pierreqi/r_code_50k · Datasets at Hugging Face

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/chapter3.R

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jhirx/IODS-project

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refs/heads/master

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2019-12-09T16:49:16

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2019-10-31T06:41:39

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chapter3.R

# rs 17.11.2019 jouni hirvionen #metadta #ja tähän sitten koodi kommentoituna #install.packages("dplyr") #luetaan data sisään, read data in #readin data in #d1=read.table("d:/yliopisto/IODS-project/data/student-mat.csv",sep=";",header=TRUE) #d2=read.table("d:/yliopisto/IODS-project/data/student-por.csv",sep=";",header=TRUE) #=merge(d1,d2,by=c("school","sex","age","address","famsize","Pstatus","Medu","Fedu","Mjob","Fjob","reason","nursery","internet")) #readtest <- read.csv("~/IODS-project/data/learning2014.csv") #first test # home readtest <- read.csv("d:/yliopisto/IODS-project/data/student-mat.csv",sep=";",header=TRUE) readtest <- read.csv("~/IODS-project/data/student-mat.csv",sep=";",header=TRUE) str(readtest) readtest2 <- read.csv("~/IODS-project/data/student-por.csv",sep=";",header=TRUE) str(readtest2) #readin data in math <- read.table("~/IODS-project/data/student-mat.csv",sep=";",header=TRUE) por <- read.table("~/IODS-project/data/student-por.csv",sep=";",header=TRUE) colnames(math) colnames(por) library(dplyr) join_by <-c("school","sex","age","address","famsize","Pstatus","Medu","Fedu","Mjob","Fjob","reason","nursery","internet") #making table math_por <- inner_join(math, por, by = join_by, suffix = c(".math", ".por")) # see the new column names colnames(math_por) # glimpse at the data glimpse(math_por) print(nrow(math_por)) # 382 students #testing data str(math_por) dim(math_por) colnames(math_po) # create a new data frame with only the joined columns alc <- select(math_por, one_of(join_by)) # columns that were not used for joining the data notjoined_columns <- colnames(math)[!colnames(math) %in% join_by] notjoined_columns # for every column name not used for joining... for(column_name in notjoined_columns) { # select two columns from 'math_por' with the same original name two_columns <- select(math_por, starts_with(column_name)) # select the first column vector of those two columns first_column <- select(two_columns, 1)[[1]] # if that first column vector is numeric... if(is.numeric(first_column)) { # take a rounded average of each row of the two columns and # add the resulting vector to the alc data frame alc[column_name] <- round(rowMeans(two_columns)) } else { # else if it's not numeric... # add the first column vector to the alc data frame alc[column_name] <- first_column } } glimpse(alc) #avarage of alcohoil consumpition # access the 'tidyverse' packages dplyr and ggplot2 library(ggplot2) # define a new column alc_use by combining weekday and weekend alcohol use alc <- mutate(alc, alc_use = (Dalc + Walc) / 2) # initialize a plot of alcohol use g1 <- ggplot(data = alc, aes(x = alc_use, fill = sex)) # define the plot as a bar plot and draw it g1 + geom_bar() # define a new logical column 'high_use' alc <- mutate(alc, high_use = alc_use > 2) # initialize a plot of 'high_use' g2 <- ggplot(alc, aes(high_use)) # draw a bar plot of high_use by sex g2 + facet_wrap("sex") + geom_bar() glimpse(alc) #Observations: 382 #Variables: 35 write.csv(alc,file="~/IODS-project/data/alc_table.csv")

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/dotplot_Fig4i/dot.grah.R

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biolchen/TNF-and-Ferroptotic-Cell-Death

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dot.grah.R

library(ggplot2) all_pval <- read.table("pvalues.txt",head=T,sep="\t",check.name=0,comment.char = "",quote="") all_means <- read.table("means.txt",head=T,sep="\t",check.name=0,comment.char = "",quote="") all_pval <- all_pval[,-c(1:11)] all_means <- all_means[,-c(1:11)] intr_pairs <- all_pval$interacting_pair selected_rows <- c("TNF_TNFRSF1A","TNF_TNFRSF1B","TGFB1_TGFbeta receptor1","TGFB1_TGFbeta receptor2","IL1 receptor_IL1A","IL1 receptor_IL1B","IL6 receptor_IL6","IL7 receptor_IL7","IL10 receptor_IL10","IL17 receptor AC_IL17A","IL15 receptor_IL15","PDGFA_PDGFRA","FGF1_FGFR1") selected_columns <- c("macrophages|macrophages","macrophages|non-sensitive","macrophages|sensitive","non-sensitive|macrophages","non-sensitive|non-sensitive","non-sensitive|sensitive","sensitive|macrophages","sensitive|non-sensitive","sensitive|sensitive") sel_pval <- all_pval[match(selected_rows, intr_pairs), selected_columns] sel_means <- all_means[match(selected_rows, intr_pairs), selected_columns] df_names <- expand.grid(selected_rows, selected_columns) pval <- unlist(sel_pval) pval[pval==0] <- 0.0009 pr = unlist(as.data.frame(sel_means)) pr[pr==0] <- 1 plot.data <- cbind(df_names,pval,log2(pr)) colnames(plot.data) <- c('pair', 'clusters', 'pvalue', 'mean') my_palette <- colorRampPalette(c("black", "blue", "yellow", "red"), alpha=TRUE)(n=399) p <- ggplot(plot.data,aes(x=clusters,y=pair)) + geom_point(aes(size=-log10(pvalue),color=mean)) + scale_color_gradientn('Log2 mean (Molecule 1, Molecule 2)', colors=my_palette)+ labs(x = "", y = "")+ theme_bw() + theme(panel.grid.minor = element_blank(),panel.grid.major = element_blank())+ theme(axis.text.x = element_text(size=16,angle=90,hjust=1),axis.text.y = element_text(size=16), legend.title = element_text(size = 16)) ggsave(p,filename = "ligand.pdf", width = 14, height = 10)

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/scripts/r/seg.distortion.project/bedtools.processing.R

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alexcoulton/phd

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refs/heads/master

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bedtools.processing.R

# ____________________________________________________________________________ # PROCESSING BEDTOOLS EXTRACTION FILE #### fafile = ReadFasta("rotation1scripts_v4/original_data/IWGSC/bed.tools.genes.only.extraction.fa") fafile[] = lapply(fafile, as.character) fafile$coords = lapply(strsplit(fafile[, 1], ":"), function(x) x[[2]]) gfffile = read.table("rotation1scripts_v4/original_data/IWGSC/iwgsc.genes.only.gff3") fafile[] = lapply(fafile, as.character) fafile$startcoord = lapply(strsplit(fafile$coords, "-"), function(x) x[[1]]) fafile$endcoord = lapply(strsplit(fafile$coords, "-"), function(x) x[[2]]) fafile$genename = "" fafile$genename = gfffile$V9 fafile$chr = lapply(strsplit(fafile$name, ":"), function(x) x[[1]]) fafile$name = paste(fafile$chr, fafile$genename) fafilenew = fafile[, 1:2] writefasta(fafilenew, "rotation1scripts_v4/original_data/IWGSC/iwgsc.full.gene.sequences.fa") fafilenew2 = ReadFasta("rotation1scripts_v4/processed_data/fasta/unique.seg.dist.genes.v2.fasta") fafilenew2 = convert.to.character.data.frame(fafilenew2) list.of.seg.genes = unlist(lapply(lapply(strsplit(fafilenew2$name, " "), function(x) x[[1]]), function(x) substr(x, 1, (nchar(x)-2)))) fafile.seg.genes.extracted = newdf(colnames(fafilenew)) for(i in 1:length(list.of.seg.genes)){ print(grep(list.of.seg.genes[i], fafilenew$name)) fafile.seg.genes.extracted = rbind(fafile.seg.genes.extracted, fafilenew[grep(list.of.seg.genes[i], fafilenew$name), ]) } fafile.seg.genes.extracted = fafile.seg.genes.extracted[-1, ] fafile.seg.genes.extracted$name = unlist(lapply(lapply(strsplit(fafile.seg.genes.extracted$name, " "), function(x) x[[2]]), function(x) substr(x, 4, nchar(x)))) fafile.seg.genes.extracted$name = paste(unlist(lapply(fafile.seg.genes.extracted$name, function(x) substr(x, 1, (nchar(x)-12)))), ".1", sep = "") writefasta(fafile.seg.genes.extracted, "rotation1scripts_v4/processed_data/fasta/unique.seg.dist.genes.full.genonmic.sequence.v3.fa") blastfilenew = read.table("bioinf/blast/genes.vs.paragon.genome/results.blast/unique.seg.dist.genes.cs.x.para.vs.para.genome.v3.outputfmt6.word_sizev3.blast") blastfilenew2 = grab.best.groups.of.hits(blastfilenew) # ____________________________________________________________________________ # NEW EXTRACTION 08032018 #### bedfa = ReadFasta("rotation1scripts_v4/processed_data/fasta/bedtools.genomic.extraction08032018.fa") bedfa = convert.to.character.data.frame(bedfa) max.orf.len = function(seq){ max.orf = max_orf(seq, reverse.strand = T) g = c(max.orf$ORF.Forward$ORF.Max.Len, max.orf$ORF.Reverse$ORF.Max.Len) g2 = max(as.numeric(g)) if(which(g == g2) == 1) for.or.rev = "forward" if(which(g == g2) == 2) for.or.rev = "reverse" if(for.or.rev == "forward"){ attr(g2, "max.orf.seq") = max.orf$ORF.Forward$ORF.Max.Seq } else { attr(g2, "max.orf.seq") = max.orf$ORF.Reverse$ORF.Max.Seq } attr(g2, "for.or.rev") = for.or.rev return(g2) } orf.lengths = unlist(lapply(bedfa$sequence, max.orf.len)) add.probe.names.to.bedtools.ex.file = function(bed.tools.df, blastdf){ fafile = bed.tools.df fafile[] = lapply(fafile, as.character) fafile$coords = lapply(strsplit(fafile[, 1], ":"), function(x) x[[2]]) fafile[] = lapply(fafile, as.character) fafile$startcoord = lapply(strsplit(fafile$coords, "-"), function(x) x[[1]]) fafile$endcoord = lapply(strsplit(fafile$coords, "-"), function(x) x[[2]]) fafile$genename = "" fafile$startcoord = as.numeric(fafile$startcoord) fafile$endcoord = as.numeric(fafile$endcoord) fafile$probenames = as.character(blastdf$V1[unlist(lapply(fafile$startcoord, function(x){ which(blastdf$V9 == (x + 2001) | blastdf$V10 == (x + 2001)) }))]) return(fafile) } bedfa2 = add.probe.names.to.bedtools.ex.file(bedfa, newblast) bedfa2 = bedfa2[which(orf.lengths > 1000), ] #NEED TO ADD PROBE NAMES TO bedfa; SEE CODE ABOVE bedfa2$max.orf.seq = unlist(lapply(bedfa2$sequence, function(x) attr(max.orf.len(x), "max.orf.seq"))) fastadf = bedfa2[, 7:8] colnames(fastadf) = c("header", "sequence") fastadf$header = unlist(lapply(fastadf$header, function(x) p(">", x))) writefasta(fastadf, "rotation1scripts_v4/processed_data/fasta/bedtools.genomic.orf.bigger.1000.fa") # ____________________________________________________________________________ # bedtools blast #### b = read.table("bioinf/blast/genes.vs.paragon.genome/results.blast/bedtools.orf.1000.cs.x.para.vs.para.genome.v3.outputfmt6.word_sizev3.blast") b = read.table("bioinf/blast/probe.vs.genes.blast/results.blast/bedtools.orfs.refined.cs.x.para.vs.genes.blast")

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/Projects Exhibit/D1 CapStoneProj_Hotels/JamesGangavarapu My Project Code.R

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jamesrohan/Projects_SampleCode_Exhibit_For_Employers

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refs/heads/master

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JamesGangavarapu My Project Code.R

############## 1 Reading CSV file into R ################# setwd("C:/Users/Nikki/Desktop/James/Data Science Internship/Assignments/Projects/CapStoneProj_Hotels") hotels.df <- read.csv(paste("Cities42.csv", sep="")) attach(hotels.df) ############ 2 Summary Stats ############ summary(hotels.df) library(psych) describe(hotels.df) #################### 3 4 5 6 7 ############################# #See if Room rent is effected by Binary Factors such as IsWeekend+IsNewYearEve+IsMetroCity+IsTouristDestination # +freeWifi+freeBreakfast+hasSwimmingPool boxplot(hotels.df$RoomRent~hotels.df$IsTouristDestination,horizontal = TRUE,main = "Room Rent vs Tourist Desti", xlab = "Room Rent" ,col = (c("green","red")), ylab = "Tourist Desti") # hotels_RoomRentLessThan.df <- hotels.df[ which(hotels.df$RoomRent <= 50000*2) , ] View(hotels_RoomRentLessThan.df) boxplot(hotels_RoomRentLessThan.df$RoomRent~hotels_RoomRentLessThan.df$IsWeekend,horizontal = TRUE, main = "Room Rent vs IsWeekend",xlab = "Room Rent",col = (c("green","red")), ylab = "IsWeekend") hist(hotels.df$IsWeekend) plot(hotels.df$IsWeekend,hotels.df$RoomRent) boxplot(hotels_RoomRentLessThan.df$RoomRent~hotels_RoomRentLessThan.df$IsNewYearEve,horizontal = TRUE, main = "Room Rent vs IsNewYearEve",xlab = "Room Rent",col = (c("green","red")), ylab = "IsNewYearEve") hist(hotels.df$IsNewYearEve) plot(hotels.df$IsNewYearEve,hotels.df$RoomRent) boxplot(hotels_RoomRentLessThan.df$RoomRent~hotels_RoomRentLessThan.df$IsMetroCity,horizontal = TRUE, main = "Room Rent vs Metro City",xlab = "Room Rent",col = (c("green","red")), ylab = "Metro City") hist(hotels.df$IsMetroCity) plot(hotels.df$IsMetroCity,hotels.df$RoomRent) boxplot(hotels_RoomRentLessThan.df$RoomRent~hotels_RoomRentLessThan.df$IsTouristDestination,horizontal = TRUE, main = "Room Rent vs IsTouristDestination",xlab = "Room Rent",col = (c("green","red")), ylab = "IsTouristDestination") hist(hotels.df$IsTouristDestination) plot(hotels.df$IsTouristDestination,hotels.df$RoomRent) boxplot(hotels_RoomRentLessThan.df$RoomRent~hotels_RoomRentLessThan.df$FreeWifi,horizontal = TRUE, main = "Room Rent vs Free Wifi",xlab = "Room Rent",col = (c("green","red")), ylab = "Free Wifi") hist(hotels.df$FreeWifi) plot(hotels.df$FreeWifi,hotels.df$RoomRent) boxplot(hotels_RoomRentLessThan.df$RoomRent~hotels_RoomRentLessThan.df$FreeBreakfast,horizontal = TRUE, main = "Room Rent vs Free Breakfast",xlab = "Room Rent",col = (c("green","red")), ylab = "Free Breakfast") hist(hotels.df$FreeBreakfast) plot(hotels.df$FreeBreakfast,hotels.df$RoomRent) boxplot(hotels_RoomRentLessThan.df$RoomRent~hotels_RoomRentLessThan.df$HasSwimmingPool,horizontal = TRUE, main = "Room Rent vs Swimming Pool",xlab = "Room Rent",col = (c("green","red")), ylab = "Has Swimming Pool") hist(hotels.df$HasSwimmingPool) plot(hotels.df$HasSwimmingPool,hotels.df$RoomRent) boxplot(hotels_RoomRentLessThan.df$RoomRent~hotels_RoomRentLessThan.df$StarRating,horizontal = TRUE, main = "Room Rent vs Star Rating",xlab = "Room Rent",col = (c("green","red")), ylab = "Star Rating") hist(hotels.df$StarRating) plot(hotels.df$StarRating,hotels.df$RoomRent) boxplot(hotels_RoomRentLessThan.df$RoomRent~hotels_RoomRentLessThan.df$Airport,horizontal = TRUE, main = "Room Rent vs Star Rating",xlab = "Room Rent",col = (c("green","red")), ylab = "Airport Dist") hist(hotels.df$Airport) boxplot(hotels_RoomRentLessThan.df$RoomRent~hotels_RoomRentLessThan.df$CityName,horizontal = TRUE, main = "Room Rent vs City",xlab = "Room Rent",col = (c("green","red")), ylab = "City") plot(hotels.df$CityName) boxplot(hotels_RoomRentLessThan.df$RoomRent~hotels_RoomRentLessThan.df$CityRank,horizontal = TRUE, main = "Room Rent vs CityRank",xlab = "Room Rent",col = (c("green","red")), ylab = "CityRank") hist(hotels.df$CityRank) plot(hotels.df$Airport,hotels.df$RoomRent) plot(hotels.df$Airport,hotels.df$RoomRent,log = 'xy') plot(hotels.df$StarRating,hotels.df$RoomRent) plot(hotels.df$StarRating,hotels.df$RoomRent,log = 'xy') IsWeekend.df <- aggregate(RoomRent ~ IsWeekend, data=hotels.df, mean) IsWeekend.df plot(IsWeekend.df) IsNewYearEve.df <- aggregate(RoomRent ~ IsNewYearEve, data=hotels.df, mean) IsNewYearEve.df IsMetroCity.df <-aggregate(RoomRent ~ IsMetroCity, data=hotels.df, mean) IsMetroCity.df IsTouristDestination.df <- aggregate(RoomRent ~ IsTouristDestination, data=hotels.df, mean) IsTouristDestination.df FreeWifi.df <- aggregate(RoomRent ~ FreeWifi, data=hotels.df, mean) FreeWifi.df HasSwimmingPool.df <-aggregate(RoomRent ~ HasSwimmingPool, data=hotels.df, mean) HasSwimmingPool.df StarRating.df <- aggregate(RoomRent ~ hotels.df$StarRating, data=hotels.df, mean) StarRating.df #AirpotDist.df <- aggregate(RoomRent ~ hotels.df$Airport, data=hotels.df, mean) #AirpotDist.df CityName.df <- aggregate(RoomRent, list(CityName),data= hotels.df,mean) CityName.df CityRank.df <- aggregate(RoomRent, list(CityRank),data= hotels.df,mean) CityRank.df library(car) scatterplotMatrix(formula = ~ FreeWifi+HasSwimmingPool+CityRank, cex=0.6,data=hotels.df) #RoomRent+IsWeekend+IsNewYearEve+Population+CityRank+IsMetroCity+IsTouristDestination ############ 8 Corrgram ################## library(corrgram) # install if needed corrgram(hotels.df, order=FALSE, lower.panel=panel.shade, upper.panel=panel.pie, text.panel=panel.txt, main="Corrgram of MBASalaries") ################ 9 Variance-Covariance Matrix ###################3 cov(hotels.df) var(hotels.df) ###################################################################### ###################################################################### ###################################################################### ###################################################################### ######################## Final Report Code ########################### ###################################################################### ###################################################################### ###################################################################### ###################################################################### ####### ###### ##### # # # # ## # # # ###### ##### #### ##### ##### # # #### ##### ###### # # ## # # # # # # # # # # # # # # # # # # # # ##### # # # # # # # ###### ##### # # # # # # # # # # # # ##### # # # # # ###### # # # # ##### # # ##### # # # # # # # # # # ## # # # # # # # # # # # # # # # # # # # # # # # # # ###### # # ###### # #### # # # ##### #### ##### ###### ###################################################################### ###################################################################### ###################################################################### ###################################################################### ######################## Final Report Code ########################### ###################################################################### ###################################################################### ###################################################################### ###################################################################### summary(hotels.df[which(hotels.df$IsTouristDestination=='1') ,]) library(psych) describe(hotels.df) #Comparing the Hotel Price based on City Rank library(lattice) bwplot(CityRank ~ RoomRent, data=hotels.df, horizontal=TRUE, xlab = "Room Rent") #Insted using data set with less than a certain room price bwplot(CityRank ~ RoomRent, data=hotels_RoomRentLessThan.df, horizontal=TRUE, xlab = "Room Rent") #Taking the log of RoomRents logRoomRent.df <- log2(hotels.df$RoomRent) hotels.df$LogRoomRent <- logRoomRent.df View(hotels.df) bwplot(CityRank ~ LogRoomRent, data=hotels.df, horizontal=TRUE, xlab = "Log Room Rent") #Comparing the Hotel Price based on City Rank and if it is Metro City bwplot(CityRank ~ LogRoomRent | IsMetroCity, data=hotels.df, horizontal=TRUE, xlab = "LogRoom Rent") #Comparing the Hotel Price based on City Rank and if it is Tourist Destination bwplot(CityRank ~ LogRoomRent | IsTouristDestination , data=hotels.df, horizontal=TRUE, xlab = "LogRoom Rent") ###Hypothesis H0: The average Price of Room Rents is equal for different Cities based on Rank. ###Hypothesis H1: The average Price of Room Rents is not equal for different Cities based on Rank. #Room Rent based on wether it is a Metro City Or Not bwplot(IsMetroCity ~ LogRoomRent , data=hotels.df, horizontal=TRUE, xlab = "LogRoom Rent") IsMetro.df <- xtabs(~IsMetroCity,data = hotels.df) prop.table(IsMetro.df)*100 bwplot(FreeWifi ~ LogRoomRent | IsMetroCity , data=hotels.df, horizontal=TRUE, notch = TRUE, xlab = "LogRoom Rent") #Room Rent Based on hasSwimming Pool and is MetroCity bwplot(HasSwimmingPool ~ LogRoomRent | IsMetroCity , data=hotels.df, horizontal=TRUE, notch = TRUE, xlab = "Log Room Rent") #Room Rent with Respect to Date bwplot( LogRoomRent ~ Date , data=hotels.df, horizontal=FALSE, xlab = "Dates") #with respecto weekend or not and new year bwplot(IsWeekend ~ LogRoomRent , data=hotels.df, horizontal=TRUE, xlab = "Log Room Rent") #with respec to new year bwplot(IsNewYearEve ~ LogRoomRent , data=hotels.df, horizontal=TRUE, xlab = "Log Room Rent") #with respec to new year bwplot(IsTouristDestination ~ LogRoomRent , data=hotels.df, horizontal=TRUE, xlab = "Log Room Rent") #with respec to new year bwplot(IsTouristDestination ~ LogRoomRent | IsMetroCity , data=hotels.df, horizontal=TRUE, xlab = "Log Room Rent") #Distance to Airport vs logRoomRent hist(hotels.df$Airport) plot(hotels.df$Airport,hotels.df$LogRoomRent, log = 'xy') plot(hotels.df$Airport[hotels.df$Airport<=20],hotels.df$LogRoomRent[hotels.df$Airport<=20]) airportDist.df <- aggregate(logRoomRent ~ Airport, data=hotels.df, mean) airportDist.df plot(airportDist.df) abline(lm(airportDist.df$Airport~airportDist.df$logRoomRent )) library(car) scatterplotMatrix(formula = ~ Airport+LogRoomRent, cex=0.6,data=hotels.df) #Free Wi-Fi vs Room Rent hist(hotels.df$FreeWifi) HotelWithFreeWifi.df <-xtabs(~hotels.df$FreeWifi) prop.table(HotelWithFreeWifi.df)*100 bwplot(FreeWifi ~ LogRoomRent , data=hotels.df, horizontal=TRUE, xlab = "Log Room Rent") library(car) scatterplotMatrix(formula = ~ FreeWifi+LogRoomRent, cex=0.6,data=hotels.df) # Free Breakfast vs Room Rent bwplot(FreeBreakfast ~ LogRoomRent , data=hotels.df, horizontal=TRUE, xlab = "Log Room Rent") HotelWithFreeBreakfast.df <-xtabs(~hotels.df$FreeBreakfast) prop.table(HotelWithFreeBreakfast.df)*100 library(car) scatterplotMatrix(formula = ~ FreeBreakfast+LogRoomRent, cex=0.6,data=hotels.df) #Hotel Capacity vs Room Rent hotelCap.df <- aggregate(logRoomRent ~ HotelCapacity, data=hotels.df, mean) hotelCap.df plot(hotelCap.df) abline(lm(hotelCap.df$logRoomRent~hotelCap.df$HotelCapacity )) hist(hotels.df$HotelCapacity) library(car) scatterplotMatrix(formula = ~ HotelCapacity+LogRoomRent, cex=0.6,data=hotels.df) #########################Hypothesis Testing############################# #2.3.1 library(car) scatterplotMatrix(formula = ~ CityRank+LogRoomRent, cex=0.6,data=hotels.df) t.test(hotels.df$LogRoomRent~hotels.df$CityRank, alternative = c("two.sided"), var.equal= TRUE ) RoomRent_CityRank <- xtabs(hotels.df$LogRoomRent~hotels.df$CityRank) RoomRent_CityRank chisq.test(RoomRent_CityRank) #2.3.2 library(car) scatterplotMatrix(formula = ~ IsMetroCity+LogRoomRent, cex=0.6,data=hotels.df) describe(hotels.df$IsMetroCity) describe(hotels.df$LogRoomRent) t.test(hotels.df$LogRoomRent~hotels.df$IsMetroCity, alternative = c("two.sided"), var.equal= FALSE) #2.3.3 library(car) scatterplotMatrix(formula = ~ HotelCapacity+LogRoomRent, cex=0.6,data=hotels.df) RoomRent_HotelCap <- xtabs(hotels.df$LogRoomRent~hotels.df$HotelCapacity) RoomRent_HotelCap chisq.test(RoomRent_HotelCap) #2.3.4 library(car) scatterplotMatrix(formula = ~ IsTouristDestination+LogRoomRent, cex=0.6,data=hotels.df) describe(hotels.df$LogRoomRent) describe(hotels.df$IsTouristDestination) t.test(hotels.df$LogRoomRent~hotels.df$IsTouristDestination, alternative = c("two.sided"), var.equal= FALSE) ############## Linear Models ####################### lm1 <- lm(LogRoomRent ~ IsWeekend+IsNewYearEve+CityRank+ IsMetroCity+IsTouristDestination +Airport+FreeWifi+FreeBreakfast+HotelCapacity+ HasSwimmingPool+StarRating,data = hotels.df) summary(lm1) lm2 <- lm(LogRoomRent ~ IsNewYearEve+CityRank+IsTouristDestination +Airport+FreeBreakfast+HotelCapacity+HasSwimmingPool +FreeWifi+StarRating,data = hotels.df) summary(lm2) confint(lm1) confint(lm2) var(hotels.df) cor(hotels.df$LogRoomRent,hotels.df$Population) cor(hotels.df$LogRoomRent, hotels.df$HotelCapacity) library(corrgram) corrgram(hotels.df, order=FALSE, lower.panel=panel.shade, upper.panel=panel.pie, text.panel=panel.txt, main="Corrgram of Hotel Data") lm3<- lm(LogRoomRent ~ HotelCapacity+HasSwimmingPool +StarRating,data = hotels.df) summary(lm3) lm4 <- lm(LogRoomRent ~ IsNewYearEve+CityRank+IsTouristDestination +Airport+FreeBreakfast+HasSwimmingPool +FreeWifi+StarRating,data = hotels.df) summary(lm4)

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PauOrtiz/R

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BayesS_ingenuo.R

install.packages("e1071") library(e1071) data(iris) View(iris) clasificador <- naiveBayes(Species ~ ., data = iris) clasificador predicción<- table(predict(clasificador, iris), iris[,5]) predicción clasificador$apriori plot(predicción,col = hcl(c(120, 10,44)))

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2021-01-17T21:22:18.602596

2017-03-10T12:37:33

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ex7.R

#esercise 7 library("R.matlab") data <- readMat("ex7data2.mat") X <- data$X source("findClosestCentroids.R") K <- 3 initial_centroids <- matrix(c(3, 3, 6, 2, 8, 5),3,2,byrow = TRUE) idx <- findClosestCentroids(X, initial_centroids) ######Compute Means source("computeCentroids.R") centroids = computeCentroids(X, idx, K); ####K means clustering K <- 3 max_iters <- 10 source("runkMeans.R") kMean <- runkMeans(X, initial_centroids, max_iters, FALSE) centroids <- kMean$centriods idx <- kMean$idx ##########pixel data <- readMat("bird_small.mat") A=data$A A=A/255 img_size=dim(A) X <- matrix(A, img_size[1] * img_size[2], 3) K <- 16 max_iters <- 10 source("kMeansInitCentroids.R") initial_centroids <- kMeansInitCentroids(X, K) kMean <- runkMeans(X, initial_centroids, max_iters) centroids <- kMean$centroids idx <- kMean$idx ########compress #########

