") print(crit.value) print(crit.value.2) } cont.table[1,1] <- sum(T0.best.w[1,]<=crit.value & T0.best.w[2,]<=crit.value.2) + sum(TA.best.w[1,]>crit.value & TA.best.w[2,]>crit.value.2) cont.table[1,2] <- sum(T0.best.w[1,]>crit.value & T0.best.w[2,]<=crit.value.2) + sum(TA.best.w[1,]<=crit.value & TA.best.w[2,]>crit.value.2) cont.table[2,1] <- sum(T0.best.w[1,]<=crit.value & T0.best.w[2,]>crit.value.2) + sum(TA.best.w[1,]>crit.value & TA.best.w[2,]<=crit.value.2) cont.table[2,2] <- sum(T0.best.w[1,]>crit.value & T0.best.w[2,]>crit.value.2) + sum(TA.best.w[1,]<=crit.value & TA.best.w[2,]<=crit.value.2) if (verbose) print("Cont table computed \n") if (verbose) print(cont.table) # } for (l in 1:w.max.index){ power.mc[l, ] <- get_power(T0[l,], TA[l,], size) } FidComm.Terms<- list(F1=Fid.Err.Term.1,F2=Fid.Err.Term.2,C=Comm.Err.Term) FidComm.Sum.Terms <- list(F1=Fid.Err.Sum.Term.1,F2=Fid.Err.Sum.Term.2,C=Comm.Err.Sum.Term) if (verbose) print(str(FidComm.Terms)) if (verbose) print("FC.ratio") if (verbose) print(str(FC.ratio)) if (verbose) print("FC.ratio.2") if (verbose) print(str(FC.ratio.2)) if (verbose) print("FC.ratio.3") if (verbose) print(str(FC.ratio.3)) print("end run.mc.replicate") list(power.mc=power.mc,power.cmp=list(cca = power.cca.mc,pom = power.pom.mc,cca.reg =power.cca.reg.mc), cont.tables=cont.table, config.dist= config.mismatch, min.stress=c(min.stress.for.w.val,pom.stress),means=means,FidComm.Terms=FidComm.Terms, FidComm.Sum.Terms = FidComm.Sum.Terms,F.to.C.ratio = FC.ratio, wtF.to.C.ratio=FC.ratio.2, F.bar.to.C.bar.ratio= FC.ratio.3,optim.power=optim.power ) } w.val.to.W.mat<-function(w,n,sep.err.w,wt.equalize){ Weight.Mat<-matrix(1-w,n,n) num.pt.pairs<- n/2 commens.entries <- cbind(1:num.pt.pairs,num.pt.pairs+(1:num.pt.pairs)) commens.entries <- rbind(commens.entries,cbind(num.pt.pairs+1:num.pt.pairs,1:num.pt.pairs)) correction.factor <- 1 if (sep.err.w==FALSE){ Weight.Mat[1:num.pt.pairs,][,num.pt.pairs+(1:num.pt.pairs)]<- 0 Weight.Mat[num.pt.pairs+(1:num.pt.pairs),][,(1:num.pt.pairs)]<- 0 correction.factor<-(1/2)*(n-2) } else {correction.factor<-(n-2) } if (wt.equalize==FALSE) correction.factor <- 1 diag(Weight.Mat)<-0 normalized.w<- w*correction.factor weights.sum <- normalized.w+(1-w) Weight.Mat[commens.entries]<-normalized.w Weight.Mat <- Weight.Mat / weights.sum return(Weight.Mat) } #JOFC.data.test.stats() JOFC.Fid.Commens.Tradeoff <-function(D,ndimens,w.vals,sep.err.w,init.conf,wt.equalize){ # if (profile.mode) Rprof("JOFC.FC.out",append=TRUE) n<- nrow(D) smacof.embed<-list() stress.vec<-c() comm.sum.vec<-c() fid1.sum.vec<-c() fid2.sum.vec<-c() comm.vec<-c() fid1.vec<-c() fid2.vec<-c() half.n<- n/2 for (w in w.vals){ Weight.Mat<-w.val.to.W.mat(w,n,sep.err.w,wt.equalize) Weight.Mat[is.na(D)]<-0 D[is.na(D)] <-1 new.embed <- smacofM(D,ndimens , W=Weight.Mat , init = init.conf, verbose = FALSE, itmax = 1000, eps = 1e-6) smacof.embed<-c(smacof.embed,list(new.embed )) stress.mat <- (as.dist(D) - dist(new.embed))^2 comm.term <- 0 fid.term.1 <-0 fid.term.2 <-0 for (i in 1:(half.n-1)) { comm.term <- comm.term + (stress.mat [n*(i-1) - i*(i-1)/2 + half.n]) for (j in (i+1):half.n) { fid.term.1 <- fid.term.1 + (stress.mat [n*(i-1) - i*(i-1)/2 + j-i]) } } i <- half.n comm.term <- comm.term + (stress.mat [n*(i-1) - i*(i-1)/2 + half.n]) for (i in (half.n+1):(n-1)) { for (j in (i+1):n) { fid.term.2 <- fid.term.2 + (stress.mat [n*(i-1) - i*(i-1)/2 + j-i]) } } fid1.sum.vec <- c(fid1.sum.vec,fid.term.1) fid2.sum.vec <- c(fid2.sum.vec,fid.term.2) comm.sum.vec <- c(comm.sum.vec,comm.term) stress.mat<-as.dist(Weight.Mat) * stress.mat stress <- sum(stress.mat) stress.vec<-c(stress.vec,stress) num.fid.terms<-half.n*(half.n-1)/2 fid1.vec <- c(fid1.vec,fid.term.1/num.fid.terms) fid2.vec <- c(fid2.vec,fid.term.2/num.fid.terms) comm.vec <- c(comm.vec,comm.term/half.n) } FC.ratio <- (fid1.sum.vec+fid2.sum.vec)/comm.sum.vec FC.ratio.2 <- ((1-w.vals)/w.vals)*(fid1.sum.vec+fid2.sum.vec)/comm.sum.vec FC.ratio.3 <- (fid1.vec + fid2.vec) / comm.vec smacof.embed<-c(smacof.embed,list(stress.vec),list(fid1.vec),list(fid2.vec),list(comm.vec) , list(fid1.sum.vec),list(fid2.sum.vec),list(comm.sum.vec),list(FC.ratio),list(FC.ratio.2),list(FC.ratio.3)) #print("length(smacof.embed)") #print(length(smacof.embed)) #print(sum(smacof.embed[[1]]-smacof.embed[[2]])) #print(sum(smacof.embed[[1]]-smacof.embed[[length(w.vals)]])) # if (profile.mode) Rprof(NULL) return(smacof.embed) } matched_rnorm_old_form<- function(n, p, q, c, r, alpha,sigma.alpha) { ## Return n pairs of matched Normal distributed random vectors, given by ## X_{ik} ~ (1-c) Norm(alpha[i] ,I) + c Norm(0,SIGMA+I), i = 1, ..., n; k = 1, 2, ## where alpha[i] gaussian distributed common means, Norm(0,SIGMA+I)is gaussian ## noise on R^q signal1 <- matrix(0, n, p) signal2 <- matrix(0, n, p) sigma.beta<- diag(p) sigma.eta <- diag(p) for (i in 1:n) { signal1[i, ] <- mvrnorm(1, alpha[i,],sigma.beta) signal2[i, ] <- mvrnorm(1, alpha[i,],sigma.eta) } sigma.X <-sigma.alpha+sigma.beta sigma.Y <-sigma.alpha+sigma.eta # eig.1 <- eigen(sigma.X) # eig.2 <- eigen(sigma.Y) noise.sigma.1<- Posdef(q, max(eigen(sigma.alpha, symmetric=TRUE, only.values = TRUE)$values)) noise.sigma.2<- noise.sigma.1 noise1 <- mvrnorm(n, rep(0,q), noise.sigma.1) noise2 <- mvrnorm(n, rep(0,q), noise.sigma.2) if (c == 0) { return(list(X1=signal1, X2=signal2)) } else { return(list(X1=cbind((1-c)*signal1, c*noise1), X2=cbind((1-c)*signal2, c*noise2))) } } matched_rnorm<- function(n, p, q, c, r, alpha,sigma.alpha,old.gauss.model.param,sigma.beta=NULL) { if (old.gauss.model.param) return (matched_rnorm_old_form(n,p,q,c,r,alpha,sigma.alpha)) ## Return n pairs of matched Normal distributed random vectors, given by ## X_{ik} ~ (1-c) Norm(alpha[i] ,I/r) + c Norm(0,(1+1/r)I), i = 1, ..., n; k = 1, 2, ## where alpha[i] gaussian distributed common means, Norm(I(1+1/r)) is gaussian ## noise on R^q signal1 <- matrix(0, n, p) signal2 <- matrix(0, n, p) if (is.null(sigma.beta)) sigma.beta<- Posdef(p,1/r) sigma.eta <- sigma.beta for (i in 1:n) { signal1[i, ] <- mvrnorm(1, alpha[i,],sigma.beta) signal2[i, ] <- mvrnorm(1, alpha[i,],sigma.eta) } noise.sigma.1<- (1+1/r)*diag(q) noise.sigma.2<- noise.sigma.1 noise1 <- matrix(mvrnorm(n, rep(0,q), noise.sigma.1),n,q) noise2 <- matrix(mvrnorm(n, rep(0,q), noise.sigma.2),n,q) if (c == 0) { return(list(X1=signal1, X2=signal2,sigma.beta=sigma.beta)) } else { return(list(X1=cbind((1-c)*signal1, c*noise1), X2=cbind((1-c)*signal2, c*noise2) ,sigma.beta=sigma.beta ) ) } } matched_rdirichlet <- function(n, p, r, q, c, alpha) { ## Return n pairs of matched Dirichlet distribtued random vectors, given by ## X_{ik} ~ (1-c) Dir(r alpha[i] + 1) + c Dir(1), i = 1, ..., n; k = 1, 2, ## where alpha are given, Dir(r alpha[i] + 1) is on Delta^p, Dir(1) is uniform ## noise on Delta^q signal1 <- matrix(0, n, p+1) signal2 <- matrix(0, n, p+1) for (i in 1:n) { signal1[i, ] <- rdirichlet(1, r*alpha[i, ]+1) signal2[i, ] <- rdirichlet(1, r*alpha[i, ]+1) } noise1 <- rdirichlet(n, rep(1, q+1)) noise2 <- rdirichlet(n, rep(1, q+1)) if (c == 0) { return(list(X1=signal1, X2=signal2)) } else { return(list(X1=cbind((1-c)*signal1, c*noise1), X2=cbind((1-c)*signal2, c*noise2))) } } get_crit_val<- function(T0,size) { n <- length(T0) T0 <- sort(T0) return(T0[round(n*(1-size))]) } get_power <- function(T0, TA, size) ## T0: values of test statistic under H0 ## TA: values of test statistic under HA { n <- length(T0) m <- length(size) T0 <- sort(T0) power <- rep(0, m) for (i in 1:m) { if (size[i] == 0) { power[i] <- 0 } else if(size[i] == 1) { power[i] <- 1 } else { power[i] <- sum(TA > T0[round(n*(1-size[i]))]) / n } } power } omnibusM <- function(D1, D2, W) { D1 <- as.matrix(D1) D2 <- as.matrix(D2) W <- as.matrix(W) rbind(cbind(D1, W), cbind(t(W), D2)) } plot.MC.evalues.with.CI<-function(evalues.mc,plot.title,plot.col,conf.int=TRUE,add=FALSE){ num.sims<-dim(evalues.mc)[1] evalue.count <- dim(evalues.mc)[2] fp.points <- 1:evalue.count num.plot.points <-evalue.count y.points<-colMeans(evalues.mc,na.rm=TRUE) var.y.points <-rep (0,num.plot.points) valid.sample.count <- rep (0,num.plot.points) for (i in 1:num.sims){ err.points <- evalues.mc[i,]-y.points err.points <- err.points^2 for (j in 1:num.plot.points){ if (!is.na(evalues.mc[i,j])){ var.y.points[j] <- var.y.points[j] + err.points[j] valid.sample.count[j] <- valid.sample.count[j] + 1 } } } var.y.points <- var.y.points/valid.sample.count std.y.points <- 2*sqrt(var.y.points) ucl <- y.points+std.y.points lcl <- y.points-std.y.points if (add){ lines(x=fp.points,y= y.points,main=plot.title, xlab="Eigenvalues",col=plot.col,xlim=c(0,1),ylim=c(0,1),lwd=2.5) } else{ plot(x=fp.points,y= y.points,main=plot.title, xlab="Eigenvalues",ylab="",col=plot.col,xlim=c(0,1),ylim=c(0,1),type='l',lwd=2.5) } if (conf.int){ arrows(fp.points,ucl,fp.points,lcl,length=.05,angle=90,code=3, lty=3,col=plot.col) } par(lty=3) abline(0,1,col="blue") par(lty=1) } plot.ROC.with.CI<-function(plot.roc.points,plot.title,plot.col,conf.int=TRUE,add=FALSE,fp.points=seq(0,1,0.01), ispowercurve=TRUE,linewd=2.5,xlim=1,ylim=1){ num.sims<-dim(plot.roc.points)[1] fp.points <- fp.points[fp.points<=xlim] num.x.pts <- length(fp.points) plot.roc.points <- plot.roc.points[,1:num.x.pts] y.points<-colMeans(plot.roc.points,na.rm=TRUE) var.y.points <-rep (0,length(fp.points)) var.y.points <- colVars(plot.roc.points,na.rm=TRUE) std.y.points <- 2*sqrt(var.y.points) ucl <- y.points+std.y.points lcl <- y.points-std.y.points #if (is.finite(max(y.points))){ # if (max(ucl) < ylim) # ylim <- max(y.points) #} if (add){ lines(x=fp.points,y= y.points,main=plot.title, xlab=expression(alpha),ylab=expression(beta),col=plot.col,xlim=c(0,xlim),ylim=c(0,1),lwd=linewd) } else{ plot(x=fp.points,y= y.points,main=plot.title, xlab=expression(alpha),ylab=expression(beta),col=plot.col,xlim=c(0,xlim),ylim=c(0,1),type='l',lwd=linewd) } if (conf.int){ arrows(fp.points,ucl,fp.points,lcl,length=.05,angle=90,code=3, lty=3,col=plot.col) } par(lty=3) abline(0,1,col="blue") par(lty=1) } plot.graph.with.CI<-function(plot.roc.points,plot.title,plot.col,conf.int=TRUE,add=FALSE,fp.points=seq(0,1,0.01),customx.labels=NULL,customy.labels=NULL,ispowercurve=TRUE){ standardx.axis <- FALSE standardy.axis <- FALSE if (is.null(customx.labels)) standardx.axis<-TRUE if (is.null(customy.labels)) standardy.axis<-TRUE num.sims<-dim(plot.roc.points)[1] y.points<-colMeans(plot.roc.points,na.rm=TRUE) var.y.points <-rep (0,length(fp.points)) var.y.points <- colVars(plot.roc.points,na.rm=TRUE) std.y.points <- 2*sqrt(var.y.points) ucl <- y.points+std.y.points lcl <- y.points-std.y.points if (add){ lines(x=fp.points,y= y.points,main=plot.title, col=plot.col,xaxt=ifelse(standardx.axis,"s","n"), yaxt=ifelse(standardy.axis,"s","n"), lwd=2.5,xlab="",ylab="") } else{ plot(x=fp.points,y= y.points,main=plot.title,xaxt=ifelse(standardx.axis,"s","n"), yaxt=ifelse(standardy.axis,"s","n"), col=plot.col,type='l',lwd=2.5,xlab="",ylab="") } if (!standardx.axis) axis(1, at=fp.points,labels=customx.labels) if (!standardy.axis) axis(2, at=y.points,labels=customy.labels) if (conf.int){ arrows(fp.points,ucl,fp.points,lcl,length=.05,angle=90,code=3, lty=3,col=plot.col) } par(lty=1) } get_epsilon_c <- function(X, Y) ## Return commensurability error { sum((X - Y)^2) / nrow(X) } get_epsilon_f <- function(D, DX) ## Return fidelity error { mean((as.dist(D) - as.dist(DX))^2) } get_epsilon <- function(D1, D1X, D2, D2X, X1t, X2t) { c(get_epsilon_f(D1, D1X), get_epsilon_f(D2, D2X), get_epsilon_c(X1t, X2t)) } get_power <- function(T0, TA, size) ## T0: values of test statistic under H0 ## TA: values of test statistic under HA { n <- length(T0) m <- length(size) T0 <- sort(T0) power <- rep(0, m) for (i in 1:m) { if (size[i] == 0) { power[i] <- 0 } else if(size[i] == 1) { power[i] <- 1 } else { power[i] <- sum(TA > T0[round(n*(1-size[i]))]) / n } } power } weight <- function(n, c = 1) ## Create the weight matrix for W=diag(0)+NA { rbind(cbind(matrix(1,n,n), c*diag(n)), cbind(c*diag(n), matrix(0,n,n))) } grassmannian <- function(Q1, Q2) { ## Q1 and Q2 are two pxd projection matrices svd(Q1 - Q2)$d[1] } ## theta <- acos(svd(t(Q1)%*%Q2)$d) ## ## then geodesic distance is sqrt(sum(theta^2)) (and there are a ## boatload of other distances computable from theta). geo_dist <- function(Q1, Q2) { theta <- acos(svd(t(Q1) %*% Q2)$d) sqrt(sum(theta^2)) ## sum(theta^2) } Haursdorf_dist <- function(Q1,Q2) { sin(geo_dist(Q1,Q2)/2) } Posdef <- function (dim, maxev = 1) ## Generating a random positive-definite matrix ## Eigenvalues are generated from uniform(0, maxev) { ev = runif(dim-1, 0, maxev) ev <- c(ev,maxev) Z <- matrix(ncol=dim, rnorm(dim^2)) decomp <- qr(Z) Q <- qr.Q(decomp) R <- qr.R(decomp) d <- diag(R) ph <- d / abs(d) O <- Q %*% diag(ph) Z <- t(O) %*% diag(ev) %*% O return(Z) } ## polarity <- function(X, Xstar) ## ## Change the signs of each column of X to best match Xstar ## ## in the sum of squared difference sense ## ## ## ## Return a ncol(X) by ncol(X) diagonal matrix Q, with {1, -1} ## ## entries, such that ||XQ - Xstar|| is minimized ## { ## d <- ncol(X) ## ss <- rep(0, 2^d) ## diag.entries <- as.matrix(expand.grid(lapply(1:d, function(i) c(1,-1)))) ## for (i in 1:2^d) { ## ss[i] <- sum((X %*% diag(diag.entries[i, ]) - Xstar)^2) ## } ## diag(diag.entries[which.min(ss), ]) ## } polarity <- function(X, Xstar) ## Change the signs of each column of X to best match Xstar ## in the sum of squared difference sense ## ## Return a ncol(X) by ncol(X) diagonal matrix Q, with {1, -1} ## entries, such that ||XQ - Xstar|| is minimized { d <- ncol(X) diagv <- rep(1L, d) for (i in 1:d) { if (sum((X[, i] - Xstar[, i])^2) > sum((-X[, i] - Xstar[, i])^2)) diagv[i] <- -1L } diag(diagv) } impute_dYX <- function(D, W, dYX, k=3) { ## get imputed distances between oos objects Y and within-sample ## objects X of different conditions. That is, d(Y2, X1) or d(Y1, X2) ## ## D = dist(X1) or dist(X2) ## W = imputed distance between X1 and X2 ## dYX = dist(Y1, X1) or dist(Y2, X2) D <- as.matrix(D) W <- as.matrix(W) n <- ncol(D) ord <- t(apply(dYX, 1, function(dyx) order(dyx, rnorm(n))))[, 1:k] imputed.dYX <- t(apply(ord, 1, function(ii) colMeans(W[ii, ]))) imputed.dYX } generateX <- function(alpha, r, q, c) { ## Dir(r alpha_i + 1) is on Delta^p, p=ncol(alpha) ## Consider uniform noise Dir(1) on Delta^q ## X_{ik} ~ (1-c) Dir(r alpha_i + 1) + c Dir(1) n <- nrow(alpha) p <- ncol(alpha) Signal1 <- matrix(0, n, p) Signal2 <- matrix(0, n, p) for (i in 1:n) { Signal1[i, ] <- rdirichlet(1, r*alpha[i, ]+1) Signal2[i, ] <- rdirichlet(1, r*alpha[i, ]+1) } Noise1 <- rdirichlet(n, rep(1, q+1)) Noise2 <- rdirichlet(n, rep(1, q+1)) if (c == 0) { return(list(X1=Signal2, X2=Signal2)) } else { return(list(X1=cbind((1-c)*Signal1, c*Noise1), X2=cbind((1-c)*Signal2, c*Noise2))) } } colVars <- function(x, na.rm=FALSE, dims=1, unbiased=TRUE, SumSquares=FALSE, twopass=FALSE) { if (SumSquares) return(colSums(x^2, na.rm, dims)) N <- colSums(!is.na(x), FALSE, dims) Nm1 <- if (unbiased) N-1 else N if (twopass) {x <- if (dims==length(dim(x))) x - mean(x, na.rm=na.rm) else sweep(x, (dims+1):length(dim(x)), colMeans(x,na.rm,dims))} (colSums(x^2, na.rm, dims) - colSums(x, na.rm, dims)^2/N) / Nm1 } ThreewayMDS.Embed.Hyp.Test <- function(D1,D2,X1,X2,Y1,Y20,Y2A,model,ndim){ Threeway.Embed<- smacofIndDiff(delta=list(D1,D2), ndim = d, weightmat = NULL, init = NULL, metric = TRUE, ties = "primary", constraint = model, verbose = FALSE, modulus = 1, itmax = 1000, eps = 1e-6) X1t <- Threeway.Embed$conf[[1]] X2t <- Threeway.Embed$conf[[2]] Y1t <- oosMDS(dist(rbind(X1, Y1)), X1t)%*% (solve(Threeway.Embed$cweights[[1]])) # Check row column matchup Y20t <- oosMDS(dist(rbind(X2, Y20)), X2t) %*% (solve(Threeway.Embed$cweights[[2]])) Y2At <- oosMDS(dist(rbind(X2, Y2A)), X2t) %*% (solve(Threeway.Embed$cweights[[2]])) T0 <- rowSums((Y1t - Y20t)^2) TA <- rowSums((Y1t - Y2At)^2) return (list(T0=T0,TA=TA)) } sign.test.cont.table <-function(cont.table.list){ suc<-0 num.trials <-0 for (tab in cont.table.list){ if (tab[1,2]>tab[2,1]){ suc <- suc + 1 } num.trials <- num.trials+1 } fail<-length(cont.table.list)-suc return(binom.test(suc,num.trials,p=0.5,alternative="greater")) } sign.rank.sum.test.cont.table <-function(cont.table.list){ num.trials <- length(cont.table.list) x<-rep(0,num.trials) y<-rep(0,num.trials) i<-1 for (tab in cont.table.list){ x[i]<-tab[1,2] y[i]<-tab[2,1] i <- i + 1 } return(wilcox.test(x,y,paired=TRUE,alternative="greater")) } binom.out <-function(cont.table.list){ num.trials <- length(cont.table.list) binomial.v<-rep(0,num.trials) i<-1 for (tab in cont.table.list){ if (tab[1,2]>tab[2,1]){ binomial.v[i] <- 1 } i<- i+1 } return (binomial.v) } omnibusM.inoos <- function(D1, D2, W) { D1 <- as.matrix(D1) D2 <- as.matrix(D2) W <- as.matrix(W) rbind(cbind(D1, W), cbind(W, D2)) }

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/man/Kmeans.Anal.Rd

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flajole/MApckg

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refs/heads/master

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Kmeans.Anal.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/Cluster_Kmeans.R \name{Kmeans.Anal} \alias{Kmeans.Anal} \title{K-means analysis} \usage{ Kmeans.Anal(dataSet, analSet, clust.num = 3) } \arguments{ \item{dataSet}{List, data set object generated by \code{\link[MSdata]{MS_to_MA}} function.} \item{analSet}{List, containing the results of statistical analysis (can be just an empty list).} \item{clust.num}{The cluster number.} } \value{ Native \code{analSet} with one added \code{$kmeans} element containing standard \code{\link[stats]{kmeans}} output. } \description{ Perform K-means analysis. Uses \code{\link[stats]{kmeans}} function. } \seealso{ \code{\link{PlotKmeans}} for plotting functions }

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/R/gaussian.R

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robertdouglasmorrison/DuffyTools

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gaussian.R

# gaussian.R - a set of probability density functions for peak fitting gaussian <- function( x, center=0, width=1, height=NULL, floor=0) { # adapted from Earl F. Glynn; Stowers Institute for Medical Research, 2007 twoVar <- 2 * width * width sqrt2piVar <- sqrt( pi * twoVar) y <- exp( -( x - center)^2 / twoVar) / sqrt2piVar # by default, the height is such that the curve has unit volume if ( ! is.null (height)) { scalefactor <- sqrt2piVar y <- y * scalefactor * height } y + floor } fit.gaussian <- function( x, y, start.center=NULL, start.width=NULL, start.height=NULL, start.floor=NULL, fit.floor=FALSE) { # try to find the best gaussian to fit the given data # make some rough estimates from the values of Y who.max <- which.max(y) if ( is.null( start.center)) start.center <- x[ who.max] if ( is.null( start.height)) start.height <- y[ who.max] if ( is.null( start.width)) start.width <- sum( y > (start.height/2)) / 2 # call the Nonlinear Least Squares, either fitting the floor too or not controlList <- nls.control( maxiter=100, minFactor=1/512, warnOnly=TRUE) if ( ! fit.floor) { starts <- list( "center"=start.center, "width"=start.width, "height"=start.height) nlsAns <- try( nls( y ~ gaussian( x, center, width, height), start=starts, control=controlList)) } else { if (is.null( start.floor)) start.floor <- quantile( y, seq(0,1,0.1))[2] starts <- list( "center"=start.center, "width"=start.width, "height"=start.height, "floor"=start.floor) nlsAns <- try( nls( y ~ gaussian( x, center, width, height, floor), start=starts, control=controlList)) } # package up the results to pass back if ( class( nlsAns) == "try-error") { centerAns <- start.center widthAns <- start.width heightAns <- start.height floorAns <- if ( fit.floor) start.floor else 0 yAns <- gaussian( x, centerAns, widthAns, heightAns, floorAns) residualAns <- y - yAns } else { coefs <-coef(nlsAns) centerAns <- coefs[1] widthAns <- coefs[2] heightAns <- coefs[3] floorAns <- if ( fit.floor) coefs[4] else 0 yAns <- fitted( nlsAns) residualAns <- residuals( nlsAns) } # always report the SD as a possitive value widthAns <- abs( widthAns) out <- list( "center"=centerAns, "width"=widthAns, "height"=heightAns, "y"=yAns, "residual"=residualAns) if ( fit.floor) { out <- c( out, "floor"=floorAns) } return( out) } lorentzian <- function( x, center=0, width=1, height=NULL, floor=0) { widSq <- width * width y <- width / ( pi * (( x - center)^2 + widSq)) # by default, the height is such that the curve has unit volume if ( ! is.null (height)) { scalefactor <- pi * width y <- y * scalefactor * height } y + floor } fit.lorentzian <- function( x, y, start.center=NULL, start.width=NULL, start.height=NULL, start.floor=NULL, fit.floor=FALSE) { # try to find the best lorentzian to fit the given data # make some rough estimates from the values of Y who.max <- which.max(y) if ( is.null( start.center)) start.center <- x[ who.max] if ( is.null( start.height)) start.height <- y[ who.max] if ( is.null( start.width)) start.width <- sum( y > (start.height/2)) / 2 # call the Nonlinear Least Squares, either fitting the floor too or not controlList <- nls.control( maxiter=100, minFactor=1/512, warnOnly=TRUE) if ( ! fit.floor) { starts <- list( "center"=start.center, "width"=start.width, "height"=start.height) nlsAns <- try( nls( y ~ lorentzian( x, center, width, height), start=starts, control=controlList)) } else { if (is.null( start.floor)) start.floor <- quantile( y, seq(0,1,0.1))[2] starts <- list( "center"=start.center, "width"=start.width, "height"=start.height, "floor"=start.floor) nlsAns <- try( nls( y ~ lorentzian( x, center, width, height, floor), start=starts, control=controlList)) } # package up the results to pass back if ( class( nlsAns) == "try-error") { centerAns <- start.center widthAns <- start.width heightAns <- start.height floorAns <- if ( fit.floor) start.floor else 0 yAns <- lorentzian( x, centerAns, widthAns, heightAns, floorAns) residualAns <- y - yAns } else { coefs <-coef(nlsAns) centerAns <- coefs[1] widthAns <- coefs[2] heightAns <- coefs[3] floorAns <- if ( fit.floor) coefs[4] else 0 yAns <- fitted( nlsAns) residualAns <- residuals( nlsAns) } # always report the SD as a possitive value widthAns <- abs( widthAns) out <- list( "center"=centerAns, "width"=widthAns, "height"=heightAns, "y"=yAns, "residual"=residualAns) if ( fit.floor) { out <- c( out, "floor"=floorAns) } return( out) } gumbel <- function( x, center=0, width=1, height=NULL, floor=0) { terms <- ( x - center) / width expTerms <- exp( terms) y <- exp( terms - expTerms) / width # by default, the height is such that the curve has unit volume if ( ! is.null (height)) { scalefactor <- exp(1) * width y <- y * scalefactor * height } y + floor } fit.gumbel <- function( x, y, start.center=NULL, start.width=NULL, start.height=NULL, start.floor=NULL, fit.floor=FALSE) { # try to find the best gumbel to fit the given data # make some rough estimates from the values of Y who.max <- which.max(y) if ( is.null( start.center)) start.center <- x[ who.max] if ( is.null( start.height)) start.height <- y[ who.max] if ( is.null( start.width)) start.width <- sum( y > (start.height/2)) / 2 # call the Nonlinear Least Squares, either fitting the floor too or not controlList <- nls.control( maxiter=100, minFactor=1/512, warnOnly=TRUE) if ( ! fit.floor) { starts <- list( "center"=start.center, "width"=start.width, "height"=start.height) nlsAns <- try( nls( y ~ gumbel( x, center, width, height), start=starts, control=controlList)) } else { if (is.null( start.floor)) start.floor <- quantile( y, seq(0,1,0.1))[2] starts <- list( "center"=start.center, "width"=start.width, "height"=start.height, "floor"=start.floor) nlsAns <- try( nls( y ~ gumbel( x, center, width, height, floor), start=starts, control=controlList)) } # package up the results to pass back if ( class( nlsAns) == "try-error") { centerAns <- start.center widthAns <- start.width heightAns <- start.height floorAns <- if ( fit.floor) start.floor else 0 yAns <- gumbel( x, centerAns, widthAns, heightAns, floorAns) residualAns <- y - yAns } else { coefs <-coef(nlsAns) centerAns <- coefs[1] widthAns <- coefs[2] heightAns <- coefs[3] floorAns <- if ( fit.floor) coefs[4] else 0 yAns <- fitted( nlsAns) residualAns <- residuals( nlsAns) } # the width for a Gumbel keeps its sign! out <- list( "center"=centerAns, "width"=widthAns, "height"=heightAns, "y"=yAns, "residual"=residualAns) if ( fit.floor) { out <- c( out, "floor"=floorAns) } return( out) }

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/man/plotConcTimeSmooth.Rd

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plotConcTimeSmooth.Rd

\name{plotConcTimeSmooth} \alias{plotConcTimeSmooth} \title{Plot up to three curves representing the concentration versus time relationship, each curve representing a different flow.} \usage{ plotConcTimeSmooth(q1, q2, q3, centerDate, yearStart, yearEnd, qUnit = 2, legendLeft = 0, legendTop = 0, concMax = NA, bw = FALSE, printTitle = TRUE, printValues = FALSE, localSample = Sample, localINFO = INFO, windowY = 10, windowQ = 2, windowS = 0.5, cex.main = 1.1, lwd = 2, ...) } \arguments{ \item{q1}{numeric This is the discharge value for the first curve to be shown on the plot. It is expressed in units specified by qUnit.} \item{q2}{numeric This is the discharge value for the second curve to be shown on the plot. It is expressed in units specified by qUnit. If you don't want a second curve then the argument must be q2=NA} \item{q3}{numeric This is the discharge value for the third curve to be shown on the plot. It is expressed in units specified by qUnit. If you don't want a third curve then the argument must be q3=NA} \item{centerDate}{string This is the time of year to be used as the center date for the smoothing. It is expressed as a month and day and must be in the form "mm-dd"} \item{yearStart}{numeric This is the starting year for the graph. The first value plotted for each curve will be at the first instance of centerDate in the year designated by yearStart.} \item{yearEnd}{numeric This is the end of the sequence of values plotted on the graph.The last value will be the last instance of centerDate prior to the start of yearEnd. (Note, the number of values plotted on each curve will be yearEnd-yearStart.)} \item{qUnit}{object of qUnit class. \code{\link{qConst}}, or numeric represented the short code, or character representing the descriptive name.} \item{legendLeft}{numeric which represents the left edge of the legend, in the units shown on x-axis of graph, default is 0, will be placed within the graph but may overprint data} \item{legendTop}{numeric which represents the top edge of the legend, in the units shown on y-axis of graph, default is 0, will be placed within the graph but may overprint data} \item{concMax}{numeric value for upper limit on concentration shown on the graph, default = NA (which causes the upper limit to be set automatically, based on the data)} \item{bw}{logical if TRUE graph is produced in black and white, default is FALSE (which means it will use color)} \item{printTitle}{logical variable if TRUE title is printed, if FALSE not printed} \item{printValues}{logical variable if TRUE the results shown on the graph are also printed to the console (this can be useful for quantifying the changes seen visually in the graph), default is FALSE (not printed)} \item{localSample}{string specifying the name of the data frame that contains the Sample data, default name is Sample} \item{localINFO}{string specifying the name of the data frame that contains the metadata, default name is INFO} \item{windowY}{numeric specifying the half-window width in the time dimension, in units of years, default is 10} \item{windowQ}{numeric specifying the half-window width in the discharge dimension, units are natural log units, default is 2} \item{windowS}{numeric specifying the half-window with in the seasonal dimension, in units of years, default is 0.5} \item{cex.main}{magnification to be used for main titles relative to the current setting of cex} \item{lwd}{line width, a positive number, defaulting to 1} \item{\dots}{arbitrary functions sent to the generic plotting function. See ?par for details on possible parameters} } \description{ These plots show how the concentration-time relationship is changing over flow. } \examples{ q1 <- 10 q2 <- 25 q3 <- 75 centerDate <- "07-01" yearStart <- 2000 yearEnd <- 2010 Sample <- exSample INFO <- exINFO plotConcTimeSmooth(q1, q2, q3, centerDate, yearStart, yearEnd) } \keyword{graphics} \keyword{statistics} \keyword{water-quality}

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Mstep.R

Mstep<-function(Z=Z,X=X,sigma_IR=sigma_IR,Ir=Ir){ alpha=hatB(Z = Z,X =X ) for (j in Ir){ sigma_IR[j]=sd(X[,j]-X%*%alpha[-1,j]) } return(list(alpha=alpha,sigma_IR=sigma_IR))#en C on fera un void }

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tbl_odata.R

tbl_odata <- function(url, name, ...){ tbl <- list() structure(tbl, class=c("tbl_odata", "tbl")) } get_odata_query <- function(x, ...){ stop("Not implemented") }

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getInfo.R

`getInfo` <- function(data) { if (is.matrix(data)) { data <- as.data.frame(data) } dc.code <- unique(unlist(lapply(data, function(x) { if (is.numeric(x)) { return(x[x < 0]) } else { return(as.character(x[is.element(x, c("-", "dc"))])) } }))) if (length(dc.code) > 1) { stopError("Multiple \"don't care\" codes found.") } fuzzy.cc <- logical(ncol(data)) hastime <- logical(ncol(data)) factor <- sapply(data, is.factor) declared <- sapply(data, function(x) inherits(x, "declared")) noflevels <- getLevels(data) attributes(noflevels) <- NULL for (i in seq(ncol(data))) { cc <- data[, i] label <- attr(cc, "label", exact = TRUE) labels <- attr(cc, "labels", exact = TRUE) if (is.factor(cc)) { cc <- as.character(cc) } if (length(dc.code) > 0 && is.element(dc.code, cc)) { cc[is.element(cc, dc.code)] <- -1 } if (possibleNumeric(cc)) { cc <- asNumeric(cc) fuzzy.cc[i] <- any(na.omit(cc) %% 1 > 0) if (!fuzzy.cc[i] & !anyNA(cc)) { if (any(na.omit(cc) < 0)) { hastime[i] <- TRUE cc[cc < 0] <- max(cc) + 1 # TODO if declared...? } } if (declared[i]) { if (min(cc) != 0 && !fuzzy.cc[i]) { # the data MUST begin with 0 and MUST be incremented by 1 for each level...! cc <- recode(cc, paste(sort(labels), seq(noflevels[i]) - 1, sep = "=", collapse = ";")) } attr(cc, "label") <- label attr(cc, "labels") <- labels class(cc) <- c("declared", class(cc)) } data[[i]] <- cc } } factor <- factor & !hastime categories <- list() columns <- colnames(data) if (any(factor | declared)) { for (i in which(factor | declared)) { if (factor[i]) { categories[[columns[i]]] <- levels(data[, i]) # the data MUST begin with 0 and MUST be incremented by 1 for each level...! data[, i] <- as.numeric(data[, i]) - 1 } else { x <- data[, i] labels <- attr(x, "labels", exact = TRUE) if (is.null(labels)) { stopError("Declared columns should have labels.") } else { if (noflevels[i] == 2) { if (length(labels) == 1) { stopError("Binary crisp columns should have labels for both presence and absence.") } } else { # noflevels > 2 (impossible less than 2) if (length(labels) != noflevels[i]) { stopError("All multi-values should have declared labels.") } } } categories[[columns[i]]] <- names(sort(labels)) } } } return( list( data = data, fuzzy.cc = fuzzy.cc, hastime = hastime, factor = factor, declared = declared, categories = categories, dc.code = dc.code, noflevels = noflevels ) ) }

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tensorflow.R

#' Install TensorFlow for use with \code{MLWIC} #' #' \code{MLWIC} requires an installation of tensorflow that can be used by Python. #' You need to use this before using \code{classify} or \code{train}. If this is your first time using #' this function, you should see additional documentation at https://github.com/mikeyEcology/MLWIC . #' This function will install tensorflow on Linux machines; if you are using Windows, #' you will need to install tensorflow on your own following the directions here: #' https://www.tensorflow.org/install/install_windows. I recommend using the installation with #' Anaconda. #' #' #' @param os The operating system on your computer. Options are "Mac" or "Ubuntu". #' Specifying "Windows" will thrown an error because we cannot automatically install #' TensorFlow on Windows at this time. #' @export tensorflow <- function(os="Mac"){ ## Check for python 2.7 vpython <- system("pyv=\"$(python -V 2>&1)\" | echo $pyv | grep \"2.7\"") ## come back to this if(vpython == TRUE){ print("Python is installed. Installing homebrew, protobuf, pip, and tensorflow.") if(os == "Mac"){ system("/usr/bin/ruby -e \"$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)\"") system("brew install protobuf") system("sudo easy_install --upgrade pip") system("sudo easy_install --upgrade six") system("sudo pip install tensorflow") ## Something to validate installation, beyond this. #system("python import_tf.py") # I think I need to add: conda install tensorflow }else if(os == "Ubuntu"){ system("sudo apt-get install python-pip python-dev") # for Python 2.7 system("pip install tensorflow") #system("python import_tf.py") }else if(os == "Windows"){ print("Sorry. MLWIC cannot install tensorflow on Windows. Please visit https://www.tensorflow.org/install/install_windows for tensorflow installation instructions.") }else{ print('Specify operating system - \"Mac\", \"Windows\", or \"Ubuntu\"') } }else{ print("Python needs to be installed. Install Python 2.7, ideally Anaconda, before proceeding. MLWIC does not work with Python 3 at this time.") } }

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/descriptive analysis/WITS_clean.R