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/man/connected_matrix.Rd

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2019-12-22T00:04:20

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connected_matrix.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/connected_matrix.R \name{connected_matrix} \alias{connected_matrix} \alias{connected_matrix.Raster} \alias{connected_matrix.SpatialPolygons} \alias{connected_matrix.SpatialLines} \alias{connected_matrix.SpatialPoints} \alias{connected_matrix.default} \title{Connected matrix} \usage{ connected_matrix(x, ...) \method{connected_matrix}{Raster}(x, directions = 4L, ...) \method{connected_matrix}{SpatialPolygons}(x, ...) \method{connected_matrix}{SpatialLines}(x, ...) \method{connected_matrix}{SpatialPoints}(x, distance, ...) \method{connected_matrix}{default}(x, ...) } \arguments{ \item{x}{\code{\link[raster]{Raster-class}} or \code{\link[sp]{Spatial-class}} object. Note that if \code{x} is a \code{\link[raster]{Raster-class}} object then it must have only one layer.} \item{...}{not used.} \item{directions}{\code{integer} If \code{x} is a \code{\link[raster]{Raster-class}} object, the number of directions in which cells should be connected: 4 (rook's case), 8 (queen's case), 16 (knight and one-cell queen moves), or "bishop" to connect cells with one-cell diagonal moves.} \item{distance}{\code{numeric} If \code{x} is a \code{\link{SpatialPoints-class}} object, the distance that planning units have to be within in order to qualify as being connected.} } \value{ \code{\link[Matrix]{dsCMatrix-class}} object. } \description{ Create a matrix showing which planning units are spatially connected to each other. } \details{ This function returns a \code{\link[Matrix]{dgCMatrix-class}} sparse matrix. Cells along the off-diagonal indicate if two planning units are connected. Cells along the diagonal are zero to reduce memory consumption. Note that for \code{\link[raster]{Raster-class}} arguments to \code{x}, pixels with \code{NA} have zeros in the returned object to reduce memory consumption and be consistent with \code{\link{boundary_matrix}}, and \code{\link{connectivity_matrix}}. } \examples{ # load data data(sim_pu_raster, sim_pu_polygons, sim_pu_lines, sim_pu_points) # create connected matrix using raster data ## crop raster to 9 cells r <- crop(sim_pu_raster, c(0, 0.3, 0, 0.3)) ## make connected matrix cm_raster <- connected_matrix(r) # create connected matrix using polygon data ## subset 9 polygons ply <- sim_pu_polygons[c(1:2, 10:12, 20:22), ] ## make connected matrix cm_ply <- connected_matrix(ply) # create connected matrix using polygon line ## subset 9 lines lns <- sim_pu_lines[c(1:2, 10:12, 20:22), ] ## make connected matrix cm_lns <- connected_matrix(lns) ## create connected matrix using point data ## subset 9 points pts <- sim_pu_points[c(1:2, 10:12, 20:22), ] # make connected matrix cm_pts <- connected_matrix(pts, distance = 0.1) # plot data and the connected matrices \donttest{ par(mfrow = c(4,2)) ## plot raster and connected matrix plot(r, main = "raster", axes = FALSE, box = FALSE) plot(raster(as.matrix(cm_raster)), main = "connected matrix", axes = FALSE, box = FALSE) ## plot polygons and connected matrix plot(r, main = "polygons", axes = FALSE, box = FALSE) plot(raster(as.matrix(cm_ply)), main = "connected matrix", axes = FALSE, box = FALSE) ## plot lines and connected matrix plot(r, main = "lines", axes = FALSE, box = FALSE) plot(raster(as.matrix(cm_lns)), main = "connected matrix", axes = FALSE, box = FALSE) ## plot points and connected matrix plot(r, main = "points", axes = FALSE, box = FALSE) plot(raster(as.matrix(cm_pts)), main = "connected matrix", axes = FALSE, box = FALSE) } }

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FastExpected.R

## Copyright 2017 <Jeremy Yee> <jeremyyee@outlook.com.au> ## Expected value function using conditional expectation matrices ################################################################################ FastExpected <- function(grid, value, disturb, weight, r_index, smooth = 1) { .Call('_rcss_FastExpected', PACKAGE = 'rcss', grid, value, r_index, disturb, weight,smooth) }

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anominate_example.R

setwd("~/Box Sync/Measurement/2014") library(anominate) library(foreign) sen <- read.dta("sen112kh.dta") senvotes <- sen[,-c(1:9)] rownames(senvotes) <- paste(sen$name,sen$lstate,sep=" ") senvotes.rc <- rollcall(senvotes, yea=1, nay=6, notInLegis=0, missing=c(7,9), legis.names=paste(sen[,9],sen[,5])) sen.nominate <- anominate(senvotes.rc, dims=1)

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hheiling/deconvolution

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sim_functions.R

# Functions to simulate gene counts, isoform probability distributions, and exon set counts #-----------------------------------------------------------------------------# # Create gene-level output for all J cell types # #-----------------------------------------------------------------------------# # Create gene_level output for all three cell types gene_level = function(total_cts, gene_alpha, seed){ set.seed(seed) # Define variables n = nrow(total_cts) J = ncol(total_cts) CT_names = rep(1:J, each = n) ref_num = rep(1:n, times = J) gene_names = names(gene_alpha) # Matrix of counts for each gene count = matrix(NA, nrow = length(gene_alpha), ncol = n*J) # Sample from UCLA Dirichlet distribution prob = t(rdirichlet(n = n*J, alpha = gene_alpha)) rownames(prob) = gene_names colnames(prob) = str_c("CT",CT_names,":","ref_",ref_num) # Using above prob vec, sample total_cts from multinomial distribution for(j in 1:J){ for(i in 1:n){ count[,i+n*(j-1)] = rmultinom(n = 1, size = total_cts[i,j], prob = prob[,i+n*(j-1)]) } } rownames(count) = rownames(prob) colnames(count) = colnames(prob) # Vector of dispersion parameters # For each sample, assign a constant dispersion parameter (applies to all genes) ## Parameterization of interest: mu + mu^2 / theta theta = sample(90:120, n*J, replace = TRUE) names(theta) = colnames(prob) gene_output = list(p_mat = prob, ct_mat = count, gene_names = names(gene_alpha), theta = theta) return(gene_output) } #-----------------------------------------------------------------------------# # Select genes for differential expression and differential isoform usage # #-----------------------------------------------------------------------------# diff_genes = function(CT1_counts, nTE_filtered, num_diff = 200, seed){ set.seed(seed) # Specify vars n = ncol(CT1_counts) num_genes = nrow(CT1_counts) gene_names = rownames(CT1_counts) # Check inputs if(num_diff %% 2 != 0){ # Note: will split num_diff into diff expression and diff usage. stop("Number of genes selected for differential expression must divisible by 2") } counts_subset = CT1_counts[which(gene_names %in% nTE_filtered$geneId),] genes_nT_limit = nTE_filtered$geneId[which(nTE_filtered$nT <= 15)] # Find genes of interest from CT1 output with counts above first p25 of counts (wrt 1000 genes of interest) q1 = apply(counts_subset, 2, function(x) quantile(x, probs = 0.25)) above_q1 = matrix(NA, nrow = nrow(counts_subset), ncol = ncol(counts_subset)) for(j in 1:ncol(counts_subset)){ above_q1[,j] = ifelse(counts_subset[,j] > q1[j], 1, 0) } gene_expCut = rownames(counts_subset)[which(rowSums(above_q1) >= n*0.9)] # Select genes for differential expression gene_choices = intersect(gene_expCut,genes_nT_limit) cat("number of gene_choices after expression level and isoform number restriction: ", length(gene_choices), "\n") all_diff = sample(gene_choices, num_diff, replace = F) diffExp = sample(all_diff, num_diff/2, replace = F) diffUsg = all_diff[-which(all_diff %in% diffExp)] genes_diff = matrix(NA, nrow = num_genes, ncol = 2) genes_diff[,1] = ifelse(gene_names %in% diffExp, 1, 0) genes_diff[,2] = ifelse(gene_names %in% diffUsg, 1, 0) colnames(genes_diff) = c("diffExp","diffUsg") rownames(genes_diff) = gene_names return(genes_diff) } #-----------------------------------------------------------------------------# # Apply fold changes to genes for differential expression for specified genes # from diff_genes() output #-----------------------------------------------------------------------------# diff_exp = function(gene_counts, n, J, CT_diffExp = 2, diff_genes_mat, propUp, seed){ set.seed(seed) # Specify vars num_genes = nrow(gene_counts) all_genes = rownames(gene_counts) # Checks if(!(CT_diffExp %in% 1:J)){ stop("CT_diffExp must bee a number in 1 to J") }else if(length(CT_diffExp) > 1){ stop("Function ony set up to handle one cell type with diff expression") } # Specify genes selected for differential expression diff_idx = which(diff_genes_mat[,"diffExp"] == 1) gene_choices = rownames(diff_genes_mat[diff_idx,]) num_diffExp = sum(diff_genes_mat[,"diffExp"]) num_upExp = round(num_diffExp*propUp) num_downExp = num_diffExp - num_upExp # Initialize log2 fold change matrix to be all 0 fc = matrix(matrix(0, nrow = num_genes, ncol = 1)) # Update fold change matrix up_diffExp = sample(gene_choices, num_upExp, replace = F) down_diffExp = gene_choices[-which(gene_choices %in% up_diffExp)] fc[which(all_genes %in% up_diffExp),1] = runif(n = num_upExp, min = log2(1.6), max = log2(2)) fc[which(all_genes %in% down_diffExp),1] = runif(n = num_downExp, min = -log2(2), max = -log2(1.6)) rownames(fc) = all_genes colnames(fc) = str_c("CT",CT_diffExp) # Apply fold change matrix to gene counts of cell type of interest ## Multiply CT_diffExp counts by 2^(fc + rnorm(1, mean = 0, sd = 0.05)) gene_cts = gene_counts[,(1+n*(CT_diffExp-1)):(n*CT_diffExp)] fc_rand = matrix(fc, nrow = nrow(fc), ncol = n) + matrix(rnorm(n = nrow(fc)*n, mean = 0, sd = 0.05), nrow = nrow(fc)) gene_cts_fc = gene_cts * 2^(fc_rand) ## Determine proportion of counts for gene_choices when no fold change propA = colSums(gene_cts[which(all_genes %in% gene_choices),]) / colSums(gene_cts) propB = colSums(gene_cts_fc[which(all_genes %in% gene_choices),]) / colSums(gene_cts_fc) prop_R = propA / propB print("proportion ratio") print(prop_R) ## Multiply ratio of proportions to the counts affected by fc gene_cts_fc[which(fc != 0),] = gene_cts_fc[which(fc != 0),] * matrix(prop_R, nrow = num_diffExp, ncol = n, byrow = T) gene_counts_new = gene_counts gene_counts_new[,(1+n*(CT_diffExp-1)):(n*CT_diffExp)] = round(gene_cts_fc) colnames(gene_counts_new) = colnames(gene_counts) rownames(gene_counts_new) = rownames(gene_counts) return(gene_counts_new) } #-----------------------------------------------------------------------------# # Determine Ending Fold Change b/w CT ref and CT j # #-----------------------------------------------------------------------------# calc_diffExp = function(gene_counts_new, gene_counts_orig, diff_genes_mat){ # Define Variables # Rows associated with genes selected for differential expression rows = which(diff_genes_mat[,"diffExp"] == 1) # Original gene counts for CT j before fold change applied orig = gene_counts_orig[rows,] # New gene counts for CT j after fold change and proportion adjustment new = gene_counts_new[rows,] fc_indiv = new / orig fc_avg = rowMeans(fc_indiv) return(list(fc_indiv = fc_indiv, fc_avg = fc_avg)) } #-----------------------------------------------------------------------------# # Simulate exon set negative binomial means # #-----------------------------------------------------------------------------# # Isoform and exon set information # Note: gene clusters chosen so number isoforms I >= 3 for all genes # iso_dist = "uniform": all probabilities will be close to 1/I (I = number isoforms) # Note: this type only uses max of alphaRange # iso_dist = "outlier": one probability will be relatively high and the remaining prob # will be approx. evenly distributed among the remaining I-1 isoforms # Note: this type uses both min and max of alphaRange # iso_dist = "paired": two probabilities will be relatively high and the remaining # probs will be approx. evenly distributed among the remaining I-2 isoforms # Note: this type uses both min and max of alphaRange iso_exon_info = function(genes_info, nTE_filtered, iso_dist, alphaRange, EffLen_info, seed = seed){ set.seed(seed) # names of 1,000 clusters of interest used in simulation clust_names = nTE_filtered$clustID # names of genes of interest ## note: rows of genes_info matrix restricted to genes in nTE_filtered object gene_names = rownames(genes_info) # Number samples n = ncol(genes_info) # Check if(length(gene_names) != length(clust_names)){ stop("nrow of genes_info does not match nrow of nTE_filtered") } # Check iso_dist iso_dist_options = unique(iso_dist) if(!all(iso_dist_options %in% c("uniform","outlier","paired"))){ stop("iso_dist elements must be one of 'uniform', 'outlier', or 'paired'") } output = list() for(clust in clust_names){ # name of gene associated with cluster gene = nTE_filtered$geneId[which(clust_names == clust)] # vector of counts for gene simulated in gene_level() function for n samples gene_ct = genes_info[which(gene_names == gene),] # Effective length matrix - ExI (num exon sets x num isoforms) X = EffLen_info[[clust]]$X # number isoforms I = ncol(X) # dirichlet alpha parameters for isoforms dir_dist = iso_dist[which(names(iso_dist) == gene)] if(dir_dist == "uniform"){ alpha = rep(alphaRange[2], times = I) }else if(dir_dist == "outlier"){ alpha = c(rep(alphaRange[1], times = (I-1)), alphaRange[2]) }else if(dir_dist == "paired"){ alpha = c(rep(alphaRange[1], times = (I-2)), rep(alphaRange[2], times = 2)) } # isoform probability matrix - Ixn (col = isoform probability vector associated with sample i) rho = t(rdirichlet(n = n, alpha = alpha)) candiIsoform = EffLen_info[[clust]]$candiIsoform rownames(rho) = colnames(candiIsoform) colnames(rho) = str_c("ref_",1:n) # scaling factor for gene r_g = numeric(n) # coefficient for mu_g = X_g %*% beta_g beta = matrix(NA, nrow = I, ncol = n) for(i in 1:n){ r_g[i] = gene_ct[i] / sum(X %*% rho[,i]) beta[,i] = rho[,i] * r_g[i] } # Find exon sets corresponding to rows of X exon_sets = EffLen_info[[clust]]$ExonSetLabels # negative binomial means for the exon sets within cluster # result: each col = neg bin means for sample i of n samples, # each row corresponds with (possible) exon sets mu = X %*% beta rownames(mu) = exon_sets colnames(mu) = str_c("ref_",1:n) output[[clust]] = list(iso_alpha = alpha, rho = rho, mu = mu, exon_sets = exon_sets) } return(output) } #-----------------------------------------------------------------------------# # Simulate exon set negative binomial means - take 2 # #-----------------------------------------------------------------------------# # Isoform and exon set information # Note: gene clusters chosen so number isoforms I >= 3 for all genes # iso_dist = "uniform": all probabilities will be close to 1/I (I = number isoforms) # Note: this type only uses max of alphaRange # iso_dist = "outlier": one probability will be relatively high and the remaining prob # will be approx. evenly distributed among the remaining I-1 isoforms # Note: this type uses both min and max of alphaRange # iso_dist = "paired": two probabilities will be relatively high and the remaining # probs will be approx. evenly distributed among the remaining I-2 isoforms # Note: this type uses both min and max of alphaRange # Note: In this situation, the isoforms with the highest alpha are different # than in the original iso_exon_info() function iso_exon_info2 = function(genes_info, nTE_filtered, iso_dist, alphaRange, EffLen_info, seed = seed){ set.seed(seed) # names of 1,000 clusters of interest used in simulation clust_names = nTE_filtered$clustID # names of genes of interest ## note: rows of genes_info matrix restricted to genes in nTE_filtered object gene_names = rownames(genes_info) # Number samples n = ncol(genes_info) # Check if(length(gene_names) != length(clust_names)){ stop("nrow of genes_info does not match nrow of nTE_filtered") } # Check iso_dist iso_dist_options = unique(iso_dist) if(!all(iso_dist_options %in% c("uniform","outlier","paired","outlier3"))){ stop("iso_dist elements must be one of 'uniform', 'outlier', or 'paired'") } output = list() for(clust in clust_names){ # name of gene associated with cluster gene = nTE_filtered$geneId[which(clust_names == clust)] # vector of counts for gene simulated in gene_level() function for n samples gene_ct = genes_info[which(gene_names == gene),] # Effective length matrix - ExI (num exon sets x num isoforms) X = EffLen_info[[clust]]$X # number isoforms I = ncol(X) # dirichlet alpha parameters for isoforms dir_dist = iso_dist[which(names(iso_dist) == gene)] if(dir_dist == "uniform"){ alpha = rep(alphaRange[2], times = I) }else if(dir_dist == "outlier"){ alpha = c(rep(alphaRange[1], times = (I-1)), alphaRange[2]) }else if(dir_dist == "paired"){ alpha = c(rep(alphaRange[2], times = 2), rep(alphaRange[1], times = (I-2))) } # isoform probability matrix - Ixn (col = isoform probability vector associated with sample i) rho = t(rdirichlet(n = n, alpha = alpha)) candiIsoform = EffLen_info[[clust]]$candiIsoform rownames(rho) = colnames(candiIsoform) colnames(rho) = str_c("ref_",1:n) # scaling factor for gene r_g = numeric(n) # coefficient for mu_g = X_g %*% beta_g beta = matrix(NA, nrow = I, ncol = n) for(i in 1:n){ r_g[i] = gene_ct[i] / sum(X %*% rho[,i]) beta[,i] = rho[,i] * r_g[i] } # Find exon sets corresponding to rows of X exon_sets = EffLen_info[[clust]]$ExonSetLabels # negative binomial means for the exon sets within cluster # result: each col = neg bin means for sample i of n samples, # each row corresponds with (possible) exon sets mu = X %*% beta rownames(mu) = exon_sets colnames(mu) = str_c("ref_",1:n) output[[clust]] = list(iso_alpha = alpha, rho = rho, mu = mu, exon_sets = exon_sets) } return(output) } #-----------------------------------------------------------------------------# # Simulate exon set negative binomial means - take 3 # #-----------------------------------------------------------------------------# # Isoform and exon set information # Note: gene clusters chosen so number isoforms I >= 3 for all genes # iso_dist = "uniform": all probabilities will be close to 1/I (I = number isoforms) # Note: this type only uses max of alphaRange # iso_dist = "outlier1": The first isoform (isoform 1 as determined by first column of # knownIsoforms matrix) of the I isoforms will have the highest probability (by a significant # margin) and the remaining isoforms will have small probabilities that are approx. uniform # across these I-1 isoforms. # Note: this type uses both min and max of alphaRange # iso_dist = "outlier2": The second isoform (isoform 2 as determined by second column of # knownIsoforms matrix) of the I isoforms will have the highest probability (by a significant # margin) and the remaining isoforms will have small probabilities that are approx. uniform # across these I-1 isoforms. # Note: this type uses both min and max of alphaRange # iso_dist = "outlier2": The third isoform (isoform 3 as determined by third column of # knownIsoforms matrix) of the I isoforms will have the highest probability (by a significant # margin) and the remaining isoforms will have small probabilities that are approx. uniform # across these I-1 isoforms. # Note: this type uses both min and max of alphaRange iso_exon_info3 = function(genes_info, nTE_filtered, iso_dist, alphaRange, EffLen_info, seed = seed){ set.seed(seed) # names of 1,000 clusters of interest used in simulation clust_names = nTE_filtered$clustID # names of genes of interest ## note: rows of genes_info matrix restricted to genes in nTE_filtered object gene_names = rownames(genes_info) # Number samples n = ncol(genes_info) # Check if(length(gene_names) != length(clust_names)){ stop("nrow of genes_info does not match nrow of nTE_filtered") } # Check iso_dist iso_dist_options = unique(iso_dist) if(!all(iso_dist_options %in% c("uniform","outlier1","outlier2","outlier3"))){ stop("iso_dist elements must be one of 'uniform', 'outlier1', 'outlier2', or 'outlier3'") } output = list() for(clust in clust_names){ # name of gene associated with cluster gene = nTE_filtered$geneId[which(clust_names == clust)] # vector of counts for gene simulated in gene_level() function for n samples gene_ct = genes_info[which(gene_names == gene),] # Effective length matrix - ExI (num exon sets x num isoforms) X = EffLen_info[[clust]]$X # number isoforms I = ncol(X) # dirichlet alpha parameters for isoforms dir_dist = iso_dist[which(names(iso_dist) == gene)] if(dir_dist == "uniform"){ alpha = rep(alphaRange[2], times = I) }else if(dir_dist == "outlier1"){ alpha = c(alphaRange[2], rep(alphaRange[1], times = (I-1))) }else if(dir_dist == "outlier2"){ alpha = c(alphaRange[1], alphaRange[2], rep(alphaRange[1], times = (I-2))) }else if(dir_dist == "outlier3"){ if(I == 3){ alpha = c(rep(alphaRange[1], times=2), alphaRange[2]) }else{ # I > 3 alpha = c(rep(alphaRange[1], times=2), alphaRange[2], rep(alphaRange[1], times = (I-3))) } } # isoform probability matrix - Ixn (col = isoform probability vector associated with sample i) rho = t(rdirichlet(n = n, alpha = alpha)) candiIsoform = EffLen_info[[clust]]$candiIsoform rownames(rho) = colnames(candiIsoform) colnames(rho) = str_c("ref_",1:n) # scaling factor for gene r_g = numeric(n) # coefficient for mu_g = X_g %*% beta_g beta = matrix(NA, nrow = I, ncol = n) for(i in 1:n){ r_g[i] = gene_ct[i] / sum(X %*% rho[,i]) beta[,i] = rho[,i] * r_g[i] } # Find exon sets corresponding to rows of X exon_sets = EffLen_info[[clust]]$ExonSetLabels # negative binomial means for the exon sets within cluster # result: each col = neg bin means for sample i of n samples, # each row corresponds with (possible) exon sets mu = X %*% beta rownames(mu) = exon_sets colnames(mu) = str_c("ref_",1:n) output[[clust]] = list(iso_alpha = alpha, rho = rho, mu = mu, exon_sets = exon_sets) } return(output) } #-----------------------------------------------------------------------------# # Simulate exon set counts for 'other' genes # #-----------------------------------------------------------------------------# # Isoform and exon set information # Note: gene clusters chosen so number isoforms I >= 3 for all genes # E = number of singular exon sets # iso_dist = "uniform": all probabilities will be close to 1/E # Note: this type only uses max of alphaRange # iso_dist = "outlier": one probability will be relatively high and the remaining prob # will be approx. evenly distributed among the remaining E-1 isoforms # Note: this type uses both min and max of alphaRange other_exonset_count = function(genes_info, nTE_other, exon_sets_other, iso_dist = rep("uniform", times = nrow(nTE_other)), alphaRange = c(20,50), seed = seed){ set.seed(seed) # names of 'other' clusters clust_names = nTE_other$clustID # names of 'other' genes ## Note: rows of genes_info matrix restricted to genes in nTE_other object gene_names = rownames(genes_info) # Number samples n = ncol(genes_info) # Check if(length(gene_names) != length(clust_names)){ stop("nrow of genes_info does not match nrow of nTE_other") } # Check iso_dist iso_dist_options = unique(iso_dist) if(!all(iso_dist_options %in% c("uniform","outlier"))){ stop("iso_dist elements must be one of 'uniform' or 'outlier'") } output = list() for(clust in clust_names){ # name of gene associated with cluster gene = nTE_other$geneId[which(clust_names == clust)] # vector of counts for gene simulated in gene_level() function for n samples gene_ct = genes_info[which(gene_names == gene),] # singular exon sets exon_sets = exon_sets_other[[clust]] # Distribute gene counts to singular exon sets according to iso_dist specification ## E = number singular exon sets E = length(exon_sets) ## dirichlet alpha parameters dir_dist = iso_dist[which(gene_names == gene)] if(dir_dist == "uniform"){ alpha = rep(alphaRange[2], times = E) }else if(dir_dist == "outlier"){ alpha = c(rep(alphaRange[1], times = (E-1)), alphaRange[2]) } # exon set probability matrix - Exn (col = exon set probability vector associated with sample i) rho = t(rdirichlet(n = n, alpha = alpha)) rownames(rho) = exon_sets colnames(rho) = str_c("ref_",1:n) # Determine exon set counts from multinomial distriution exon_set_cts = matrix(NA, nrow = E, ncol = n) for(i in 1:n){ exon_set_cts[,i] = rmultinom(n = 1, size = gene_ct[i], prob = rho[,i]) } rownames(exon_set_cts) = exon_sets colnames(exon_set_cts) = colnames(genes_info) output[[clust]] = list(exon_sets = exon_sets, exon_set_cts = exon_set_cts) } return(output) } #-----------------------------------------------------------------------------# # Create Pure CT Reference Count Files # #-----------------------------------------------------------------------------# counts_output = function(exonInfo_1000, exonInfo_other, theta, file_labels, folder, seed){ set.seed(seed) # Checks # Define variables ## Number cell types J = length(exonInfo_1000) ## Number samples per cell type n = length(theta) / J output_1000 = list() for(ct in 1:J){ ct_info = exonInfo_1000[[ct]] ct_theta = theta[(1 + n*(ct-1)):(n*ct)] for(clust in names(ct_info)){ mu = ct_info[[clust]]$mu exon_sets = ct_info[[clust]]$exon_sets # Initialize ES_labels (exon set labels) and record of counts (counts_record) in first cluster if(names(ct_info)[1] == clust){ ES_labels = exon_sets counts_record = matrix(NA, nrow = length(exon_sets), ncol = n) for(i in 1:n){ counts_record[,i] = rnegbin(n = length(mu[,i]), mu = mu[,i], theta = ct_theta[i]) } }else{ # End IF ES_labels = c(ES_labels, exon_sets) counts_subset = matrix(NA, nrow = length(exon_sets), ncol = n) for(i in 1:n){ counts_subset[,i] = rnegbin(n = length(mu[,i]), mu = mu[,i], theta = ct_theta[i]) } counts_record = rbind(counts_record, counts_subset) } # End ELSE of IF-ELSE } # End clust for-loop rownames(counts_record) = ES_labels output_1000[[ct]] = list(ES_labels = ES_labels, counts = counts_record) } # End ct for-loop output_other = list() for(ct in 1:J){ ct_info = exonInfo_other[[ct]] for(clust in names(ct_info)){ exon_sets = ct_info[[clust]]$exon_sets # Initialize ES_labels (exon set labels) and record of counts (counts_record) in first cluster if(names(ct_info)[1] == clust){ ES_labels = exon_sets counts_record = ct_info[[clust]]$exon_set_cts }else{ # End IF ES_labels = c(ES_labels, exon_sets) counts_record = rbind(counts_record, ct_info[[clust]]$exon_set_cts) } # End ELSE of IF-ELSE } # End clust for-loop output_other[[ct]] = list(ES_labels = ES_labels, counts = counts_record) } # End ct for-loop for(ct in 1:J){ ct_files = file_labels[(1 + n*(ct-1)):(n*ct)] counts_combo = rbind(output_1000[[ct]]$counts, output_other[[ct]]$counts) ES_labels_all = c(output_1000[[ct]]$ES_labels, output_other[[ct]]$ES_labels) for(i in 1:n){ df = data.frame(counts = counts_combo[,i], exons = ES_labels_all) write.table(df, file = sprintf("%s/%s.txt", folder, ct_files[i]), row.names = F, col.names = F) } # End i for-loop } # End ct for-loop } #-----------------------------------------------------------------------------# # Simulate Mixture Count Files # #-----------------------------------------------------------------------------# mix_creation = function(set_mixSim, out_folder, file_labels, total_cts, probs, seed){ set.seed(seed) # Define variables ## Number mixture replicates to create mix_rep = nrow(total_cts) ## Number cell types J = ncol(probs) ## Number pure reference samples per cell type (assume equal across all cell types) M = length(set_mixSim[[1]]) # Checks if(any(rowSums(probs) != 1)){ stop("rows of probs must add to 1") } # List of pure reference sample count data.frames df_list = list() for(j in 1:J){ pure_files = set_mixSim[[j]] files_list = list() for(f in 1:length(pure_files)){ df = read.table(file = pure_files[f], as.is = T) colnames(df) = c("count","exons") files_list[[f]] = df } df_list[[j]] = files_list } # exon set labels (assume same across all pure reference samples) exon_sets = df_list[[1]][[1]]$exons # Number exon sets (assume equal across all pure reference samples) E = length(exon_sets) for(k in 1:nrow(probs)){ # Identify prob vector p = probs[k,] # Randomly select counts files from each cell type pure_counts = matrix(NA, nrow = E, ncol = J) for(j in 1:J){ counts_vec = df_list[[j]][[sample(1:M, size = 1)]]$count pure_counts[,j] = counts_vec } # Calculate ratio of total counts between mixture replicate and pure reference counts cts_Ratio = matrix(NA, nrow = mix_rep, ncol = J) for(rep in 1:mix_rep){ cts_Ratio[rep,] = total_cts[rep,k] / colSums(pure_counts) } # Multiply p and cts_Ratio to appropriate columns of pure_counts to get mixture sample components ## Round results and add results across exon sets mixture = list() for(rep in 1:mix_rep){ mix_components = pure_counts * matrix(p, nrow = nrow(pure_counts), ncol = J, byrow = T) * matrix(cts_Ratio[rep,], nrow = nrow(pure_counts), ncol = J, byrow = T) mixture[[rep]] = rowSums(round(mix_components)) } # Save mixture results in counts.txt files for(rep in 1:mix_rep){ label = file_labels[rep,k] df_mix = data.frame(count = mixture[[rep]], exons = exon_sets) write.table(df_mix, file = sprintf("%s/%s.txt", out_folder, label), col.names = F, row.names = F) } } } mix_creation2 = function(set_mixSim, out_folder, file_labels, total_cts, probs, seed){ set.seed(seed) # Define variables ## Number cell types J = ncol(probs) ## Number pure reference samples per cell type (assume equal across all cell types) M = length(set_mixSim[[1]]) # Checks if(any(rowSums(probs) != 1)){ stop("probs must add to 1") } # List of pure reference sample count data.frames df_list = list() for(j in 1:J){ pure_files = set_mixSim[[j]] files_list = list() for(f in 1:length(pure_files)){ df = read.table(file = pure_files[f], as.is = T) colnames(df) = c("count","exons") files_list[[f]] = df } df_list[[j]] = files_list } # exon set labels (assume same across all pure reference samples) exon_sets = df_list[[1]][[1]]$exons # Number exon sets (assume equal across all pure reference samples) E = length(exon_sets) for(k in 1:nrow(probs)){ # Identify prob vector p = probs[k,] # Randomly select counts files from each cell type pure_counts = matrix(NA, nrow = E, ncol = J) for(j in 1:J){ counts_vec = df_list[[j]][[sample(1:M, size = 1)]]$count pure_counts[,j] = counts_vec } # Calculate ratio of total counts between mixture sample and pure reference counts # Goal: standardize total counts from each pure sample, then take desired proportion cts_Ratio = total_cts[k] / colSums(pure_counts) # Multiply p and cts_Ratio to appropriate columns of pure_counts to get mixture sample components mix_componenets = pure_counts * matrix(p, nrow = nrow(pure_counts), ncol = J, byrow = T) * matrix(cts_Ratio, nrow = nrow(pure_counts), ncol = J, byrow = T) mixture = rowSums(round(mix_components)) # Save mixture results in counts.txt files label = file_labels[k] df_mix = data.frame(count = mixture, exons = exon_sets) write.table(df_mix, file = sprintf("%s/%s.txt", out_folder, label), col.names = F, row.names = F) } # # Identify prob vector # p = probs # # Randomly select counts files from each cell type # pure_counts = matrix(NA, nrow = E, ncol = J) # for(j in 1:J){ # counts_vec = df_list[[j]][[sample(1:M, size = 1)]]$count # pure_counts[,j] = counts_vec # } # # # Calculate ratio of total counts between mixture replicate and pure reference counts # cts_Ratio = matrix(NA, nrow = mix_rep, ncol = J) # for(rep in 1:mix_rep){ # cts_Ratio[rep,] = total_cts[rep,k] / colSums(pure_counts) # } # # # Multiply p and cts_Ratio to appropriate columns of pure_counts to get mixture sample components # ## Round results and add results across exon sets # mixture = list() # for(rep in 1:mix_rep){ # mix_components = pure_counts * matrix(p, nrow = nrow(pure_counts), ncol = J, byrow = T) * # matrix(cts_Ratio[rep,], nrow = nrow(pure_counts), ncol = J, byrow = T) # mixture[[rep]] = rowSums(round(mix_components)) # } # # # Save mixture results in counts.txt files # for(rep in 1:mix_rep){ # label = file_labels[rep,k] # df_mix = data.frame(count = mixture[[rep]], exons = exon_sets) # write.table(df_mix, file = sprintf("%s/%s.txt", out_folder, label), col.names = F, row.names = F) # } } #-----------------------------------------------------------------------------# # Simulate Fragment Length Distribution Files # #-----------------------------------------------------------------------------# fragLens_out = function(total_reads, mean = 300, SD = 50, lenMin = 150, lenMax = 600, out_folder, file_names, seed){ set.seed(seed) # Define variables mix_rep = nrow(total_reads) num_pCombos = ncol(total_reads) for(rep in 1:mix_rep){ for(p in 1:num_pCombos){ # fragLens_dist() in geneModel_code/fragLens_dist.cpp file freq_dist = fragLens_dist(total_reads[rep,p], mean, SD, lenMin, lenMax) freq_dist = freq_dist[which(freq_dist[,1] > 0),] write.table(freq_dist, file = sprintf("%s/%s.txt",out_folder,file_names[rep,p]), col.names = F, row.names = F) } } }