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WITS_clean.R

library(data.table) library(plyr) library(dplyr) setwd(dirname(path.expand("~"))) DataPath <- file.path(paste(getwd(),"Dropbox/Customs Evasion", sep="/")) #####CLEAN MFN DATA FOR MERGE##### load(paste(DataPath,"Raw Data/hs12_MFN.Rda", sep = "/")) EUmembers <- read.csv(paste(DataPath,"raw data/EUmembers.csv", sep = "/")) EUmembers$Reporter_ISO_N <- as.character(EUmembers$Reporter_ISO_N) hs12_MFN$Reporter_ISO_N <- as.character(hs12_MFN$Reporter_ISO_N) hs12_MFN <- merge(hs12_MFN, EUmembers, by = c("Reporter_ISO_N"), allow.cartesian = T, all.x = T) hs12_MFN$Reporter_ISO_N[!is.na(hs12_MFN$CountryCode)] <- hs12_MFN$CountryCode[!is.na(hs12_MFN$CountryCode)] hs12_MFN <- hs12_MFN[ !(Reporter_ISO_N == 191 & Country == "Croatia" & Year ==2012)| is.na(Reporter_ISO_N == 191 & Country == "Croatia" & Year ==2012)] hs12_MFN <- hs12_MFN[ !(Reporter_ISO_N == 191 & Country == "Croatia" & Year ==2013)| is.na(Reporter_ISO_N == 191 & Country == "Croatia" & Year ==2013)] hs12_MFN <- hs12_MFN[, c("Country", "DateJoined", "CountryCode") :=NULL] hs12_MFN$ProductCode[hs12_MFN$ProductCode<100000] <- paste("0", hs12_MFN$ProductCode[hs12_MFN$ProductCode<100000], sep="") hs12_MFN$Year <- as.character(hs12_MFN$Year) hs12_MFN$Reporter_ISO_N[hs12_MFN$Reporter_ISO_N==250] <- 251 #France hs12_MFN$Reporter_ISO_N[hs12_MFN$Reporter_ISO_N==380] <- 381 #Italy hs12_MFN$Reporter_ISO_N[hs12_MFN$Reporter_ISO_N==578] <- 579 #Norway hs12_MFN$Reporter_ISO_N[hs12_MFN$Reporter_ISO_N==756] <- 757 #Switzerland hs12_MFN$Reporter_ISO_N[hs12_MFN$Reporter_ISO_N==840] <- 842 #USA hs12_MFN$Reporter_ISO_N[hs12_MFN$Reporter_ISO_N==356] <- 699 #India hs12_MFN$MFN <- 1 #####MERGE VALUE DATA W MFN TARIFFS###### #REPLACE WITH QTY OR VALUE load(paste(DataPath,"Analysis Data/hs12_qty.Rda", sep = "/")) hs12_all_tariffs <- merge(hs12_MFN, hs12_qty, by.x = c("Year", "Reporter_ISO_N", "ProductCode"), by.y = c("Period", "Reporter Code", "Commodity Code")) save(hs12_all_tariffs,file = "Documents/hs2012/hs12_all_tariffs_qty.Rda") rm(hs12_qty, hs12_MFN, hs12_all_tariffs) #####MERGE EU COUNTRIES WITH PREFERENTIAL TARIFFS#### load(paste(DataPath,"raw data/hs12_pref.Rda", sep = "/")) hs12_pref$Reporter_ISO_N <- as.character(hs12_pref$Reporter_ISO_N) hs12_pref$Partner <- as.character(hs12_pref$Partner) hs12_pref$Partner <- substr(hs12_pref$Partner, regexpr("[^0]",hs12_pref$Partner), nchar(hs12_pref$Partner)) hs12_pref <- merge(hs12_pref, EUmembers, by = c("Reporter_ISO_N"), allow.cartesian = T, all.x = T) hs12_pref$Reporter_ISO_N[!is.na(hs12_pref$CountryCode)] <- hs12_pref$CountryCode[!is.na(hs12_pref$CountryCode)] hs12_pref <- hs12_pref[ !(Reporter_ISO_N == 191 & Country == "Croatia" & Year ==2012)| is.na(Reporter_ISO_N == 191 & Country == "Croatia" & Year ==2012)] hs12_pref <- hs12_pref[ !(Reporter_ISO_N == 191 & Country == "Croatia" & Year ==2013)| is.na(Reporter_ISO_N == 191 & Country == "Croatia" & Year ==2013)] hs12_pref <- hs12_pref[, c("Country", "DateJoined", "CountryCode") :=NULL] save(hs12_pref,file = "Documents/hs2012/hs12_pref.Rda") rm(hs12_pref) #####CLEAN BENEFICIARY CODES#### load(paste(DataPath,"raw data/TRAINS_preference_beneficiaries.Rda", sep = "/")) TRAINS_preference_beneficiaries$Partner <- as.character(TRAINS_preference_beneficiaries$Partner) TRAINS_preference_beneficiaries$Partner <- substr(TRAINS_preference_beneficiaries$Partner, regexpr("[^0]",TRAINS_preference_beneficiaries$Partner), nchar(TRAINS_preference_beneficiaries$Partner)) TRAINS_preference_beneficiaries <- merge(TRAINS_preference_beneficiaries, EUmembers, by.x = c("Partner"), by.y = c("Reporter_ISO_N"), allow.cartesian = T, all.x = T) TRAINS_preference_beneficiaries$Partner[!is.na(TRAINS_preference_beneficiaries$CountryCode)] <- TRAINS_preference_beneficiaries$CountryCode[!is.na(TRAINS_preference_beneficiaries$CountryCode)] TRAINS_preference_beneficiaries <- TRAINS_preference_beneficiaries[, c("Country", "DateJoined", "CountryCode") :=NULL] TRAINS_preference_beneficiaries <- rename(TRAINS_preference_beneficiaries, "Partner Code" = "Partner") #####CLEAN and SPLIT PREFERENCE TARIFFS##### #Function for cleaning each section of pref tariffs pref_split <- function(hs12_pref) { hs12_pref <- merge(hs12_pref, TRAINS_preference_beneficiaries, by.x = c("Partner"), by.y = c("RegionCode"), allow.cartesian = T, all.x = T) hs12_pref <- hs12_pref[!(`Partner Code` == 191 & PartnerName == "European Union" & Year == 2012)| is.na(`Partner Code` == 191 & PartnerName == "European Union" & Year == 2012)] hs12_pref <- hs12_pref[!(`Partner Code` == 191 & PartnerName == "European Union" & Year == 2013)| is.na(`Partner Code` == 191 & PartnerName == "European Union" & Year == 2013)] hs12_pref$ProductCode[hs12_pref$ProductCode<100000] <- paste("0", hs12_pref$ProductCode[hs12_pref$ProductCode<100000], sep="") hs12_pref$`Partner Code` <- as.character(hs12_pref$`Partner Code`) hs12_pref$Partner <- as.character(hs12_pref$Partner) hs12_pref$`Partner Code`[is.na(hs12_pref$`Partner Code`)] <- hs12_pref$Partner[is.na(hs12_pref$`Partner Code`)] hs12_pref[,Partner:=NULL] hs12_pref$Reporter_ISO_N <- as.character(hs12_pref$Reporter_ISO_N) hs12_pref$Year <- as.character(hs12_pref$Year) hs12_pref$ProductCode <- as.character(hs12_pref$ProductCode) hs12_pref$Reporter_ISO_N[hs12_pref$Reporter_ISO_N==250] <- 251 #France hs12_pref$Reporter_ISO_N[hs12_pref$Reporter_ISO_N==380] <- 381 #Italy hs12_pref$Reporter_ISO_N[hs12_pref$Reporter_ISO_N==578] <- 579 #Norway hs12_pref$Reporter_ISO_N[hs12_pref$Reporter_ISO_N==756] <- 757 #Switzerland hs12_pref$Reporter_ISO_N[hs12_pref$Reporter_ISO_N==840] <- 842 #USA hs12_pref$Reporter_ISO_N[hs12_pref$Reporter_ISO_N==356] <- 699 #India hs12_pref$`Partner Code`[hs12_pref$`Partner Code`==250] <- 251 #France hs12_pref$`Partner Code`[hs12_pref$`Partner Code`==380] <- 381 #Italy hs12_pref$`Partner Code`[hs12_pref$`Partner Code`==578] <- 579 #Norway hs12_pref$`Partner Code`[hs12_pref$`Partner Code`==756] <- 757 #Switzerland hs12_pref$`Partner Code`[hs12_pref$`Partner Code`==840] <- 842 #USA hs12_pref$`Partner Code`[hs12_pref$`Partner Code`==356] <- 699 #India hs12_pref$pref <- 1 return(hs12_pref) } #Pref 1 load("Documents/hs2012/hs12_pref.Rda") hs12_pref_1 <- hs12_pref[Reporter_ISO_N <= 251, ] rm(hs12_pref) hs12_pref_1 <- pref_split(hs12_pref_1) save(hs12_pref_1,file = "Documents/hs2012/hs12_pref_1.Rda") rm(hs12_pref_1) #Pref 2 load("Documents/hs2012/hs12_pref.Rda") hs12_pref_2 <- hs12_pref[Reporter_ISO_N > 251 & Reporter_ISO_N <= 400 | Reporter_ISO_N == 699, ] rm(hs12_pref) hs12_pref_2 <- pref_split(hs12_pref_2) save(hs12_pref_2,file = "Documents/hs2012/hs12_pref_2.Rda") rm(hs12_pref_2) #Pref 3 load("Documents/hs2012/hs12_pref.Rda") hs12_pref_3 <- hs12_pref[Reporter_ISO_N > 400 & Reporter_ISO_N <= 500, ] rm(hs12_pref) hs12_pref_3 <- pref_split(hs12_pref_3) save(hs12_pref_3,file = "Documents/hs2012/hs12_pref_3.Rda") rm(hs12_pref_3) #Pref 4 load("Documents/hs2012/hs12_pref.Rda") hs12_pref_4 <- hs12_pref[Reporter_ISO_N > 500 & Reporter_ISO_N <= 700 & Reporter_ISO_N!=699, ] rm(hs12_pref) hs12_pref_4 <- pref_split(hs12_pref_4) save(hs12_pref_4,file = "Documents/hs2012/hs12_pref_4.Rda") rm(hs12_pref_4) #Pref 5 load("Documents/hs2012/hs12_pref.Rda") hs12_pref_5 <- hs12_pref[Reporter_ISO_N > 700, ] rm(hs12_pref) hs12_pref_5 <- pref_split(hs12_pref_5) save(hs12_pref_5,file = "Documents/hs2012/hs12_pref_5.Rda") rm(hs12_pref_5, TRAINS_preference_beneficiaries) #MERGE TRADE/MFN WITH PREF##### load("Documents/hs2012/hs12_all_tariffs_qty.Rda") #Section 1 hs12_all_tariffs_1 <- hs12_all_tariffs[Reporter_ISO_N <= 251, ] load("Documents/hs2012/hs12_pref_1.Rda") hs12_all_tariffs_1 <- merge(hs12_all_tariffs_1, hs12_pref_1, by=c("Reporter_ISO_N", "Year", "ProductCode", "Partner Code"), all.x = T) rm(hs12_pref_1) #Section 2 hs12_all_tariffs_2 <- hs12_all_tariffs[Reporter_ISO_N > 251 & Reporter_ISO_N <= 400 | Reporter_ISO_N == 699, ] load("Documents/hs2012/hs12_pref_2.Rda") hs12_all_tariffs_2 <- merge(hs12_all_tariffs_2, hs12_pref_2, by=c("Reporter_ISO_N", "Year", "ProductCode", "Partner Code"), all.x = T) rm(hs12_pref_2) #Section 3 hs12_all_tariffs_3 <- hs12_all_tariffs[Reporter_ISO_N > 400 & Reporter_ISO_N <= 500, ] load("Documents/hs2012/hs12_pref_3.Rda") hs12_all_tariffs_3 <- merge(hs12_all_tariffs_3, hs12_pref_3, by=c("Reporter_ISO_N", "Year", "ProductCode", "Partner Code"), all.x = T) rm(hs12_pref_3) #Section 4 hs12_all_tariffs_4 <- hs12_all_tariffs[Reporter_ISO_N > 500 & Reporter_ISO_N <= 700 & Reporter_ISO_N != 699, ] load("Documents/hs2012/hs12_pref_4.Rda") hs12_all_tariffs_4 <- merge(hs12_all_tariffs_4, hs12_pref_4, by=c("Reporter_ISO_N", "Year", "ProductCode", "Partner Code"), all.x = T) rm(hs12_pref_4) #Section 5 hs12_all_tariffs_5 <- hs12_all_tariffs[Reporter_ISO_N > 700, ] load("Documents/hs2012/hs12_pref_5.Rda") hs12_all_tariffs_5 <- merge(hs12_all_tariffs_5, hs12_pref_5, by=c("Reporter_ISO_N", "Year", "ProductCode", "Partner Code"), all.x = T) rm(hs12_pref_5) hs12_all_tariffs <- do.call("rbind", list(hs12_all_tariffs_1, hs12_all_tariffs_2, hs12_all_tariffs_3, hs12_all_tariffs_4, hs12_all_tariffs_5)) rm(hs12_all_tariffs_1, hs12_all_tariffs_2, hs12_all_tariffs_3, hs12_all_tariffs_4, hs12_all_tariffs_5) #####CLEAN FULL TARIFF DATA##### #Remove duplicates combinations with same tariff rates hs12_all_tariffs <- hs12_all_tariffs[!duplicated(hs12_all_tariffs[, c("Reporter_ISO_N", "Year", "ProductCode", "Partner Code", "SimpleAverage.y", "Max_Rate.y")])] #If more than one avg tariff rate, keep lower of two hs12_all_tariffs <- hs12_all_tariffs %>% group_by(Reporter_ISO_N, Year, ProductCode, `Partner Code`) %>% filter(SimpleAverage.y==min(SimpleAverage.y)|is.na(SimpleAverage.y)) #Take lower max rate if still some non-unique rates hs12_all_tariffs <- hs12_all_tariffs %>% group_by(Reporter_ISO_N, Year, ProductCode, `Partner Code`) %>% filter(Max_Rate.y==min(Max_Rate.y)|is.na(Max_Rate.y)) hs12_all_tariffs <- as.data.table(hs12_all_tariffs) hs12_all_tariffs$Sum_Of_Rates.x[!is.na(hs12_all_tariffs$Sum_Of_Rates.y)] <- hs12_all_tariffs$Sum_Of_Rates.y[!is.na(hs12_all_tariffs$Sum_Of_Rates.y)] hs12_all_tariffs$Min_Rate.x[!is.na(hs12_all_tariffs$Min_Rate.y)] <- hs12_all_tariffs$Min_Rate.y[!is.na(hs12_all_tariffs$Min_Rate.y)] hs12_all_tariffs$Max_Rate.x[!is.na(hs12_all_tariffs$Max_Rate.y)] <- hs12_all_tariffs$Max_Rate.y[!is.na(hs12_all_tariffs$Max_Rate.y)] hs12_all_tariffs$SimpleAverage.x[!is.na(hs12_all_tariffs$SimpleAverage.y)] <- hs12_all_tariffs$SimpleAverage.y[!is.na(hs12_all_tariffs$SimpleAverage.y)] hs12_all_tariffs$TotalNoOfLines.x[!is.na(hs12_all_tariffs$TotalNoOfLines.y)] <- hs12_all_tariffs$TotalNoOfLines.y[!is.na(hs12_all_tariffs$TotalNoOfLines.y)] hs12_all_tariffs$Nbr_NA_Lines.x[!is.na(hs12_all_tariffs$Nbr_NA_Lines.y)] <- hs12_all_tariffs$Nbr_NA_Lines.y[!is.na(hs12_all_tariffs$Nbr_NA_Lines.y)] hs12_all_tariffs <- hs12_all_tariffs[, c("Sum_Of_Rates.y", "Min_Rate.y", "Max_Rate.y", "SimpleAverage.y", "TotalNoOfLines.y", "Nbr_NA_Lines.y", "EstCode.y", "NomenCode.y") :=NULL] colnames(hs12_all_tariffs)[grep(".x",colnames(hs12_all_tariffs))] <- gsub(".x$","",colnames(hs12_all_tariffs)[grep(".x",colnames(hs12_all_tariffs))]) save(hs12_all_tariffs,file = paste(DataPath,"Analysis Data","hs12_all_tariffs_qty.Rda", sep = "/"))

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prepData.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/general_functions.R \name{prepData} \alias{prepData} \title{Prepare imported data} \usage{ prepData(Tab) } \arguments{ \item{Tab}{Genotypes dataframe.} } \description{ Prepare imported data for processing, checks, and analysis. } \note{ This function is for internal BIGDAWG use only. }

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/Simulation/ACS_Prep.R

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conor-ryan/Imperfect_Insurance_Competition_Code

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ACS_Prep.R

rm(list=ls()) library(doBy) library(randtoolbox) library(data.table) setwd("C:/Users/Conor/Documents/Research/Imperfect_Insurance_Competition") ## Run # run = "2019-03-12" #### 2015 Subsidy Percentage Function #### subsPerc <- function(FPL){ x = FPL[!is.na(FPL)] y = rep(100,length(x)) y[x>=1&x<1.33] = 2.01 + (x-1)[x>=1&x<1.33]/(1.33-1)*(3.02-2.01) y[x>=1.33&x<1.5] = 3.02 + (x-1.33)[x>=1.33&x<1.5]/(1.5-1.33)*(4.02-3.02) y[x>=1.5&x<2] = 4.02 + (x-1.5)[x>=1.5&x<2]/(2-1.5)*(6.34-4.02) y[x>=2&x<2.5] = 6.34 + (x-2)[x>=2&x<2.5]/(2.5-2)*(8.1-6.34) y[x>=2.5&x<3] = 8.1 + (x-2.5)[x>=2.5&x<3]/(3-2.5)*(9.56-8.1) y[x>=3&x<=4] = 9.56 HHcont = rep(NA,length(FPL)) HHcont[!is.na(FPL)] = y/100 return(HHcont) } #### Read in ACS Exchange Elligible Data #### acs = read.csv("Data/2015_ACS/exchangePopulation2015.csv") acs = as.data.table(acs) setkey(acs,STATEFIP,PUMA) #Uninsured Rate with(acs,sum(uninsured*PERWT)/sum(PERWT)) acs$person = rownames(acs) #### Match PUMA to Rating Area #### areaMatch = read.csv("Intermediate_Output/Zip_RatingArea/PUMA_to_RatingArea.csv") areaMatch = as.data.table(areaMatch) acs = merge(acs,areaMatch[,c("PUMA","RatingArea","ST","STATEFIP","alloc")],by=c("STATEFIP","PUMA"),all.x=TRUE,allow.cartesian = TRUE) # Distribute weight by population prob that observation is in a given Rating Area acs[,PERWT:=PERWT*alloc] acs[,household:=as.factor(paste(household,gsub("Rating Area ","",RatingArea),sep="-"))] acs[,insured:=!all(uninsured),by="household"] acs = acs[,c("household","HHincomeFPL","HH_income","AGE","SEX","PERWT","RatingArea","ST","insured")] names(acs) = c("household","HHincomeFPL","HH_income","AGE","SEX","PERWT","AREA","ST","insured") #### Household Characteristics #### rating = read.csv("Data/AgeRating.csv") rating = as.data.table(rating) # Create truncated Age variable acs$AgeMatch = acs$AGE acs$AgeMatch[acs$AGE<14] = 14 acs$AgeMatch[acs$AGE>64] = 64 # Merge in Default and State-Specific Age Rating Curves acs = merge(acs,rating[rating$State=="Default",c("Age","Rating")],by.x="AgeMatch",by.y="Age",all.x=TRUE) acs = merge(acs,rating[rating$State!="Default",],by.x=c("ST","AgeMatch"),by.y=c("State","Age"),all.x=TRUE) acs$ageRate = acs$Rating.x acs$ageRate[!is.na(acs$Rating.y)] = acs$Rating.y[!is.na(acs$Rating.y)] # Drop redundant rating variables acs = acs[,c("Rating.x","Rating.y"):=NULL] rm(rating) # Merge in Age-specific HHS-HCC Risk Adjustment Factors HCC = read.csv("Risk_Adjustment/2014_HHS_HCC_AgeRA_Coefficients.csv") names(HCC) = c("Sex","Age","PlatHCC_Age","GoldHCC_Age","SilvHCC_Age","BronHCC_Age","CataHCC_Age") acs[,AgeMatch:= pmax(floor(AGE/5)*5,21)] acs = merge(acs,HCC,by.x=c("AgeMatch","SEX"),by.y=c("Age","Sex")) #Count Members setkey(acs,household) acs$MEMBERS=1 #Age of HoH acs[,MaxAge:=max(AGE),by="household"] acs[,AvgAge:=AGE*PERWT] #Count Children acs[,childRank:=rank(AGE,ties.method="first"),by="household"] acs$childRank[acs$AGE>18] = NA acs$ageRate[!is.na(acs$childRank)&acs$childRank>3]=0 acs$catas_cnt = as.numeric(acs$AGE<=30) acs$ageRate_avg = acs$ageRate*acs$PERWT acs[,PlatHCC_Age:=PlatHCC_Age*PERWT] acs[,GoldHCC_Age:=GoldHCC_Age*PERWT] acs[,SilvHCC_Age:=SilvHCC_Age*PERWT] acs[,BronHCC_Age:=BronHCC_Age*PERWT] acs[,CataHCC_Age:=CataHCC_Age*PERWT] acs = acs[,lapply(.SD,sum),by=c("household","HHincomeFPL","HH_income","MaxAge","AREA","ST","insured"), .SDcols = c("MEMBERS","AvgAge","ageRate","ageRate_avg","PERWT","catas_cnt", "PlatHCC_Age","GoldHCC_Age","SilvHCC_Age","BronHCC_Age","CataHCC_Age")] names(acs) = c("household","HHincomeFPL","HH_income","AGE","AREA","ST","insured", "MEMBERS","AvgAge","ageRate","ageRate_avg","PERWT","catas_cnt", "PlatHCC_Age","GoldHCC_Age","SilvHCC_Age","BronHCC_Age","CataHCC_Age") acs[,AvgAge:=AvgAge/PERWT] acs$ageRate_avg = with(acs,ageRate_avg/PERWT) acs[,PlatHCC_Age:=PlatHCC_Age/PERWT] acs[,GoldHCC_Age:=GoldHCC_Age/PERWT] acs[,SilvHCC_Age:=SilvHCC_Age/PERWT] acs[,BronHCC_Age:=BronHCC_Age/PERWT] acs[,CataHCC_Age:=CataHCC_Age/PERWT] acs$FAMILY_OR_INDIVIDUAL = "INDIVIDUAL" acs$FAMILY_OR_INDIVIDUAL[acs$MEMBERS>1] = "FAMILY" acs$catas_elig = acs$catas_cnt==acs$MEMBERS save(acs,file="Intermediate_Output/Simulated_BaseData/acs_unrest.rData") # Drop heads of household that are under 18 - 2,041 acs = acs[AGE>=18,] save(acs,file="Intermediate_Output/Simulated_BaseData/acs_prepped.rData")

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/Clusterização/Municípios de SP/municipios_sp_kmeans.R

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hernandesmjunior/R-Machine-Learning

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municipios_sp_kmeans.R

############################################################# # # CLUSTER NAO HIERARQUICO - Municípios de SP # ############################################################# # carregando as bibliotecas library(tidyverse) # pacote para manipulação de dados library(cluster) # algoritmo de cluster library(factoextra) # algoritmo de cluster e visualização library(fpc) # algoritmo de cluster e visualização library(gridExtra) # para a funcao grid arrange library(readxl) # leitura dos dados #carregar base municipio municipios <- read.table("dados/municipios.csv", sep = ";", header = T, dec = ",") rownames(municipios) <- municipios[,1] municipios <- municipios[,-1] #padronizar dados municipios.padronizado <- scale(municipios) #Agora vamos rodar de 3 a 6 centros e visualizar qual a melhor divisao municipios.k3 <- kmeans(municipios.padronizado, centers = 3) municipios.k4 <- kmeans(municipios.padronizado, centers = 4) municipios.k5 <- kmeans(municipios.padronizado, centers = 5) municipios.k6 <- kmeans(municipios.padronizado, centers = 6) #Graficos G1 <- fviz_cluster(municipios.k3, geom = "point", data = municipios.padronizado) + ggtitle("k = 3") G2 <- fviz_cluster(municipios.k4, geom = "point", data = municipios.padronizado) + ggtitle("k = 4") G3 <- fviz_cluster(municipios.k5, geom = "point", data = municipios.padronizado) + ggtitle("k = 5") G4 <- fviz_cluster(municipios.k6, geom = "point", data = municipios.padronizado) + ggtitle("k = 6") #Criar uma matriz com 4 graficos grid.arrange(G1, G2, G3, G4, nrow = 2) #VERIFICANDO ELBOW fviz_nbclust(municipios.padronizado, FUN = hcut, method = "wss") #juntando dados municipios2 <- read.table("dados/municipios.csv", sep = ";", header = T, dec = ",") municipiosfit <- data.frame(municipios.k6$cluster) #Agrupar cluster e base MunicipioFinal <- cbind(municipios2, municipiosfit)

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/man/dot-optimalClass.Rd

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cran/DynTxRegime

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dot-optimalClass.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/J_class_OptimalClass.R \name{.optimalClass} \alias{.optimalClass} \title{Perform Classification Step} \usage{ .optimalClass( moPropen, moMain, moCont, moClass, data, response, txName, iter, fSet, suppress, step ) } \arguments{ \item{moPropen}{model object(s) for propensity regression} \item{moMain}{model object(s) for main effects of outcome regression or NULL} \item{moCont}{model object(s) for contrasts of outcome regression or NULL} \item{moClass}{model object(s) for classification procedure} \item{data}{data.frame of covariates and treatment history} \item{response}{vector of responses} \item{txName}{character of column header of data containing tx} \item{iter}{maximum number of iterations for outcome regression or NULL} \item{fSet}{function defining subsets or NULL} \item{suppress}{T/F indicating screen printing preference} \item{step}{integer indicating step of algorithm} } \value{ an object of class OptimalClass } \description{ Perform Classification Step } \keyword{internal}

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poorjanos/Work-NBA-Process

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r

grouped_EDA_with_nested_tables.R

library(bupaR) library(processmapR) library(DiagrammeR) library(tidyverse) library(lubridate) library(broom) # Load dataset t_event_log_app <- read.csv(here::here("Data", "t_event_log.csv"), stringsAsFactors = FALSE) %>% mutate( TIMESTAMP = ymd_hms(TIMESTAMP), PRODUCT_LINE = as.factor(PRODUCT_LINE), SALES_CHANNEL = as.factor(SALES_CHANNEL), MEDIUM_TYPE = as.factor(MEDIUM_TYPE), AUTOUW = as.factor(case_when( .$AUTOUW == "I" ~ "Automatikus", TRUE ~ "Manuális" )) ) # Select cols to transform to eventlog with bupar::eventlog t_event_log_clean <- t_event_log_app %>% select(CASE_ID, EVENT_NAME, TIMESTAMP, ACTIVITY_INST_ID, LIFECYCLE_ID, PARTNER_NAME, PRODUCT_LINE) # Simple nestting # by_product <- t_event_log_clean %>% # group_by(PRODUCT_LINE) %>% # nest() # Data manipulation funcs to use in purrr::map trace_num <- function(df){ number_of_traces( eventlog( df, case_id = "CASE_ID", activity_id = "EVENT_NAME", activity_instance_id = "ACTIVITY_INST_ID", lifecycle_id = "LIFECYCLE_ID", timestamp = "TIMESTAMP", resource_id = "PARTNER_NAME" )) } trace_cov <- function(df){ trace_coverage( eventlog( df, case_id = "CASE_ID", activity_id = "EVENT_NAME", activity_instance_id = "ACTIVITY_INST_ID", lifecycle_id = "LIFECYCLE_ID", timestamp = "TIMESTAMP", resource_id = "PARTNER_NAME" ), level = "trace") } # Returns df 7x1 -> unnest will fail # through_time <- function(df) { # throughput_time( # eventlog( # df, # case_id = "CASE_ID", # activity_id = "EVENT_NAME", # activity_instance_id = "ACTIVITY_INST_ID", # lifecycle_id = "LIFECYCLE_ID", # timestamp = "TIMESTAMP", # resource_id = "PARTNER_NAME" # ), # level = "log", units = "day" # )[c("mean", "median", "min", "max", "st_dev", "q1", "q3")] # } # Returns df 1x7 -> unnest will work through_time <- function(df) { tidyr::spread( data = data.frame( metric = c("mean", "median", "min", "max", "st_dev", "q1", "q3"), values = throughput_time( eventlog( df, case_id = "CASE_ID", activity_id = "EVENT_NAME", activity_instance_id = "ACTIVITY_INST_ID", lifecycle_id = "LIFECYCLE_ID", timestamp = "TIMESTAMP", resource_id = "PARTNER_NAME" ), level = "log", units = "day" )[c("mean", "median", "min", "max", "st_dev", "q1", "q3")], row.names = NULL ), key = metric, value = values ) } trace_len <- function(df) { tidyr::spread( data = data.frame( metric = c("mean", "median", "min", "max", "st_dev", "q1", "q3", "iqr"), values = trace_length( eventlog( df, case_id = "CASE_ID", activity_id = "EVENT_NAME", activity_instance_id = "ACTIVITY_INST_ID", lifecycle_id = "LIFECYCLE_ID", timestamp = "TIMESTAMP", resource_id = "PARTNER_NAME" ), level = "log", units = "day" )[c("mean", "median", "min", "max", "st_dev", "q1", "q3", "iqr")], row.names = NULL ), key = metric, value = values ) } # Gen nested tables with aggregated stats in nested tables by_product <- t_event_log_clean %>% group_by(PRODUCT_LINE) %>% nest() %>% mutate( trace_number = map(data, trace_num), through_time = map(data, through_time), trace_length = map(data, trace_len) ) # Retrieve aggregates by_product %>% select(PRODUCT_LINE, trace_number) %>% unnest() by_product %>% select(PRODUCT_LINE, through_time) %>% unnest() by_product %>% select(PRODUCT_LINE, trace_length) %>% unnest()

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a5c8aa1ed795d6b34b53ec33bc39a0670e1ca29f

/code/02-dgirt/holdover/clean-cces.R

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no_license

mikedecr/dissertation

6a15f45647f1205f6f3fe901108a5917f231e473

06ec3d304821ee09a28e42ba0bc11318d774b5ed

refs/heads/main

2021-06-24T08:52:45.349588

2020-11-14T17:57:54

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r

clean-cces.R

# ---------------------------------------------------- # CCES cleaning # run on 2012 data (112th congress from 2010-2012) # (written fall or winter 2018/2019; CCES data used in June 2019) # (CCES work will be generalized into 'survey-algo') # (Hierarchical Covariates already redone a little bit) # ---------------------------------------------------- library("here") library("magrittr") library("tidyverse") library("ggplot2") library("scales") library("labelled") library("broom") library("boxr"); box_auth() # library("broom") library("latex2exp") library("rstan") rstan_options(auto_write = TRUE) options(mc.cores = min(parallel::detectCores(), 10)) # will show nothing on linstat (if no data pushed) list.files("data/cces-cdf") theme_set( ggthemes::theme_base(base_family = "Source Sans Pro", base_size = 14) + theme(plot.background = element_blank(), axis.ticks = element_line(lineend = "square"), axis.ticks.length = unit(0.25, "lines")) ) # ---------------------------------------------------- # data # ---------------------------------------------------- # meta data (come back to this) meta_cces <- data_frame(firm = "cces", date = "cdf", roper_id = as.character(NA), wt = "weight", Qs = as.character(NA)) %>% print() # CCES responses cc <- box_read(369447961216) %>% # cc <- haven::read_dta(here("data/cces-cdf/cces_common_cumulative_4.dta")) %>% as_tibble() %>% mutate_all(remove_labels) %>% print() # state fips codes fips <- box_read(377757394379) %>% # read_csv(here("data", "census", "census-state-fips.csv")) %>% as_tibble() %>% mutate_if(is.integer, as.numeric) %>% print() # district covariates # at-large coded as 1 for 'cd' variable fm_raw <- box_read(377781507739) %>% as_tibble() %>% # read_csv(here("data", "secondary", "foster-molina", # "allCongressDataPublish.csv")) %>% print() # tools for finding the Congress number 1789 - 2 + (93 * 2) # dime data dime_raw <- box_read(379360058073) %>% as_tibble() %>% print() # who's in DWDIME that has lost a primary?? # is DWDIME a route (not important rn though) dime_raw %>% filter(ran.primary == 1 | p.elec.stat %in% c("W", "L")) %>% count(!is.na(dwdime), p.elec.stat) # worth investigating more dime_raw %>% count(seat) # ---------------------------------------------------- # Recoding # ---------------------------------------------------- # ---- CCES items ----------------------- # racial_resent_special_favors # 5pt, 3 is no opinion # racial_resent_slavery # 5pt, 3 is no opinion # jobs_environment # 5 pt, 3 and 6 are NA # Recode items # merge FIPS to fix state and district numbers # recode at-large districts = 1 cc_rc <- cc %>% mutate( q_r_favors = case_when(racial_resent_special_favors %in% c(1, 2) ~ 1, racial_resent_special_favors %in% c(4, 5) ~ 0), q_r_slavery = case_when(racial_resent_slavery %in% c(4, 5) ~ 1, racial_resent_slavery %in% c(1, 2) ~ 0), q_e_jobs.env = case_when(jobs_environment %in% c(4, 5) ~ 1, jobs_environment %in% c(1, 2) ~ 0), q_e_ss.priv = case_when(soc_sec_private %in% c(1, 2) ~ 1, soc_sec_private %in% c(4, 5) ~ 0), q_s_imm.status = case_when(immig_legal_status == 1 ~ 0, immig_legal_status == 2 ~ 1), q_s_imm.guestwork = case_when(immig_guest_worker == 1 ~ 0, immig_guest_worker == 2 ~ 1), q_s_imm.fines = case_when(immig_fine_businesses == 1 ~ 1, immig_fine_businesses == 2 ~ 0), q_s_imm.patrol = case_when(immig_border_patrol == 1 ~ 1, immig_border_patrol == 2 ~ 0), q_s_imm.birthcit = case_when(immig_auto_citizenship == 1 ~ 1, immig_auto_citizenship == 2 ~ 0), q_s_imm.police = case_when(immig_police_question == 1 ~ 1, immig_police_question == 2 ~ 0), q_s_imm.wall = case_when(immig_border_wall == 1 ~ 1, immig_border_wall == 2 ~ 0), q_s_imm.public = case_when(immig_hosp_school == 1 ~ 1, immig_hosp_school == 2 ~ 0), q_r_aff.action = case_when(affirm_action %in% c(1, 2) ~ 0, affirm_action %in% c(3, 4) ~ 1), q_r_aff.action = case_when(affirm_action_06 %in% c(1, 2, 3) ~ 0, affirm_action_06 %in% c(5, 6, 7) ~ 1), q_s_gay.marry = case_when(gay_marriage_amendment == 1 ~ 1, gay_marriage_amendment == 2 ~ 0), q_s_gay.marry = case_when(gay_marriage_amendment_06 %in% c(1, 2) ~ 1, gay_marriage_amendment_06 %in% c(3, 4) ~ 0), q_s_gun.control = case_when(gun_control == 1 ~ 0, gun_control == 2 ~ 1), q_s_stem.cells = case_when(stem_cell_research == 1 ~ 0, stem_cell_research == 2 ~ 1), q_s_imm.citizenship = case_when(opinion_immig_citizenship == 1 ~ 0, opinion_immig_citizenship == 2 ~ 1), q_e_min.wage = case_when(opinion_minwage == 1 ~ 0, opinion_minwage == 2 ~ 1), q_s_partial.birth = case_when(opinion_partial_birth == 1 ~ 0, opinion_partial_birth == 2 ~ 1), q_e_stimulus = case_when(opinion_stimulus == 1 ~ 0, opinion_stimulus == 2 ~ 1), q_e_aca = case_when(opinion_affordablecareact == 1 ~ 0, opinion_affordablecareact == 2 ~ 1), q_e_cap.trade = case_when(opinion_captrade == 1 ~ 0, opinion_captrade == 2 ~ 1), q_s_dadt.repeal = case_when(opinion_dadt_repeal == 1 ~ 0, opinion_dadt_repeal == 2 ~ 1), party = case_when(pid3 == 1 ~ "D", pid3 == 2 ~ "R") ) %>% left_join(fips, by = c("state_pre" = "state_FIPS")) %>% mutate(state_n = as.numeric(as.factor(state)), dist_n = case_when(congdist_pre == 0 ~ 1, TRUE ~ congdist_pre)) %>% print() # how many responses per question? cc_rc %>% filter(year == 2012) %>% gather(key = item, value = value, starts_with("q_")) %>% group_by(year) %>% count(item, value) %>% filter(value %in% c(0, 1)) %>% print(n = nrow(.)) # identify other relevant data # party (x), state (x), district (x), covariates ( ) cc_rc %>% count(state, state_n, party, dist_n) %>% spread(key = party, value = n) count(cc_rc, congdist_pre, dist_n) %>% print(n = nrow(.)) count(cc, state_pre, congdist_pre) %>% arrange(congdist_pre) count(cc, state_pre) %>% print(n = nrow(.)) # ---- covariates from Foster-Molina ----------------------- # 112th Congress is Jan 2011--2013, most appropes for 2012 opinion data? # concepts: # income: medianIncome, gini, # under10k, over10k, over15k, over100k, over150k, over200k # ed: prcntHS, prcntBA # race: prcntWhite, prcntWhiteAll, prcntNotHisp, # presidential vote/partisanship (DIME) fm_cong <- fm_raw %>% filter(congNum == 112) %>% filter(state %in% c("DC", state.abb)) %>% select( stateDist, medianIncome, gini, under10k, over10k, over15k, over100k, over150k, over200k, prcntHS, prcntBA, prcntWhite, prcntWhiteAll, prcntNotHisp, icpsr, state, district, cd, statenm) %>% mutate( dist_num = ifelse(is.na(cd), str_split(stateDist, pattern = "[.]", simplify = TRUE)[,2], cd) %>% as.numeric(), dist_num = ifelse(dist_num == 0, 1, dist_num), state_dist = case_when(nchar(stateDist) == 4 ~ str_glue("{state}_0{dist_num}"), nchar(stateDist) == 5 ~ str_glue("{state}_{dist_num}")) %>% as.character() ) %>% rename(state.dist = stateDist, district_raw = district) %>% print() # at-large to equal 1 eventually? fm_cong %>% select(state.dist, dist_num) # figure out if at-larges are overlapping with any others? # ---- covaraites from Dime ----------------------- # district.partisanship, district.pres.vs # need to match CD and State code # - fix some district codes to match the rest of the scheme # are the covariates unique per case dime_raw %>% filter(seat == "federal:house") %>% group_by(cycle, district) %>% summarize(dist_pres = n_distinct(district.pres.vs), dist_partisan = n_distinct(district.partisanship)) %>% count(dist_pres, dist_partisan) %>% print(n = nrow(.)) # aggregate dime_cong <- dime_raw %>% filter(seat == "federal:house") %>% filter(cycle == 2012) %>% filter(state %in% c("DC", state.abb)) %>% mutate(district = case_when(nchar(district) == 1 ~ str_glue("{state}0{district}"), nchar(district) == 2 ~ str_glue("{state}{district}"), TRUE ~ district)) %>% group_by(state, district) %>% summarize(past_repvote = unique(district.pres.vs), past_kernell = unique(district.partisanship)) %>% ungroup() %>% mutate(dist_padded = str_sub(district, -2L, -1L), dist_num = as.numeric(dist_padded), state_dist = str_glue("{state}_{dist_padded}") %>% as.character()) %>% rename(statedist = district) %>% print() count(dime_cong, dist_padded, dist_num) %>% print(n = nrow(.)) # how does this handle at-larges? dime_cong %>% filter(dist_padded %in% "NA") # ---------------------------------------------------- # Merge # ---------------------------------------------------- # ---- district level ----------------------- anti_join(fm_cong, dime_cong) %>% select(state.dist, state, state_dist, dist_num) anti_join(dime_cong, fm_cong) %>% filter(is.na(dist_num) == FALSE) %>% select(statedist, state, state_dist, dist_num) d_level <- inner_join(fm_cong, dime_cong, by = c("state", "dist_num")) %>% filter(is.na(cd) == FALSE | is.na(dist_num) == FALSE) %>% # comment to turn "duplicates" on group_by(state, dist_num) %>% # comment to turn "duplicates" on sample_n(1) %>% # comment to turn "duplicates" on print() fm_cong %>% filter(state.dist == "OH.8") %>% pull(cd) dime_cong %>% filter(statedist == "OH08") %>% pull(dist_num) # duplicate districts in the district data? # (turn duplicates on to investigate) d_dupes <- d_level %>% count(state, dist_num) %>% filter(n > 1) %>% print() %$% str_glue("{state}_{dist_num}") %>% print() names(d_level) # where don't we have unique data? d_level %>% filter(str_glue("{state}_{dist_num}") %in% d_dupes) %>% group_by(state.dist) %>% nest() %>% mutate( dist = map(data, ~ .x %>% mutate_all(n_distinct) %>% select_if(function(x) 2 %in% x) ) ) %>% unnest(dist) %>% print() # some NA for cd (dropped above) # non-agreeing ICPSR numbers (dropped above) d_level %>% filter(str_glue("{state}_{dist_num}") %in% d_dupes) %>% select(state.dist, icpsr, cd, statenm, medianIncome, gini, prcntBA, prcntWhite, past_kernell) %>% pull(statenm) # ---- i_level ----------------------- i_level <- cc_rc %>% filter(year == 2012) %>% filter(party %in% c(1, 2)) %>% select(year, weight, starts_with("q_"), party, state_nm = state, state = state_abb, state_num = state_n, dist_num = dist_n) %>% print() count(i_level, state) i_level %>% count(state, dist_num) # ---- join em ----------------------- anti_join(i_level, d_level, by = c("state", "dist_num")) anti_join(d_level, i_level, by = c("state", "dist_num")) # how does this add cases? joiny <- inner_join(i_level, d_level, by = c("state", "dist_num")) %>% filter(state != "DC") %>% ungroup() %>% mutate(group_num = str_glue("{state}-{dist_num}--{party}") %>% as.factor() %>% as.numeric()) %>% print() # 435! joiny %>% count(state, dist_num) # elongate the joined data by item longo <- joiny %>% gather(key = item_name, value = response, starts_with("q_")) %>% filter(is.na(response) == FALSE) %>% mutate(item_num = as.numeric(as.factor(item_name)), response = as.factor(response), party = as.factor(party)) %>% print() # we're missing partisans from one district in NY, # ---- create binomial data ----------------------- # create Y data, weighted Y and N for each item response grouped_responses <- longo %>% group_by(state, dist_num, party, group_num, item_num) %>% count(response, wt = weight) %>% # ungroup() %>% # complete(state, dist_num, party, group_num, item_num, # fill = list(n = 0)) rename(y_j = n) %>% group_by(group_num, item_num) %>% mutate(n_j = sum(y_j), n_j = case_when(n_j < 1 ~ 1, is.na(n_j) ~ 1, TRUE ~ round(n_j)), y_j = round(y_j), y_j = case_when(y_j > n_j ~ n_j, is.na(y_j) ~ sample(c(0, 1), size = 1), TRUE ~ y_j)) %>% ungroup() %>% filter(response == 1) %>% mutate(party = as.numeric(party)) %>% print() grouped_responses %>% count(n_j) %>% arrange(n_j) grouped_responses %>% count(y_j) longo %>% group_by(state, dist_num) %>% summarize(parties = n_distinct(party)) %>% filter(parties == 1) i_level %>% filter(state == "NY" & dist_num == 6) %>% count(party) # spread out the Ns # if there are empty cells, make them n = 1 n_spread <- grouped_responses %>% select(state, dist_num, party, group_num, item_num, n_j) %>% spread(key = item_num, value = n_j) %>% mutate_at(vars(-state, -dist_num, -group_num), function(x) case_when(is.na(x) ~ 1, TRUE ~ x)) %>% inner_join(., d_level) %>% print() # spread out Ys, # empty cells are n = 1, make y = 1 with 50% probability y_spread <- grouped_responses %>% select(state, dist_num, party, group_num, item_num, y_j) %>% spread(key = item_num, value = y_j) %>% mutate_at(vars(-state, -dist_num, -group_num), function(x) case_when(is.na(x) ~ 1, TRUE ~ x)) %>% inner_join(., d_level) %>% print() # ---- matrix forms ----------------------- y_matrix <- y_spread %>% select(`1`:`12`) %>% as.matrix() %>% print() n_matrix <- n_spread %>% select(`1`:`12`) %>% as.matrix() %>% print() # there should be no 0s or NAs, no Y > N sum(n_matrix == 0, na.rm = TRUE) sum(is.na(n_matrix)) sum(is.na(y_matrix)) sum(y_matrix > n_matrix) # log income, standardize everything design_matrix <- y_spread %>% select(medianIncome, gini, prcntBA, prcntWhite, past_kernell) %>% mutate(median_income_log = log(medianIncome)) %>% select(-medianIncome) %>% mutate_all(scale) %>% print() ggplot(design_matrix, aes(x = prcntBA, y = median_income_log)) + geom_point() bayes_data <- list( Y = y_matrix, N = n_matrix, G = nrow(y_matrix), J = ncol(y_matrix), P = n_distinct(longo$party), S = n_distinct(y_spread$group_num), party = y_spread$party, geo = y_spread$group_num, X = as.matrix(design_matrix), k = ncol(design_matrix), prior_mean_party_1 = 0, prior_mean_party_2 = 0 ) bayes_data %>% lapply(length) # ---- sampler hyperparameters ----------------------- n_iterations <- 2000 n_warmup <- 1000 n_chains <- if (parallel::detectCores() < 10) parallel::detectCores() else 10 n_thin <- 1 # ---- homoskedastic model ----------------------- c_homo <- stanc(file = here("code", "dgirt", "stan", "cd", "cd-static-homo.stan")) c_het <- stanc(file = here("code", "dgirt", "stan", "cd", "cd-static-het.stan")) (compiled_homo <- stan_model(stanc_ret = c_homo, verbose = TRUE)) (compiled_het <- stan_model(stanc_ret = c_het, verbose = TRUE)) # homoskedastic cces_homo <- sampling(object = compiled_homo, data = bayes_data, iter = n_iterations, warmup = n_warmup, init = 0, chains = n_chains, thin = n_thin, pars = c("theta", "cutpoint", "discrimination", "sigma_in_g", "theta_hypermean", "scale_theta", "z_theta", "party_int", "party_coefs"), # diagnostic_file = # here(mcmc_dir, "diagnostics-static-noncenter.csv"), verbose = TRUE) saveRDS(cces_homo, here("data", "dgirt", "test-static", "mcmc", "static-homo-test.RDS"), compress = TRUE) box_ul(dir_id = 63723791862, file = here("data", "dgirt", "test-static", "mcmc", "static-homo-test.RDS")) # heteroskedastic cces_het <- sampling(object = compiled_het, data = bayes_data, iter = n_iterations, warmup = n_warmup, init = 0, chains = n_chains, thin = n_thin, pars = c("theta", "cutpoint", "discrimination", "sigma_in_g", "theta_hypermean", "scale_theta", "z_theta", "party_int", "party_coefs"), # diagnostic_file = # here(mcmc_dir, "diagnostics-static-noncenter.csv"), verbose = TRUE) beepr::beep(2) saveRDS(cces_het, here("data", "dgirt", "test-static", "mcmc", "static-het-test.RDS"), compress = TRUE) box_ul(dir_id = 63723791862, file = here("data", "dgirt", "test-static", "mcmc", "static-het-test.RDS")) # box_write(cces_het, "static-het-test.RDS", dir_id = 63723791862, compress = TRUE) cces_homo <- readRDS(here("data", "dgirt", "test-static", "mcmc", "static-homo-test.RDS")) cces_het <- readRDS(here("data", "dgirt", "test-static", "mcmc", "static-het-test.RDS")) g_params <- cces_het %>% recover_types() %>% spread_draws(theta_hypermean[g], theta[g], sigma_in_g[g]) %>% print() g_params %>% group_by(g) %>% nest() %>% sample_n(20) %>% unnest() %>% ggplot(aes(x = .iteration, y = theta)) + geom_line(aes(color = as.factor(.chain)), show.legend = FALSE) + facet_wrap(~ g) thetas <- cces_het %>% tidy(conf.int = TRUE) %>% mutate(index = parse_number(term), par = str_split(term, pattern = "\\[", simplify = TRUE)[,1]) %>% filter(par == "theta") %>% left_join(y_spread, by = c("index" = "group_code")) %>% print() # what's up with the party swapping? ggplot(thetas, aes(x = index, y = estimate)) + geom_pointrange(aes(ymin = conf.low, ymax = conf.high, color = as.factor(party))) ggplot(thetas, aes(x = rank(estimate), y = estimate)) + geom_pointrange(aes(ymin = conf.low, ymax = conf.high), shape = 21, fill = "white") + coord_flip() + labs(y = TeX("$\\theta_g$"), x = "Rank") ggplot(thetas, aes(x = medianIncome, y = estimate)) + geom_pointrange(aes(ymin = conf.low, ymax = conf.high, color = as.factor(party))) # compare thetas: is the regression good compare_thetas <- cces_het %>% tidy(conf.int = TRUE) %>% mutate(index = parse_number(term), par = str_split(term, pattern = "\\[", simplify = TRUE)[,1]) %>% inner_join(. %>% filter(par == "theta"), . %>% filter(par == "theta_hypermean"), by = "index") print()