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app_output3_plotlydygraph.R

library(shiny) library(plotly) library(dygraphs) # UI ui <- fluidPage( # outputs fluidRow( column(6, plotlyOutput("grafico1", height = 300)), column(6, dygraphOutput("grafico2", height = 300)) ) ) # SERVER server <- function(input, output) { output$grafico1 <- renderPlotly({ plot_ly(data = iris, x = ~Sepal.Length, y = ~Petal.Length, symbol = ~Species) }) output$grafico2 <- renderDygraph({ dygraph(AirPassengers) %>% dySeries("V1", label = "AirPassengers", color = "#000000") %>% dyRangeSelector() %>% dyLegend(labelsSeparateLines = T, show = "always") }) } # Executar a aplicação shinyApp(ui = ui, server = server)

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gopalpenny/anem

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get_ROI.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/anem_potentials.R \name{get_ROI} \alias{get_ROI} \title{Estimate the radius of influence of a well} \usage{ get_ROI(..., method) } \arguments{ \item{...}{Variable parameters, depending on the method used. Single numbers or vectors (of equal length)} \item{method}{String containing the name of the desired method} } \value{ A numeric value indicating the horizontal radius of influence of the well. } \description{ Estimate the radius of influence of a well } \section{Methods}{ The methods below are taken from Fileccia (2015). Acque Sotterranee - Italian Journal of Groundwater (\url{http://www.doi.org/10.7343/AS-117-15-0144}). The following strings can be input for the \code{method} variable, and must be accompanied by the corresponding variables as part of the \code{...} input: \describe{ \item{"cooper-jacob":}{\eqn{R=\sqrt{2.25 Tr t / S}}, for confined aquifer after short pumping period. (Cooper and Jacob, 1946)} \item{"aravin-numerov":}{\eqn{R=\sqrt{1.9 Ksat h t / n}}, for unconfined aquifers (Aravin and Numerov, 1953)} \item{"sichardt":}{\eqn{R=3000 s \sqrt{Ksat}}, Sichardt formula for unconfined aquifers (Cashman and Preene, 2001)} } Where: \itemize{ \item R = radius of influence [m] \item Tr = transmissivity [m^2/s] \item t = time [s] \item S = storage \item h = height of the water table above substratum [m] \item n = effective porosity \item Ksat = saturated hydraulic conductivity [m/s] \item s = drawdown in the borehole [m] } These inputs can be single numbers or vectors of equal length. } \examples{ get_ROI(Tr=0.01,t=3600*12,S=1,method="cooper-jacob") get_ROI(Ksat=0.0001,h=50,t=3600*12,n=0.4,method="aravin-numerov") get_ROI(Ksat=0.0001,s=10,method="sichardt") }

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/man/linCorrKTS.Rd

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linCorrKTS.Rd

\name{linCorrKTS} \alias{linCorrKTS} %- Also NEED an '\alias' for EACH other topic documented here. \title{ linCorrKTS: linear correlation plot } \description{ This function plots the autocorrelation and partial autocorrelation functions or the cross correlation function, depending on the number of input time series. It is used internally through the Linear correlation button in the Plots menu. } %- maybe also 'usage' for other objects documented here. \author{ Marina Saez Andreu }

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/R/nearest_neighbors.R

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nearest_neighbors.R

#' #' #' nearest_neighbors <- function(U, k){ require(FNN) require(magrittr) require(plyr) FNN::get.knn(U, k = k) %>% magrittr::extract2("nn.index") %>% magrittr::set_rownames(row.names(U)) %>% plyr::aaply(1, function(x){ magrittr::extract(row.names(U), x) }) } query_distance <- function(query, U, row_names, summary = T){ require(magrittr) if(length(dim(U)) < 3){ U %<>% array(c(1, dim(U))) } plyr::alply(query, 1, function(q){ all_res <- plyr::aaply(U, 1, function(u){ u %<>% magrittr::set_rownames(row_names) u %>% apply(1, function(uu){ sum(uu*u[q, ]) / (sqrt(sum(uu^2))*sqrt(sum(u[q, ]^2))) }) }) if(length(dim(all_res)) < 2){ all_res %<>% array(c(1, length(all_res))) %>% magrittr::set_colnames(row_names) } if(summary){ all_res %>% plyr::adply(2, function(col){ data.frame(mean = mean(col), sd = sd(col), lwr = quantile(col, 0.025), upr = quantile(col, 0.975)) }) %>% dplyr::mutate(query = q) %>% dplyr::rename(reference = X1) %>% dplyr::select(query, reference, mean, sd, lwr, upr) %>% dplyr::arrange(mean) } else { all_res } }) %>% magrittr::set_names(query) } nearest_neighbor_distribution <- function(U, k, row_names){ require(FNN) require(magrittr) require(plyr) interim_res <- plyr::aaply(1:nrow(U), 1, function(i){ FNN::get.knn(U[i,,], k = k) %>% magrittr::extract2("nn.index") %>% magrittr::set_rownames(row_names) %>% plyr::aaply(1, function(x){ magrittr::extract(row_names, x) }) }) plyr::adply(1:(dim(interim_res)[2]), 1, function(i){ m <- interim_res[,i,] plyr::adply(1:ncol(m), 1, function(j){ plyr::count(m[,j]) %>% dplyr::mutate(neighbor_k = j) %>% dplyr::mutate(anchor = row_names[i]) %>% dplyr::mutate(freq = freq / nrow(m)) }) }) %>% dplyr::select(anchor, x, neighbor_k, freq) %>% plyr::arrange(anchor, neighbor_k, 1/freq) }

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/annotatedTreeReader.R

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annotatedTreeReader.R

# # @author Marc A. Suchard # # A class for reading Newick formatted trees with BEAST-style annotations # strip.annotations = function(text) { annotations = list() end = 1 pattern = "\\[&.*?\\]" repeat { match = regexpr(pattern=pattern,text=text) if (!(match[1] > 0)) { break } annotations[[end]] = regmatches(text, match) text = sub(pattern,paste("[",end,"]",sep=""), text) end = end + 1 } return(list(annotations=annotations,tree=text)) } .split.tree.names = function(text) { text = gsub(pattern="\\[.*?\\]=",x=text,replacement="") text = gsub(pattern="^tree",x=text,replacement="") return(text) } .split.tree.traits = function(text) { # Pull out annotation text = regmatches(text,regexpr(pattern="\\[.*?\\]",text)) # Remove leading and trailing delimitors text = substring(text,3,nchar(text)-1) return(text) } parse.value = function(text) { value = text if (length(grep("^\\{",value))) { # starts with { save = value value = substring(value, 2, nchar(value)-1) depth = 0 r = regexpr(pattern="\\{+",value,perl=TRUE) match.length = attr(r, "match.length") if (match.length > 0) { depth = match.length } if (depth == 0) { split = "," } else { split = paste( "(?<=",rep("\\}",depth),")", ",", "(?=" ,rep("\\{",depth),")", sep="") } if (depth >= 1) { return(save) # TODO Still error in recursion } part = strsplit(value, split, perl=TRUE)[[1]] value = list() for (i in 1:length(part)) { value[[i]] = parse.value(part[i]) } # TODO Unlist when simple array? } else { if (!is.na(suppressWarnings(as.numeric(value)))) { # is a number value = as.numeric(value) } } return(value) } parse.traits = function(text, header=FALSE) { if (header == TRUE) { text = substring(text,3,nchar(text)-1) } pattern = "(\"[^\"]*\"+|[^,=\\s]+)\\s*(=\\s*(\\{[^=]*\\}|\"[^\"]*\"+|[^,]+))?" rgx = gregexpr(pattern,text,perl=TRUE) n = length(attr(rgx[[1]],"match.length")) traits = list() start = attr(rgx[[1]],"capture.start") names = attr(rgx[[1]],"capture.names") length = attr(rgx[[1]],"capture.length") names = attr(rgx[[1]],"capture.names") for (i in 1:n) { s = start[i,3] e = s + length[i,3] - 1 value = substring(text,s,e) s = start[i,1] e = s + length[i,1] - 1 key = substring(text,s,e) traits[[key]] = parse.value(value) } return(traits) } # THE CODE BELOW COMES FROM 'ape'. MY GOAL IS TO DERIVE FROM THIS TO READ IN BEAST-STYLE ANNOTATIONS annotated.tree.build = function (tp) { add.internal <- function() { edge[j, 1] <<- current.node edge[j, 2] <<- current.node <<- node <<- node + 1L index[node] <<- j j <<- j + 1L } add.terminal <- function() { edge[j, 1] <<- current.node edge[j, 2] <<- tip index[tip] <<- j X <- unlist(strsplit(tpc[k], ":")) tip.label[tip] <<- X[1] edge.length[j] <<- as.numeric(X[2]) k <<- k + 1L tip <<- tip + 1L j <<- j + 1L } go.down <- function() { l <- index[current.node] X <- unlist(strsplit(tpc[k], ":")) node.label[current.node - nb.tip] <<- X[1] edge.length[l] <<- as.numeric(X[2]) k <<- k + 1L current.node <<- edge[l, 1] } if (!length(grep(",", tp))) { obj <- list(edge = matrix(c(2L, 1L), 1, 2)) tp <- unlist(strsplit(tp, "[\$\$:;]")) obj$edge.length <- as.numeric(tp[3]) obj$Nnode <- 1L obj$tip.label <- tp[2] if (tp[4] != "") obj$node.label <- tp[4] class(obj) <- "phylo" return(obj) } result = strip.annotations(tp) annotations = result$annotations new.tp.stripped = result$tree annotations = lapply(annotations, parse.traits, header=TRUE) tp.stripped = gsub("\\[.*?\\]","",tp) tpc <- unlist(strsplit(tp.stripped, "[\$\$,;]")) tpc <- tpc[nzchar(tpc)] tsp <- unlist(strsplit(tp.stripped, NULL)) skeleton <- tsp[tsp %in% c("(", ")", ",", ";")] nsk <- length(skeleton) nb.node <- sum(skeleton == ")") nb.tip <- sum(skeleton == ",") + 1 nb.edge <- nb.node + nb.tip node.label <- character(nb.node) tip.label <- character(nb.tip) edge.length <- numeric(nb.edge) edge <- matrix(0L, nb.edge, 2) current.node <- node <- as.integer(nb.tip + 1) edge[nb.edge, 2] <- node index <- numeric(nb.edge + 1) index[node] <- nb.edge j <- k <- tip <- 1L for (i in 2:nsk) { if (skeleton[i] == "(") add.internal() if (skeleton[i] == ",") { if (skeleton[i - 1] != ")") add.terminal() } if (skeleton[i] == ")") { if (skeleton[i - 1] == ",") { add.terminal() go.down() } if (skeleton[i - 1] == ")") go.down() } } edge <- edge[-nb.edge, ] obj <- list(edge = edge, Nnode = nb.node, tip.label = tip.label) root.edge <- edge.length[nb.edge] edge.length <- edge.length[-nb.edge] if (!all(is.na(edge.length))) obj$edge.length <- edge.length if (is.na(node.label[1])) node.label[1] <- "" if (any(nzchar(node.label))) obj$node.label <- node.label if (!is.na(root.edge)) obj$root.edge <- root.edge class(obj) <- "phylo" attr(obj, "order") <- "cladewise" obj$annotations = annotations obj } read.annontated.tree = function (file = "", text = NULL, tree.names = NULL, skip = 0, comment.char = "#", keep.multi = FALSE, ...) { unname <- function(treetext) { nc <- nchar(treetext) tstart <- 1 while (substr(treetext, tstart, tstart) != "(" && tstart <= nc) tstart <- tstart + 1 if (tstart > 1) return(c(substr(treetext, 1, tstart - 1), substr(treetext, tstart, nc))) return(c("", treetext)) } if (!is.null(text)) { if (!is.character(text)) stop("argument `text' must be of mode character") tree <- text } else { tree <- scan(file = file, what = "", sep = "\n", quiet = TRUE, skip = skip, comment.char = comment.char, ...) } if (identical(tree, character(0))) { warning("empty character string.") return(NULL) } tree <- gsub("[ \n\t]", "", tree) tree <- gsub("\\[&R\\]", "", tree) tree <- unlist(strsplit(tree, NULL)) y <- which(tree == ";") Ntree <- length(y) x <- c(1, y[-Ntree] + 1) if (is.na(y[1])) return(NULL) STRING <- character(Ntree) for (i in 1:Ntree) STRING[i] <- paste(tree[x[i]:y[i]], sep = "", collapse = "") tmp <- unlist(lapply(STRING, unname)) tmpnames <- tmp[c(TRUE, FALSE)] STRING <- tmp[c(FALSE, TRUE)] if (is.null(tree.names) && any(nzchar(tmpnames))) tree.names <- tmpnames colon <- grep(":", STRING) if (!is.null(tree.names)) { traits.text = lapply(tree.names, .split.tree.traits) tree.names = lapply(tree.names, .split.tree.names) tree.traits = lapply(traits.text, parse.traits) } if (!length(colon)) { stop(paste("Annotated clado.build is not yet implemented.\n")) obj <- lapply(STRING, annotated.clado.build) } else if (length(colon) == Ntree) { obj <- lapply(STRING, annotated.tree.build) } else { obj <- vector("list", Ntree) obj[colon] <- lapply(STRING[colon], annotated.tree.build) nocolon <- (1:Ntree)[!1:Ntree %in% colon] obj[nocolon] <- lapply(STRING[nocolon], clado.build) } for (i in 1:Ntree) { ROOT <- length(obj[[i]]$tip.label) + 1 if (sum(obj[[i]]$edge[, 1] == ROOT) == 1 && dim(obj[[i]]$edge)[1] > 1) stop(paste("The tree has apparently singleton node(s): cannot read tree file.\n Reading Newick file aborted at tree no.", i)) } if (Ntree == 1 && !keep.multi) obj <- obj[[1]] else { if (!is.null(tree.names)) { names(obj) <- tree.names } class(obj) <- "multiPhylo" } obj } read.annotated.nexus = function (file, tree.names = NULL) { X <- scan(file = file, what = "", sep = "\n", quiet = TRUE) LEFT <- grep("\\[", X) RIGHT <- grep("\\]", X) # browser() # # if (length(LEFT)) { # w <- LEFT == RIGHT # if (any(w)) { # s <- LEFT[w] # X[s] <- gsub("\\[[^]]*\\]", "", X[s]) # } # w <- !w # if (any(w)) { # s <- LEFT[w] # X[s] <- gsub("\\[.*", "", X[s]) # sb <- RIGHT[w] # X[sb] <- gsub(".*\\]", "", X[sb]) # if (any(s < sb - 1)) # X <- X[-unlist(mapply(":", (s + 1), (sb - 1)))] # } # } endblock <- grep("END;|ENDBLOCK;", X, ignore.case = TRUE) semico <- grep(";", X) i1 <- grep("BEGIN TREES;", X, ignore.case = TRUE) i2 <- grep("TRANSLATE", X, ignore.case = TRUE) translation <- if (length(i2) == 1 && i2 > i1) TRUE else FALSE if (translation) { end <- semico[semico > i2][1] x <- X[(i2 + 1):end] x <- unlist(strsplit(x, "[,; \t]")) x <- x[nzchar(x)] TRANS <- matrix(x, ncol = 2, byrow = TRUE) TRANS[, 2] <- gsub("['\"]", "", TRANS[, 2]) n <- dim(TRANS)[1] } start <- if (translation) semico[semico > i2][1] + 1 else semico[semico > i1][1] end <- endblock[endblock > i1][1] - 1 tree <- X[start:end] # browser() rm(X) tree <- tree[tree != ""] semico <- grep(";", tree) Ntree <- length(semico) if (Ntree == 1 && length(tree) > 1) STRING <- paste(tree, collapse = "") else { if (any(diff(semico) != 1)) { STRING <- character(Ntree) s <- c(1, semico[-Ntree] + 1) j <- mapply(":", s, semico) if (is.list(j)) { for (i in 1:Ntree) STRING[i] <- paste(tree[j[[i]]], collapse = "") } else { for (i in 1:Ntree) STRING[i] <- paste(tree[j[, i]], collapse = "") } } else STRING <- tree } rm(tree) # browser() STRING <- STRING[grep("^[[:blank:]]*tree.*= *", STRING, ignore.case = TRUE)] Ntree <- length(STRING) STRING <- gsub("\\[&R\\]", "", STRING) # TODO Parse out tree-level traits nms.trees <- sub(" *= *.*", "", STRING) nms.trees <- sub("^ *tree *", "", nms.trees, ignore.case = TRUE) STRING <- sub("^.*?= *", "", STRING) STRING <- gsub("\\s", "", STRING) # browser() colon <- grep(":", STRING) if (!length(colon)) { stop("annotated.clado.build is not yet implemented.\n") trees <- lapply(STRING, annotated.clado.build) } else if (length(colon) == Ntree) { # trees <- if (translation) { # browser() # stop("treeBuildWithTokens is not yet implemented.\n") # lapply(STRING, .treeBuildWithTokens) # } # else lapply(STRING, annotated.tree.build) trees <- lapply(STRING, annotated.tree.build) # browser() } else { # trees <- vector("list", Ntree) # trees[colon] <- lapply(STRING[colon], annotated.tree.build) # nocolon <- (1:Ntree)[!1:Ntree %in% colon] # trees[nocolon] <- lapply(STRING[nocolon], annotated.clado.build) # if (translation) { # for (i in 1:Ntree) { # tr <- trees[[i]] # for (j in 1:n) { # ind <- which(tr$tip.label[j] == TRANS[, 1]) # tr$tip.label[j] <- TRANS[ind, 2] # } # if (!is.null(tr$node.label)) { # for (j in 1:length(tr$node.label)) { # ind <- which(tr$node.label[j] == TRANS[, # 1]) # tr$node.label[j] <- TRANS[ind, 2] # } # } # trees[[i]] <- tr # } # translation <- FALSE # } stop("Unknown error in read.annotated.nexus.\n") } for (i in 1:Ntree) { tr <- trees[[i]] if (!translation) n <- length(tr$tip.label) ROOT <- n + 1 if (sum(tr$edge[, 1] == ROOT) == 1 && dim(tr$edge)[1] > 1) { stop(paste("The tree has apparently singleton node(s): cannot read tree file.\n Reading NEXUS file aborted at tree no.", i, sep = "")) } } if (Ntree == 1) { trees <- trees[[1]] if (translation) { trees$tip.label <- if (length(colon)) TRANS[, 2] else TRANS[, 2][as.numeric(trees$tip.label)] } } else { if (!is.null(tree.names)) names(trees) <- tree.names if (translation) { if (length(colon) == Ntree) attr(trees, "TipLabel") <- TRANS[, 2] else { for (i in 1:Ntree) trees[[i]]$tip.label <- TRANS[, 2][as.numeric(trees[[i]]$tip.label)] trees <- .compressTipLabel(trees) } } class(trees) <- "multiPhylo" if (!all(nms.trees == "")) names(trees) <- nms.trees } trees }

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/data/genthat_extracted_code/Biograph/examples/Trans.Rd.R

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surayaaramli/typeRrh

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Trans.Rd.R

library(Biograph) ### Name: Trans ### Title: Transitions by age ### Aliases: Trans ### ** Examples data (GLHS) y<- Parameters(GLHS) z <- Trans (GLHS)

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/vis/circlize.R

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wallingTACC/oncourse

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circlize.R

library(circlize) library(data.table) library(reshape2) setwd("/home/walling/dev/git/oncourse/vis") data = fread("./data/fice_transfers.csv", na.strings = '#NULL!', colClass=c('character', rep('numeric', 113))) # Drop last column 'Total' data = data[,1:(ncol(data)-1)] # Drop last 2 rows data = data[1:(nrow(data)-2),] # Clean up column names colnames(data) = substring(colnames(data), 5) colnames(data)[1] = 'giving_fice' # Form Matrix m = as.matrix(data[,2:ncol(data)]) m.w = melt(m) # Remove NAs m.w = m.w[!is.na(m.w$value),] # Limit to those where flow > 10 m.w = m.w[m.w$value > 50,] # Plot #chordDiagram(m.w) # Advanced Plot chordDiagram(m.w, annotationTrack = "grid", preAllocateTracks = list(track.height = 0.1)) circos.trackPlotRegion(track.index = 1, panel.fun = function(x, y) { xlim = get.cell.meta.data("xlim") xplot = get.cell.meta.data("xplot") ylim = get.cell.meta.data("ylim") sector.name = get.cell.meta.data("sector.index") if(abs(xplot[2] - xplot[1]) < 10) { circos.text(mean(xlim), ylim[1], sector.name, facing = "clockwise", niceFacing = TRUE, adj = c(0, 0.5)) } else { circos.text(mean(xlim), ylim[1], sector.name, facing = "inside", niceFacing = TRUE, adj = c(0.5, 0)) } }, bg.border = NA)

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/app/r/deconstruct.r

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keller-mark/mutation-visualization

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deconstruct.r

#!/usr/bin/r library(deconstructSigs) library(SignatureEstimation) library(BSgenome.Hsapiens.UCSC.hg19) selected_columns <- c("icgc_sample_id", "chromosome", "chromosome_start", "reference_genome_allele", "mutated_to_allele") somatic_mutations_path <- "/app/data_temp/extracted_data.tsv" somatic_mutations <- read.table(somatic_mutations_path, sep = '\t', header = TRUE, colClasses = c("factor")) somatic_mutations_stripped <- somatic_mutations[, which(names(somatic_mutations) %in% selected_columns)] sample.name.to.id <- function(sample.name) { return(as.numeric(gsub("SA", "", as.character(sample.name)))) } chr.prefix <- function(chr.input) { return(paste("chr", as.character(chr.input), sep = "")) } start.numeric <- function(start.input) { return(as.numeric(as.character(start.input))) } somatic_mutations_stripped[,2] <- sapply(somatic_mutations_stripped[,2], chr.prefix) somatic_mutations_stripped[,3] <- sapply(somatic_mutations_stripped[,3], start.numeric) somatic_mutations_stripped[,4] <- sapply(somatic_mutations_stripped[,4], as.character) somatic_mutations_stripped[,5] <- sapply(somatic_mutations_stripped[,5], as.character) somatic_mutations_stripped <- somatic_mutations_stripped[!(somatic_mutations_stripped$chromosome=="chrMT"),] sigs.input <- mut.to.sigs.input( mut.ref = somatic_mutations_stripped, sample.id = "icgc_sample_id", chr = "chromosome", pos = "chromosome_start", ref = "reference_genome_allele", alt = "mutated_to_allele" ) sigs.input <- sigs.input[, order(colnames(sigs.input))] tricontext.fractions <- t(getTriContextFraction(sigs.input, trimer.counts.method = 'default')) sample.names <- colnames(tricontext.fractions) signature.distributions <- data.frame(matrix(ncol = 30, nrow = 0)) for (i in 1:ncol(tricontext.fractions)) { signature.distributions[sample.names[i],] <- decomposeQP(tricontext.fractions[,i], signaturesCOSMIC) } #boxplot.matrix(as.matrix(signature.distributions)) write.csv(t(signature.distributions), file = "/app/static/data/signature_distributions_t.csv")

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/utils.R \name{convertIdx} \alias{convertIdx} \title{importFrom DT convertIdx} \usage{ convertIdx(i, names, n = length(names), invert = FALSE) } \description{ importFrom DT convertIdx }

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/R/rasterFromCells.R

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rasterFromCells.R

# Author: Robert J. Hijmans # Date : April 2009 # Version 0.9 # Licence GPL v3 rasterFromCells <- function(x, cells, values=TRUE) { x <- raster(x) u <- stats::na.omit(unique(cells)) # now removing NAs 2018-02-22 u <- u[ u > 0 & u <= ncell(x) ] if (length(u) == 0) { stop('no valid cells') } cols <- colFromCell(x, u) rows <- rowFromCell(x, u) res <- res(x) x1 <- xFromCol(x, min(cols)) - 0.5 * res[1] x2 <- xFromCol(x, max(cols)) + 0.5 * res[1] y1 <- yFromRow(x, max(rows)) - 0.5 * res[2] y2 <- yFromRow(x, min(rows)) + 0.5 * res[2] e <- extent(x1, x2, y1, y2) r <- crop(x, e) if (values) { r <- setValues(r, cellsFromExtent(x, e)) } return(r) }

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/man/subSpecials.Rd

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subSpecials.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/subspecials.r \name{subSpecials} \alias{subSpecials} \title{Sub special characters out of character vectors.} \usage{ subSpecials(..., specialChars = c("!", "(", ")", "-", "=", "*", ".")) } \arguments{ \item{\dots}{Character vectors that will be altered by subbing the special characters with their escaped equivalents} \item{specialChars}{The characters to be subbed out} } \value{ The provided vectors are returned with any of the defined specialChars subbed out for their escaped equivalents. Each vector is returned as an element of a list. } \description{ Converts each of the special characters to their escaped equivalents in each element of each vector. } \details{ Each element in the specialChar vector is subbed for its escaped equivalent in each of the elements of each vector passed in } \examples{ subSpecials(c("Hello", "(parens)", "Excited! Mark")) subSpecials(c("Hello", "(parens)", "Excited! Mark"), specialChars=c("!", "(")) subSpecials(c("Hello", "(parens)", "Excited! Mark"), c("This is a period. And this is an asterisk *"), specialChars=c("!", "(")) subSpecials(c("Hello", "(parens)", "Excited! Mark"), c("This is a period. And this is an asterisk *"), specialChars=c("!", "(", "*")) } \seealso{ \code{\link{sub}} \code{\link{subOut}} } \author{ Jared P. Lander www.jaredlander.com } \keyword{string} \keyword{text}

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/sample_data_Save.R

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tfrance/Bias-Variance-Tradeoff

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sample_data_Save.R

filePathRoot <- 'C:\\Users\\deifen\\Documents\\Projects\\Bias and overfitting trade offs\\Project\\SampleData\\sampledata' n <- 25 r <- 20 vect <- seq(from= -pi, to =pi, by=.1) signal<-function(n) {sin(n)} for(i in 1:10) { x <- sample(vect, r) noise<- rnorm(r, mean=0, sd=.5) y <- signal(x) + noise Data <- data.frame(x, y) write.csv(Data, file=paste(filePathRoot, i, collapse=NULL), quote=FALSE, row.names=FALSE) } plot(y ~ x, Data) curve(signal(x), from=-pi, to=pi, , xlab="x", ylab="y", add=TRUE)

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/scripts/tema1/03-NBL-install.R

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ferreret/r-basic

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install.packages("tidyverse", dependencies = TRUE) library(tidyverse) install.packages("magic", dependencies = TRUE) library(magic) magic(6) installed.packages()

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test_that("Able to create package with validated package basics", { withr::with_tempdir({ quite <- capture.output({ vt_create_package(".", open = FALSE) }) expect_true( devtools::is.package(devtools::as.package(".")) ) expect_true( dir.exists("vignettes/validation") ) }) }) test_that("throws standard error when unable to create the package", { withr::with_tempdir({ expect_error( vt_create_package("temp_package"), "Failed to create package. Error" ) }) })

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/sprintf_data_frame.R \name{sprintf_data_frame} \alias{sprintf_data_frame} \alias{sprintf_data_frame_single} \title{Create new columns in a data.frame with sprintf results} \usage{ sprintf_data_frame(data, ..., factor_out_if_factor_in = TRUE, ordered = NULL) sprintf_data_frame_single( data, format, factor_out_if_factor_in = TRUE, ordered = NULL ) } \arguments{ \item{data}{the data to use for formatting} \item{...}{a named list of character vectors. Names are new columns for \code{data}, and values are sent to \code{format} in \code{sprintf_data_frame_single}.} \item{factor_out_if_factor_in}{If any of the input columns are factors, make the output column a factor in the same order as the input column factors} \item{ordered}{If \code{factor_out_if_factor_in} converts the output to a factor, pass to \code{base::factor}. If \code{NULL}, then it is set to \code{TRUE} if any of the input columns are ordered factors.} \item{format}{A named character vector where the names are column names in \code{data} and the values are sprintf format strings for the column.} } \value{ The data frame with columns added for the names of \code{...}. A character vector with one element per row of \code{data}. } \description{ Create new columns in a data.frame with sprintf results } \section{Functions}{ \itemize{ \item \code{sprintf_data_frame_single()}: Generate a character vector based on sprintf input formats }} \examples{ sprintf_data_frame( data=mtcars, cyl_mpg=c(mpg="\%g miles/gallon, ", cyl="\%g cylinders"), disp_hp=c(disp="\%g cu.in. displacement, ", hp="\%g hp") ) sprintf_data_frame_single( data=mtcars, format=c(mpg="\%g miles/gallon, ", cyl="\%g cylinders") ) }

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/Discrete generation anadromy model two sexes.R

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Discrete generation anadromy model two sexes.R

############################################################################################################ # Discrete generation, simple model of evolution of anadromy. # The evolvable trait here is the "underlying threshold for residency", i.e. the condition value above which # residency occurs, below which anadromy occurs. # "Condition" here refers to a trait that varies across individuals randomly (variation is purely environmentally # driven in this simple model, i.e. there is no genetic basis to condition). ############################################################################################################ rm(list=ls()) # clear all ## PARAMETERS: nyears <- 10 # number of years for each simulation run nreps<- 1 # number of simulations to run mu_thresh <- 0 # initial mean genetic value of the treshold trait h2 <- 0.5 # heritability of the threshold trait Vp <- 1 # phenotypic variance of the threshold trait Va <- Vp*h2 # additive genetic variance of the threshold trait Ve <- Vp - Va # environmental variance of the threshold trait (Vp = Va + Ve) mu_cond <- 0 # mean value of the condition trait V_cond <- 1 # variance of the condition trait (in this case, all the phenotypic variance is environmental) N_females <- 500 # Number of females N_males <- 500 # Number of males N_init <- N_females + N_males # initial population size (number of fry) is the sum of males and females # NB**** If any of the following 4 parameters are changed, this will drive evolution towards one or other tactic # NB**** (e.g. if S_anadromous is increased, then the pop will evolve towards a higher fraction of anadromous fish) S_anadromousF <- 0.35 # fraction of female anadromous fish that survive to breeding age S_anadromousM <- 0.35 # fraction of male anadromous fish that survive to breeding age S_residentF <- 0.5 # fraction of female resident fish that survive to breeding age S_residentM <- 0.5 # fraction of male resident fish that survive to breeding age F_anadromous <- 10 # fecundity of anadromous fish (number of offspring) F_resident <- 5 # fecundity of resident fish (number of offspring) K <- 100 # Carrying capacity (this is a simple ceiling population size, above which individuals # are randomly culled, to avoid the population sky-rocketing to huge sizes # The following bits simply create empty vectors, in which the results of each model run can be stored: sim <- c() # Store simulation number year <- c() # Store year number pop.size1 <- c() # Store population size of before survival and fecundity selection pop.size1_F <- c() # Store population size of females before survival and fecundity selection pop.size1_M <- c() # Store population size of males before survival and fecundity selection pop.size2 <- c() # Store population size of after survival and fecundity selection pop.size2_F <- c() # Store population size of females after survival and fecundity selection pop.size2_M <- c() # Store population size of males after survival and fecundity selection mean.thresh <- c() # Store realised mean genetic threshold across all individuals va.realiz<- c() # Store realised variance in genetic thresholds across all individuals frac.anadF <- c() # Store realised fraction of anadromous female fish (number from 0 to 1) frac.anadM <- c() # Store realised fraction of anadromous male fish (number from 0 to 1) real.surv.anadF <- c()# Store realised survival of anadromous female fish real.surv.anadM <- c()# Store realised survival of anadromous male fish real.surv.resF <- c() # Store realised survival of resident female fish real.surv.resM <- c() # Store realised survival of resident male fish ########################################################### ### SIMULATION STARTS HERE!!! ########################################################### ### cycle over simulations for (Sim in 1:nreps) { ## Initiate the population with N_init number of breeding values for the threshold trait. These are the individual-specific, ## genetic values for the evolvable trait a_threshF <- rnorm(N_females, mu_thresh, (sqrt(Va))) a_threshM <- rnorm(N_males, mu_thresh, (sqrt(Va))) threshSize<-length(a_threshF) + length(a_threshM) ### cycle over years for (Y in 1:nyears) { ### additional random mortality if K exceeded, to keep population size in check if (length(a_threshF) + length(a_threshM)>K) { surv <- ifelse(runif(threshSize,0,1)<(K/threshSize),1,0) # sum(surv==1) # runif draws a random number from a uniform #distribution, here between 0 and 1. If this number #is less than the desired fraction of survivors (K/N), #that individual survives, otherwise it dies a_threshF <- a_threshF[surv==1] # subset the breeding values for only those "individuals" that survive a_threshM <- a_threshM[surv==1] # subset the breeding values for only those "individuals" that survive a_threshF <- na.omit(a_threshF) a_threshM <- na.omit(a_threshM) } pop.size1 <- c(pop.size1,length(a_threshF) + length(a_threshM)) # calculate and store pop size ( = length of a_thresh vector) IDF <- 1:length(a_threshF) # allocate arbitraty identities to each "individual" IDM <- 1:length(a_threshM) + 500 # allocate arbitraty identities to each "individual" e_threshF <- rnorm(length(a_threshF), 0, (sqrt(Ve))) # draw environmental deviations for the threshold trait e_threshM <- rnorm(length(a_threshM), 0, (sqrt(Ve))) # draw environmental deviations for the threshold trait z_threshF <- a_threshF + e_threshF # the phenotypic value (z) for each individual is the sum of the genetic # (breeding) value and the environmental deviation# z_threshM <- a_threshM + e_threshM condF <- rnorm(length(a_threshF), mu_cond, sqrt(V_cond)) # draw random condition values for each individual condM <- rnorm(length(a_threshM), mu_cond, sqrt(V_cond)) # draw random condition values for each individual anadromousF <- ifelse(condF > z_threshF, 0, 1) # define migration tactics based on threshold model (if an individual's # condition is > it's threshold value, it becomes resident (i.e. anadromous==0). # otherwise it becomes anadromous (anadromous == 1)) anadromousM <- ifelse(condF > z_threshF, 0, 1) frac.anadF <- c(frac.anadF, mean(anadromousF)) # calculate and store the fraction of anadromous fish in the pop at this timepoint frac.anadM <- c(frac.anadM, mean(anadromousM)) # calculate and store the fraction of anadromous fish in the pop at this timepoint anad_fishF <- IDF[anadromousF==1] # pull out the IDs for the anadromous fish res_fishF <- IDF[anadromousF==0] # pull out the IDs for the resident fish anad_fishM <- IDM[anadromousM==1] # pull out the IDs for the anadromous fish res_fishM <- IDM[anadromousM==0] # pull out the IDs for the resident fish # Calculate survival of anadromous fish by drawing a random number from uniform distribution between 0 and 1. If this number # is < S_anadromous (i.e. the inputted expected survival of anadromous fish), then that individual survives, otherwise it dies surv_anadF <- runif(length(anad_fishF)) < rep(S_anadromousF, length(anad_fishF)) surv_anadM <- runif(length(anad_fishM)) < rep(S_anadromousM, length(anad_fishM)) # Do same as above for the resident fish: surv_resF <- runif(length(res_fishF)) < rep(S_residentF, length(res_fishF)) surv_resM <- runif(length(res_fishM)) < rep(S_residentM, length(res_fishM)) real.surv.anadF <- c(real.surv.anadF, mean(surv_anadF)) # Calculate and store the realised mean survival of anadromous fish, as a check real.surv.anadM <- c(real.surv.anadM, mean(surv_anadM)) # Calculate and store the realised mean survival of anadromous fish, as a check real.surv.resF <- c(real.surv.resF, mean(surv_resF)) # Calculate and store the realised mean survival of resident fish, as a check real.surv.resM <- c(real.surv.resM, mean(surv_resM)) # Calculate and store the realised mean survival of resident fish, as a check anad_fishF <- anad_fishF[surv_anadF] # Extract the IDs of the surviving anadromous fish anad_fishM <- anad_fishM[surv_anadM] # Extract the IDs of the surviving anadromous fish res_fishF <- res_fishF[surv_resF] # Extract the IDs of the resident anadromous fish res_fishM <- res_fishM[surv_resM] # Extract the IDs of the resident anadromous fish parents <- c(anad_fishF,anad_fishM,res_fishF,res_fishM) # Make a new vector of parent IDs by stringing together the anadromous and resident IDs parents <- data.frame(parents) parents$gender <- ifelse(parents > 500,"male", "female") parents <- data.frame(parents) head(parents) # Make a vector of family sizes (number of offspring) for each parent, which will be used below in the mating and reproduction loop # This vector is ordered the same way as the vector of parents' IDs (anadromous fish first, then residents). family_size <- c( rep(F_anadromous, (length(anad_fishF)) + length(anad_fishM) ) , rep(F_resident, (length(res_fishF) + length(res_fishM)))) # check with this length(family_size) == length(parents$parents) # Mating and reproduction: # sum(parents$parents<0) & sum(parents$parents>500)>=2 # FALSE condition to test if (sum(parents$parents<500,na.rm = T) & sum(parents$parents>500,na.rm=T)>=2) # if more than 1 individual, mating happens { # sum(parents$parents<500) + sum(parents$parents>500) ### mating # npairs <- length(parents)%/%2 # number of mating pairs = no. of parents ÷ 2, and rounded down motherPotential <- sample(parents$parents[parents$parents<500], replace=F) # randomly select n=npairs "mothers" out of the list of parents IDs fatherPotential <- sample(parents$parents[parents$parents>500], replace=F) # randomly select n=npairs "fathers" out of the list of parents IDs # we have to correct for the fact that the numbers of mothers and fathers won't match, this ifelse statement # does that by sampling from the longer parent vector by the length of the smaller one if(length(motherPotential) > length(fatherPotential)){ father<-fatherPotential; mother<-sample(motherPotential,length(fatherPotential), replace = FALSE) } else { mother<-motherPotential; father<-sample(fatherPotential,length(motherPotential), replace = FALSE) } a_thresh_fath <- a_threshM[match(father,IDM)] # extract the corresponding breeding values for these fathers from the vector a_thresh a_thresh_moth <- a_threshF[match(mother,IDF)] # extract the corresponding breeding values for these mothers from the vector a_thresh mid <- (a_thresh_fath + a_thresh_moth)/2 # calculate the mid-parental genetic value (mean of breeding values of each parent) ### breeding BV <- c() # create an empty vector (BV= "breeding values") to store the new genetic values of the offspring for (i in 1:length(mother)) # cycle over the n mothers { # Generate new genetic values for the offpring that are centred on the mid-parental values of their parents, # plus a random deviation drawn from 0.5Va. This essentially generates genetic variation among siblings, # with the expected genetic variation among siblings equal to half the population-wide additive genetic variance. # This comes directly from quantitative genetic theory, see Chapter 4 of Roff 2009 book on "Modelling Evolution" BVo <- rep(mid[i] , family_size[parents==mother[i]]) + rnorm(family_size[parents==mother[i]], 0, sqrt(0.5*Va)) # Add these offspring genetic values for each family to the vector BV: BV <- c(BV, BVo) } idx <- sample.int(length(BV),size=length(BV)/2,replace=FALSE) a_threshF <- BV[idx] a_threshM <- BV[-idx] # Now we replace the parental genetic values with a new list of offspring genetic values. # This is because we here assume that all parents die immediately after reproduction, along with their # genetic values! ### store results sim <- c(sim,Sim) year <- c(year,Y) pop.size2 <- c(pop.size2,length(a_threshF) + length(a_threshM)) # store population size after survival and mating mean.thresh <- c(mean.thresh,mean(c(a_threshF,a_threshM, na.rm=T))) # store realised mean genetic value. va.realiz<- c(va.realiz,var(c(a_threshF,a_threshM, na.rm=T))) # store realised variance in genetic values. } else { sim <- c(sim,Sim) year <- c(year,Y) pop.size2 <- c(pop.size2,0) # If the number of parents is <2, then simply store a 0 for pop size mean.thresh <- c(mean.thresh,NA) # And store an NA here va.realiz<- c(va.realiz, NA) # And store an NA here } } # close the year loop } # close the simulation replicate loop # Create a data frame called r1 ("results 1") to store all the results: r1 <- data.frame(sim,year,pop.size1,pop.size2,mean.thresh,va.realiz,frac.anadF,frac.anadM,real.surv.anadF,real.surv.anadM ,real.surv.resF, real.surv.resM) # Calculate the mean population size per year across all replicate simulations: mN <- tapply(r1$pop.size1, r1$year, quantile, 0.5, na.rm=T) lciN <- tapply(r1$pop.size1, r1$year, quantile, 0.05, na.rm=T) # calculate the lower confidence interval for this variable uciN <- tapply(r1$pop.size1, r1$year, quantile, 0.95, na.rm=T) # calculate the upper confidence interval for this variable # Calculate the mean fraction of anadromous fish per year across all replicate simulations: mA <- tapply(r1$frac.anad, r1$year, quantile, 0.5, na.rm=T) lcA <- tapply(r1$frac.anad, r1$year, quantile, 0.05, na.rm=T) ucA <- tapply(r1$frac.anad, r1$year, quantile, 0.95, na.rm=T) # Calculate the mean genetic threshold value per year across all replicate simulations: mT <- tapply(r1$mean.thresh, r1$year, quantile, 0.5, na.rm=T) lcT <- tapply(r1$mean.thresh, r1$year, quantile, 0.05, na.rm=T) ucT <- tapply(r1$mean.thresh, r1$year, quantile, 0.95, na.rm=T) # Calculate the mean realised survival of anadromous fish per year across all replicate simulations: mSA <- tapply(r1$real.surv.anad, r1$year, quantile, 0.5, na.rm=T) lcSA <- tapply(r1$real.surv.anad, r1$year, quantile, 0.05, na.rm=T) ucSA <- tapply(r1$real.surv.anad, r1$year, quantile, 0.95, na.rm=T) # Calculate the mean realised survival of resident fish per year across all replicate simulations: mSR <- tapply(r1$real.surv.res, r1$year, quantile, 0.5, na.rm=T) lcSR <- tapply(r1$real.surv.res, r1$year, quantile, 0.05, na.rm=T) ucSR <- tapply(r1$real.surv.res, r1$year, quantile, 0.95, na.rm=T) yr<- 1:nyears # create a vector of year IDs, for plotting purposes below par(mfrow=c(2,2)) # open a 2 x 2 tiled plotting window # Plot 1 = Population size against year: plot(yr, mN, xlab='Year', ylab='Population size', type='l', lwd=1, ylim=c(0,250), cex.lab=1.2) points(yr, lciN, type='l', lwd=1, lty=2, ylim=c(0,250)) points(yr, uciN, type='l', lwd=1, lty=2, ylim=c(0,250)) # Plot 2 = Survival or anadromous and resident fish against year: plot(yr, mSA, xlab='Year', ylab='Survival anadromous/resident', type='l', lwd=1, ylim=c(0,1), cex.lab=1.2, col="blue") points(yr, mSR, type='l', lwd=1, ylim=c(0,1), cex.lab=1.2, col="red") legend("topright",legend=c("andadromous","resident"), col=c("blue","red"), lty=c(1,1)) # Plot 3 = Fraction of anadromous fish against year: # ***NB This fraction will change as the population evolves!! plot(yr, mA, xlab='Year', ylab='Fraction anadromous', type='l', lwd=1, ylim=c(0,1), cex.lab=1.2) points(yr, lcA, type='l', lwd=1, lty=2, ylim=c(0,1)) points(yr, ucA, type='l', lwd=1, lty=2, ylim=c(0,1)) # Plot 4 = Mean genetic threshold against year: # ***NB The mean genetic threshold will change as the population evolves!! plot(yr, mT, xlab='Year', ylab='Mean genetic threshold', type='l', lwd=1, ylim=c(-5,5), cex.lab=1.2) points(yr, lcT, type='l', lwd=1, lty=2, ylim=c(0,1)) points(yr, ucT, type='l', lwd=1, lty=2, ylim=c(0,1))

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/tests/testthat/test-document.R

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schuemie/PatientLevelPrediction

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0b59c97a53ab4c6aaf6236048d5bcc9363c2716e

refs/heads/master

2020-09-05T00:50:10.021513

2019-11-06T07:46:44

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test-document.R

# Copyright 2019 Observational Health Data Sciences and Informatics # # This file is part of PatientLevelPrediction # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. library("testthat") context("Document.R") # Test unit for the document creation test_that("document creation parameters", { #test createPlpDocument inputs expect_error(createPlpJournalDocument(plpResult=NULL)) expect_error(createPlpJournalDocument(plpResult=1:5)) plpResult <- list(1) class(plpResult) <- 'plpModel' # target name not character expect_error(createPlpJournalDocument(plpResult=plpResult, targetName=1)) # outcomeName not character expect_error(createPlpJournalDocument(plpResult=plpResult, targetName='target test', outcomeName=1)) # characterisationSettings not list expect_error(createPlpJournalDocument(plpResult=plpResult, targetName='target test', outcomeName='outcome test',characterisationSettings=1 )) # includeTrain not logical expect_error(createPlpJournalDocument(plpResult=plpResult, targetName='target test', outcomeName='outcome test',characterisationSettings=list(), includeTrain='Y')) # includeTest not logical expect_error(createPlpJournalDocument(plpResult=plpResult, targetName='target test', outcomeName='outcome test',characterisationSettings=list(), includeTrain=T, includeTest='Y')) # includePredictionPicture not logical expect_error(createPlpJournalDocument(plpResult=plpResult, targetName='target test', outcomeName='outcome test',characterisationSettings=list(), includeTrain=T, includeTest=T, includePredictionPicture='Y')) # includeAttritionPlot not logical expect_error(createPlpJournalDocument(plpResult=plpResult, targetName='target test', outcomeName='outcome test',characterisationSettings=list(), includeTrain=T, includeTest=T, includePredictionPicture=T, includeAttritionPlot='Y')) #set.seed(1234) #data(plpDataSimulationProfile) #sampleSize <- 2000 #plpData <- PatientLevelPrediction::simulatePlpData(plpDataSimulationProfile, n = sampleSize) #population <- PatientLevelPrediction::createStudyPopulation(plpData, outcomeId=2, # riskWindowEnd = 365) #modelset <- PatientLevelPrediction::setLassoLogisticRegression() #plpResult <- PatientLevelPrediction::runPlp(population, plpData, modelset, saveModel = F) #doc <- PatientLevelPrediction::createPlpJournalDocument(plpResult=plpResult, plpData = plpData, # targetName='target test', # outcomeName='outcome test', # includeTrain=T, includeTest=T, # includePredictionPicture=T, # includeAttritionPlot=T) #expect_equal(doc, TRUE) ## clean up #file.remove(file.path(getwd(), 'plp_journal_document.docx')) }) data(plpDataSimulationProfile) sampleSize <- 2000 plpData <- PatientLevelPrediction::simulatePlpData(plpDataSimulationProfile, n = sampleSize) population <- PatientLevelPrediction::createStudyPopulation(plpData, outcomeId=2, riskWindowEnd = 365) modelset <- PatientLevelPrediction::setCoxModel() plpResult <- PatientLevelPrediction::runPlp(population=population, plpData=plpData, modelSettings = modelset, savePlpData = F, saveEvaluation = F, savePlpResult = F, savePlpPlots = F, verbosity = 'NONE') test_that("createPlpJournalDocument document works", { doc <- PatientLevelPrediction::createPlpJournalDocument(plpResult=plpResult, plpData = plpData, targetName='target test', outcomeName='outcome test', includeTrain=T, includeTest=T, includePredictionPicture=T, includeAttritionPlot=T, save=F) expect_equal(class(doc), "rdocx") }) test_that("createPlpReport document works", { doc <- createPlpReport(plpResult=plpResult, plpValidation=NULL, plpData = plpData, targetName = '<target population>', outcomeName = '<outcome>', targetDefinition = NULL, outcomeDefinition = NULL, outputLocation=file.path(getwd(), 'plp_report.docx'), save = F) expect_equal(class(doc), "rdocx") })

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/BATCH_simulation-multComp-type1.R

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bozenne/Article-lvm-multiple-comparisons

4bbf4f2ea9b8601bf600c9c35204f809c4761b70

7ca5a1a4b17336e275f0b673a7c4467a8c35b09a

refs/heads/master

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2022-02-04T11:11:56

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BATCH_simulation-multComp-type1.R