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/data/genthat_extracted_code/geomorph/examples/gpagen.Rd.R

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surayaaramli/typeRrh

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refs/heads/master

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gpagen.Rd.R

library(geomorph) ### Name: gpagen ### Title: Generalized Procrustes analysis of points, curves, and surfaces ### Aliases: gpagen ### Keywords: analysis ### ** Examples # Example 1: fixed points only data(plethodon) Y.gpa <- gpagen(plethodon$land,PrinAxes=FALSE) summary(Y.gpa) plot(Y.gpa) # Example 2: points and semilandmarks on curves data(hummingbirds) ###Slider matrix hummingbirds$curvepts # Using Procrustes Distance for sliding Y.gpa <- gpagen(hummingbirds$land,curves=hummingbirds$curvepts) summary(Y.gpa) plot(Y.gpa) # Using bending energy for sliding Y.gpa <- gpagen(hummingbirds$land,curves=hummingbirds$curvepts,ProcD=FALSE) summary(Y.gpa) plot(Y.gpa) # Example 3: points, curves and surfaces data(scallops) # Using Procrustes Distance for sliding Y.gpa <- gpagen(A=scallops$coorddata, curves=scallops$curvslide, surfaces=scallops$surfslide) # NOTE can summarize as: summary(Y.gpa) # NOTE can plot as: plot(Y.gpa)

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/orestar_scrape/checkFilesForDlLimit.R

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stochasticTreat/hackOregonBackEnd

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refs/heads/master

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checkFilesForDlLimit.R

#!/usr/bin/Rscript #check all files for dl limit setwd("~/data_infrastructure/orestar_scrape/") source('./runScraper.R') args <- commandArgs(trailingOnly=TRUE) fromXLS=args[1] indir="./" destDir = "./transConvertedToTsv/" if(is.null(fromXLS)) fromXLS="txt" if(is.na(fromXLS)) fromXLS="txt" if(fromXLS == "xls"){ cat("\nLoading xls files from the current working directory..\n") fromXLS=TRUE }else{ cat("\nLoading tsv or txt files from the './transConvertedToTsv/' directory\n") fromXLS=FALSE } if(fromXLS){ converted = importAllXLSFiles(remEscapes=T, grepPattern="^[0-9]+(-)[0-9]+(-)[0-9]+(_)[0-9]+(-)[0-9]+(-)[0-9]+(.xls)$", remQuotes=T, forceImport=T, indir=indir, destDir=destDir) } fileDir = destDir converted = dir(fileDir) converted = converted[grepl(pattern=".txt$|.tsv$", x=converted)] converted = paste0(fileDir, converted) checkHandleDlLimit(converted=converted) tranTableName="raw_committee_transactions" dbname="hackoregon" transactionsFolder="./transConvertedToTsv/" scrapedTransactionsToDatabase(tsvFolder=transactionsFolder, tableName=tranTableName, dbname=dbname)

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gapminder analysis.R

download.file("https://raw.githubusercontent.com/swcarpentry/r-novice-gapminder/gh-pages/_episodes_rmd/data/gapminder-FiveYearData.csv", destfile = "gapminder-FiveYearData.csv") gapminder <- read.csv("gapminder-FiveYearData.csv") #head #ncol #nrow #summary #view is_africa <- gapminder@continent == "Africa"instal is_2007 <- gapfinder$year == 2007 africa_2007 <- gapminder [is_2007 & is_africa, c("country","lifeExp")] #how to make plots with ggplot2 ggplot(data=gapminder, aes(x=gdpPercap, y=lifeExp))+geom_point() ggplot(data=gapminder, aes(x=year, y=lifeExp))+geom_point() #plotting with points on top of the lines ggplot(data=gapminder, aes(x=year, y=lifeExp, by = country, color = continent))+geom_line()+geom_point() #to get the size to be as big as the gdpPercap ggplot(data=gapminder, aes(x=year, y=lifeExp, size =gdpPercap, by = country, color = continent))+geom_line()+geom_point() #to add line color line ggplot(data=gapminder, aes(x=year, y=lifeExp, size =gdpPercap, by = country, color = continent))+geom_line(color="black")+geom_point(aes(size=gdpPercap))+ facet_grid(.~continent) #note from carpentary workshop modified but code remains the same #facet_grid feater add to catalog the plot defined by continent ggsave(filename = "year_vs_lifeexp_percont.png", width = 5, height = 4, units = "in")

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/fitPacs.R \name{fitPacs} \alias{fitPacs} \title{fitPacs function} \usage{ fitPacs(Y, X, lambda = 0.5, betaInput, epsPACS = 1e-05, nItMax = 1000) } \arguments{ \item{Y}{[numeric]: The vector of observed responses - size \code{n}.} \item{X}{[matrix]: The matrix of predictors - size \code{n} rows and \code{p} columns.} \item{lambda}{[numeric]: A non-negative penalty term that controls simultaneouly clusetering and sparsity.} \item{betaInput}{[numeric]: A vector of initial guess of the model parameters. The authors suggest to use coefficients obtained after fitting a ridge regression with the shrinkage parameter selected using AIC criterion.} \item{epsPACS}{[numeric]: A tolerance threshold that control the convergence of the algorithm. The default value fixed in Bondell's initial script is 1e-5.} \item{nItMax}{[numeric]: Maximum number of iterations in the algorithm.} } \value{ Object of class \code{\linkS4class{Pacs}} containing all the input parameters plus parameter \code{a0} the intercept and parameter \code{K} the dimensionality of the model. } \description{ This function implements the PACS (Pairwise Absolute Clustering and Sparsity) methodology of Sharma DB et al. (2013). This methodology proposes to estimate the regression coefficients by solving a penalized least squares problem. It imposes a constraint on Beta (the vector of regression coefficients) that is a weighted combination of the L1 norm and the pairwise L-infinity norm. Upper-bounding the pairwise L-infinity norm enforces the covariates to have close coefficients. When the constraint is strong enough, closeness translates into equality achieving thus a grouping property. For PACS, no software was available. Only an R script was released on Bondell's webpage (http://www4.stat.ncsu.edu/~bondell/Software/PACS/PACS.R.r). Since this R script was running very slowly, we decided to reimplement it in C++ and interfaced it with the present R package clere. This corresponds to the option \code{type=1} in Bondell's script. } \examples{ n <- 100 p <- 20 Beta <- rep(c(0,2),10) eps <- rnorm(n,sd=3) x <- matrix(rnorm(n*p), nrow = n, ncol = p) y <- as.numeric(10+x\%*\%Beta+eps) bInit <- lm(y~scale(x))$coefficients[-1] mod <- fitPacs(Y=y,X=x,lambda=1.25,betaInput=bInit,epsPACS=1e-5,nItMax=1000) } \seealso{ Overview : \code{\link{clere-package}} \cr Classes : \code{\linkS4class{Clere}}, \code{\linkS4class{Pacs}} \cr Methods : \code{\link{plot}}, \code{\link{clusters}}, \code{\link{predict}}, \code{\link{summary}} \cr Functions : \code{\link{fitClere}}, \code{\link{fitPacs}} Datasets : \code{\link{numExpRealData}}, \code{\link{numExpSimData}}, \code{\link{algoComp}} }

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#' Equivalence Class #' #' @param dat \code{qigrp} object #' @param QI Quasi-Identifiers #' @param TA Target Attribute #' @param ... #' @return Equivalence Class and Size eclass <- function(dat, QI, TA, ...){ # Description : Equivalence Class and Size # # Arguments # dat : QIgrouping Data Object # QI : QI # TA : TA # 필요 패키지 불러오기 require(reshape2) require(pbapply) equi.res <- pblapply(1:length(dat), function(x){ data.QI.sort <- dat[[x]][order(dat[[x]][, QI]), ] # QI grouping 결과 데이터를 QI마다 ordering을 시킴 # 즉, equivalence class끼리 볼 수 있도록 하는 것임 rownames(data.QI.sort) <- NULL # equivalence class size 산출 equi.class <- melt(data.QI.sort, id.vars = QI, measure.vars = TA) equi.class <- dcast(equi.class, ... ~ variable, length) colnames(equi.class)[ncol(equi.class)] <- 'equi.size' attr(equi.class, 'NequiClass') <- nrow(equi.class) attr(equi.class, 'equi.size') <- equi.class$equi.size equi.class }) # class 지정 class(equi.res) <- c('eclass', 'list') # Attributes 추가 attr(equi.res, 'n') <- length(equi.res) # equivalence class의 갯수 return(equi.res) }

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draw_logistic.R

library(ggplot2) script.dir <- dirname(sys.frame(1)$ofile) setwd(script.dir) xseq <- seq(-20, 20, 0.01) px = dlogis(xseq, location = 1.1) cdfx = plogis(xseq, location = 1.1) # print(px) plt.df <- data.frame(x = xseq, y = px) ggplot(plt.df, aes(x = x, y = y)) + geom_line() ggsave("./logis-pdf.png") cdf.plt.df <- data.frame(x = xseq, y = cdfx) ggplot(cdf.plt.df, aes(x = x, y = y)) + geom_line() ggsave("./logis-cdf.png")

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## DataMakers ## Uso del pivot_longer (o spread) ## Referencias: https://tidyr.tidyverse.org/articles/pivot.html ## Carga de librerías library(tidyverse) ## Carga de datos animales <- read_csv('data/04_pivot_longer.csv') animales ## Uso del pivot_longer animales %>% pivot_longer(cols = c(Peso, Talla), names_to = "Medida", values_to = "Valor") ## Uso del gather (Versión obsoleta* del pivot_longer) animales %>% gather(key = "Medida", value = "Valor", Peso:Talla) animales %>% gather(c(Peso, Talla), key = "Variable", value = "Valor") %>% as_tibble() ## Uso del gather (Versión obsoleta* del pivot_longer) animales %>% pivot_longer(cols = -Especie, names_to = "Medida", values_to = "Valor") ## Argumento values_drop_na animales %>% pivot_longer(cols = c(Peso, Talla), names_to = "Medida", values_to = "Valor", values_drop_na = TRUE) ## Uso de prefijos en las columnas billboard billboard %>% pivot_longer( cols = starts_with("wk"), names_to = "week", names_prefix = "wk", names_transform = list(week = as.integer), values_to = "rank", values_drop_na = TRUE, ) ## Múltiples variables en el nombre de la columna who who %>% pivot_longer( cols = new_sp_m014:newrel_f65, names_to = c("diagnosis", "gender", "age"), names_pattern = "new_?(.*)_(.)(.*)", values_to = "count", values_drop_na = TRUE ) ## Múltiples observaciones por fila ### Ejemplo 1 animales2 <- read_csv('data/04_pivot_longer2.csv') animales2 %>% pivot_longer(!Fecha, names_to = c(".value", "animal"), names_sep = "_", values_drop_na = TRUE) ### Ejemplo 2 anscombe anscombe %>% pivot_longer(everything(), names_to = c(".value", "set"), names_pattern = "(.)(.)" )