## path <- "P:/Cluster/LVMproject/article-multipleComparisons" ## setwd(path) ## source("BATCH_simulation-multComp-type1.R") ## * seed iter_sim <- as.numeric(Sys.getenv("SGE_TASK_ID")) n.iter_sim <- as.numeric(Sys.getenv("SGE_TASK_LAST")) if(is.na(iter_sim)){iter_sim <- 97} if(is.na(n.iter_sim)){n.iter_sim <- 100} cat("iteration ",iter_sim," over ",n.iter_sim,"\n", sep = "") set.seed(1) seqSeed <- sample(1:max(1e5,n.iter_sim),size=n.iter_sim,replace=FALSE) iSeed <- seqSeed[iter_sim] set.seed(iSeed) cat("seed: ",iSeed,"\n") ## * path path <- "." path.res <- file.path(path,"Results","simulation-multComp-type1") if(dir.exists(path.res)==FALSE){ dir.create(path.res) } path.output <- file.path(path,"output","simulation-multComp-type1") if(dir.exists(path.output)==FALSE){ dir.create(path.output) } ## * libraries library(lava) library(data.table) library(multcomp) library(lavaSearch2) ## * settings seqN <- c(30,50,75,100,150,200,300,500) seqCor <- c(0.1,0.2,0.35,0.65,1,5)##c(0,0.5,1,1.5,3,5) n.Cor <- length(seqCor) n.rep <- 100 ## * model ## ** generative model m.sim <- lvm(c(log.thalamus, log.pallidostriatum, log.neocortex, log.midbrain, log.pons, log.cingulateGyrus, log.hippocampus, log.supramarginalGyrus, log.corpusCallosum)~ a * eta + genotypeHAB + b * groupconcussion) latent(m.sim) <- ~eta ## ** investigator model m.test <- lvm(c(log.thalamus, log.pallidostriatum, log.neocortex, log.midbrain, log.pons, log.cingulateGyrus, log.hippocampus, log.supramarginalGyrus, log.corpusCallosum)~ eta + genotypeHAB + groupconcussion) latent(m.test) <- ~eta ## * prepare n.N <- length(seqN) n.Cor <- length(seqCor) ## ** value for the simulation sim.coef <- c("log.pallidostriatum" = 0.07255, "log.neocortex" = -0.08699, "log.midbrain" = 0.25676, "log.pons" = 0.28991, "log.cingulateGyrus" = 0.09924, "log.hippocampus" = 0.09823, "log.supramarginalGyrus" = -0.1254, "log.corpusCallosum" = -0.00549, "eta" = 1.43044, "log.thalamus~genotypeHAB" = 0.60113, "log.thalamus~groupconcussion" = 0.11999, "log.pallidostriatum~eta" = 0.83452, "log.pallidostriatum~genotypeHAB" = 0.57197, "log.pallidostriatum~groupconcussion" = 0.11358, "log.neocortex~eta" = 0.85006, "log.neocortex~genotypeHAB" = 0.57948, "log.neocortex~groupconcussion" = 0.04283, "log.midbrain~eta" = 0.84739, "log.midbrain~genotypeHAB" = 0.61591, "log.midbrain~groupconcussion" = 0.09895, "log.pons~eta" = 0.86516, "log.pons~genotypeHAB" = 0.53958, "log.pons~groupconcussion" = 0.01545, "log.cingulateGyrus~eta" = 0.76682, "log.cingulateGyrus~genotypeHAB" = 0.65551, "log.cingulateGyrus~groupconcussion" = 0.15936, "log.hippocampus~eta" = 0.76147, "log.hippocampus~genotypeHAB" = 0.57525, "log.hippocampus~groupconcussion" = 0.11901, "log.supramarginalGyrus~eta" = 0.87999, "log.supramarginalGyrus~genotypeHAB" = 0.57436, "log.supramarginalGyrus~groupconcussion" = 0.05089, "log.corpusCallosum~eta" = 0.67779, "log.corpusCallosum~genotypeHAB" = 0.57192, "log.corpusCallosum~groupconcussion" = 0.17416, "log.thalamus~~log.thalamus" = 0.01308, "log.pallidostriatum~~log.pallidostriatum" = 0.00987, "log.neocortex~~log.neocortex" = 0.00603, "log.midbrain~~log.midbrain" = 0.00402, "log.pons~~log.pons" = 0.01053, "log.cingulateGyrus~~log.cingulateGyrus" = 0.00451, "log.hippocampus~~log.hippocampus" = 0.01247, "log.supramarginalGyrus~~log.supramarginalGyrus" = 0.00612, "log.corpusCallosum~~log.corpusCallosum" = 0.01602, "eta~~eta" = 0.06319) ## ** give appropriate name dfType <- coefType(lava::estimate(m.sim,lava::sim(m.sim,1e2)), as.lava = FALSE)[,c("name","param","lava")] ## name2lava <- setNames(dfType[!is.na(dfType$lava),"lava"],dfType[!is.na(dfType$lava),"name"]) sim.coefLava <- sim.coef[setdiff(names(sim.coef), dfType[is.na(dfType$lava),"name"])] ## dfType[is.na(dfType$lava),"name"] ## ** null hypotheses name.test <- paste0(c("log.thalamus", "log.pallidostriatum", "log.neocortex", "log.midbrain", "log.pons", "log.cingulateGyrus", "log.hippocampus", "log.supramarginalGyrus", "log.corpusCallosum"),"~groupconcussion") n.test <- length(name.test) ## * loop dt <- NULL pb <- txtProgressBar(max = n.Cor) dt.res <- NULL for(iN in 1:n.N){ # iN <- 5 for(iCor in 1:n.Cor){ # iCor <- 1 cat("sample size=",seqN[iN],", correlation=",seqCor[iCor],": ", sep = "") n.tempo <- seqN[iN] a.tempo <- seqCor[iCor] for(iRep in 1:n.rep){ # iRep <- 1 cat(iRep," ") ls.max <- list() ## ** Simulate data dt.data <- lava::sim(m.sim, n = n.tempo, p = c(a = a.tempo, sim.coefLava, b = 0), latent = FALSE) ## ** models e.lvm <- estimate(m.test, data = dt.data) ## summary(e.lvm) ##setdiff(c(endogenous(m.test),exogenous(m.test)), names(dt.data)) ## e.lvm <- estimate(m.test, data = dt.data, control = list(constrain = TRUE, starterfun = "startvalues")) ## e.lvm <- estimate(m.test, data = dt.data, control = list(constrain = TRUE, starterfun = "startvalues0")) ## e.lvm <- estimate(m.test, data = dt.data, control = list(constrain = TRUE, starterfun = "startvalues1")) ## e.lvm <- estimate(m.test, data = dt.data, control = list(starterfun = "startvalues")) ## e.lvm <- estimate(m.test, data = dt.data, control = list(starterfun = "startvalues0")) ## e.lvm <- estimate(m.test, data = dt.data, control = list(starterfun = "startvalues1")) if (e.lvm$opt$convergence == 1) { next } if (any(eigen(getVarCov2(e.lvm))$values <= 0)) { next } name.coef <- names(coef(e.lvm)) n.coef <- length(name.coef) ## ** create contrast matrix Ccontrast <- matrix(0, ncol = n.coef, nrow = n.test, dimnames = list(name.test,name.coef)) diag(Ccontrast[name.test,name.test]) <- 1 ## ** adjustment for multiple comparison ## *** lava e.glht <- glht(e.lvm, linfct = Ccontrast) cor.test <- cov2cor(e.glht$vcov[name.test,name.test]) medianCor.test <- median(abs(cor.test[lower.tri(cor.test)])) e0.glht <- summary(e.glht, test = univariate()) eB.glht <- summary(e.glht, test = adjusted(type = "bonferroni")) eHochberg.glht <- summary(e.glht, test = adjusted(type = "hochberg")) eHommel.glht <- summary(e.glht, test = adjusted(type = "hommel")) eS.glht <- summary(e.glht, test = adjusted(type = "single-step")) name.X <- names(e0.glht$test$coef) p.value.none <- as.double(e0.glht$test$pvalues) p.value.bonf <- as.double(eB.glht$test$pvalues) p.value.hoch <- as.double(eHochberg.glht$test$pvalues) p.value.homm <- as.double(eHommel.glht$test$pvalues) p.value.max <- as.double(eS.glht$test$pvalues) ### *** lavaSearch 2 e.glht2 <- try(glht2(e.lvm, linfct = Ccontrast, rhs = rep(0, n.test)), silent = TRUE) ## e.glht2 <- try(glht2(e.lvm, linfct = Ccontrast), silent = TRUE) if("try-error" %in% class(e.glht2) || is.na(e.glht2$df) || (e.glht2$df<0)){ p.value.none2 <- NA p.value.bonf2 <- NA p.value.max2 <- NA medianCor.test2 <- NA }else{ cor.test2 <- cov2cor(e.glht2$vcov[name.test,name.test]) medianCor.test2 <- median(abs(cor.test2[lower.tri(cor.test)])) e0.glht2 <- summary(e.glht2, test = univariate()) eB.glht2 <- summary(e.glht2, test = adjusted(type = "bonferroni")) eHochberg.glht2 <- summary(e.glht2, test = adjusted(type = "hochberg")) eHommel.glht2 <- summary(e.glht2, test = adjusted(type = "hommel")) eS.glht2 <- summary(e.glht2, test = adjusted(type = "single-step")) p.value.none2 <- as.double(e0.glht2$test$pvalues) p.value.bonf2 <- as.double(eB.glht2$test$pvalues) p.value.hoch2 <- as.double(eHochberg.glht2$test$pvalues) p.value.homm2 <- as.double(eHommel.glht2$test$pvalues) p.value.max2 <- as.double(eS.glht2$test$pvalues) } ## ** store results dt.tempo <- rbind(data.table(method = "none", variable = name.X, p.value = p.value.none), data.table(method = "Bonferroni", variable = name.X, p.value = p.value.bonf), data.table(method = "Hochberg", variable = name.X, p.value = p.value.hoch), data.table(method = "Hommel", variable = name.X, p.value = p.value.homm), data.table(method = "Max", variable = name.X, p.value = p.value.max), data.table(method = "none2", variable = name.X, p.value = p.value.none2), data.table(method = "Bonferroni2", variable = name.X, p.value = p.value.bonf2), data.table(method = "Hochberg2", variable = name.X, p.value = p.value.hoch2), data.table(method = "Hommel2", variable = name.X, p.value = p.value.homm2), data.table(method = "Max2", variable = name.X, p.value = p.value.max2) ) dt.tempo[, n := n.tempo] dt.tempo[, a := a.tempo] dt.tempo[, rep := iRep] dt.tempo[, seed := iSeed] dt.tempo[, corTest := medianCor.test] dt.tempo[, corTest2 := medianCor.test2] dt.res <- rbind(dt.res, dt.tempo) } cat("\n") } filename <- paste0("type1error-S",iter_sim,"(tempo).rds") saveRDS(dt.res, file = file.path(path.res,filename)) } ## * export filename <- paste0("type1error-S",iter_sim,".rds") saveRDS(dt.res, file = file.path(path.res,filename)) ## * display print(sessionInfo())

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/Utils/ArgoExtract.R

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MAST-ULiege/ArgoDiva

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ArgoExtract.R

ArgoExtract<- function(fulldf, flist){ sdf <- ddply(fulldf, ~ aprofile+Platform, function(profile){ flistoutputs <- ldply(flist,function(f){ f<- match.fun(f) f(profile) } ) onelinedf <- data.frame( qc = max(profile$qc), # TODO several variables , etc ... lon = mean(profile$lon), lat = mean(profile$lat) , day = profile$day[1], month = profile$month[1], year = profile$year[1], juld = profile$juld[1]) return( cbind(flistoutputs, onelinedf) ) }) return(sdf) }

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run_analysis.R

##Working Directory work_dir <- "C:/Coursera/Getting and Cleaning Data/ProgrammingAssignmentWeek4" ##data Directory data_dir <- "Data" ##download URL download_URL <- "https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip" ##file Name file_name <- "Datasets.zip" #create working directory and data subdirectory if not there if (!file.exists(work_dir)) dir.create(work_dir) if (!file.exists(file.path(work_dir,data_dir))) dir.create(file.path(work_dir,data_dir)) setwd(work_dir) ##set working directory datafile <- file.path(work_dir,data_dir,file_name) if (!file.exists(datafile)) { download.file(download_URL,datafile) unzip(datafile,exdir=file.path(work_dir,data_dir)) } udir <- "UCI HAR Dataset" ##Unzipped files dir ##Step 1 - Merge training and test sets to create a combined set dataset_train <- read.table(file.path(work_dir,data_dir,udir,"train/X_train.txt")) dataset_test <- read.table(file.path(work_dir,data_dir,udir,"test/X_test.txt")) label_train <- read.table(file.path(work_dir,data_dir,udir,"train/y_train.txt")) label_test <- read.table(file.path(work_dir,data_dir,udir,"test/y_test.txt")) subject_train <- read.table(file.path(work_dir,data_dir,udir,"train/subject_train.txt")) subject_test <- read.table(file.path(work_dir,data_dir,udir,"test/subject_test.txt")) dataset_combined <- rbind(dataset_train,dataset_test) label_combined <- rbind(label_train,label_test) subject_combined <- rbind(subject_train,subject_test) ##Step 2 - Extract only measurements on mean and std dev ##read in the features features_list <- read.table(file.path(work_dir,data_dir,udir,"features.txt")) ##we take only the columns with mean and stdev meanstd_cols <- grep(".*mean.*|.*std.*",features_list[,2]) ##take only that subset of columns, and name them based on the description dataset_meanstd <- dataset_combined[,meanstd_cols] names(dataset_meanstd) <- features_list[meanstd_cols, 2] ##Step 3 use the descriptive activity names activity <- read.table(file.path(work_dir,data_dir,udir,"activity_labels.txt")) label_combined[,1] <- activity[label_combined[,1],2] ##Step 4 create combined tidy dataset names(label_combined) <- "activity" names(subject_combined) <- "subject" dataset_tidy <- cbind(subject_combined,label_combined,dataset_meanstd) write.table(dataset_tidy,"tidydata.txt") ##Step 5 create second tidy data set for average of each variable for each activity and subject dataset_tidy$subject <-factor(dataset_tidy$subject) #make factor ##"melt and recast data using reshape library" library(reshape2) dataset_melted <- melt(dataset_tidy, id=c("subject","activity")) dataset_means <- dcast(dataset_melted, subject + activity ~ variable, mean) write.table(dataset_means, "tidydatameans.txt")

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jefferys/SamSeq

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testthat.R

library(testthat) library(SamSeq) test_check("SamSeq")

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s3io_write.R

#' @title Write a file to AWS S3 #' @param obj <anything> The object to write. #' @param bucket <chr(1)> AWS S3 bucket name. #' @param key <chr(1)> AWS S3 object key. #' @param writefun <function> A write function where the first argument is the object to write and the second argument is a filepath. #' The function's signature should look like \code{writefun(obj, file, ...)}. #' @param ... Additional arguments passed on to \code{writefun}. #' @param .localfile <chr(1)> The local filepath for the initial write-to-disk. #' @param .rm_localfile <lgl(1)> Remove \code{localfile} once the copy-to-S3 is complete? #' @param .opts <dict> Additional --opts for the AWS CLI `aws s3api put-object` command. #' A common option you may want to specify, e.g., is content-type: \code{.opts = list("content-type" = "application/json")}. #' @return The returned value from \code{writefun}. #' @examples #' \dontrun{ #' s3io_write(iris, "mybucket", "my/object/key.csv", readr::write_csv, col_names = TRUE) #' } #' @export ## TODO: Possibly split .opts into .copy_opts and .aws_config to splice the values into the correct/expected positions in the final awscli command. ## Same with s3io_read. s3io_write <- function(obj, bucket, key, writefun, ..., .localfile = fs::file_temp(), .rm_localfile = TRUE, .aws_config = NULL, .put_object_opts = NULL, .opts = .aws_config) { if (isTRUE(.rm_localfile)) on.exit(try_file_remove(.localfile), add = TRUE) retval <- withVisible(writefun(obj, .localfile, ...)) retval <- if (isTRUE(retval$visible)) retval$value else invisible(retval$value) rlang::inject(awscli2::awscli( c("s3api", "put-object"), "--bucket" = bucket, "--key" = key, "--body" = .localfile, !!!.put_object_opts, .config = .aws_config )) retval }

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DrOppenheimer/predict_depth

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watstats.r

watstats <- function ( num_reads = 1000000, percent_data = 20, # use to get num reads genome_length = 4000000, read_length = 125, min_overlap = 30, verbose=TRUE ) { taxa_num_reads = ( num_reads * ( percent_data/100 ) ) # alpha <<- ( taxa_num_reads/genome_length ) # $alpha=$N/$GM; # $GM = $G*1000; (input in original was in KB) theta <<- ( min_overlap/read_length ) # $theta=$T/$L; sigma <<- ( 1-theta ) # $sigma=1-$theta; coverage_redundancy <<- ( (read_length*taxa_num_reads)/genome_length ) # $c=$L*$N/$GM; num_contigs <<- taxa_num_reads*exp(-coverage_redundancy*sigma) # $i =$N*exp(-$c*$sigma); if ( num_contigs < 1 ){ num_contigs <<- 1 } # $i=1 if $i < 1; if ( num_contigs > num_reads ){ num_contigs <<- num_reads } # $i =$N if $i > $N; seqs_per_contig <<- exp(coverage_redundancy*sigma) # exp($c*$sigma); if ( seqs_per_contig > num_reads ){ seqs_per_contig <<- num_reads } # $iii=$N if $iii > $N; if ( seqs_per_contig < 1 ){ seqs_per_contig <<- 1 } # $iii=1 if $iii < 1; contig_length <<- read_length*(((exp(coverage_redundancy*sigma)-1)/coverage_redundancy)+(1-sigma)) # $iv=int($L*(((exp($c*$sigma)-1)/$c)+(1-$sigma))); if ( contig_length > genome_length ){ contig_length <<- genome_length } # $iv=$GM if $iv > $GM; percent_coverage <<- 100*num_contigs*contig_length/genome_length # $compl=int(100*$i*$iv/$GM); if ( percent_coverage > 100 ){ percent_coverage <- 100 } if( verbose==TRUE ){ print("INPUT") print(paste("num_reads :", round(taxa_num_reads, digits=0))) print(paste("percent_data :", round(percent_data, digits=1))) print(paste("genome_length :", round(genome_length, digits=0))) print(paste("read_length :", round(read_length,digits=0))) print(paste("min_overlap :", round(min_overlap, digits=0))) print("") print("OUTPUT") print(paste("coverage_redundancy :", round(coverage_redundancy, digits=1))) print(paste("num_contigs :", round(num_contigs, digits=1))) print(paste("seqs_per_contig :", round(seqs_per_contig, digits=1))) print(paste("contig_length :", round(contig_length, digits=1))) print(paste("percent_coverage :", round(percent_coverage, digits=1), "%")) print('------------------------------------------------------') } watstats_command <- paste("watstats.pl -g", genome_length, "-n", taxa_num_reads, "-l", read_length, "-t", min_overlap, "intern=TRUE") my_watstats <<- system(watstats_command) #watstats_command <- paste("watstats.pl -g", genome_size, "-n", taxa_num_reads, "-l", read_length, "-t", min_overlap, "intern=TRUE") # system2(watstats_command, stdout=my_watstats.2) #my_watstats.2 <<- capture.output(system(watstats_command), file = NULL, append = FALSE) my_date <<- system("date", intern=TRUE) } # watstats.pl -g 5000 -n 1000 -l 480 -t 30 # watstats 1.1 ; Based on # Lander, E. S. and Waterman, M. S., "Genomic mapping by fingerprinting # random clones: a mathematical analysis", Genomics 2, 231-239 (1988).

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/run_twitch_script.R

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rcrondeau/twitch-stream-data

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2020-03-09T20:43:24.304984

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run_twitch_script.R

source("twitch_libraries.R") source("twitch_api.R") source("twitch_boxart.R") source("twitch_mergefiles.R")

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/man/likJ.Rd

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thoree/inbred

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2022-11-29T05:24:39.076623

2021-03-25T17:33:32

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likJ.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/likJ.R \name{likJ} \alias{likJ} \title{Likelihood for two Possibly Inbred Individuals as a function of the Condensed 'Jacquard' Coefficients} \usage{ likJ(a, b, cc, d, pa, pb, pc, pd, Delta) } \arguments{ \item{a}{Vector of positive integers (allele 1 of individual 1)} \item{b}{Vector of positive integers (allele 2 of individual 1)} \item{cc}{Vector of positive integers (allele 1 of individual 2)} \item{d}{Vector of positive integers (allele 2 of individual 2)} \item{pa}{Double vector of allele frequencies} \item{pb}{Double vector of allele frequencies} \item{pc}{Double vector of allele frequencies} \item{pd}{Double vector of allele frequencies} \item{Delta}{Double vector of length 9 summing to unity} } \description{ Likelihood for two Possibly Inbred Individuals as a function of the Condensed 'Jacquard' Coefficients } \examples{ p = c("1" = 0.1, "2" = 0.9) a = c(rep(1,6), rep(2,3)) b = c(rep(1,3), rep(2,6)) cc = rep(c(1,1,2), 3) d = rep(c(1,2,2), 3) pa = p[a] pb = p[b] pc = p[cc] pd = p[d] Delta = c(0, 0, 0, 0, 0, 0, 1,0, 0) l2 = likJ(a,b,cc,d, pa,pb,pc,pd, Delta = Delta) sum(l2) == 1 }

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/tests/testthat/test_t_test_outlier.R

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AnthonyTedde/utilitR

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test_t_test_outlier.R

################################################################################ ### dataset mtcars used to perform the following test. Please use the same ### if you want to create new tests. Otherwise update that comment and mention ### which set do you use. ################################################################################ testthat::test_that("recipes are accepted", { recipe <- recipes::recipe(hp ~ mpg + cyl, data = mtcars) %>% recipes::step_center(recipes::all_predictors()) %>% recipes::step_scale(recipes::all_predictors()) methods <- list( list(method = "lm") ) testthat::expect_output( subset <- utilitR::t_outlier_test(recipe, data = mtcars, method = methods), regexp = "[[:digit:]]+/[[:digit:]]+ rows removed" ) testthat::expect_vector(subset, ptype = logical(), size = nrow(mtcars)) }) testthat::test_that("Formulae are accepted", { methods <- list( list(method = "lm") ) testthat::expect_output( subset <- utilitR::t_outlier_test(hp ~ mpg + cyl, data = mtcars, method = methods, preProcess = c("center", "scale", "nzv")), regexp = "[[:digit:]]+/[[:digit:]]+ rows removed" ) testthat::expect_vector(subset, ptype = logical(), size = nrow(mtcars)) })

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energyfirefox/Shiny_Titanic

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server.R

library(shiny) library(ggplot2) library(caret) titanic <- read.csv("cutted_titanic.csv") titanic$survived[titanic$survived == 0] <- 'No' titanic$survived[titanic$survived == 1] <- 'Yes' titanic$survived <- as.factor(titanic$survived) surviving_prob <- function(age, sex){ tree_mod <- train(survived ~ ., data=titanic, method="rpart") prediction <- predict(tree_mod, data.frame(age=age, sex=sex)) return(prediction) } shinyServer(function(input, output) { output$prediction <- renderPrint({as.character(surviving_prob(input$age, input$sex))}) output$distPlot <- renderPlot({ titanic$user_age <- input$age titanic$user_sex <- input$sex p <- ggplot(titanic, aes(age, sex, color=survived))+ geom_point(pch = 19) + geom_point(aes(user_age, user_sex), color="black", cex=10, pch=4) + geom_text(aes(user_age+3, user_sex), color="darkblue", label="You") p }) })

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surayaaramli/typeRrh

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bot_token.Rd.R

library(telegram.bot) ### Name: bot_token ### Title: Get a token from environment ### Aliases: bot_token ### ** Examples ## Not run: ##D bot_token("RTelegramBot") ## End(Not run)

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# Question 2 ####################################################################################################################### # # Using the correct type of plot of the previous exercises, plot the relationship between runs and at_bats, # using at_bats as the explanatory variable. # # The relationship is... # ####################################################################################################################### 1 linear 2 negative 3 horseshoe-shaped ( ∩∩ ) 4 u-shaped ( ∪∪ ) plot(mlb11$at_bats,mlb11$runs) Answer - 1 linear

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DTC_genomics_convertor.r

### DTC Genomics Convertor v0.1 ### ### A command line tool to go between 23andMe, AncestryDNA, and FTDNA raw data formats ### ### Austin Reynolds ### ### awreynolds@utexas.edu ### ### June 2017 ### #note: make sure your file paths do not have spaces in them. for whatever reason the shell gets confused with spaces when you call from R # opt<-list() # opt$file<-"~/Desktop/FTDNA.csv" # opt$format<-"FTDNA" # opt$convert_to<-"23andMe" # opt$out<-"~/test.txt" #welcome text intro<-"\n" intro<-paste(intro,"-------------------------------------------------------------------\n") intro<-paste(intro,"-------------------------------------------------------------------\n") intro<-paste(intro," DTC Genomics Convertor\n") intro<-paste(intro," by Austin Reynolds\n") intro<-paste(intro," v0.1 (June 2017)\n") intro<-paste(intro,"-------------------------------------------------------------------\n") intro<-paste(intro,"-------------------------------------------------------------------\n\n") cat(intro) #import required packages library(optparse) library(data.table) library(stringr) #define options option_list <- list( make_option(c("-i", "--file"), action="store", default="", help="Path to input file."), make_option(c("-f", "--format"), action="store", default="", help="The format of the input file. Accepted options: \"23andMe\",\"AncestryDNA\",\"FTDNA\" "), make_option(c("-c", "--convert_to"), action="store", default="", help="The format that you want the output file to be. Accepted options: \"23andMe\",\"AncestryDNA\",\"FTDNA\" "), make_option(c("-o", "--out"), action="store", default="", help="Path to output file") ) opt<-parse_args(OptionParser(option_list=option_list)) #define functions convertor_Ancto23<-function(x){ #hold header of AncestryDNA file recover_header_command<-paste("sed '/^rsid/q' ",opt$file," | sed '$d' > temp_header.txt",sep = "") system(recover_header_command) #convert to 23andMe format x$genotype<-paste(x$allele1,x$allele2,sep = "") x<-x[,c(1,2,3,6)] colnames(x)[1]<-"# rsid" write.table(x,"temp_tailer.txt",sep = "\t",col.names = TRUE,row.names = FALSE,quote = FALSE) #combine header and tailer combine_command<-paste("cat temp_header.txt temp_tailer.txt > ",opt$out,sep = "") system(combine_command) #cleanup cleanup_command<-paste("rm temp_header.txt temp_tailer.txt") system(cleanup_command) } convertor_FTto23<-function(x){ #convert to 23andMe format colnames(x)<-c("# rsid","chromosome","position","genotype") write.table(x,opt$out,sep = "\t",col.names = TRUE,row.names = FALSE,quote = FALSE) } convertor_23toAnc<-function(x){ #hold header of 23andMe file recover_header_command<-paste("sed '/^# rsid/q' ",opt$file," | sed '$d' > temp_header.txt",sep = "") system(recover_header_command) #convert to 23andMe format genotypes<-as.data.frame(str_split_fixed(x$genotype,"",2)) x<-cbind(x[,c(1,2,3)],genotypes) colnames(x)<-c("rsid","chromosome","position","allele1","allele2") write.table(x,"temp_tailer.txt",sep = "\t",col.names = TRUE,row.names = FALSE,quote = FALSE) #combine header and tailer combine_command<-paste("cat temp_header.txt temp_tailer.txt > ",opt$out,sep = "") system(combine_command) #cleanup cleanup_command<-paste("rm temp_header.txt temp_tailer.txt") system(cleanup_command) } convertor_FTtoAnc<-function(x){ #convert to AncestryDNA format genotypes<-as.data.frame(str_split_fixed(x$RESULT,"",2)) x<-cbind(x[,c(1,2,3)],genotypes) colnames(x)<-c("rsid","chromosome","position","allele1","allele2") write.table(x,opt$out,sep = "\t",col.names = TRUE,row.names = FALSE,quote = FALSE) } convertor_23toFT<-function(x){ #convert to FTDNA format new_colnames<-list("RSID","CHROMOSOME","POSITION","RESULT") write.table(new_colnames,"temp_header.txt",sep=",",col.names = FALSE, row.names = FALSE,quote = FALSE) x<-data.frame(lapply(x, as.character), stringsAsFactors=FALSE) write.table(x,"temp_tailer.txt",sep = ",",col.names = FALSE, row.names = FALSE,quote = TRUE) #combine header and tailer combine_command<-paste("cat temp_header.txt temp_tailer.txt > ",opt$out,sep = "") system(combine_command) #cleanup cleanup_command<-paste("rm temp_header.txt temp_tailer.txt") system(cleanup_command) } convertor_AnctoFT<-function(x){ #convert to FTDNA format x$genotype<-paste(x$allele1,x$allele2,sep = "") x<-x[,c(1,2,3,6)] x<-data.frame(lapply(x, as.character), stringsAsFactors=FALSE) new_colnames<-list("RSID","CHROMOSOME","POSITION","RESULT") write.table(new_colnames,"temp_header.txt",sep=",",col.names = FALSE, row.names = FALSE,quote = FALSE) write.table(x,"temp_tailer.txt",sep = ",",col.names = FALSE, row.names = FALSE,quote = TRUE) #combine header and tailer combine_command<-paste("cat temp_header.txt temp_tailer.txt > ",opt$out,sep = "") system(combine_command) #cleanup cleanup_command<-paste("rm temp_header.txt temp_tailer.txt") system(cleanup_command) } #workflow if (opt$format=="23andMe"){ #read in 23andMe data input_df<-fread(opt$file) if (opt$convert_to=="23andMe"){ print("Input and output format are the same.") quit() } else if (opt$convert_to=="AncestryDNA"){ convertor_23toAnc(input_df) } else if (opt$convert_to=="FTDNA"){ convertor_23toFT(input_df) } else{ print("--convert_to option not recognized. Type '--help' for accepted options.") quit() } } else if (opt$format=="AncestryDNA"){ #read in AncestryDNA data input_df<-fread(opt$file) if (opt$convert_to=="AncestryDNA"){ print("Input and output format are the same.") quit() } else if (opt$convert_to=="23andMe"){ convertor_Ancto23(input_df) } else if (opt$convert_to=="FTDNA"){ convertor_AnctoFT(input_df) } else{ print("--convert_to option not recognized. Type '--help' for accepted options.") quit() } } else if (opt$format=="FTDNA"){ #read in FTDNA data input_df<-fread(opt$file) if (opt$convert_to=="FTDNA"){ print("Input and output format are the same.") quit() } else if (opt$convert_to=="23andMe"){ convertor_FTto23(input_df) } else if (opt$convert_to=="AncestryDNA"){ convertor_FTtoAnc(input_df) } else{ print("--convert_to option not recognized. Type '--help' for accepted options.") quit() } } else { print("--format option not recognized. Type '--help' for accepted options.") quit() } print("Conversion complete!")

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context("Testing extraction of parameters") df <- data.frame( stringsAsFactors = FALSE, "treatment" = c("Suni", "Ifn", "Suni", "Pazo"), "study" = c("Study 1", "Study 1", "Study 2", "Study 2"), "baseline" = c("Suni", "Suni", "Suni", "Suni"), "filepath" = sapply(c("Mota_OS_Suni_KM.txt", "Mota_OS_Ifn_KM.txt", "Mot_OS_Suni_KM.txt", "Mot_OS_Pazo_KM.txt"), function(x) system.file("extdata", "narrow", x, package="survnma", mustWork=TRUE)) ) nma <- survnma(df, "weibull", min_time_change = 0.05) test_that("Rubbish study/ treatment causes an error", { expect_error(extract_mu(nma, "wrong study")) expect_error(extract_d(nma, "wrong treatment")) }) test_that("Checking format", { expect_is(extract_mu(nma, "Study 1"), "matrix") expect_is(extract_d(nma, "Ifn"), "matrix") }) test_that("Testing if baseline", { global.base <- names(which(nma$trt_labels == 1)) expect_equal(unique(c(extract_d(nma, global.base))), 0) for(trt in nma$treatments[-1]){ expect_gt(length(unique(c(extract_d(nma, trt)))), 1) } expect_equal(unique(c(relative_d_in_study(nma, "Suni", "Study 2"))), 0) expect_gt(length(unique(c(relative_d_in_study(nma, "Pazo", "Study 2")))), 1) })

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/networkmanager_operations.R \name{networkmanager_get_transit_gateway_peering} \alias{networkmanager_get_transit_gateway_peering} \title{Returns information about a transit gateway peer} \usage{ networkmanager_get_transit_gateway_peering(PeeringId) } \arguments{ \item{PeeringId}{[required] The ID of the peering request.} } \description{ Returns information about a transit gateway peer. See \url{https://www.paws-r-sdk.com/docs/networkmanager_get_transit_gateway_peering/} for full documentation. } \keyword{internal}

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#' Conditional posterior distribution of latent U #' #' This function simulates from the conditional posterior distribution of the #' latent U. #' #' For internal use. #' #' @keywords internal #' @examples #' #' ## The function is currently defined as #' function(ut, n = 200, r = 20, alpha = 1, kappa = 1, gama = 1 / 2, #' delta = 2) { #' w <- ut #' ratio <- NaN #' while (is.nan(ratio)) { #' v <- ustar <- rgamma(1, shape = delta, rate = delta / ut) #' vw <- v / w #' vb <- v + kappa #' wb <- w + kappa #' A <- vw^(n - 2 * delta) #' B <- (vb / wb)^(r * gama - n) #' D <- vb^gama - wb^gama #' E <- 1 / vw - vw #' ratio <- A * B * exp(-alpha / gama * D - delta * E) #' } #' p <- min(1, ratio) #' u <- ifelse(runif(1) <= p, ustar, ut) #' return(u) #' } gs3 <- function(ut, n, r, alpha, kappa, gama, delta) { w <- ut ratio <- NaN while (is.nan(ratio)) { v <- ustar <- rgamma(1, shape = delta, rate = delta / ut) vw <- v / w vb <- v + kappa wb <- w + kappa A <- vw^(n - 2 * delta) B <- (vb / wb)^(r * gama - n) D <- vb^gama - wb^gama E <- 1 / vw - vw ratio <- A * B * exp(-alpha / gama * D - delta * E) } p <- min(1, ratio) u <- ifelse(runif(1) <= p, ustar, ut) return(u) } #' Target logdensity of U given the data #' #' @keywords internal #' logf_u_cond_y <- function(u, n, r, gamma, kappa, a) { (n - 1) * log(u) + (r * gamma - n) * log(u + kappa) - a / gamma * (u + kappa)^gamma } #' Contribution of the target logdensity of logU to the Metropolis-Hastings ratio #' #' @keywords internal #' logf_logu_cond_y <- function(logu, n, r, gamma, kappa, a) { logu + logf_u_cond_y(u = exp(logu), n = n, r = r, gamma = gamma, kappa = kappa, a = a) } #' Contribution of the proposal kernel logdensity to the Metropolis-Hastings ratio #' #' @keywords internal #' logdprop_logu <- function(logu_prime, logu, delta) { dnorm(x = logu_prime, mean = logu, sd = delta, log = T) } #' Proposal distribution for logU #' #' This function makes a proposal for a new value of logU #' #' @inheritParams logacceptance_ratio_logu #' @keywords internal #' rprop_logu <- function(logu, delta) { rnorm(n = 1, mean = logu, sd = delta) } #' Metropolis-Hastings ratio for the conditional of logU #' #' This function computes the Metropolis-Hastings ratio to decide whether to accept or reject a new value for logU. #' #' @param logu Real, log of the latent variable U at the current iteration. #' @param logu_prime Real, log of the new proposed latent variable U. #' @param a Positive real. Total mass of the centering measure. #' @inheritParams gs3_log #' #' @keywords internal #' logacceptance_ratio_logu <- function(logu, logu_prime, n, r, gamma, kappa, a, delta) { log_ratio <- logf_logu_cond_y(logu_prime, n, r, gamma, kappa, a) - logf_logu_cond_y(logu, n, r, gamma, kappa, a) + logdprop_logu(logu, logu_prime, delta) - logdprop_logu(logu_prime, logu, delta) return(min(0, log_ratio)) } #' Conditional posterior distribution of latent logU #' #' This function simulates from the conditional posterior distribution of a log transformation of the #' latent U. #' #' @param logut Real, log of the latent variable U at the current iteration. #' @param n Integer, number of data points. #' @param r Integer, number of clusters. #' @param alpha Positive real. Total mass of the centering measure. #' @param kappa Positive real. A parameter of the NRMI process. #' @param gama Real. \eqn{0\leq \texttt{gama} \leq 1}{0 <= gama <= #' 1}. See details. #' #' @param delta Scale of the Metropolis-Hastings proposal distribution #' #' @keywords internal #' gs3_log <- function(logut, n, r, alpha, kappa, gama, delta) { logu_prime <- rprop_logu(logu = logut, delta = delta) logq1 <- logacceptance_ratio_logu(logu = logut, logu_prime = logu_prime, n = n, r = r, gamma = gama, kappa = kappa, a = alpha, delta = delta) if (log(runif(n = 1)) < logq1) { return(logu_prime) } else { return(logut) } } #' Conditional posterior distribution of latent U #' #' This function simulates from the conditional posterior distribution of the #' latent U, with an adaptive proposal #' #' @keywords internal #' gs3_adaptive3 <- function(ut, n, r, alpha, kappa, gama, delta, U, iter, adapt = FALSE) { target_acc_rate <- 0.44 batch_size <- 100 if (adapt && (iter %% batch_size == 0)) { acc_rate <- length(unique(U[(iter - batch_size + 1):iter])) / batch_size logincrement <- 2 * min(0.25, 1 / sqrt(iter)) # increment = min(0.5, 5 / sqrt(iter)) if (acc_rate < 0.44) { delta_i <- delta * exp(-logincrement) } else { delta_i <- delta * exp(+logincrement) } } else { delta_i <- delta } logu_prime <- gs3_log(logut = log(ut), n = n, r = r, alpha = alpha, kappa = kappa, gama = gama, delta = delta_i) return(list(u_prime = exp(logu_prime), delta = delta_i)) }

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/utilityFunctions.R \name{getAOAFeature} \alias{getAOAFeature} \title{getAOAFeature} \usage{ getAOAFeature(unitCode, aoaExtent = "km30") } \arguments{ \item{unitCode}{unitCode One NPS unit code as a string} \item{aoaExtent}{aoaExtent one of park, km3 or km30 as a string. Default is "km30"} } \description{ Function retrieves a GeoJSON-formatted area of analysis (AOA) polygon in the NAD83 geographic coordinate reference system (CRS). }

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library(ape) testtree <- read.tree("11639_0.txt") unrooted_tr <- unroot(testtree) write.tree(unrooted_tr, file="11639_0_unrooted.txt")

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\alias{gtkWidgetSetCanDefault} \name{gtkWidgetSetCanDefault} \title{gtkWidgetSetCanDefault} \description{Specifies whether \code{widget} can be a default widget. See \code{\link{gtkWidgetGrabDefault}} for details about the meaning of "default".} \usage{gtkWidgetSetCanDefault(object, can.default)} \arguments{ \item{\verb{object}}{a \code{\link{GtkWidget}}} \item{\verb{can.default}}{whether or not \code{widget} can be a default widget.} } \details{Since 2.18} \author{Derived by RGtkGen from GTK+ documentation} \keyword{internal}

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% Generated by roxygen2 (4.0.2): do not edit by hand \name{calc_roc.rfsrc} \alias{calc_roc} \alias{calc_roc.randomForest} \alias{calc_roc.rfsrc} \title{Internal Reciever Operator Characteristic calculator for randomForest objects} \usage{ calc_roc.rfsrc(rf, dta, which.outcome = "all", oob = TRUE) } \arguments{ \item{rf}{\code{randomForestSRC::rfsrc} or \code{randomForestSRC::predict} object containing predicted response} \item{dta}{True response variable} \item{which.outcome}{If defined, only show ROC for this response.} \item{oob}{Use OOB estimates, the normal validation method (TRUE)} } \description{ Internal Reciever Operator Characteristic calculator for randomForest objects } \details{ Given the randomForest or randomForestSRC prediction and the actual response value, calculate the specificity (1-False Positive Rate) and sensitivity (True Positive Rate) of a predictor. This is a helper function for the \code{\link{gg_roc}} functions, and not intended for use by the end user. } \examples{ \dontrun{ ## ## Taken from the gg_roc example iris.obj <- rfsrc(Species ~ ., data = iris) gg_dta <- calc_roc.rfsrc(iris.obj, iris.obj$yvar, which.outcome=1, oob=TRUE) } } \seealso{ \code{\link{calc_auc}} \code{\link{gg_roc}} \code{\link{plot.gg_roc}} }

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utils-p_values.R

#' Helper function which calculates p-value via chi-square or fisher #' Uses `survey::svychisq` for weighted tests, otherwise uses `stats::chisq.test` or `stats::fisher.test` depending on cell counts #' #' @param df <`tbl_df`> Dataframe that has variable and treatment columns of interest #' @param var <`character(1)`> Name of variable column #' @param treatment <`character(1)`> Name of treatment column #' @param weight_var <`character(1)`> Name of variable with observation weights #' #' @return <`numeric(1)`> p-value #' #' @importFrom survey svydesign svychisq #' @import purrr #' @importFrom rlang !! #' #' @noRd p_chi_fisher <- function(df, var, treatment, weight_var) { if (any(df[[weight_var]] != 1)) { df <- df %>% tidyr::drop_na(!!var, !!treatment) survey_obj <- svydesign(~1, data = df, weights = df[[weight_var]]) p_val <- var %>% paste0("~", ., " + ", treatment) %>% stats::as.formula() %>% svychisq(design = survey_obj) %>% pluck("p.value") %>% as.numeric() return(p_val) } chisq_wrapper <- function(var, df, treatment) { stats::chisq.test( x = as.factor(df[[var]]), y = as.factor(df[[treatment]]) ) %>% pluck("p.value") %>% as.numeric() } fisher_wrapper <- function(var, df, treatment) { p_val <- stats::fisher.test( x = as.factor(df[[var]]), y = as.factor(df[[treatment]]), simulate.p.value = TRUE ) %>% pluck("p.value") } chisq_wrapper <- purrr::quietly(chisq_wrapper) chisq <- chisq_wrapper(var, df, treatment) if (length(chisq$warnings) == 0) { return(chisq$result) } else { return(fisher_wrapper(var, df, treatment)) } } #' Helper function which calculates p-value via anova #' Uses `survey::svyglm` and `survey::regTermTest` for weighted tests and `stats::lm` and `stats::anova` otherwise #' #' @inheritParams p_chi_fisher #' #' @return <`numeric(1)`> p-value #' #' @import dplyr #' @importFrom survey svydesign svyglm regTermTest #' @importFrom rlang !! #' #' @noRd p_anova <- function(df, var, treatment, weight_var) { if (any(df[[weight_var]] != 1)) { df <- df %>% tidyr::drop_na(!!var, !!treatment) survey_obj <- svydesign(~1, data = df, weights = df[[weight_var]]) p_val <- var %>% paste0(" ~ ", treatment) %>% stats::as.formula() %>% svyglm(design = survey_obj) %>% regTermTest( test.terms = treatment, method = "Wald" ) %>% purrr::pluck("p") %>% as.numeric() return(p_val) } paste0(var, " ~ ", treatment) %>% stats::lm(data = df) %>% stats::anova() %>% pull(`Pr(>F)`) %>% purrr::pluck(1) } #' Helper function which calculates p-value via Kruskal-Wallis #' Uses `survey::svyranktest` for weighted tests and `stats::kruskal.test` otherwise #' #' @inheritParams p_chi_fisher #' #' @return <`numeric(1)`> p-value #' #' @importFrom survey svydesign svyranktest #' @importFrom rlang !! #' #' @noRd p_kruskal <- function(df, var, treatment, weight_var) { if (any(df[[weight_var]] != 1)) { df <- df %>% tidyr::drop_na(!!var, !!treatment) survey_obj <- svydesign(~1, data = df, weights = df[[weight_var]]) p_val <- var %>% paste0(" ~ ", treatment) %>% stats::as.formula() %>% svyranktest(design = survey_obj) %>% purrr::pluck("p.value") %>% as.numeric() return(p_val) } paste0(var, " ~ ", treatment) %>% stats::as.formula() %>% stats::kruskal.test(data = df) %>% purrr::pluck("p.value") }

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/tests/testthat/test_polygon_geometry.R

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test_polygon_geometry.R

# Automatically generated by openapi-generator (https://openapi-generator.tech) # Please update as you see appropriate context("Test PolygonGeometry") model.instance <- PolygonGeometry$new() test_that("coordinates", { # tests for the property `coordinates` (array[Array]) # An array of linear rings # uncomment below to test the property #expect_equal(model.instance$`coordinates`, "EXPECTED_RESULT") }) test_that("type", { # tests for the property `type` (character) # The literal string \"Polygon\" # uncomment below to test the property #expect_equal(model.instance$`type`, "EXPECTED_RESULT") })

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/R/dograph.R

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byadu/libcubolap

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dograph.R

#' @title dograph #' @description create an aggregate dataframe from selected measures, dimensions and filters #' @param M is database connection for Meta data #' @param D is database connection fro data #' @param measures are measures selected #' @param dims are dimensions selected #' @param filters are filters selected #' @param gtype is graph type #' #' @import dplyr #' @import DBI #' @import RMySQL #' @import libcubmeta #' @export #' dograph<- function(M, D, measures, dims, filters, gtype) { mddf<- getmddf(M, D, measures, dims) mdd<- mddf$mdd; mdf<- mddf$mdf; measures<- mddf$measures series<- c() timings<- c() for(i in 1:length(measures)) { if(grepl("]", mdf[[i]]$md_column)) timings[i]<- system.time(s<- calcol(M, D, mdf[[i]], mdd, filters))[3] else timings[i]<- system.time(s<- doseries(M, D$mydata, mdf[[i]], mdd, filters))[3] thisdxy<- s$dxy series[i]<- s$series if(i>1) { dxy<- merge(dxy, thisdxy, all=T) } else dxy<- thisdxy } gp<- setgp(mdd, mdf, series, timings, measures, dims, filters, gtype) #gp$gfid<- addgraph(gp) return(list(dxy=dxy, gp=gp)) } doseries<- function(M, my_data, mdf, mdd, filters) { # g<- isolate(rg$g) sel<- makeaggsel(M, mdf, mdd, filters) dxy<-dbGetQuery(my_data, sel) my_cfg<- M$mycfg if(ncol(dxy)<8) series<- addxy(my_cfg, dxy) addseries(my_cfg, series, M$uid, mdf, mdd, filters) if(!is.null(mdd)) dimnames<- c(sapply(mdd, '[[', 'md_name')) if(!is.null(mdf)) measnames<- mdf$md_name colnames(dxy)<- c(dimnames, measnames) return(list(dxy=dxy, series=series)) } aggcol<- function(tab, col, aggfun) { paste0(aggfun, '(', ifelse(isacol(col), paste0(tab, "."), ""), col, ')') } makeagg<- function(mdf, mdd) { if(is.null(mdd)) "count(*)" else aggcol(mdf$md_table, mdf$md_column, mdf$md_sumcnt) } makeaggsel<- function(M, mdf, mdd, filters) { sp<- olapdims(M, mdf, mdd, filters) agg<- makeagg(mdf, mdd) sel<- paste("select", sp$dimcols, ",", agg , "from", sp$frm , ifelse(!is.null(sp$w), paste("where", sp$w), ''), "group by", sp$dimcols) # if(!is.null(f$having)) # sel<- paste(sel, f$having) print(sel) # qry$sql<- sel sel }

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/R/explore_mobloc_functions.R

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mtennekes/mobloc

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refs/heads/master

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explore_mobloc_functions.R

move_cp_to_direction <- function(cp, distance = 100) { cp$x2 <- cp$x + ifelse(cp$small | is.na(cp$direction), 0, (SIN(cp$direction) * distance)) cp$y2 <- cp$y + ifelse(cp$small | is.na(cp$direction), 0, (COS(cp$direction) * distance)) cp2 <- st_set_geometry(cp, NULL) st_as_sf(cp2, coords = c("x2", "y2"), crs = st_crs(cp)) } create_connection_lines <- function(cp1, cp2) { c1 <- st_coordinates(cp1) c2 <- st_coordinates(cp2) st_sf(geometry = do.call(st_sfc, lapply(1:nrow(c1), function(i) { co <- rbind(c1[i,], c2[i,]) st_linestring(co) })), cell = cp1$cell, crs = st_crs(cp1)) } get_leafletCRS <- function(epsg) { if (epsg == 3035) { leafletCRS(crsClass = "L.Proj.CRS", code='EPSG:3035', proj4def="+proj=laea +lat_0=52 +lon_0=10 +x_0=4321000 +y_0=3210000 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs", resolutions = 2^(7:0)) } else { leafletCRS(crsClass = "L.CRS.EPSG3857") } } get_epsg_tiles <- function(map, epsg) { if (epsg == 3035) { addWMSTiles(map, "https://image.discomap.eea.europa.eu/arcgis/services/GioLandPublic/DEM/MapServer/WmsServer", layers = "Image") } else { addTiles(map) } } base_map <- function(cp, offset, epsg) { cp2 <- move_cp_to_direction(cp, offset) cp_lines <- create_connection_lines(cp, cp2) lf <- leaflet(options = leafletOptions(crs = get_leafletCRS(epsg))) %>% addPolylines(data = cp_lines %>% st_transform(crs = 4326), color = "#777777", opacity = 1, weight = 3, group = "Cell locations") %>% get_epsg_tiles(epsg) } viz_p <- function(cp, rst, var, trans, pnames, offset, rect) { cp$sel <- factor(ifelse(cp$sel == 2, "Selected", ifelse(cp$small, "Small cell", "Normal cell")), levels = c("Selected", "Small cell", "Normal cell")) cp2 <- move_cp_to_direction(cp, offset) cp_lines <- create_connection_lines(cp, cp2) pal <- colorFactor(c("red", "gray70", "gold"), levels = c("Selected", "Small cell", "Normal cell")) if (all(is.na(rst[]))) var <- "empty" title <- switch(var, dBm = "Signal strength in dBm", s = "Signal dominance - s", bsm = "Best server map", #lu = "Land use prior (in %)", pag = "Connection likelihood - P(a|g)<br>(in 1 / 1,000)", pg = "Composite prior - P(g)<br>(in 1/1,000,000)", pga = "Location posterior - P(g|a)<br>(in 1/1,000,000)", paste("Prior", pnames[var], " - P(g)<br>(in 1/1,000,000)")) cls <- if (var == "dBm") { dBm_classes } else { qty_classes } numpal <- ifelse(var %in% c("dBm", "s"), "Blues", ifelse(var == "pga", "viridis", ifelse(var == "pag", "Greens", "Blues"))) if (var %in% c("dBm", "s")) { pal2 <- colorBin(cls$colors, bins = cls$breaks, na.color = "#00000000")#, dBm_classes$labels) #rst2 <- raster::projectRaster(rst, crs = st_crs(4326)$proj4string) rst2 <- rst } else if (var == "bsm") { rst2 <- raster::projectRaster(rst, crs = st_crs(3857)$proj4string, method = "ngb") lvls <- raster::levels(rst)[[1]] cols <- rep(RColorBrewer::brewer.pal(8, "Dark2"), length.out = nrow(lvls)) pal2 <- colorFactor(palette = cols, domain = lvls$ID, na.color = "transparent") } else if (var != "empty") { rst2 <- raster::projectRaster(rst, crs = st_crs(4326)$proj4string, method = "bilinear") if (any(is.nan(rst2[]))) { rst2 <- raster::projectRaster(rst, crs = st_crs(4326)$proj4string, method = "ngb") } if (var == "pag") { values <- pmin(pmax(rst2[] * 1000, 0), 1000) } else { values <- pmin(pmax(rst2[] * 1000000, 0), 1000000) } rst2[] <- values rng <- range(values, na.rm = TRUE) pal2 <- colorNumeric(palette = numpal, rng, reverse = (numpal != "viridis"), na.color = "transparent") } lf <- leafletProxy("map") %>% clearMarkers() %>% clearImages() %>% clearControls() %>% clearShapes() if (offset > 0) { lf <- lf %>% addPolylines(data = cp_lines %>% st_transform(crs = 4326), color = "#777777", opacity = 1, weight = 3, group = "Cell locations") %>% addCircleMarkers(data = cp2 %>% st_transform(crs = 4326), fillColor = ~pal(sel), color = "black", fillOpacity = 1, radius = 5, weight = 1, group = "Cell locations", layerId = ~cell) } else { lf <- lf %>% addCircleMarkers(data = cp %>% st_transform(crs = 4326), fillColor = ~pal(sel), color = "black", fillOpacity = 1, radius = 5, weight = 1, group = "Cell locations", layerId = ~cell) } if (var %in% c("dBm", "s")) { lf <- lf %>% addRasterImage(x = rst2, opacity = trans, group = title, colors = pal2) %>% leaflet::addLayersControl(overlayGroups = c("Cell locations", title), position = "topleft", options = layersControlOptions(collapsed = FALSE)) %>% addLegend(colors = cls$colors, labels = cls$labels, opacity = trans, title = title) } else if (var == "bsm") { lf <- lf %>% addRasterImage(x = rst2, opacity = trans, group = title, colors = cols) %>% leaflet::addLayersControl(overlayGroups = c("Cell locations", title), position = "topleft", options = layersControlOptions(collapsed = FALSE)) %>% addLegend(colors = cols, labels = as.character(lvls$cell), opacity = trans, title = title) } else if (var == "empty") { lf <- lf %>% leaflet::addLayersControl(overlayGroups = c("Cell locations"), position = "topleft") } else { lf <- lf %>% addRasterImage(x = rst2, opacity = trans, group = title, colors = pal2) %>% leaflet::addLayersControl(overlayGroups = c("Cell locations", title), position = "topleft", options = layersControlOptions(collapsed = FALSE)) %>% addLegend(pal = pal2, values = rng, opacity = trans, title = title) } lf %>% addPolygons(data = rect, color = "#000000", weight = 1, fill = FALSE) }