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plotting.r

if(!require(lubridate)){ install.packages("lubridate") } if(!require(ggplot2)){ install.packages("ggplot2") } # --- reading, converting library(lubridate) df <- read.csv("stats.csv", sep=",", dec=".", comment.char="#") df$date <- as.Date(df$date, "%d.%m.%Y") df$day <- wday(df$date, label=TRUE) df$year <- format(df$date, "%Y") df$year_month <- format(df$date, "%Y-%m") df$km.day <- as.numeric(as.character(df$km.day)) df$time.day <- hms(df$time.day) df$time.day.hours <- period_to_seconds(df$time.day)/3600 df$V_mean <- as.numeric(as.character(df$V_mean)) df$V_max <- as.numeric(as.character(df$V_max)) df$total.time <- hms(df$total.time) df$total.time.hours <- period_to_seconds(df$total.time)/3600 # ..debug data.. #lapply(df, class) #str(df) #which(is.na(df$V_max)) #df[rowSums(is.na(df)) > 0,] # --- plotting library(ggplot2) png("stats_%d.png", width=2200, height=1000, res=120) # ..distance related ggplot(df, aes(x=date, y=total.km, group=bike, color=bike, fill=bike)) + geom_point() + geom_line(alpha=.35) + ggtitle("Total distance") + labs(x="Date", y="Total distance\n(km)") + facet_wrap(bike~year, scales="free") ggplot(df, aes(x=date, y=km.day, group=bike, color=bike, fill=bike)) + geom_bar(stat="identity") + #geom_area(alpha=.35) + ggtitle("Daily distance") + labs(x="Date", y="Distance\n(km)") + facet_wrap(bike~year, scales="free_x") ggplot(df, aes(day, km.day, fill=bike, color=bike)) + geom_violin(adjust=.75, alpha=.35) + ggtitle("Daily distance by day of week") + labs(x="Day of week", y="Distance\n(km)") + facet_wrap(bike~year) # ..time related ggplot(df, aes(x=date, y=total.time.hours, group=bike, color=bike, fill=bike)) + geom_point() + geom_line(alpha=.35) + ggtitle("Total time") + labs(x="Date", y="Total time\n(hours)") + facet_wrap(bike~year, scales="free") ggplot(df, aes(x=date, y=time.day.hours, group=bike, color=bike, fill=bike)) + geom_bar(stat="identity") + #geom_area(alpha=.35) + ggtitle("Daily time") + labs(x="Date", y="Time\n(hours)") + facet_wrap(bike~year, scales="free_x") ggplot(df, aes(day, time.day.hours, fill=bike, color=bike)) + geom_violin(adjust=.75, alpha=.35) + ggtitle("Daily time by day of week") + labs(x="Day of week", y="Time\n(hours)") + facet_wrap(bike~year) # ..speed related ggplot(df, aes(day, V_max, fill=bike, color=bike)) + geom_violin(adjust=.75, alpha=.35) + ggtitle("Max. speed by day of week") + labs(x="Day of week", y="Speed\n(km/h)") + facet_wrap(bike~year) ggplot(df, aes(day, V_mean, fill=bike, color=bike)) + geom_violin(adjust=.75, alpha=.35) + ggtitle("Mean speed by day of week") + labs(x="Day of week", y="Speed\n(km/h)") + facet_wrap(bike~year)

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#----- stan 練習 1 -----# #-----線形回帰------# library(rstan) library(ggplot2) x <- runif(100, 1, 100) y <- x * 2/3 + 220 + rnorm(length(x), 0, 10) ggplot() + geom_point(aes(x=x,y=y)) stan_code <- ' data{ int<lower=1> N; real x[N]; real y[N]; } parameters{ real beta1; real beta2; real<lower=0> sigma; } transformed parameters{ real yhat[N]; for(i in 1:N) yhat[i] <- beta1 + beta2 * x[i]; } model{ for(i in 1:N) y[i] ~ normal(yhat[i], sigma); } ' dat_list <- list(x = x, y = y, N = length(x)) fit <- stan(model_code = stan_code, data = dat_list, iter = 110, warmup = 10, chain = 1) d <- extract(fit) traceplot(fit) library(gridExtra) p1 <- ggplot() + geom_density(aes(x=d$beta1)) p2 <- ggplot() + geom_density(aes(x=d$beta2)) grid.arrange(p1, p2, ncol = 2) pred <- NULL for(i in 1:10000){ for(j in c(min(x),max(x))){ pred <- c(pred, d$beta1[i] + d$beta2[i] * j) } } predc <- data.frame(pred = pred, iter = rep(1:10000, each = 2), x = rep(c(min(x),max(x)), 10000)) ggplot() + geom_line(data = predc, aes(x=x, y=pred, group=iter), colour = "pink", lwd = 1, alpha=.004) + geom_point(aes(x=x,y=y)) + geom_abline(aes(intercept=mean(d$beta1), slope=mean(d$beta2)), colour = "red", lwd = 1.3)

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#' Scatterplot based on d3 #' #' #' #' @import htmlwidgets #' #' @export d3Scatter <- function(data, col='black', dotsize =3.5, xlab='', ylab='', title=NULL, subtitle=NULL, callback='ScatterSelection', tooltip=NULL, legend=NULL,legend_title=NULL,legend_pos='topright', legend_right_offset = 100, width = NULL, height = NULL, xrange=NULL, yrange=NULL, margins = NULL, col_scale = RColorBrewer::brewer.pal(11,"RdBu")[11:1], elementId = NULL, collection = FALSE) { if (is.null(margins)){ margins <- list(top = 40, right = 20, bottom = 50, left = 60) } #if a numeric value was provided use the color scale to transform if (is.numeric(col)){ if (length(col) != nrow(data)){ stop('If "col" is numeric there has to be a value for each data row') } breaks <- seq(min(col, na.rm = T), max(col, na.rm = T), length = length(col_scale) + 1 ) grps <- cut(col, breaks = breaks, include.lowest = TRUE) col <- col_scale[grps] }else{ #Fix the coloring col <- gplots::col2hex(col) if(length(col)==1){ col <- rep(col,nrow(data)) } } data$col <- col if (!is.null(xrange) && length(xrange) != 2){ stop("If xrange is specified it needs to have a length of 2") } if (!is.null(yrange) && length(yrange) != 2){ stop("If yrange is specified it needs to have a length of 2") } #Add names as separate column instead of rownames data$name <- rownames(data) rownames(data) <- NULL #fix the labels if (xlab==''){ xlab <- names(data)[1] } if (ylab==''){ ylab <- names(data)[2] } names(data)[1:2] <- c('x','y') #figure out legend positioning if (!is.null(legend)){ if (legend_pos == 'topleft'){ legend_pos <- c(0,0) }else if (legend_pos == 'top'){ legend_pos <- c(0,1) }else if (legend_pos == 'topright'){ legend_pos <- c(0,2) }else if (legend_pos == 'right'){ legend_pos <- c(1,2) }else if (legend_pos == 'bottomright'){ legend_pos <- c(2,2) }else if (legend_pos == 'bottom'){ legend_pos <- c(2,1) }else if (legend_pos == 'bottomleft'){ legend_pos <- c(2,0) }else if (legend_pos == 'left'){ legend_pos <- c(1,0) }else{ stop('legend_pos needs to be "top","right","left","bottom","bottomright",..') } } # forward options using x x = list( type = "d3Scatter", data = data, dotsize = dotsize, xlab = xlab, ylab = ylab, xrange = xrange, yrange = yrange, title = title, subtitle = subtitle, tooltip=tooltip, legend=legend, legend_title=legend_title, legend_pos=legend_pos, legend_right_offset=legend_right_offset, margins=margins, callback = callback ) if (collection){ return(x) }else{ # create widget htmlwidgets::createWidget( name = 'd3Scatter', x, width = width, height = height, package = 'd3Toolbox', elementId = elementId, sizingPolicy = htmlwidgets::sizingPolicy(browser.fill = TRUE) ) } } #' Shiny bindings for d3Scatter #' #' Output and render functions for using D3Scatter within Shiny #' applications and interactive Rmd documents. #' #' @param outputId output variable to read from #' @param width,height Must be a valid CSS unit (like \code{'100\%'}, #' \code{'400px'}, \code{'auto'}) or a number, which will be coerced to a #' string and have \code{'px'} appended. #' @param expr An expression that generates a D3Scatter #' @param env The environment in which to evaluate \code{expr}. #' @param quoted Is \code{expr} a quoted expression (with \code{quote()})? This #' is useful if you want to save an expression in a variable. #' #' @name d3Scatter-shiny #' #' @export d3ScatterOutput <- function(outputId, width = '100%', height = '400px'){ htmlwidgets::shinyWidgetOutput(outputId, 'd3Scatter', width, height, package = 'd3Toolbox') } #' @rdname d3Scatter-shiny #' @export renderd3Scatter <- function(expr, env = parent.frame(), quoted = FALSE) { if (!quoted) { expr <- substitute(expr) } # force quoted htmlwidgets::shinyRenderWidget(expr, d3ScatterOutput, env, quoted = TRUE) }

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net_calc.r

#' Network Topology Metrics #' #' Calculates stream network topology metrics #' #' Requires /NHDPlusAttributes directory (see \code{\link{net_nhdplus}}) #' #' Length and area measures are scaled by M values #' #' @param netdelin output from \code{net_delin} #' @param vpu NHDPlusV2 Vector Processing Unit #' @param nhdplus_path Directory for NHDPlusV2 files (\code{\link{net_nhdplus}}) #' #' @return \code{data.frame}: \code{$group.comid} stream network root COMID; #' \code{$vpu} NHDPlusV2 vector processing unit;\code{M} Position of sampling #' point on COMID, as proportion of COMID from upstream end; \code{WS.ord} #' strahler order for root node;\code{$head.h2o} number of headwater reaches; #' \code{$trib.jun} number of tributary junctions; \code{reach.cnt} number of #' reaches in network; \code{diver.cnt} count of divergent flow paths; #' \code{$AREASQKM} drainage area (km^2); \code{$LENGTHKM} total lenght of #' network flowlines (km); \code{drain.den} drainage density (\code{LENGTHKM} #' / \code{AREASQKM}) #' #' @examples #' # identify NHDPlusV2 COMID #' a <- net_sample(nhdplus_path = getwd(), vpu = "01", ws_order = 6, n = 5) #' # delineate stream network #' b <- net_delin(group_comid = as.character(a[,"COMID"]), nhdplus_path = getwd(), vpu = "01") #' calculate topology summary #' c <- net_calc(netdelin = b, vpu = "01", nhdplus_path = getwd()) #' @export net_calc <- function(netdelin, vpu, nhdplus_path){ directory <- grep(paste(vpu, "/NHDPlusAttributes", sep = ""), list.dirs(nhdplus_path, full.names = T), value = T) Vaa <- grep("PlusFlowlineVAA.dbf", list.files(directory[1], full.names = T), value = T) slope <- grep("elevslope.dbf", list.files(directory, full.names = T), value = T) flow.files <- grep("PlusFlow.dbf", list.files(directory[1], full.names = T), value = T) flow <- foreign::read.dbf(flow.files) vaa <- foreign::read.dbf(Vaa) slope <- foreign::read.dbf(slope) names(slope) <- toupper(names(slope)) names(vaa) <- toupper(names(vaa)) full.net <- unique(netdelin$Network) reach.data <- Reduce(function(x, y) merge(x, y, by.x = "net.comid", by.y = "COMID", all.x = T), list(full.net, vaa, slope)) #calculate network order WS.ord <- reach.data[as.character(reach.data[,"group.comid"]) == as.character(reach.data[,"net.comid"]), c("net.id","M", "STREAMORDE")] names(WS.ord) <- c("net.id", "M", "WS.ord") #catchemnts catchment area #group by, substract, multiply #value at end of flowline cat.area <- aggregate(reach.data[, c("AREASQKM", "LENGTHKM")], by = list(net.id = reach.data[, "net.id"], group.comid = reach.data[,"group.comid"]), sum) incr <- reach.data[as.character(reach.data[,"group.comid"]) == as.character(reach.data[,"net.comid"]), c("net.id", "AREASQKM","LENGTHKM", "M")] incr <- merge(incr, cat.area, by = "net.id") area <- (incr[,"AREASQKM.y"] - incr[,"AREASQKM.x"]) + incr[,"AREASQKM.x"]*incr[,"M"] len <- (incr[,"LENGTHKM.y"] - incr[,"LENGTHKM.x"]) + incr[,"LENGTHKM.x"]*incr[,"M"] #scaled length and catchment vlaues cat.area <- data.frame(net.id = incr[,"net.id"], AreaSQKM = area, LengthKM = len) drain.den <- cat.area[ ,"LengthKM"] / cat.area[ ,"AreaSQKM"] cat.area <- data.frame(cat.area, drain.den) #diversion feature count #counts minor flow paths of divergences if (any(reach.data[,c("STREAMORDE")] != reach.data[,"STREAMCALC"] & reach.data[,"DIVERGENCE"]==2)){ div.rm <- reach.data[reach.data[,c("STREAMORDE")] != reach.data[,"STREAMCALC"] & reach.data[, "DIVERGENCE"] == 2, c("net.id", "net.comid", "group.comid")] diver.cnt <- aggregate(div.rm[, "group.comid"], by = list(div.rm[,"net.id"]), length) names(diver.cnt) <- c("net.id", "diver.cnt") } else { diver.cnt <- data.frame(net.id = 99999, diver.cnt = 999999) } #headwaters & Tribs head.h2o <- aggregate(reach.data[ reach.data[,"STARTFLAG"] == 1, "STREAMORDE"], by = list(reach.data[reach.data[,"STARTFLAG"] == 1, "net.id"]), length) names(head.h2o) <- c("net.id", "head.h2o") trib.jun <- as.numeric(as.character(head.h2o[, "head.h2o"])) - 1 head.h2o <- data.frame(head.h2o, trib.jun) #edge count edges <- head.h2o[,"head.h2o"] + head.h2o[,"trib.jun"] reach.cnt <- data.frame(net.id = head.h2o[,"net.id"], reach.cnt = edges) #relief - at outlet; I want to move this to basin metric #maxelev <- aggregate(reach.data[,"MAXELEVSMO"], # by = list(reach.data[,"group.comid"]), # max) #minelev <- aggregate(reach.data[, "MINELEVSMO"], # by = list(reach.data[, "group.comid"]), # min) #relief <- maxelev[,"x"]-minelev[,"x"] #relief <- data.frame(COMID = maxelev[,"Group.1"], # maxelev = maxelev[,"x"], # minelev = minelev[,"x"], # releif = relief) #aggregate table for summaries of group comid data.out <- unique(full.net[, c("net.id","group.comid", "vpu")]) names(data.out)[2] <- "COMID" data.out <- Reduce(function(x, y) merge(x, y, by = "net.id", all.x = T), list(data.out, WS.ord,head.h2o, reach.cnt, diver.cnt, cat.area))#, relief)) names(data.out)[2] <- "group.comid" return(data.out) }

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/code/code.main/scripts-easydiff.r

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NYU-BFX/hic-bench

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1d9efa8bb0265e3eef3c328434133c49584ecfcc