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/R/fx_modelResample.R

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fishpm/nruPredict

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fx_modelResample.R

## * fx_modelResample (documentation) ##' @title Apply Machine Learning Framework ##' @description Apply machine learning framework to specified dataset ##' ##' @param df0 data frame including all observations (data frame) ##' @param cv.type cross-validation type ('loocv', 'ltocv', 'n-fold', 'numeric') (string) ##' @param covar list of df0 column names for "covariate" (not of specific interest) features (string/list) ##' @param voi list of df0 column names for variables/features of interest (string/list) ##' @param outcome df0 column name for outcome measure to be predicted (string) ##' @param model.type machine learning model ('rf', 'logistic', 'regression', 'rf.regression', 'svm') (string) ##' @param nresample number of resamples (numeric) ##' @param dthresh decision threshold (numeric) ##' @param z.pred standardize predictive features (boolean) ##' @param n.cores number of cores (parallel processes) (numeric/integer) ##' @param balance.col df0 column name used for ensuring balanced columns ##' @param partitions pre-defined train/test partitions ##' ##' @return A list of length five, containing the following elements: ##' \itemize{ ##' \item "perfMetrics" Model performance metrics for each individual fold and "across" and "within". ##' \cr "across": sum or mean of metric across folds ##' \cr "within": mean of metric across folds ##' \item "cmat.covar": confusion matrix of covariate model (at "dthresh" decision threshold) ##' ##' \item "cmat.full": confusion matrix of full model (at "dthresh" decision threshold) ##' ##' \item "df.allfolds": data frame for test-related model predictions ##' ##' \item "parameters": list of relevant specified parameters ##' } ##' ##' @return A list of length five, containing the following elements: ##' \itemize{ ##' \item "perfMetrics" Model performance metrics for each individual fold and "across" and "within". ##' \cr "across": sum or mean of metric across folds ##' \cr "within": mean of metric across folds ##' \itemize{ ##' \item TP: true positive ##' \item FP: false positive ##' \item TN: true negative ##' \item FN: false negative ##' \item sens: sensitivity ##' \item spec: specificity ##' \item ppv: positive predictive value ##' \item npv: negative predictive value ##' \item acc: accuracy ##' \item auc.ROC: area under the curve of ROC curve ##' \item optThresh: optimal decision threshold determined from training data ##' } ##' ##' \item "cmat.covar": confusion matrix of covariate model (at "dthresh" decision threshold) ##' ##' \item "cmat.full": confusion matrix of full model (at "dthresh" decision threshold) ##' ##' \item "df.allfolds": data frame for test-related model predictions ##' \itemize{ ##' \item orig.df.row: row in original data frame for specific observation, ##' \item fold: fold assignment ##' \item pred.prob.covar: predicted probability of class membership from covariate model ##' \item pred.prob.full: predicted probability of class membership from full model ##' \item pred.class.covar: predicted class from covariate model ##' \item pred.class.full: predicted class from full model ##' \item actual.class: actual class membership ##' } ##' ##' \item "parameters": list of relevant specified parameters ##' \itemize{ ##' \item "sample.type": cross-validation sampling procedure ##' \item "class.levels": class levels ##' \item "model.type": machine learning model framework ##' \item "covar": specified covariates ##' \item "voi": specified variables of interest ##' \item "outcome": name of class being predicted ##' \item "formula.covar": formula object for covariate model ##' \item "formula.full": formula object for full model ##' \item "data.frame": data frame specified (CURRENTLY NOT CORRECTLY SPECIFIED) ##' \item "cmat.descrip": key for how to understand confusion matrices () ##' \item "negative.class": class assigned to probability = 0 ##' \item "positive.class": class assigned to probability = 1 ##' \item "dthresh": decision threshold ##' \item "z.pred": whether z-scoring of features is specified ##' \item "nresample": number of resamples ##' } ##' } ## * fx_modelResample (example) ##' @examples ##' #### Generate data #### ##' n <- 100 ##' ##' set.seed(1) ##' group <- factor(sample(c('MDD','HC'),n,replace=T)) ##' age <- rnorm(n,25,5) ##' sex <- factor(sample(c('male','female'),n,replace=T)) ##' rand.vals1 <- rnorm(n,0,0.75) ##' set.seed(2) ##' rand.vals2 <- rnorm(n,0,0.75) ##' dd <- data.frame(group = group, ##' age = age, ##' sex = sex, ##' f1 = rand.vals1 + as.numeric(group), ##' f2 = rand.vals2) ##' ##' #### MODEL EXAMPLE 1 ##### ##' ## covariates ##' covar <- c('age','sex') ##' ## variables of interest ##' voi <- c('f1','f2') ##' ## class outcome ##' y <- 'group' ##' ##' ## resamples and permutations ##' nresample <- 10 ##' nperm <- 10 ##' n.cores <- 1 ## 10 ##' ##' ## fit classification model ##' modelObj <- fx_modelResample(df0 = dd, ##' cv.type = '5-fold', ##' covar = covar, ##' voi = voi, ##' outcome = y, ##' model.type = 'rf', ##' nresample = nresample, ##' dthresh = 0.5, ##' z.pred = F, ##' balance.col = y, ##' n.cores = n.cores) ##' ##' ## determine overall model performance ##' modelPerfObj <- fx_modelResamplePerf(modelResampleObj = modelObj) ##' ## permutation testing ##' permObj <- fx_perm(df0 = dd, modelObj = modelObj, nperm = nperm, n.cores = n.cores) ##' ## determine permutation test performance ##' permPerfObj <- fx_permPerf(permObj = permObj, modelResamplePerf = modelPerfObj) ##' ##' ## Summary of performance measures based on observed data ##' modelPerfObj$df.summary ##' ## Outcome metrics for each resample ##' modelPerfObj$df.iter ##' ## Summary of permutation test outcomes ##' permPerfObj$df.summary ##' ## Outcome metrics for each permutation ##' permPerfObj$df.iter ##' ## create roc curve plot ##' fx_rocPlot(modelObj = modelObj, modelPerfObj = modelPerfObj, permPerfObj = permPerfObj, title.text = 'My Title') ## * fx_modelResample (code) ##' @export fx_modelResample <- function(df0, cv.type = NULL, covar = NULL, voi = NULL, outcome = NULL, model.type = NULL, nresample = 1, dthresh = 0.5, z.pred = F, n.cores = 20, balance.col = NULL, partitions = NULL){ # For visual update on progress updateMarks <- seq(from = 0, to = nresample, length.out = 11) # only one resample if loocv if(cv.type == 'loocv'){ nresample <- 1 writeLines('LOOCV - resetting nresamples to 1...') } else { writeLines('Generating resample results...') } # fit model object for each resample modelResamplePerfObj <- lapply(seq(nresample), function(j){ # update on progress if (j%in%updateMarks){ writeLines(paste0('\tResample: ', j, ' (', (j/nresample)*100, '% complete)')) } # partition data in to folds if(is.null(partitions)){ partition.list <- fx_partition(df0, type = cv.type, balance.col = balance.col) } else { partition.list <- partitions[[j]] } # apply machine learning framework modelObj <- parallel::mclapply(seq(length(partition.list)), function(i){ fx_model(fx_sample(df0,partition.list[[i]]), covar = covar, voi = voi, outcome = outcome, model.type = model.type, z.pred = z.pred)}, mc.cores = n.cores) # summarize model performance modelPerfObj <- fx_modelPerf(modelObj, dthresh=dthresh) # parameters saved only once and as it's own list element modelPerfObj$parameters <- NULL return(modelPerfObj) }) # run model once to extract parameter information partition.list <- fx_partition(df0, type = cv.type, balance.col = balance.col) modelObj <- parallel::mclapply(seq(length(partition.list)), function(i){ fx_model(fx_sample(df0,partition.list[[i]]), covar = covar, voi = voi, outcome = outcome, model.type = model.type)}, mc.cores = n.cores) modelPerfObj <- fx_modelPerf(modelObj, dthresh=dthresh) # update parameter information parameters <- modelPerfObj$parameters parameters$z.pred <- z.pred parameters$nresample <- nresample writeLines('Model fitting completed!') return(list(modelResamplePerfObj = modelResamplePerfObj, parameters = parameters)) }

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/geotopbricks/R/getProjection.R

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ingted/R-Examples

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getProjection.R

NULL #' #' It reads the CRS metadata utilzed in a GEOtop Simulation #' #' @param x name and full path of the file containimg CRS information #' @param cond logical value. If \code{FALSE} the function returns \code{NA}. Default is \code{TRUE}. #' @param ... futher arguments #' #' @export #' @return A string corresponding the projection and CRS if the argument \code{cond} is \code{TRUE}. #' @examples #' library(geotopbricks) #' wpath <- "http://www.rendena100.eu/public/geotopbricks/simulations/idroclim_test1" #' x <- paste(wpath,"geotop.proj",sep="/") #' #' #' crs <- getProjection(x) #' getProjection <- function(x,cond=TRUE,...) { out <- NA open <- FALSE if (cond) { out <- as.character(scan(x,what="list",sep="\n",n=1)) } return(out) }

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/ancestral_populations_on_map.R

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marclagu/ddRADseq_brown_trout_Iceland

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r

ancestral_populations_on_map.R

# calculating the number of ancestral populations library(LEA) vcf2geno("olfusa.filtered.vcf", "olfusa.geno") pc = pca("olfusa.geno", scale = TRUE) tw = tracy.widom(pc) tw$pvalues[1:5] plot(tw$percentage) project = NULL project = snmf("olfusa.geno", K = 6:11, entropy = TRUE, repetitions = 2500, seed = 1236, CPU = 4, alpha = 100, project = "new") par(family="Times") plot(project, col = "#d73027", pch = 19, cex = 1.2) library(dplyr) # selecting the best run for K = 8 best = which.min(cross.entropy(project, K = 8)) my.colors <- c("#ffffbf", "#abd9e9", "#313695", "#f46d43", "#d73027", "#a50026", "#fee090", "#74add1") barchart(project, K = 8, run = best, border = NA, space = 0, sort.by.Q = TRUE, col = my.colors, xlab = "Individuals", ylab = "Ancestry proportions", main = "Ancestry matrix (K=8)") # exporting matrix matrix8 <- Q(project, K=8, run=best) write.csv(matrix8, "matrix8") ####################################################################################### # plotting ancestral populations on map par(mfrow=c(1,1)) # spatial packages library(raster) library(rgeos) library(rgdal) library(ggmap) library(sp) library(terra) # colors library(colorspace) # Read in the shapefile (obtained from the Cartographic Service of Iceland at https://www.lmi.is/) IS1<-readOGR(dsn="./Maps/", layer="IS1") IS2<-readOGR(dsn="./Maps/", layer="IS2") IS3<-readOGR(dsn="./Maps/", layer="IS3") zones_clipped_1 <- raster::crop(IS1, extent(1569500,1650000,170000,230000)) zones_clipped_2 <- raster::crop(IS2, extent(1569500,1650000,170000,230000)) zones_clipped_3 <- raster::crop(IS3, extent(1569500,1650000,170000,230000)) qmatrix = Q(project, K=8, run=best) popmap <- read.table("popmap.olfusa.clean.QGIS.tsv", header=TRUE) k<- data.frame(qmatrix) k$ID <- popmap$POP k %>% group_by(k$ID) %>% summarize(mean_V1 = mean(V1, na.rm=TRUE), mean_V2 = mean(V2, na.rm=TRUE), mean_V3 = mean(V3, na.rm=TRUE), mean_V4 = mean(V4, na.rm=TRUE), mean_V5 = mean(V5, na.rm=TRUE), mean_V6 = mean(V6, na.rm=TRUE), mean_V7 = mean(V7, na.rm=TRUE), mean_V8 = mean(V8, na.rm=TRUE)) k_summary <- k %>% group_by(k$ID) %>% summarize(mean_V1 = mean(V1, na.rm=TRUE), mean_V2 = mean(V2, na.rm=TRUE), mean_V3 = mean(V3, na.rm=TRUE), mean_V4 = mean(V4, na.rm=TRUE), mean_V5 = mean(V5, na.rm=TRUE), mean_V6 = mean(V6, na.rm=TRUE), mean_V7 = mean(V7, na.rm=TRUE), mean_V8 = mean(V8, na.rm=TRUE)) coord <- data.frame(popmap$POP, popmap$LAT, popmap$LONG) coord_summary <- coord %>% group_by(popmap.POP) %>% summarize(mean_LAT = mean(popmap.LAT, na.rm=TRUE), mean_LONG = mean(popmap.LONG, na.rm=TRUE)) k_number <- k %>% group_by(k$ID) %>% tally() k_summary$number <- k_number$n data <- data.frame(k_summary) data$LAT <- coord_summary$mean_LAT data$LONG <- coord_summary$mean_LONG y<- data$LAT x<- data$LONG data$lon=x data$lat=y coordinates(data) <- c("lon", "lat") proj4string(data) <- CRS("+init=epsg:4326") # WGS 84 CRS.new <- CRS("+proj=lcc +lat_1=64.25 +lat_2=65.75 +lat_0=65 +lon_0=-19 +x_0=1700000 +y_0=300000 +ellps=GRS80 +units=m +no_defs") d <- spTransform(data, CRS.new) d1 <- data.frame(d) par(mai=c(0.1,0.1,0.1,0.1)) plot(zones_clipped_1, yaxs = 'i', xaxs = 'i', lwd=2) plot(zones_clipped_2, add=T, col="light gray",border="dark gray", yaxs = 'i', xaxs = 'i') plot(zones_clipped_3, add=T, col="light gray", yaxs = 'i', xaxs = 'i') plot(d, add=TRUE, pch=1, cex=0.01, alpha=0) library(maps) library(plotrix) library(scales) library(seqinr) points(d1$lon, d1$lat, cex = d1$number/1800, col='white', pch=19) my.colors <- c("#ffffbf", "#abd9e9", "#313695", "#f46d43", "#d73027", "#a50026", "#fee090", "#74add1") for (i in 1:(2)) {my.colors[i]<-col2alpha(color=my.colors[i], alpha=1)} #for same size of pies for (x in 1:nrow(d)){floating.pie(d1$lon[x],d1$lat[x], c(d1$mean_V1[x],d1$mean_V2[x],d1$mean_V3[x],d1$mean_V4[x], d1$mean_V5[x], d1$mean_V6[x],d1$mean_V7[x],d1$mean_V8[x]), radius=1200, col =my.colors)}

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/data/genthat_extracted_code/genoPlotR/examples/plot_gene_map.Rd.R

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plot_gene_map.Rd.R

library(genoPlotR) ### Name: plot_gene_map ### Title: Plot gene and genome maps ### Aliases: plot_gene_map ### Keywords: hplot ### ** Examples old.par <- par(no.readonly=TRUE) data("three_genes") ## Segments only plot_gene_map(dna_segs=dna_segs) ## With comparisons plot_gene_map(dna_segs=dna_segs, comparisons=comparisons) ## Tree names <- c("A_aaa", "B_bbb", "C_ccc") names(dna_segs) <- names tree <- newick2phylog("(((A_aaa:4.2,B_bbb:3.9):3.1,C_ccc:7.3):1);") plot_gene_map(dna_segs=dna_segs, comparisons=comparisons, tree=tree) ## Increasing tree width plot_gene_map(dna_segs=dna_segs, comparisons=comparisons, tree=tree, tree_width=3) ## Annotations on the tree tree2 <- newick2phylog("(((A_aaa:4.2,B_bbb:3.9)97:3.1,C_ccc:7.3)78:1);") plot_gene_map(dna_segs=dna_segs, comparisons=comparisons, tree=tree2, tree_width=3) plot_gene_map(dna_segs=dna_segs, comparisons=comparisons, tree=tree2, tree_width=3, tree_branch_labels_cex=0.5) plot_gene_map(dna_segs=dna_segs, comparisons=comparisons, tree=tree2, tree_width=3, tree_branch_labels_cex=0) ## Annotation ## Calculating middle positions mid_pos <- middle(dna_segs[[1]]) # Create first annotation annot1 <- annotation(x1=mid_pos, text=dna_segs[[1]]$name) plot_gene_map(dna_segs=dna_segs, comparisons=comparisons, annotations=annot1) ## Exploring options annot2 <- annotation(x1=c(mid_pos[1], dna_segs[[1]]$end[2]), x2=c(NA, dna_segs[[1]]$end[3]), text=c(dna_segs[[1]]$name[1], "region1"), rot=c(30, 0), col=c("grey", "black")) plot_gene_map(dna_segs=dna_segs, comparisons=comparisons, annotations=annot2, annotation_height=1.3) ## xlims ## Just returning a segment plot_gene_map(dna_segs, comparisons, xlims=list(NULL, NULL, c(Inf,-Inf)), dna_seg_scale=TRUE) ## Removing one gene plot_gene_map(dna_segs, comparisons, xlims=list(NULL, NULL, c(-Inf,2800)), dna_seg_scale=TRUE) ## offsets offsets <- c(0, 0, 0) plot_gene_map(dna_segs=dna_segs, comparisons=comparisons, offsets=offsets) offsets <- c(200, 400, 0) plot_gene_map(dna_segs=dna_segs, comparisons=comparisons, offsets=offsets) ## main plot_gene_map(dna_segs=dna_segs, comparisons=comparisons, main="Comparison of A, B and C") plot_gene_map(dna_segs=dna_segs, comparisons=comparisons, main="Comparison of A, B and C", main_pos="left") ## dna_seg_labels plot_gene_map(dna_segs=dna_segs, comparisons=comparisons, dna_seg_labels=c("Huey", "Dewey", "Louie")) ## dna_seg_labels size plot_gene_map(dna_segs=dna_segs, comparisons=comparisons, dna_seg_labels=c("Huey", "Dewey", "Louie"), dna_seg_label_cex=2) ## dna_seg_line plot_gene_map(dna_segs=dna_segs, comparisons=comparisons, dna_seg_line=c("FALSE", "red", grey(0.6))) ## gene_type plot_gene_map(dna_segs=dna_segs, comparisons=comparisons, gene_type="side_blocks") ## ## From here on, using a bigger dataset from a 4-genome comparison ## data("barto") ## Adding a tree tree <- newick2phylog("(BB:2.5,(BG:1.8,(BH:1,BQ:0.8):1.9):3);") ## Showing only subsegments xlims1 <- list(c(1380000, 1445000), c(10000, 83000), c(15000, 98000), c(5000, 82000)) ## Reducing dataset size for speed purpose for (i in 1:length(barto$dna_segs)){ barto$dna_segs[[i]] <- trim(barto$dna_segs[[i]], xlim=xlims1[[i]]) if (i < length(barto$dna_segs)) barto$comparisons[[i]] <- trim(barto$comparisons[[i]], xlim1=xlims1[[i]], xlims1[[i+1]]) } plot_gene_map(barto$dna_segs, barto$comparisons, tree=tree, xlims=xlims1, dna_seg_scale=TRUE) ## Showing several subsegments per genome xlims2 <- list(c(1445000, 1415000, 1380000, 1412000), c( 10000, 45000, 50000, 83000, 90000, 120000), c( 15000, 36000, 90000, 120000, 74000, 98000), c( 5000, 82000)) ## dna_seg_scale, global_color_scheme, size, number, color of dna_seg_scale, ## size of dna_seg_scale labels plot_gene_map(barto$dna_segs, barto$comparisons, tree=tree, xlims=xlims2, dna_seg_scale=c(TRUE, FALSE, FALSE, TRUE), scale=FALSE, dna_seg_label_cex=1.7, dna_seg_label_col=c("black", "grey", "blue", "red"), global_color_scheme=c("e_value", "auto", "grey", "0.7"), n_scale_ticks=3, scale_cex=1) ## Hand-made offsets: size of all gaps offsets2 <- list(c(10000, 10000), c(2000, 2000, 2000), c(10000, 5000, 2000), c(10000)) plot_gene_map(barto$dna_segs, barto$comparisons, tree=tree, #annotations=annots, xlims=xlims2, offsets=offsets2, dna_seg_scale=TRUE) ## ## Exploring and modifying a previously plotted gene map plot ## ## View viewports current.vpTree() ## Go down to one of the viewports, add an xaxis, go back up to root viewport downViewport("dna_seg_scale.3.2") grid.rect() upViewport(0) ## Get all the names of the objects grobNames <- getNames() grobNames ## Change the color ot the scale line grid.edit("scale.lines", gp=gpar(col="grey")) ## Remove first dna_seg_lines grid.remove("dna_seg_line.1.1") ## ## Plot genoPlotR logo ## col <- c("#B2182B", "#D6604D", "#F4A582", "#FDDBC7", "#D1E5F0", "#92C5DE", "#4393C3", "#2166AC") cex <- 2.3 ## First segment start1 <- c(150, 390, 570) end1 <- c( 1, 490, 690) genoR <- c(270, 530) ## Second segment start2 <- c(100, 520, 550) end2 <- c(240, 420, 650) Plot <- c(330) ## dna_segs ds1 <- as.dna_seg(data.frame(name=c("", "", ""), start=start1, end=end1, strand=rep(1, 3), col=col[c(2, 6, 1)], stringsAsFactor=FALSE)) ds_genoR <- as.dna_seg(data.frame(name=c("geno", "R"), start=genoR, end=genoR, strand=rep(1, 2), col=c(col[8], "black"), stringsAsFactor=FALSE), cex=cex, gene_type="text") ds2 <- as.dna_seg(data.frame(name=c("", "", ""), start=start2, end=end2, strand=rep(1, 3), col=col[c(5, 3, 7)], stringsAsFactor=FALSE)) ds_Plot <- as.dna_seg(data.frame(name="Plot", start=Plot, end=Plot, strand=1, col=col[c(1)], stringsAsFactor=FALSE), cex=cex, gene_type="text") ## comparison c1 <- as.comparison(data.frame(start1=start1, end1=end1, start2=start2, end2=end2, col=grey(c(0.6, 0.8, 0.5)))) ## Generate genoPlotR logo ## Not run: ##D cairo_pdf("logo.pdf", h=0.7, w=3) ## End(Not run) par(fin=c(0.7, 3)) plot_gene_map(dna_segs=list(c(ds1, ds_genoR), c(ds2, ds_Plot)), comparisons=list(c1), scale=FALSE, dna_seg_scale=FALSE, dna_seg_line=grey(0.7), offsets=c(-20,160)) ## Not run: ##D dev.off() ## End(Not run) par(old.par)

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/tests/testthat/test-term_stats.R

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test-term_stats.R

context("term_stats") test_that("'term_stats' works", { expect_equal(term_stats("A rose is a rose is a rose."), structure(data.frame(term = c("a", "rose", "is", "."), count = c(3, 3, 2, 1), support = c(1, 1, 1, 1), stringsAsFactors = FALSE), class = c("corpus_frame", "data.frame"))) }) test_that("'term_stats' can use a filter", { f <- text_filter(drop_punct = TRUE, drop = stopwords_en) expect_equal(term_stats("A rose is a rose is a rose.", f), structure(data.frame(term = c("rose"), count = c(3), support = c(1), stringsAsFactors = FALSE), class = c("corpus_frame", "data.frame"))) }) test_that("'term_stats' can count ngrams", { expect_equal(term_stats("A rose is a rose is a rose.", ngrams = 2), structure(data.frame(term = c("a rose", "is a", "rose is", "rose ."), count = c(3, 2, 2, 1), support = c(1, 1, 1, 1), stringsAsFactors = FALSE), class = c("corpus_frame", "data.frame"))) }) test_that("'term_stats' can count ngrams above count_min", { expect_equal(term_stats("A rose is a rose is a rose.", ngrams = 2, min_count = 2), structure(data.frame(term = c("a rose", "is a", "rose is"), count = c(3, 2, 2), support = c(1, 1, 1), stringsAsFactors = FALSE), class = c("corpus_frame", "data.frame"))) }) test_that("'term_stats' can count ngrams above support_min", { expect_equal(term_stats(c("A rose is a rose is a rose.", "Rose Red"), ngrams = 1, min_support = 2), structure(data.frame(term = c("rose"), count = c(4), support = c(2), stringsAsFactors = FALSE), class = c("corpus_frame", "data.frame"))) }) test_that("'term_stats' can output types", { expect_equal(term_stats("A rose is a rose is a rose.", ngrams = 2, min_count = 2, types = TRUE), structure(data.frame(term = c("a rose", "is a", "rose is"), type1 = c("a", "is", "rose"), type2 = c("rose", "a", "is"), count = c(3, 2, 2), support = c(1, 1, 1), stringsAsFactors = FALSE), class = c("corpus_frame", "data.frame"))) }) test_that("'term_stats' can select terms", { expect_equal(term_stats("A rose is a rose is a rose.", subset = term %in% c("rose", "a")), structure(data.frame(term = c("a", "rose"), count = c(3, 3), support = c(1, 1), stringsAsFactors = FALSE), class = c("corpus_frame", "data.frame"))) }) test_that("'term_stats' errors for invalid 'subset' argument", { expect_error(term_stats("A rose is a rose is a rose.", subset = "rose"), "'subset' must be logical") }) test_that("'term_stats' errors for invalid 'count', 'support' arguments", { expect_error(term_stats("hello", min_count = c(1, 2)), "'min_count' must have length 1") expect_error(term_stats("hello", max_count = NA), "'max_count' must be a numeric value (or NULL)", fixed = TRUE) }) test_that("'term_stats' errors for invalid 'ngrams' argument", { expect_error(term_stats("hello", ngrams = "1"), "'ngrams' must be NULL or an integer vector") expect_error(term_stats("hello", ngrams = c(NA, 1)), "'ngrams' entries must be positive integer values") expect_error(term_stats("hello", ngrams = c(1, 0)), "'ngrams' entries must be positive integer values") expect_error(term_stats("hello", ngrams = 128), "'ngrams' entries must be below 128") expect_error(term_stats("hello", ngrams = integer()), "'ngrams' argument cannot have length 0") })

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/man/add_default_decisions.Rd

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add_default_decisions.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/add_default_decisions.R \name{add_default_decisions} \alias{add_default_decisions} \title{Add default decisions} \usage{ add_default_decisions(x) } \arguments{ \item{x}{\code{\link{ConservationProblem-class}} object.} } \description{ This function adds the default decision types to a conservation planning \code{\link{problem}}. The default types are binary and are added using the \code{\link{add_binary_decisions}} function. } \seealso{ \code{\link{decisions}}. }

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anova2x2_se.R

#'Compute power for Simple Effects in a Two by Two Between Subjects ANOVA with two levels for each factor. #'Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by user #'@param m1.1 Cell mean for First level of Factor A, First level of Factor B #'@param m1.2 Cell mean for First level of Factor A, Second level of Factor B #'@param m2.1 Cell mean for Second level of Factor A, First level of Factor B #'@param m2.2 Cell mean for Second level of Factor A, Second level of Factor B #'@param s1.1 Cell standard deviation for First level of Factor A, First level of Factor B #'@param s1.2 Cell standard deviation for First level of Factor A, Second level of Factor B #'@param s2.1 Cell standard deviation for Second level of Factor A, First level of Factor B #'@param s2.2 Cell standard deviation for Second level of Factor A, Second level of Factor B #'@param n1.1 Cell sample size for First level of Factor A, First level of Factor B #'@param n1.2 Cell sample size for First level of Factor A, Second level of Factor B #'@param n2.1 Cell sample size for Second level of Factor A, First level of Factor B #'@param n2.2 Cell sample size for Second level of Factor A, Second level of Factor B #'@param alpha Type I error (default is .05) #'examples #'anova2x2_se(m1.1=0.85, m1.2=0.85, m2.1=0.00, m2.2=0.60, #'s1.1=1.7, s1.2=1.7, s2.1=1.7, s2.2=1.7, #'n1.1=250, n1.2=250, n2.1=250, n2.2=250, alpha=.05) #'@return Power for Simple Effects Tests in a Two By Two ANOVA #'@export #' #' anova2x2_se<-function(m1.1=NULL,m1.2=NULL,m2.1=NULL,m2.2=NULL, s1.1=NULL,s1.2=NULL,s2.1=NULL,s2.2=NULL, n1.1=NULL,n1.2=NULL,n2.1=NULL,n2.2=NULL, alpha=.05){ oldoption<-options(contrasts=c("contr.helmert", "contr.poly")) oldoption on.exit(options(oldoption)) x<-stats::rnorm(n1.1,m1.1,s1.1) X<-x MEAN<-m1.1 SD<-s1.1 Z <- (((X - mean(X, na.rm = TRUE))/stats::sd(X, na.rm = TRUE))) * SD y<-MEAN + Z A<-rep("A1",n1.1) B<-rep("B1",n1.1) l1.1<-data.frame(y, A, B) x<-stats::rnorm(n1.2,m1.2,s1.2) X<-x MEAN<-m1.2 SD<-s1.2 Z <- (((X - mean(X, na.rm = TRUE))/stats::sd(X, na.rm = TRUE))) * SD y<-MEAN + Z A<-rep("A1",n1.2) B<-rep("B2",n1.2) l1.2<-data.frame(y, A, B) x<-stats::rnorm(n2.1,m2.1,s2.1) X<-x MEAN<-m2.1 SD<-s2.1 Z <- (((X - mean(X, na.rm = TRUE))/stats::sd(X, na.rm = TRUE))) * SD y<-MEAN + Z A<-rep("A2",n2.1) B<-rep("B1",n2.1) l2.1<-data.frame(y, A, B) x<-stats::rnorm(n2.2,m2.2,s2.2) X<-x MEAN<-m2.2 SD<-s2.2 Z <- (((X - mean(X, na.rm = TRUE))/stats::sd(X, na.rm = TRUE))) * SD y<-MEAN + Z A<-rep("A2",n2.2) B<-rep("B2",n2.2) l2.2<-data.frame(y, A, B) simdat<-rbind(l1.1,l1.2,l2.1,l2.2) dataA1<-subset(simdat, A=="A1") dataA2<-subset(simdat, A=="A2") dataB1<-subset(simdat, B=="B1") dataB2<-subset(simdat, B=="B2") options(contrasts=c("contr.sum", "contr.poly")) anova<-stats::aov(y~A*B, data=simdat) anova<-car::Anova(anova, type="III") SSwin<-anova[5,1] #row column dfwin<-anova[5,2] SSA<-anova[2,1] #column, row SSB<-anova[3,1] SSAB<-anova[4,1] SST<-SSA+SSB+SSAB+SSwin MSwin<-SSwin/dfwin options(contrasts=c("contr.sum", "contr.poly")) anoAatB1<-stats::aov(y~A, data=dataB1) anoAatB1<-car::Anova(anoAatB1, type="III") options(contrasts=c("contr.sum", "contr.poly")) anoAatB2<-stats::aov(y~A, data=dataB2) anoAatB2<-car::Anova(anoAatB2, type="III") options(contrasts=c("contr.sum", "contr.poly")) anoBatA1<-stats::aov(y~B, data=dataA1) anoBatA1<-car::Anova(anoBatA1,type="III") options(contrasts=c("contr.sum", "contr.poly")) anoBatA2<-stats::aov(y~B, data=dataA2) anoBatA2<-car::Anova(anoBatA2, type="III") dfwinSE<-dfwin+2 SSBatA1<-anoBatA1[2,1] dfBatA1<-anoBatA1[2,2] eta2BatA1<-SSBatA1/SST f2BatA1<-eta2BatA1/(1-eta2BatA1) lambdaBatA1<-f2BatA1*dfwinSE minusalpha<-1-alpha FtBatA1<-stats::qf(minusalpha, dfBatA1, dfwinSE) power.BatA1<-round(1-stats::pf(FtBatA1, dfBatA1,dfwinSE,lambdaBatA1),3) SSBatA2<-anoBatA2[2,1] dfBatA2<-anoBatA2[2,2] eta2BatA2<-SSBatA2/SST f2BatA2<-eta2BatA2/(1-eta2BatA2) lambdaBatA2<-f2BatA2*dfwinSE FtBatA2<-stats::qf(minusalpha, dfBatA2, dfwinSE) power.BatA2<-round(1-stats::pf(FtBatA2, dfBatA2,dfwinSE,lambdaBatA2),3) SSAatB1<-anoAatB1[2,1] dfAatB1<-anoAatB1[2,2] dfwinAat<-anoAatB1[3,2] eta2AatB1<-SSAatB1/SST f2AatB1<-eta2AatB1/(1-eta2AatB1) lambdaAatB1<-f2AatB1*dfwinSE FtAatB1<-stats::qf(minusalpha, dfAatB1, dfwinSE) power.AatB1<-round(1-stats::pf(FtAatB1, dfAatB1,dfwinSE,lambdaAatB1),3) SSAatB2<-anoAatB2[2,1] dfAatB2<-anoAatB2[2,2] eta2AatB2<-SSAatB2/SST f2AatB2<-eta2AatB2/(1-eta2AatB2) lambdaAatB2<-f2AatB2*dfwinSE FtAatB2<-stats::qf(minusalpha, dfAatB2, dfwinSE) power.AatB2<-round(1-stats::pf(FtAatB2, dfAatB2,dfwinSE,lambdaAatB2),3) message("Simple Effect Comparing M = ",m1.1, " and M = ", m2.1,". Power = ", power.AatB1) message("Simple Effect Comparing M= ",m1.2, " and M = ", m2.2,". Power = ", power.AatB2) message("Simple Effect Comparing M = ",m1.1, " and M = ", m1.2,". Power = ", power.BatA1) message("Simple Effect Comparing M = ",m2.1, " and M = ", m2.2,". Power = ", power.BatA2) result <- data.frame(matrix(ncol = 8)) colnames(result) <- c("Eta-squared A at B1","Power A at B1","Eta-squared A at B2","Power A at B2","Eta-squared B at A1","Power B at A1","Eta-squared B at A2","Power B at A2") result[, 1]<-eta2AatB1 result[, 2]<-power.AatB1 result[, 3]<-eta2AatB2 result[, 4]<-power.AatB2 result[, 5]<-eta2BatA1 result[, 6]<-power.BatA1 result[, 7]<-eta2BatA2 result[, 8]<-power.BatA2 output<-na.omit(result) rownames(output)<- c() invisible(output) }