refs/heads/master

2023-02-15T12:26:52.246291

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r

scripts-easydiff.r

#!/usr/bin/Rscript # global variables VERSION = '1.0' normalize_matrix <- function(D) { D_norm <- normalize.quantiles(D); dimnames(D_norm) <- dimnames(D); return(D_norm); } calc_fdr_cutoff <- function(pos,neg,fdr) { if (fdr<=0) return(Inf); pos <- sort(pos); neg <- sort(neg); kpos <- 1; kneg <- 1; while ((kpos<=length(pos))&(kneg<=length(neg))) { if ((length(neg)-kneg+1)/(length(pos)-kpos+1)<=fdr) { break; } if (pos[kpos]<neg[kneg]) { kpos <- kpos+1; } else if (pos[kpos]>neg[kneg]) { kneg <- kneg+1; } else { kpos <- kpos+1; kneg <- kneg+1; } } if (kpos>length(pos)) { y <- 1.01*pos[length(pos)] } else { y <- pos[kpos]; } return(y); } calc_fdr_cutoff_with_bins <- function(values,obs_scores,exp_scores,fc_cutoff,fdr,fdr_bin_size) { # values: values (e.g. RPKMs) as a function of which the fdr will be computed # obs_scores: observed scores (e.g. fold-changes across samples) # exp_scores: scores expected by chance (e.g. fold-changes within replicates) # fc_cutoff: minimum required fold change # fdr: false discovery rate cutoff # fdr_bin_size: minimum number of instances per bin where FDR will be computed (as a function of value) n = length(values) ivalues = order(values) value_bin = c() t_cutoff = c() t_score = rep(0,n) m = 2 # smoothing parameter, number of micro-bins per bin = 2m+1 mbin_size = fdr_bin_size/(2*m+1) # micro-bin size mbin_starts = seq(1,n,by=mbin_size) k = 1 for (s in mbin_starts) { imbin = ivalues[s:min(n,s+mbin_size-1)] bin = max(1,s-m*mbin_size):min(n,s+(m+1)*mbin_size-1) # flank microbin by m*mbin_size ibin = ivalues[bin] # values inside bin t_bin_cutoff = max(calc_fdr_cutoff(obs_scores[ibin],exp_scores[ibin],fdr),fc_cutoff) # cutoff is determined using all values in bin t_score[imbin] = obs_scores[imbin]/t_bin_cutoff # score is only updated in micro-bin t_cutoff = c(t_cutoff,t_bin_cutoff,t_bin_cutoff) value_bin = c(value_bin,min(values[imbin]),max(values[imbin])) k = k + 1 if (k%%10==0) write(paste('* ',round(100*k/length(mbin_starts),0),'% complete',sep=''),stderr()) } return(list(value_bin=value_bin,t_cutoff=t_cutoff,t_score=t_score)) } score <- function(x,y,method) # method = { 'mean', 'paired', 'pairwise' } { if (method=='paired') { s = mean(x/y) } else if (method=='mean') { s = mean(x)/mean(y) } else if (method=='pairwise') { if (length(y)==0) { z = combn(x,2) # s = mean(c(z[1,]/z[2,],z[2,]/z[1,])) s = mean(z[1,]/z[2,]) } else { z = expand.grid(x,y) s = mean(z[,1]/z[,2]) } } else { s = NA } return(s) } my_ttest <- function(x,y,alternative,paired) { return(0) # TODO: fix this t.test(x,y,alternative='two.sided',paired=paired)$'p.value' } diff_peaks.calc <- function(D,signal_cols,ref_cols,fdr,fdr_bin_size,fold_cutoff,method) { write('Initializing...',stderr()) diffpeaks = {} diffpeaks$signal_cols = signal_cols diffpeaks$ref_cols = ref_cols diffpeaks$fdr = fdr diffpeaks$fdr_bin_size = fdr_bin_size diffpeaks$fold_cutoff = fold_cutoff write('Enforcing positive lower bound on matrix values...',stderr()) lbound = min(D[D>0]) D[D<lbound] = lbound diffpeaks$D = D write('Computing fold-changes between samples...',stderr()) diffpeaks$gain = apply(D,1,function(x) score(x=x[signal_cols],y=x[ref_cols],method=method)) diffpeaks$loss = apply(D,1,function(x) score(x=x[ref_cols],y=x[signal_cols],method=method)) write('Computing p-values...',stderr()) diffpeaks$pval = apply(log2(D),1,function(z) my_ttest(z[signal_cols],z[ref_cols],alternative='two.sided',paired=ifelse(method=='paired',TRUE,FALSE))) write('Computing fold-changes within replicates...',stderr()) diffpeaks$loss_bg = apply(D,1,function(x) score(x=x[ref_cols],y=NULL,method='pairwise')) diffpeaks$gain_bg = apply(D,1,function(x) score(x=x[signal_cols],y=NULL,method='pairwise')) write('Computing FDR on peak gains...',stderr()) diffpeaks$gain_cutoff = calc_fdr_cutoff_with_bins(apply(D[,ref_cols,drop=FALSE],1,mean),diffpeaks$gain,diffpeaks$gain_bg,fold_cutoff,fdr,fdr_bin_size) write('Computing FDR on peak losses...',stderr()) diffpeaks$loss_cutoff = calc_fdr_cutoff_with_bins(apply(D[,signal_cols,drop=FALSE],1,mean),diffpeaks$loss,diffpeaks$loss_bg,fold_cutoff,fdr,fdr_bin_size) diffpeaks$gain_significant = (diffpeaks$gain>=fold_cutoff)&(diffpeaks$gain_cutoff$t_score>=1) diffpeaks$loss_significant = (diffpeaks$loss>=fold_cutoff)&(diffpeaks$loss_cutoff$t_score>=1) write(paste('Gain = ',sum(diffpeaks$gain_significant),sep=''),stderr()) write(paste('Loss = ',sum(diffpeaks$loss_significant),sep=''),stderr()) return(diffpeaks) } diff_peaks.plot <- function(diffpeaks,scale) { f = function(z) { z } if (scale=='log2') f = log2 x = apply(diffpeaks$D[,diffpeaks$ref_cols,drop=FALSE],1,mean) y = apply(diffpeaks$D[,diffpeaks$signal_cols,drop=FALSE],1,mean) vlim = f(c(min(c(x,y)),max(c(x,y)))) x_lab = paste(colnames(diffpeaks$D)[diffpeaks$ref_cols[1]],' mean (',scale,')',sep='') y_lab = paste(colnames(diffpeaks$D)[diffpeaks$signal_cols[1]],' mean (',scale,')',sep='') fclim = log2(c(min(c(diffpeaks$gain,diffpeaks$loss)),max(c(diffpeaks$gain,diffpeaks$loss)))) # x=ref y=sig/ref smoothScatter(f(x),log2(diffpeaks$gain),ylim=fclim,xlab=x_lab,ylab='fold-change (log2)',main='signal vs reference') lines(f(diffpeaks$gain_cutoff$value_bin),log2(diffpeaks$gain_cutoff$t_cutoff),col='red') # x=sig y=ref/sig smoothScatter(f(y),log2(diffpeaks$loss),ylim=fclim,xlab=y_lab,ylab='fold-change (log2)',main='reference vs signal') lines(f(diffpeaks$loss_cutoff$value_bin),log2(diffpeaks$loss_cutoff$t_cutoff),col='green') # x=ref y=sig smoothScatter(f(x),f(y),xlim=vlim,ylim=vlim,xlab=x_lab,ylab=y_lab,main='differential peaks') igain = diffpeaks$gain_significant points(f(x[igain]),f(y[igain]),pch=18,col='red') iloss = diffpeaks$loss_significant points(f(x[iloss]),f(y[iloss]),pch=18,col='green') # boxplots boxplot(log2(diffpeaks$gain),log2(diffpeaks$loss),log2(diffpeaks$gain_bg),log2(diffpeaks$loss_bg)) } diff_peaks.store <- function(x,y,w_signal,w_ref,diffpeaks,out_prefix) { # compute mean/min for reference and signal samples y_filt = y[w_signal&w_ref,] ref_mean = apply(y_filt[,colnames(y)=='reference'],1,mean) ref_min = apply(y_filt[,colnames(y)=='reference'],1,min) sig_mean = apply(y_filt[,colnames(y)=='signal'],1,mean) sig_min = apply(y_filt[,colnames(y)=='signal'],1,min) # save score data score_file <- paste(out_prefix,'.score',sep='') scores <- cbind(rownames(y_filt),round(log2(diffpeaks$gain),3),diffpeaks$pval,ref_mean,sig_mean,ref_min,sig_min,y_filt) colnames(scores)[1] <- 'locus' colnames(scores)[2] <- 'fold-change(log2)'; colnames(scores)[3] <- 'p-value'; write.table(scores,score_file,quote=F,row.names=F,col.names=T,sep='\t') # save gain/loss reg files write.table(scores[diffpeaks$gain_significant,c(1,2),drop=FALSE],paste(out_prefix,'.gain',sep=''),quote=F,row.names=F,col.names=F,sep='\t'); write.table(scores[diffpeaks$loss_significant,c(1,2),drop=FALSE],paste(out_prefix,'.loss',sep=''),quote=F,row.names=F,col.names=F,sep='\t'); # save outlier data outlier_file <- paste(out_prefix,'.outliers',sep=''); d <- rbind(y[!w_signal,],y[!w_ref,]); outliers <- round(d,digits=6); outliers <- cbind(rownames(d),outliers); colnames(outliers)[1] <- 'locus'; write.table(outliers,outlier_file,quote=F,row.names=F,col.names=T,sep='\t'); # create RData file save(x,y,w_signal,w_ref,diffpeaks,file=paste(out_prefix,'.RData',sep='')) } remove_outliers <- function(D,outlier_prob,scale) { w <- 1:nrow(D) if (ncol(D)==1) return(w) # enforce lower bound lbound = min(D[D>0]) D[D<lbound] = lbound # scale f = function(z) { z } if (scale=='log2') f = log2 signal_z <- f(D) vlim = c(min(signal_z[signal_z>-Inf]),max(signal_z)) signal_label <- colnames(D)[1] smoothScatter(signal_z,xlab=paste(signal_label,' #1 (',scale,')',sep=''),xlim=vlim,ylim=vlim,ylab=paste(signal_label,' #2 (',scale,')',sep=''),main='replicate reproducibility'); if (outlier_prob>0) { n_sample = 20000; i_extrema <- as.vector(c(which(signal_z[,1]==min(signal_z[,1]))[1],which(signal_z[,1]==max(signal_z[,1]))[1])); i <- c(i_extrema,sample(nrow(signal_z),n_sample,rep=T)) fit <- loess(signal_z[i,2] ~ signal_z[i,1],span=0.5,degree=1); x <- sort(signal_z[i,1]); lines(x,predict(fit,x),col='magenta'); r <- signal_z[,2]-predict(fit,signal_z[,1]); w <- dnorm(r,mean(r,na.rm=T),sd(r,na.rm=T))>outlier_prob; points(signal_z[!w,],pch=19,col='brown'); } return(w); } # ############################################## # op_easydiff # ############################################## op_easydiff <- function(cmdline_args) { # usage usage = "\ easydiff.r [OPTIONS] INPUT-MATRIX\ \ Function:\ Identifies differences between two samples. Use --help for list of options.\ \ Input files:\ INPUT-MATRIX tab-separated input data file (columns are samples), use --help for more details\ \ Output files:\ diff.RData RData file containing all relevant data structures used for the analysis \ diff.gain gains \ diff.loss losses \ diff.outliers outliers \ diff.pdf scatter plots \ diff.score all scores and data \ " # process command-line arguments option_list <- list( make_option(c("-v","--verbose"), action="store_true",default=FALSE, help="Print more messages."), make_option(c("-o","--output-dir"), default="", help="Output directory (required) [default=\"%default\"]."), make_option(c("--nref"), default=0, help="Number of reference samples [default=%default]."), make_option(c("--normalize"), default="none", help="Matrix normalization: none or normq [default=%default]."), make_option(c("--scale"), default="none", help="Scale to be used for plotting: none or log2 [default=%default]."), make_option(c("--method"), default="paired", help="Method for fold change computations: paired, mean, pairwise [default=%default]."), make_option(c("--outlier-prob"), default=0.0, help="Outlier probability cutoff [default=%default]."), make_option(c("--fdr-cutoff"), default=0.05, help="False discovery rate cutoff [default=%default]."), make_option(c("--fc-cutoff"), default=1.5, help="Fold-change cutoff [default=%default]."), make_option(c("--val-cutoff"), default=-Inf, help="Value cutoff [default=%default].") ) # get command line options (if help option encountered print help and exit) arguments <- parse_args(args=cmdline_args,OptionParser(usage=usage,option_list=option_list),positional_arguments=c(0,Inf)); opt <- arguments$options files <- arguments$args if (length(files)!=1) { write(usage,stderr()); quit(save='no'); } # process input parameters data_file = files[1] nref = opt$'nref' out_dir = opt$'output-dir' normalization = opt$'normalize' scale = opt$'scale' method = opt$'method' outlier_prob = opt$'outlier-prob' fdr = opt$'fdr-cutoff' fold_cutoff = opt$'fc-cutoff' val_cutoff = opt$'val-cutoff' # check parameters if (nref<=0) { write('Error: number of reference samples must be greater than zero!',stderr()); quit(save='no') } # read data x = as.matrix(read.table(data_file,check.names=F,header=T,row.names=1,sep='\t')) ref_cols = 1:nref signal_cols = (nref+1):ncol(x) sample_labels = c('reference','signal') # check parameters if (length(signal_cols)!=length(ref_cols)) write('Warning: number of reference samples not equal to number of signal samples!',stderr()) # create output directory if (out_dir=="") { write('Error: please specify output directory!',stderr()); quit(save='no') } if (file.exists(out_dir)==FALSE) { dir.create(out_dir) } else { write('Warning: output directory already exists, results will be overwritten!',stderr()) } out_prefix = paste(out_dir,'/diff',sep='') image_file <- paste(out_prefix,'.pdf',sep='') # set column labels colnames(x)[ref_cols] = sample_labels[1] colnames(x)[signal_cols] = sample_labels[2] # normalize if (normalization == 'normq') { y = normalize_matrix(x) } else { y = x } # filter based on original values if (opt$verbose) write('Filtering out low values...',stderr()) i_filtered = (apply(x[,signal_cols],1,mean)<val_cutoff)&(apply(x[,ref_cols],1,mean)<val_cutoff) y = y[i_filtered==FALSE,] # setup pdf pdf(image_file,width=5,height=7); par(mfrow=c(3,2),cex=0.5,mar=c(4,4,4,4)) # remove outliers if (opt$verbose&(outlier_prob>0)) write('Removing outliers...',stderr()) w_ref <- remove_outliers(y[,ref_cols,drop=FALSE],outlier_prob=outlier_prob,scale=scale) w_sig <- remove_outliers(y[,signal_cols,drop=FALSE],outlier_prob=outlier_prob,scale=scale) z = y[w_sig&w_ref,] # determine FDR bin size fdr_bin_size = min(floor(nrow(z)/10),10000) if (opt$verbose) write(paste("FDR bin size = ",fdr_bin_size,sep=''),stderr()) # find differential peaks diffpeaks = diff_peaks.calc(D=z,signal_cols=signal_cols,ref_cols=ref_cols,fdr=fdr,fdr_bin_size=fdr_bin_size,fold_cutoff=fold_cutoff,method=method) # plot peak differences if (opt$verbose) write('Plotting differences...',stderr()) diff_peaks.plot(diffpeaks,scale=scale) dev.off() # store results if (opt$verbose) write('Storing results...',stderr()) diff_peaks.store(x,y,w_sig,w_ref,diffpeaks,out_prefix) if (opt$verbose) write('Done.',stderr()) } # ################################################################## # MAIN PROGRAM # ################################################################## # process command-line arguments args <- commandArgs(trailingOnly=T) # install packages for (p in c('optparse','preprocessCore','MASS')) if (!require(p,character.only=TRUE,quietly=TRUE,warn.conflicts=FALSE)) { install.packages(p,repos="http://cran.rstudio.com/") library(p,character.only=TRUE,verbose=FALSE) } # run op_easydiff(args) quit(save='no')

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/dipsaus/R/queue-abstract.R

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akhikolla/TestedPackages-NoIssues

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refs/heads/master

2023-03-01T09:10:17.227119

2021-01-25T19:44:44

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queue-abstract.R

#' @title Defines abstract queue class #' #' @description This class is inspired by \url{https://cran.r-project.org/package=txtq}. #' The difference is \code{AbstractQueue} introduce an abstract class that can #' be extended and can queue not only text messages, but also arbitrary R #' objects, including expressions and environments. All the queue types in this #' package inherit this class. #' #' @name AbstractQueue #' #' @section Abstract Public Methods: #' #' Methods start with \code{@@...} are not thread-safe. Most of them are not #' used directly by users. However, you might want to override them if you #' inherit this abstract class. Methods marked as "(override)" are not #' implemented, meaning you are supposed to implement the details. Methods #' marked as "(optional)" usually have default alternatives. #' #' \describe{ #' \item{\code{initialize(...)} (override)}{ #' The constructor. Usually three things to do during the process: #' 1. set \code{get_locker} \code{free_locker} if you don't want to use the #' default lockers. 2. set lock file (if using default lockers). 3. call #' \code{self$connect(...)} #' } #' \item{\code{get_locker()}, \code{free_locker()} (optional)}{ #' Default is \code{NULL} for each methods, and queue uses an internal #' \code{private$default_get_locker} and \code{private$default_free_locker}. #' These two methods are for customized locker, please #' implement these two methods as functions during \code{self$initialization} #' \code{get_locker} obtains and lock access (exclusive), and \code{free_locker} #' frees the locker. Once implemented, \code{private$exclusive} will take care #' the rest. Type: function; parameters: none; return: none #' } #' \item{\code{@@get_head()}, \code{@@set_head(v)} (override)}{ #' Get head so that we know where we are in the queue \code{self$@@get_head()} #' should return a integer indicating where we are at the queue #' \code{self$@@set_head(v)} stores that integer. Parameter \code{v} is always #' non-negative, this is guaranteed. Users are not supposed to call these #' methods directly, use \code{self$head} and \code{self$head<-} instead. #' However, if you inherit this class, you are supposed to override the methods. #' } #' \item{\code{@@get_total()}, \code{@@set_total(v)} (override)}{ #' Similar to \code{@@get_head} and \code{@@set_head}, defines the total items #' ever stored in the queue. total-head equals current items in the queue. #' } #' \item{\code{@@inc_total(n=1)} (optional)}{ #' Increase total, usually this doesn't need to be override, unless you are #' using files to store total and want to decrease number of file connections #' } #' \item{\code{@@append_header(msg, ...)} (override)}{ #' \code{msg} will be vector of strings, separated by "|", containing encoded #' headers: `time`, `key`, `hash`, and `message`. to decode what's inside, you #' can use \code{self$print_items(stringr::str_split_fixed(msg, '\\|', 4))}. #' \strong{Make sure} to return a number, indicating number of items stored. #' Unless handled elsewhere, usually \code{return(length(msg))}. #' } #' \item{\code{@@store_value(value, key)} (override)}{ #' Defines how to store value. `key` is unique identifier generated from #' time, queue ID, and value. Usually I use it as file name or key ID in #' database. value is an arbitrary R object to store. you need to store value #' somewhere and return a string that will be passed as `hash` in #' \code{self$restore_value}. #' } #' \item{\code{restore_value(hash, key, preserve = FALSE)} (override)}{ #' Method to restore value from given combination of `hash` and `key`. #' `hash` is the string returned by \code{@@store_value}, and `key` is the same #' as key in \code{@@store_value}. preserve is a indicator of whether to #' preserve the value for future use. If set to \code{FALSE}, then you are #' supposed to free up the resource related to the value. (such as free memory #' or disk space) #' } #' \item{\code{@@log(n = -1, all = FALSE) (override)}}{ #' get \code{n} items from what you saved to during \code{@@append_header}. #' \code{n} less equal than 0 means listing all possible items. #' If \code{all=TRUE}, return all items (number of rows should equals to #' \code{self$total}), including popped items. If \code{all=FALSE}, only #' return items in the queue (number of rows is \code{self$count}). The #' returned value should be a \code{n x 4} matrix. Usually I use #' \code{stringr::str_split_fixed(..., '\\|', 4)}. Please see all other #' types implemented for example. #' } #' \item{\code{@@reset(...)} (override)}{ #' Reset queue, remove all items and reset head, total to be 0. #' } #' \item{\code{@@clean()} (override)}{ #' Clean the queue, remove all the popped items. #' } #' \item{\code{@@validate()} (override)}{ #' Validate the queue. Stop if the queue is broken. #' } #' \item{\code{@@connect(con, ...)} (override)}{ #' Set up connection. Usually should be called at the end of #' \code{self$initialization} to connect to a database, a folder, or an #' existing queue you should do checks whether the connection is new or it's #' an existing queue. #' } #' \item{\code{connect(con, ...)} (optional)}{ #' Thread-safe version. sometimes you need to override this function instead #' of \code{@@connect}, because \code{private$exclusive} requires \code{lockfile} #' to exist and to be locked. If you don't have lockers ready, or need to set #' lockers during the connection, override this one. #' } #' \item{\code{destroy()} (optional)}{ #' Destroy a queue, free up space and call #' \code{delayedAssign('.lockfile', {stop(...)}, assign.env=private)} to raise #' error if a destroyed queue is called again later. #' } #' } #' #' @section Public Methods: #' #' Usually don't need to override unless you know what you are doing. #' #' \describe{ #' \item{\code{push(value, message='',...)}}{ #' Function to push an arbitrary R object to queue. \code{message} is a string #' giving notes to the pushed item. Usually message is stored with header, #' separated from values. The goal is to describe the value. \code{...} is #' passed to \code{@@append_header} #' } #' \item{\code{pop(n = 1, preserve = FALSE)}}{ #' Pop \code{n} items from the queue. \code{preserve} indicates whether not to #' free up the resources, though not always guaranteed. #' } #' \item{\code{print_item(item)}, \code{print_items(items)}}{ #' To decode matrix returned by \code{log()}, returning named list or data frame #' with four heads: `time`, `key`, `hash`, and `message`. #' } #' \item{\code{list(n=-1)}}{ #' List items in the queue, decoded. If \code{n} is less equal than 0, then #' list all results. The result is equivalent to #' \code{self$print_items(self$log(n))} #' } #' \item{\code{log(n=-1,all=FALSE)}}{ #' List items in the queue, encoded. This is used with \code{self$print_items}. #' When \code{all=TRUE}, result will list the records ever pushed to the queue #' since the last time queue is cleaned. When \code{all=FALSE}, results will be #' items in the queue. \code{n} is the number of items. #' } #' } #' #' @section Public Active Bindings: #' #' \describe{ #' \item{\code{id}}{ #' Read-only property. Returns unique ID of current queue. #' } #' \item{\code{lockfile}}{ #' The lock file. #' } #' \item{\code{head}}{ #' Integer, total number of items popped, i.e. inactive items. #' } #' \item{\code{total}}{ #' Total number of items ever pushed to the queue since last cleaned, integer. #' } #' \item{\code{count}}{ #' Integer, read-only, equals to total - head, number of active items in the #' queue #' } #' } #' #' @section Private Methods or properties: #' #' \describe{ #' \item{\code{.id}}{ #' Don't use directly. Used to store queue ID. #' } #' \item{\code{.lockfile}}{ #' Location of lock file. #' } #' \item{\code{lock}}{ #' Preserve the file lock. #' } #' \item{\code{exclusive(expr,...)}}{ #' Function to make sure the methods are thread-safe #' } #' \item{\code{default_get_locker()}}{ #' Default method to lock a queue #' } #' \item{\code{default_free_locker}}{ #' Default method to free a queue #' } #' } NULL not_implemented <- function(msg = 'Not yet implemented', default = 0){ warning(msg) default } rand_string <- function(length = 50){ paste(sample(c(letters, LETTERS, 0:9), length, replace = TRUE), collapse = '') } null_item <- data.frame( time = character(0), key = character(0), hash = character(0), message = character(0) ) #' @rdname AbstractQueue #' @export AbstractQueue <- R6::R6Class( classname = "AbstractQueue", portable = TRUE, cloneable = TRUE, private = list( .id = character(0), # Lock file that each queue should have # If lock file is locked, then we should wait till the next transaction period .lockfile = character(0), lock = NULL, # Run expr making sure that locker is locked to be exclusive (for write-only) exclusive = function(expr, ...) { on.exit({ if(is.function(self$free_locker)){ self$free_locker() }else{ private$default_free_locker() } }) if(is.function(self$get_locker)){ self$get_locker(...) }else{ private$default_get_locker(...) } force(expr) }, default_get_locker = function(timeout = 5){ dipsaus_lock(self$lockfile, timeout = timeout) }, default_free_locker = function(){ dipsaus_unlock(self$lockfile) } ), public = list( # By default, queue uses file locker, if you have customized locker, please # implement these two methods as functions: # get_locker obtain and lock access (exclusive) # free_locker free the lock # private$exclusive will take care the rest get_locker = NULL, free_locker = NULL, # Get head so that we know where we are in the queue # @get_head should return a integer indicating where we are at the queue # @set_head stores that integer # param `v` is always non-negative, this is guaranteed # Users are not supposed to call these methods directly, # they use self$head and self$head<- `@get_head` = function(){ not_implemented() }, `@set_head` = function(v){ not_implemented() }, # Get total number of items in the queue, similar to @get_head and @set_head `@get_total` = function(){ not_implemented() }, `@set_total` = function(v){ not_implemented() }, # Increase total, usually this doesn't need to be override, unless you are # using files to store total and want to decrease number of file connections `@inc_total` = function(n=1){ self$total <- self$total + n }, # msg will be vector of strings, separated by "|", containing encoded headers # 1. time, key, hash, and message, to view what's inside, you can use # self$print_items(stringr::str_split_fixed(msg, '\\|', 4)) # to decode # # Make **sure** to return a number as $push() function uses the returned # value as indicator of how many items are stored # Unless handled elsewhere, usually return length of msg `@append_header` = function(msg, ...){ not_implemented() return(length(msg)) }, # Defines how to store value. `key` is unique identifier generated from # time, queue ID, and value, you can use it as file name # value is an arbitrary R object to store. you need to store value somewhere # and return a string (hash, or key or whatever) that will be used in # restore_value # For example, in rds_queue, I use key as file name and saveRDS(value) to # that file. and in `restore_value` I use `hash` to retrive the file name # and read the value # # Make sure return a string, it'll be encoded and stored as `hash` `@store_value` = function(value, key){ not_implemented() }, # hash is the string returned by `@store_value`, and # key is the same as key in `@store_value` # preserve is a indicator of whether to preserve the value for future use # or remove the value to free memory/disk space restore_value = function(hash, key, preserve = FALSE){ not_implemented() }, # Fixed usage, don't override unless you know what's inside push = function(value, message = '', ...){ time <- safe_urlencode(microtime()) digest_val <- digest::digest(message) key <- digest::digest(list(self$id, time, digest_val)) hash <- safe_urlencode(self$`@store_value`(value, key)) message <- safe_urlencode(message) if(length(hash) != 1){ cat2('store_value returns hash value that has length != 1', level = 'FATAL') } out <- paste( time, key, hash, message, sep = "|" ) private$exclusive({ n <- self$`@append_header`(msg = out, ...) if( n > 0 ){ self$`@inc_total`( n ) } }) }, # decode headers and return a data.frame # items should be a nx4 matrix. Easiest example is the matrix returned by # `log()` print_items = function(items){ # Take the results from log() and translate into a data.frame with time, key, hash, and message do.call('rbind', apply(items, 1, function(item){ as.data.frame(self$print_item(item), stringsAsFactors = FALSE) })) }, # Print single item, similar to `print_items`, returns a list print_item = function(item){ list( time = safe_urldecode(item[[1]]), key = item[[2]], hash = safe_urldecode(item[[3]]), message = safe_urldecode(item[[4]]) ) }, # List n items in the queue. if n <= 0, then list all # value will not be obtained during the process, # only time, key, hash, and message will be returned, as obtaining value # is usually much heavier. However, you can use # self$restore_value(hash, key, preserve=TRUE) to # obtain the value. The value is not always available though. list = function(n = -1){ out <- self$log(n=n, all=FALSE) if( !length(out) ){ return(null_item) } if( !is.matrix(out) && !is.data.frame(out) ){ cat2('list must return a matrix or a data.frame', level = 'FATAL') } nrows <- nrow(out) if(!nrows){ return( null_item ) } out <- lapply(seq_len(nrows), function(ii){ re <- self$print_item(out[ii, ]) as.data.frame(re, stringsAsFactors=FALSE) }) do.call('rbind', out) }, # pop first n items from queue, `preserve` will be passed to `restore_value` # Don't override unless you know what's inside pop = function(n = 1, preserve = FALSE) { private$exclusive({ # Check count first, in this case, we don't read header file count <- self$count if(count < 0.5){ return(list()) } out <- self$`@log`(n = n) if( !length(out) ){ return(list()) } if( !is.matrix(out) && !is.data.frame(out) ){ cat2('list must return a matrix or a data.frame', level = 'FATAL') } nrows <- nrow(out) if(!nrows){ return( list() ) } # parse time, key, hash out <- lapply(seq_len(nrows), function(ii){ re <- self$print_item(out[ii, ]) re$value <- self$restore_value( re$hash, re$key, preserve = preserve ) re }) self$head <- self$head + nrows out }) }, # get n items from what you saved to during `@append_header`. n<=0 means # list all possible items. # If all=TRUE, return all items (#items=self$total), including popped items # If all=FALSE, only return items in the queue # The returned value should be a nx4 matrix # I use stringr::str_split_fixed(..., '\\|', 4) in all queues implemented `@log` = function(n = -1, all = FALSE){ not_implemented() }, # log with locks (thread-safe) log = function(n=-1, all=FALSE){ private$exclusive({ self$`@log`(n=n, all=all) }) }, # Remove all items and reset head=total=0 `@reset` = function(...) { not_implemented() }, # thread-safe version reset = function(...) { private$exclusive({ self$`@reset`(...) }) }, # clean all popped items. Usually you don't have to do this manually as # pop(..., preserve=FALSE) will clean automatically (except for `text_queue`) `@clean` = function(...) { not_implemented() }, # thread-safe version clean = function(...){ private$exclusive({ self$`@clean`(...) }) }, # check the validity of queue. Usually the followings need to be checked # 1. head<=total, and non-negative # 2. all the necessary files exist # 3. all the connections exist `@validate` = function(...) { not_implemented() }, validate = function(...){ private$exclusive({ self$`@validate`(...) }) }, # Usually should be called at the end of `initialization` to connect to # a database, a folder, or an existing queue # you should do checks whether the connection is new or it's an existing # queue `@connect` = function(con = NULL, ...){ not_implemented() }, # thread-safe version. sometimes you need to override this function instead # of `@connect`, because `private$exclusive` requires lockfile to be locked # If you don't have lockers ready, or need to set lockers during the # connection, override this one connect = function(...){ private$exclusive({ self$`@connect`(...) }) }, # will be called during Class$new(...), three tasks, # 1. set `get_locker` `free_locker` if lock type is not a file # 2. set lockfile (if using default lockers) # 3. call self$connect initialize = function(con = NULL, lockfile, ...){ self$lockfile <- lockfile self$connect(con, ...) }, # destroy a queue, free up space # and call `delayedAssign('.lockfile', {stop(...)}, assign.env=private)` # to raise error if a destroyed queue is called again later. destroy = function(){ private$default_free_locker() delayedAssign('.lockfile', { cat2("Queue is destroyed", level = 'FATAL') }, assign.env=private) } ), active = list( # read-only version of self$id. It's safer than private$.id as the latter # one does not always exist id = function(){ if(length(private$.id) != 1){ private$.id <- rand_string() } private$.id }, # set/get lock file. Don't call private$.lockfile directly lockfile = function(v){ if(!missing(v)){ private$default_free_locker() private$.lockfile <- v }else if(!length(private$.lockfile)){ private$.lockfile <- rand_string() } private$.lockfile }, # a safe wrapper for `@get_head` and `@set_head` head = function(v) { if(missing(v)){ return(as.integer(self$`@get_head`())) } if( length(v) != 1 ){ cat2('head must be a number',level = 'FATAL') } if( !is.numeric(v) || v < 0 ){ cat2('head must be a non-negative integer', level = 'FATAL') } if( v > self$total ){ cat2('head must not exceed total', level = 'FATAL') } self$`@set_head`( v ) }, # a safe wrapper for `@get_total` and `@set_total` total = function(v){ if(missing(v)){ return(as.integer(self$`@get_total`())) } if( length(v) != 1 ){ cat2('total must be a number', level = 'FATAL') } if( !is.numeric(v) || v < 0 ){ cat2('total must be a non-negative integer', level = 'FATAL') } self$`@set_total`( v ) }, # How many items in the queue right now, = total - head count = function(){ tryCatch({ self$total - self$head }, error = function(e){ warning('Cannot get count, return 0') 0 }) } ) )

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/R/PlotPDFsOLE.R

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PlotPDFsOLE.R

#'Plotting two probability density gaussian functions and the optimal linear #'estimation (OLE) as result of combining them. #' #'@author Eroteida Sanchez-Garcia - AEMET, //email{esanchezg@aemet.es} #' #'@description This function plots two probability density gaussian functions #'and the optimal linear estimation (OLE) as result of combining them. #' #'@param pdf_1 A numeric array with a dimension named 'statistic', containg #' two parameters: mean' and 'standard deviation' of the first gaussian pdf #' to combining. #'@param pdf_2 A numeric array with a dimension named 'statistic', containg #' two parameters: mean' and 'standard deviation' of the second gaussian pdf #' to combining. #'@param nsigma (optional) A numeric value for setting the limits of X axis. #' (Default nsigma = 3). #'@param legendPos (optional) A character value for setting the position of the #' legend ("bottom", "top", "right" or "left")(Default 'bottom'). #'@param legendSize (optional) A numeric value for setting the size of the #' legend text. (Default 1.0). #'@param plotfile (optional) A filename where the plot will be saved. #' (Default: the plot is not saved). #'@param width (optional) A numeric value indicating the plot width in #' units ("in", "cm", or "mm"). (Default width = 30). #'@param height (optional) A numeric value indicating the plot height. #' (Default height = 15). #'@param units (optional) A character value indicating the plot size #' unit. (Default units = 'cm'). #'@param dpi (optional) A numeric value indicating the plot resolution. #' (Default dpi = 300). #' #'@return PlotPDFsOLE() returns a ggplot object containing the plot. #' #'@examples #'# Example 1 #'pdf_1 <- c(1.1,0.6) #'attr(pdf_1, "name") <- "NAO1" #'dim(pdf_1) <- c(statistic = 2) #'pdf_2 <- c(1,0.5) #'attr(pdf_2, "name") <- "NAO2" #'dim(pdf_2) <- c(statistic = 2) #' #'PlotPDFsOLE(pdf_1, pdf_2) #'@import ggplot2 #'@export PlotPDFsOLE <- function(pdf_1, pdf_2, nsigma = 3, legendPos = 'bottom', legendSize = 1.0, plotfile = NULL, width = 30, height = 15, units = "cm", dpi = 300) { y <- type <- NULL if(!is.null(plotfile)){ if (!is.numeric(dpi)) { stop("Parameter 'dpi' must be numeric.") } if (length(dpi) > 1) { warning("Parameter 'dpi' has length greater than 1 and ", "only the first element will be used.") dpi <- dpi[1] } if (!is.character(units)) { stop("Parameter 'units' must be character") } if (length(units) > 1) { warning("Parameter 'units' has length greater than 1 and ", "only the first element will be used.") units <- units[1] } if(!(units %in% c("in", "cm", "mm"))) { stop("Parameter 'units' must be equal to 'in', 'cm' or 'mm'.") } if (!is.numeric(height)) { stop("Parameter 'height' must be numeric.") } if (length(height) > 1) { warning("Parameter 'height' has length greater than 1 and ", "only the first element will be used.") height <- height[1] } if (!is.numeric(width)) { stop("Parameter 'width' must be numeric.") } if (length(width) > 1) { warning("Parameter 'width' has length greater than 1 and ", "only the first element will be used.") width <- width[1] } if (!is.character(plotfile)) { stop("Parameter 'plotfile' must be a character string ", "indicating the path and name of output png file.") } } if (!is.character(legendPos)) { stop("Parameter 'legendPos' must be character") } if(!(legendPos %in% c("bottom", "top", "right", "left"))) { stop("Parameter 'legendPos' must be equal to 'bottom', 'top', 'right' or 'left'.") } if (!is.numeric(legendSize)) { stop("Parameter 'legendSize' must be numeric.") } if (!is.numeric(nsigma)) { stop("Parameter 'nsigma' must be numeric.") } if (length(nsigma) > 1) { warning("Parameter 'nsigma' has length greater than 1 and ", "only the first element will be used.") nsigma <- nsigma[1] } if (!is.array(pdf_1)) { stop("Parameter 'pdf_1' must be an array.") } if (!is.array(pdf_2)) { stop("Parameter 'pdf_2' must be an array.") } if (!is.numeric(pdf_1)) { stop("Parameter 'pdf_1' must be a numeric array.") } if (!is.numeric(pdf_2)) { stop("Parameter 'pdf_2' must be a numeric array.") } if (is.null(names(dim(pdf_1))) || is.null(names(dim(pdf_2)))) { stop("Parameters 'pdf_1' and 'pdf_2' ", "should have dimmension names.") } if(!('statistic' %in% names(dim(pdf_1)))) { stop("Parameter 'pdf_1' must have dimension 'statistic'.") } if(!('statistic' %in% names(dim(pdf_2)))) { stop("Parameter 'pdf_2' must have dimension 'statistic'.") } if (length(dim(pdf_1)) != 1) { stop("Parameter 'pdf_1' must have only dimension 'statistic'.") } if (length(dim(pdf_2)) != 1) { stop("Parameter 'pdf_2' must have only dimension 'statistic'.") } if ((dim(pdf_1)['statistic'] != 2) || (dim(pdf_2)['statistic'] != 2)) { stop("Length of dimension 'statistic'", "of parameter 'pdf_1' and 'pdf_2' must be equal to 2.") } if(!is.null(attr(pdf_1, "name"))){ if(!is.character(attr(pdf_1, "name"))){ stop("The 'name' attribute of parameter 'pdf_1' must be a character ", "indicating the name of the variable of parameter 'pdf_1'.") } } if(!is.null(attr(pdf_2, "name"))){ if(!is.character(attr(pdf_2, "name"))){ stop("The 'name' attribute of parameter 'pdf_2' must be a character ", "indicating the name of the variable of parameter 'pdf_2'.") } } if(is.null(attr(pdf_1, "name"))){ name1 <- "variable 1" } else { name1 <- attr(pdf_1, "name") } if(is.null(attr(pdf_2, "name"))){ name2 <- "Variable 2" } else { name2 <- attr(pdf_2, "name") } #----------------------------------------------------------------------------- # Set parameters of gaussian distributions (mean and sd) #----------------------------------------------------------------------------- mean1 <- pdf_1[1] sigma1 <- pdf_1[2] mean2 <- pdf_2[1] sigma2 <- pdf_2[2] pdfBest <- CombinedPDFs(pdf_1, pdf_2) meanBest <- pdfBest[1] sigmaBest <- pdfBest[2] #----------------------------------------------------------------------------- # Plot the gaussian distributions #----------------------------------------------------------------------------- nameBest <- paste0(name1, " + ", name2) graphicTitle <- "OPTIMAL LINEAR ESTIMATION" xlimSup <- max(nsigma * sigmaBest + meanBest, nsigma * sigma1 + mean1, nsigma * sigma2 + mean2) xlimInf <- min(-nsigma * sigmaBest+meanBest, - nsigma * sigma1 + mean1, -nsigma * sigma2 + mean2) # deltax <- 0.02 deltax <- (xlimSup - xlimInf) / 10000 x <- seq(xlimInf, xlimSup, deltax) df1 <- data.frame(x = x, y = dnorm(x, mean = mean1, sd = sigma1), type = name1) df2 <- data.frame(x = x, y = dnorm(x, mean = mean2, sd = sigma2), type = name2) df3 <- data.frame(x = x, y = dnorm(x, mean = meanBest, sd = sigmaBest), type = nameBest) df123 <- rbind(df1, df2, df3) label1 <- paste0(name1, ": N(mean=",round(mean1, 2), ", sd=", round(sigma1, 2), ")") label2 <- paste0(name2, ": N(mean=",round(mean2, 2), ", sd=", round(sigma2, 2), ")") labelBest <- paste0(nameBest, ": N(mean=",round(meanBest,2), ", sd=", round(sigmaBest, 2), ")") cols <- c("#DC3912", "#13721A", "#1F5094") names(cols) <- c(name1, name2, nameBest) g <- ggplot(df123) + geom_line(aes(x, y, colour = type), size = rel(1.2)) g <- g + scale_colour_manual(values = cols, limits = c(name1, name2, nameBest), labels = c(label1, label2, labelBest)) g <- g + theme(plot.title=element_text(size=rel(1.1), colour="black", face= "bold"), axis.text.x = element_text(size=rel(1.2)), axis.text.y = element_text(size=rel(1.2)), axis.title.x = element_blank(), legend.title = element_blank(), legend.position = legendPos, legend.text = element_text(face = "bold", size=rel(legendSize))) g <- g + ggtitle(graphicTitle) g <- g + labs(y="probability", size=rel(1.9)) g <- g + stat_function(fun = dnorm_limit, args = list(mean=mean1, sd=sigma1), fill = cols[name1], alpha=0.2, geom="area") g <- g + stat_function(fun = dnorm_limit, args = list(mean=mean2, sd=sigma2), fill = cols[name2], alpha=0.2, geom="area") g <- g + stat_function(fun = dnorm_limit, args = list(mean=meanBest, sd=sigmaBest), fill = cols[nameBest], alpha=0.2, geom="area") #----------------------------------------------------------------------------- # Save to plotfile if needed, and return plot #----------------------------------------------------------------------------- if (!is.null(plotfile)) { ggsave(plotfile, g, width = width, height = height, units = units, dpi = dpi) } return(g) } # Auxiliar function to plot CombinedPDFs <- function(pdf_1, pdf_2) { mean_1 <- pdf_1[1] sigma_1 <- pdf_1[2] mean_2 <- pdf_2[1] sigma_2 <- pdf_2[2] a_1 <- (sigma_2^2)/((sigma_1^2)+(sigma_2^2)) a_2 <- (sigma_1^2)/((sigma_1^2)+(sigma_2^2)) pdf_mean <- a_1*mean_1 + a_2*mean_2 pdf_sigma <- sqrt((sigma_1^2)*(sigma_2^2)/((sigma_1^2)+(sigma_2^2))) data <- c(pdf_mean, pdf_sigma) dim(data) <- c(statistic = 2) return(data) } dnorm_limit <- function(x,mean,sd){ y <- dnorm(x,mean,sd) y[x<mean | x > mean+sd] <- NA return(y) }

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3,590

rd

sem.aic.Rd

\name{sem.aic} \alias{sem.aic} \title{ Extracts AIC scores for piecewise SEM } \description{ Extracts the AIC and AICc (corrected for small sample size) values from a piecewise structural equation model (SEM). } \usage{ sem.aic(modelList, data, corr.errors, add.vars, grouping.vars, grouping.fun, adjust.p, basis.set, pvalues.df, model.control, .progressBar) } \arguments{ \item{modelList}{ a \code{list} of regressions representing the structural equation model. } \item{data}{ a \code{data.frame} used to construct the structured equations. } \item{corr.errors}{ a vector of variables with correlated errors (separated by "~~"). } \item{add.vars}{ a vector of additional variables whose independence claims should be evaluated, but which do not appear in the model list. } \item{grouping.vars}{ an optional variable that represents the levels of data aggregation for a multi-level dataset. } \item{grouping.fun}{ a function defining how variables are aggregated in \code{grouping.vars}. Default is \code{mean}. } \item{adjust.p}{ whether p-values degrees of freedom should be adjusted (see below). Default is \code{FALSE}. } \item{basis.set}{ provide an optional basis set. } \item{pvalues.df}{ an optional \code{data.frame} corresponding to p-values for independence claims. } \item{model.control}{ a \code{list} of model control arguments to be passed to d-sep models. } \item{.progressBar}{ enable optional text progress bar. Default is \code{TRUE}. } } \details{ This function calculates AIC and AICc (corrected for small sample sizes) values for a piecewise structural equation model (SEM). For linear mixed effects models, p-values can be adjusted to accommodate the full model degrees of freedom using the argument \code{p.adjust = TRUE}. For more information, see Shipley 2013. } \value{ Returns a \code{data.frame} where the first entry is the AIC score, and the second is the AICc score, and the third is the likelihood degrees of freedom (K). } \references{ Shipley, Bill. "The AIC model selection method applied to path analytic models compared using a d-separation test." Ecology 94.3 (2013): 560-564. } \author{ Jon Lefcheck } \examples{ # Load example data data(shipley2009) # Reduce dataset for example shipley2009.reduced = shipley2009[1:200, ] # Load model packages library(lme4) library(nlme) # Create list of models shipley2009.reduced.modlist = list( lme(DD ~ lat, random = ~1|site/tree, na.action = na.omit, data = shipley2009.reduced), lme(Date ~ DD, random = ~1|site/tree, na.action = na.omit, data = shipley2009.reduced), lme(Growth ~ Date, random = ~1|site/tree, na.action = na.omit, data = shipley2009.reduced), glmer(Live ~ Growth+(1|site)+(1|tree), family=binomial(link = "logit"), data = shipley2009.reduced) ) # Get AIC and AICc values for the SEM sem.aic(shipley2009.reduced.modlist, shipley2009.reduced) \dontrun{ # Repeat with full dataset as in Shipley (2009) # Create list of models shipley2009.modlist = list( lme(DD ~ lat, random = ~1|site/tree, na.action = na.omit, data = shipley2009), lme(Date ~ DD, random = ~1|site/tree, na.action = na.omit, data = shipley2009), lme(Growth ~ Date, random = ~1|site/tree, na.action = na.omit, data = shipley2009), glmer(Live ~ Growth+(1|site)+(1|tree), family=binomial(link = "logit"), data = shipley2009) ) # Get AIC and AICc values for the SEM sem.aic(shipley2009.modlist, shipley2009) } }