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Connecting.R

## ---- echo = FALSE, message = FALSE------------------------------------------- library(DatabaseConnector) ## ----eval=FALSE--------------------------------------------------------------- # Sys.setenv("DATABASECONNECTOR_JAR_FOLDER" = "c:/temp/jdbcDrivers") ## ----eval=FALSE--------------------------------------------------------------- # install.packages("usethis") # usethis::edit_r_environ() ## ----eval=FALSE--------------------------------------------------------------- # Sys.setenv("DATABASECONNECTOR_JAR_FOLDER" = "c:/temp/jdbcDrivers") ## ----eval=FALSE--------------------------------------------------------------- # downloadJdbcDrivers("postgresql") ## ----echo=FALSE--------------------------------------------------------------- writeLines("DatabaseConnector JDBC drivers downloaded to 'c:/temp/jdbcDrivers'.") ## ----eval=FALSE--------------------------------------------------------------- # install.packages("RSQLite") ## ----eval=FALSE--------------------------------------------------------------- # conn <- connect(dbms = "postgresql", # server = "localhost/postgres", # user = "joe", # password = "secret") ## ----echo=FALSE--------------------------------------------------------------- writeLines("Connecting using PostgreSQL driver") ## ----eval=FALSE--------------------------------------------------------------- # disconnect(conn) ## ----eval=FALSE--------------------------------------------------------------- # conn <- connect(dbms = "postgresql", # connectionString = "jdbc:postgresql://localhost:5432/postgres", # user = "joe", # password = "secret") ## ----echo=FALSE--------------------------------------------------------------- writeLines("Connecting using PostgreSQL driver") ## ----eval=FALSE--------------------------------------------------------------- # details <- createConnectionDetails(dbms = "postgresql", # server = "localhost/postgres", # user = "joe", # password = "secret") # conn <- connect(details) ## ----echo=FALSE--------------------------------------------------------------- writeLines("Connecting using PostgreSQL driver") ## ----------------------------------------------------------------------------- conn <- connect(dbms = "sqlite", server = tempfile()) # Upload cars dataset as table: insertTable(connection = conn, tableName = "cars", data = cars) querySql(conn, "SELECT COUNT(*) FROM main.cars;") disconnect(conn)

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/R/intervention_proportion.R

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2020-04-16T17:44:59.654202

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intervention_proportion.R

#' Calculate proportion of intervention group #' #' @inheritParams beta_par #' #' @export intervention_proportion <- function(n, proportion, error) { par <- beta_par(proportion, error) rbeta(n, shape1 = par$alpha, shape2 = par$beta) }

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/data/genthat_extracted_code/bPeaks/examples/bPeaks-package.Rd.R

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bPeaks-package.Rd.R

library(bPeaks) ### Name: bPeaks-package ### Title: bPeaks: an intuitive peak-calling strategy to detect ### transcription factor binding sites from ChIP-seq data in small ### eukaryotic genomes ### Aliases: bPeaks-package bPeaks ### Keywords: peak calling ChIP-seq protein binding sites protein-DNA ### interactions deep sequencing small eukaryotic genomes ### ** Examples # get library library(bPeaks) # STEP 1: get PDR1 data (ChIP-seq experiments, IP and control samples, # related to the transcription factor Pdr1 in yeast Saccharomyces # cerevisiae) data(dataPDR1) # STEP 2 : bPeaks analysis (only 10 kb of chrIV are analyzed here, # as an illustration) bPeaksAnalysis(IPdata = dataPDR1$IPdata[40000:50000,], controlData = dataPDR1$controlData[40000:50000,], cdsPositions = dataPDR1$cdsPositions, windowSize = 150, windowOverlap = 50, IPcoeff = 4, controlCoeff = 2, log2FC = 1, averageQuantiles = 0.5, resultName = "bPeaks_example") # --> Result files (PDF and BED) are written in the working directory. ## Not run: ##D # -> bPeaks analysis, all chromosome IV and default parameters (optimized for yeasts) ##D ##D # STEP 1: get PDR1 data (ChIP-seq experiments, IP and control samples, ##D # related to the transcription factor Pdr1 in yeast Saccharomyces ##D # cerevisiae) ##D data(dataPDR1) ##D ##D # STEP 2: bPeaks analysis ##D bPeaksAnalysis(IPdata = dataPDR1$IPdata, ##D controlData = dataPDR1$controlData, ##D cdsPositions = dataPDR1$cdsPositions, ##D windowSize = 150, windowOverlap = 50, ##D IPcoeff = 2, controlCoeff = 2, ##D log2FC = 2, averageQuantiles = 0.9, ##D resultName = "bPeaks_PDR1", ##D peakDrawing = TRUE) ##D ##D # STEP 3 : procedure to locate peaks according to ##D # gene positions ##D peakLocation(bedFile = "bPeaks_PDR1_bPeaks_allGenome.bed", ##D cdsPositions = yeastCDS$Saccharomyces.cerevisiae, ##D outputName = "bPeakLocation_finalPDR1", promSize = 800) ##D ##D # -> Note that cds (genes) positions are stored in bPeaks package for several yeast ##D # species ##D data(yeastCDS) ##D ##D summary(yeastCDS) ##D # Length Class Mode ##D # Debaryomyces.hansenii 31370 -none- character ##D # Eremothecium.gossypii 23615 -none- character ##D # Kluyveromyces.lactis 25380 -none- character ##D # Pichia.sorbitophila 55875 -none- character ##D # Saccharomyces.kluyveri 27790 -none- character ##D # Yarrowia.lipolytica 32235 -none- character ##D # Zygosaccharomyces.rouxii 24955 -none- character ##D # Saccharomyces.cerevisiae 5 data.frame list ##D # Candida.albicans 5 data.frame list ##D # Candida.glabrata 5 data.frame list ## End(Not run)

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/HypoPub_LoadPackages.R

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brianherb/DevHumanHypothalamus

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HypoPub_LoadPackages.R

#qlogin -P "sament-lab" -l mem_free=100G -q interactive.q -pe thread 16 .libPaths('/local/projects-t3/idea/bherb/software/R_lib/R_3_6') library(Seurat,lib.loc='/usr/local/packages/r-3.6.0/lib64/R/library') ## 3.0.1 #library(Seurat) library(BiocGenerics) library(monocle3) library(projectR) library(plotrix) # updated library(xlsx) library(ggplot2) library(impute) library(preprocessCore) library(AnnotationDbi) library(GO.db) library(dendextend) library(matrixStats) library(Matrix) library(scrattch.hicat) library(scDblFinder) #library(future) library(RColorBrewer) library(GENIE3) library(feather) library(slingshot) library(gam) library(tradeSeq) library(RColorBrewer) library(SingleCellExperiment) library(WGCNA) library(AUCell) library(MetaNeighbor) #library(topGO) library('GOstats') library('GO.db') library('org.Hs.eg.db') library("biomaRt") #library(scran,lib.loc='/usr/local/packages/r-3.6.0/lib64/R/library') library(EnhancedVolcano) library( cicero )

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/db_old/axes.R

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laurentBesnainou/Rcode

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axes.R

#cirle sur les clients par rapport aux offres #On constitue la matrice des connaissancess library(circlize) library(dplyr) pilotage_2017 <- pilotage_data tmp <- pilotage_2017 %>% filter( WEEK ==5, STEP %in% c( "3 - Emise")) %>% select(GROUPE,OFFRE_PRINCIPALE,CA_BT__N__KE) matriceConnais <- xtabs(CA_BT__N__KE~ GROUPE + OFFRE_PRINCIPALE, na.omit(tmp)) to <- paste(unique(colnames(matriceConnais)),sep = ",") from <- paste(rownames(matriceConnais),sep = ",") mat <- matrix(0, nrow = length(unique(from)), ncol = length(unique(to))) col <- matrix(0, nrow = length(unique(from)), ncol = length(unique(to))) rownames(mat) = unique(from) colnames(mat) = unique(to) noms <- c(from,to) names(gripCol) <- noms for (i in 1:length(from)) { for (j in 1:length(to)) { mat[i,j] <- matriceConnais[i,j] } } # for(i in from ) { # # if (i != input$consultant_id ) { # # col[which(from == i), 1] = "#FFFFFF00" # col[which(from == i), 2] = "#FFFFFF00" # col[which(from == i), 3] = "#FFFFFF00" # col[which(from == i), 4] = "#FFFFFF00" # col[which(from == i), 5] = "#FFFFFF00" # } # } #= = = = = initialize = = = = = # par(mar = c(1, 1, 1, 1)) circos.par(gap.degree = c(rep(2, nrow(mat)-1), 10, rep(2, ncol(mat)-1), 10)) # = = = = = plot 'circlize' = = = = = # chordDiagram(mat, annotationTrack = "grid", transparency = 0.8, preAllocateTracks = list(track.height = 0.1), col = matrix(rainbow(nrow(mat)),nrow=nrow(mat),ncol=ncol(mat)), row.col = 1) # = = = = = add labels = = = = = # circos.trackPlotRegion(track.index = 1, panel.fun = function(x, y) { xrange = get.cell.meta.data("xlim") labels = get.cell.meta.data("sector.index") circos.text(mean(xrange), 0, labels = labels, niceFacing = TRUE) }, bg.border = NA) circos.clear() circos.axis(mat, sector.index, track.index) chordDiagram(mat, directional = TRUE, niceFacing=TRUE, transparency = 0.2) circos.clear() circos.trackText(from,to,labels =union(from,to), factors = union(from,to), col = "#EEEEEE", font = 2, facing = "downward") # = = = = = add labels = = = = = # factors = 1:20# just indicate there are 20 sectors circos.par(gap.degree = 0, cell.padding =c(0, 0, 0, 0),start.degree = 360/20/2, track.margin =c(0, 0), clock.wise = FALSE) circos.initialize(factors = factors, xlim =c(0, 1)) circos.trackPlotRegion(ylim =c(0, 1), factors = factors, bg.col = "black",track.height = 0.15) circos.trackText(rep(0.5, 20),rep(0.5, 20), labels =c(13, 4, 18, 1, 20, 5, 12, 9, 14, 11, 8, 16, 7, 19, 3, 17, 2, 15, 10, 6), factors = factors, col = "#EEEEEE", font = 2, facing = "bending.outside") circos.trackPlotRegion(ylim =c(0, 1), factors = factors,bg.col =rep(c("#E41A1C", "#4DAF4A"), 10), bg.border = "#EEEEEE", track.height = 0.05) circos.trackPlotRegion(ylim =c(0, 1), factors = factors,bg.col =rep(c("black", "white"), 10), bg.border = "#EEEEEE", track.height = 0.275) circos.trackPlotRegion(ylim =c(0, 1), factors = factors,bg.col =rep(c("#E41A1C", "#4DAF4A"), 10), bg.border = "#EEEEEE", track.height = 0.05) circos.trackPlotRegion(ylim =c(0, 1), factors = factors,bg.col =rep(c("black", "white"), 10), bg.border = "#EEEEEE", track.height = 0.375) draw.sector(center =c(0, 0), start.degree = 0, end.degree = 360,rou1 = 0.1, col = "#4DAF4A", border = "#EEEEEE") draw.sector(center =c(0, 0), start.degree = 0, end.degree = 360,rou1 = 0.05, col = "#E41A1C", border = "#EEEEEE")

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/RScripts/00_apresenta_curso.R

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rdosreis/MAT02018

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2022-10-04T12:55:53.010897

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00_apresenta_curso.R

## ----echo=FALSE, fig.align='center', message=FALSE, warning=FALSE, out.width='80%', out.height='80%', paged.print=FALSE---- knitr::include_graphics(here::here('images', 'hi_my_name_is.png')) ## ----echo=FALSE, fig.align='center', message=FALSE, warning=FALSE, out.width='100%', paged.print=FALSE---- knitr::include_graphics(here::here('images','covid-recomendacoes.jpg')) ## ----echo=FALSE, fig.align='center', message=FALSE, warning=FALSE, out.width='10%', paged.print=FALSE---- knitr::include_graphics(here::here('images','Rlogo.png')) ## ---- echo=FALSE, eval=TRUE------------------------------------------------------------------- x <- rnorm(n = 100, mean = 10, sd = 1) ## ---- echo=TRUE, eval=TRUE, fig.align='center', out.width='50%'------------------------------- hist(x, col = 'black', border = 'white') ## ----echo=FALSE, fig.align='right', message=FALSE, warning=FALSE, out.width='15%', paged.print=FALSE---- knitr::include_graphics(here('images','ctanlion.png')) ## ----echo=FALSE, fig.align='center', message=FALSE, warning=FALSE, out.width='100%', out.height='80%', paged.print=FALSE---- knitr::include_graphics(here::here('images', 'estat1.jpg')) ## ----echo=FALSE, fig.align='center', message=FALSE, warning=FALSE, out.width='100%', out.height='80%', paged.print=FALSE---- knitr::include_graphics(here::here('images', 'estat2.jpg')) ## ----echo=FALSE, fig.align='center', message=FALSE, warning=FALSE, out.width='100%', out.height='80%', paged.print=FALSE---- knitr::include_graphics(here::here('images', 'estat3.png')) ## ----echo=FALSE, fig.align='center', message=FALSE, warning=FALSE, out.width='100%', out.height='80%', paged.print=FALSE---- knitr::include_graphics(here::here('images', 'estat4.jpg')) ## ----echo=FALSE, fig.align='center', message=FALSE, warning=FALSE, out.width='100%', out.height='80%', paged.print=FALSE---- knitr::include_graphics(here::here('images', 'Descritiva_Inferencia.png')) ## ----echo=FALSE, fig.align='center', message=FALSE, warning=FALSE, out.width='50%', out.height='50%', paged.print=FALSE---- knitr::include_graphics(here::here('images', 'Statistically-Insignificant-8.jpg'))

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/Scripts/compareNetworks.R

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gazwb/compositionalCor_v3

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refs/heads/master

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compareNetworks.R

setwd("~/MAARS_p2/MB") source("/home/gaz/MAARS_p2/Scripts/compositionalCor_v3/Scripts/inference_functions.R") source("/home/gaz/MAARS_p2/Scripts/compositionalCor_v3/Scripts/analysis_functions.R") source("/home/gaz/rand/multiplot/multiplot.R") nets <- readNetworks() # read in these networks net.ADL <- nets[[1]] # net.ADNL <- read.table(file="/home/gaz/MAARS_p2/Scripts/compositionalCor/mainNetworks/edgeData_AD_NON_LES", header = TRUE) net.CTRL <- nets[[2]] # net.PSONL <- read.table(file="/home/gaz/MAARS_p2/Scripts/compositionalCor/mainNetworks/edgeData_PSO_NON_LES", header = TRUE) net.PSOL <- nets[[3]] node <- readAttributes() # read in these networks node.ADL <- node[[1]] # net.ADNL <- read.table(file="/home/gaz/MAARS_p2/Scripts/compositionalCor/mainNetworks/edgeData_AD_NON_LES", header = TRUE) node.CTRL <- node[[2]] # net.PSONL <- read.table(file="/home/gaz/MAARS_p2/Scripts/compositionalCor/mainNetworks/edgeData_PSO_NON_LES", header = TRUE) node.PSOL <- node[[3]] mbdat <- load_MB_data() getHubNodes(net.ADL) # call functions for module realigning olm.CTRL_PSOL <- defOverlapMatrix(node.CTRL ,node.PSOL) olm.CTRL_ADL <- defOverlapMatrix(node.CTRL ,node.ADL) # pass module memberships for networks and overlap jac.CTRL_PSOL <- defJaccardMatrix(node.CTRL ,node.PSOL,olm.CTRL_PSOL) jac.CTRL_ADL <- defJaccardMatrix(node.CTRL ,node.ADL,olm.CTRL_ADL) # realign node.PSOL.fx <- reAlign.2(node.PSOL,jac.CTRL_PSOL) node.ADL.fx <- reAlign.2(node.ADL,jac.CTRL_ADL) node.CTRL.fx <- reAlign.ctrl(node.CTRL) # create a new list of the network attributes with the fixed nodes node.fx <- list(node.ADL.fx,node.CTRL.fx,node.PSOL.fx) # write out to file #write_realignment(node.ADL.fx,node.CTRL.fx,node.PSOL.fx) #define the order of the lists netOrder <- c("AD L","CTRL","PSO L") # call network stats netStats <- calcStats(node,nets, netOrder) # call differential connectivity diffConect <- diffConnectivity(nets) diffConectivityPerms <- readPermutations() perm.ADL <- diffConectivityPerms[[1]] perm.CTRL.ADL <- diffConectivityPerms[[2]] perm.PSOL <- diffConectivityPerms[[3]] perm.CTRL.PSOL <- diffConectivityPerms[[4]] netSizes <- c(sum(modStats.ADL[[3]]),sum(modStats.CTRL[[3]]),sum(modStats.PSOL[[3]])) diffPerm.ADL <- difConexPermutation(perm.ADL,perm.CTRL.ADL) diffPerm.PSOL <- difConexPermutation(perm.PSOL,perm.CTRL.PSOL) # function to calculate p values diffConnect.P <- calculatePermutationPval(diffConect, diffPerm.ADL,diffPerm.PSOL) tax <- mbdat[[2]][match(rownames(diffConnect.P),mbdat[[2]]$OTU_id),] #fix tax tax[tax$Family == "",6] <- "F." tax[tax$Genus == "",7] <- "G." tax[tax$Species == "",8] <- "sp." tax$joinName <- as.factor(paste0(tax$Genus, " ", tax$Species)) ## what modules are these nodes in? cms <- node.CTRL.fx[match(rownames(diffConnect.P),node.CTRL.fx$nodeName),10] pms <- node.PSOL.fx[match(rownames(diffConnect.P),node.PSOL.fx$nodeName),10] ams <- node.ADL.fx[match(rownames(diffConnect.P),node.ADL.fx$nodeName),10] diffConnect.tax.P <- cbind(tax[,c(6,7,8,10)],ams,cms,pms, diffConnect.P) rownames(diffConnect.tax.P) <- rownames(diffConnect.P) # diffConnect.tax.P[diffConnect.tax.P$ad.p < 0.05,] # diffConnect.tax.P[diffConnect.tax.P$pso.p < 0.05,] fold.changes <- calcDifferentialAbundance(diffConnect.tax.P) diffConnect.tax.P.fc <- cbind(diffConnect.tax.P ,fold.changes) # plot the differential connectivity by module AD.diffconnect <- diffConnect.tax.P.fc[!is.na(diffConnect.tax.P.fc$ams),] PSO.diffconnect <- diffConnect.tax.P.fc[!is.na(diffConnect.tax.P.fc$pms),] AD.diffconnect$ad.BH <- p.adjust(AD.diffconnect$ad.p,method = "BH") PSO.diffconnect$pso.BH <- p.adjust(PSO.diffconnect$pso.p,method = "BH") # set color scale PSO.diffconnect$pms <- as.factor(PSO.diffconnect$pms) myColors <- brewer.pal(8,"Dark2") names(myColors) <- levels(PSO.diffconnect$pms) colScale <- scale_colour_manual(name = "pms",values = myColors) # plot qplot(Diffcvpso, PSOvCTRL.FC, data=PSO.diffconnect, colour=as.factor(pms),xlab = "Differential Connectivity", ylab = "log10(Fold Change)", main = "PSO Differential connectivity vs Fold change") + geom_point(aes(size = 1)) + colScale qplot(Diffcvad, ADvCTRL.FC, data=AD.diffconnect, colour=as.factor(ams),xlab = "Differential Connectivity", ylab = "log10(Fold Change)",main = "AD Differential connectivity vs Fold change")+ geom_point(aes(size = 1)) + colScale # get significant differentially connected bacterias sig.dc.PSO <- PSO.diffconnect[PSO.diffconnect$pso.BH < 0.1,] sig.dc.AD <- AD.diffconnect[AD.diffconnect$ad.BH < 0.1,] # factor for order sig.dc.PSO <- sig.dc.PSO[order(sig.dc.PSO$Diffcvpso,decreasing = FALSE),] sig.dc.PSO$joinName<- as.character(sig.dc.PSO$joinName) sig.dc.PSO$joinName <- factor(sig.dc.PSO$joinName, levels=unique(sig.dc.PSO$joinName)) # factor for order sig.dc.AD<- sig.dc.AD[order(sig.dc.AD$Diffcvad,decreasing = FALSE),] sig.dc.AD$joinName<- as.character(sig.dc.AD$joinName) sig.dc.AD$joinName <- factor(sig.dc.AD$joinName, levels=unique(sig.dc.AD$joinName)) sig.dc.AD$rn <- rownames(sig.dc.AD) sig.dc.AD$rn <- factor(sig.dc.AD$rn, levels=unique(sig.dc.AD$rn)) ggplot(data=sig.dc.PSO, aes(x=joinName, y=Diffcvpso, fill=joinName)) + geom_bar(colour="black", stat="identity",width=.8) + guides(fill=FALSE) + labs(y = "Differential Connectivity", x = "") + ggtitle("PSO v CTRL") + coord_flip() + theme_bw() ggplot(data=sig.dc.AD, aes(x=rn, y=Diffcvad, fill=joinName)) + geom_bar(colour="black", stat="identity",width=.8) + guides(fill=FALSE) + scale_x_discrete(labels = sig.dc.AD$joinName)+ labs(y = "Differential Connectivity", x = "") + ggtitle("AD v CTRL") + coord_flip() + theme_bw() # more plots moduleDegreePlot() plotBetweenessCentrality(nets) # extract positive and negative subgraphs # 1 is positive links, 2 is negative links, 3 iis positive nodes, 4 is negative nodes directionalSubgraphs <- extractDirectionalSubgraphs(nets,node.fx) ###get module statistics modStats.ADL <- calcModuleStats(node.ADL.fx) modStats.CTRL <- calcModuleStats(node.CTRL.fx) modStats.PSOL <- calcModuleStats(node.PSOL.fx) o <- o2PropNetworkPlot(node.fx) # plot of o2 proportions per modules p <- o2PropPlot(node.fx) # works now # plot of module similarity j <- moduleOverlapPlot(node.fx) k <- orderNetworkPlot(node.fx) l <- taxNetworkPlot(node.fx) readPermutations rawUnionDat <- readRawProportionalCountDat() AD.raw <- t(rawUnionDat[[1]]) CTRL.raw <- t(rawUnionDat[[2]]) PSO.raw <- t(rawUnionDat[[3]]) # exec one SparCC #sparCC.PSO <- sparcc(PSO.raw)

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184180d341d2928ab7c5a626d94f2a9863726c65

/dataanalysiscode/libfuzz.R

c7828041547b583c1dca51b216f65bd5e83c299b

[]

no_license

akhikolla/RcppDeepStateTest

f102ddf03a22b0fc05e02239d53405c8977cbc2b

97e73fe4f8cb0f8e5415f52a2474c8bc322bbbe5

refs/heads/master

2023-03-03T12:19:31.725234

2021-02-12T21:50:12

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libfuzz.R

deepstate_pkg_create_LibFuzzer<-function(path){ path <- normalizePath(path,mustWork=TRUE) insts.path <- normalizePath("~",mustWork=TRUE) deepstate <- file.path(insts.path,".RcppDeepState") deepstate.path <- file.path(deepstate,"deepstate-master") inst_path <- file.path(path, "inst") test_path <- file.path(inst_path,"testfiles") if(!(file.exists(file.path(insts.path,".RcppDeepState/deepstate-master/build/libdeepstate32.a")) && file.exists(file.path(insts.path,"/.RcppDeepState/deepstate-master/build/libdeepstate.a")))){ RcppDeepState::deepstate_make_run() } LF.a <- file.path(deepstate.path,"build_libfuzzer/libdeepstate_LF.a") if(!file.exists(LF.a)){ deepstate_make_libFuzzer() #print("lib not exists") } exists_flag = 0 if(!file.exists(file.path(path,"src/*.so"))){ system(paste0("R CMD INSTALL ",path),intern = FALSE,ignore.stderr =TRUE,ignore.stdout = TRUE) } functions.list <- RcppDeepState::deepstate_get_function_body(path) fun_names <- unique(functions.list$funName) for(f in fun_names){ libfuzzer.fun.path <- file.path(test_path,f,paste0("libFuzzer_",f)) libfuzzer.harness.path <- file.path(libfuzzer.fun.path,paste0(f,"_DeepState_TestHarness")) input_dir <- file.path(libfuzzer.fun.path,"libfuzzer_inputs") inputs.list <- Sys.glob(file.path(input_dir,"*")) if(!dir.exists(libfuzzer.fun.path)){ exists_flag = 1 dir.create(libfuzzer.fun.path,showWarnings = FALSE) } function.path <- file.path(test_path,f) harness.path <- file.path(function.path,paste0(f,"_DeepState_TestHarness.cpp")) makefile.path <- file.path(function.path,"Makefile") if(file.exists(harness.path) && file.exists(makefile.path) ){ executable <- gsub(".cpp$","",harness.path) object <- gsub(".cpp$",".o",harness.path) o.logfile <- file.path(libfuzzer.fun.path,paste0("/",f,"_log")) logfile <- file.path(libfuzzer.fun.path,paste0("/libfuzzer_",f,"_log")) output_dir <- file.path(libfuzzer.fun.path,paste0("/libfuzzer_",f,"_output")) if(!dir.exists(output_dir)) { dir.create(output_dir,showWarnings = FALSE) } if(!dir.exists(input_dir)) { dir.create(input_dir,showWarnings = FALSE) } #writing harness file harness_lines <- readLines(harness.path,warn=FALSE) harness_lines <- gsub("RInside R;","static int rinside_flag = 0;\n if(rinside_flag == 0)\n {\n rinside_flag = 1;\n RInside R;\n } std::time_t current_timestamp = std::time(0);" ,harness_lines,fixed=TRUE) k <- nc::capture_all_str(harness_lines, "qs::c_qsave","\\(", save=".*",",\"",l=".*","\"") for(i in seq_along(k$l)){ harness_lines <- gsub(paste0("\"",k$l[i],"\""),paste0(gsub(".qs","",basename(k$l[i])),"_t"),harness_lines,fixed=TRUE) harness_lines <- gsub(paste0("qs::c_qsave(",gsub(".qs","",basename(k$l[i]))),paste0("std::string ",gsub(".qs","",basename(k$l[i])),"_t = ","\"",dirname(dirname(k$l[i])), "/",basename(libfuzzer.fun.path),"/libfuzzer_inputs/\" + std::to_string(current_timestamp) + \"_",basename(k$l[i]),"\"",";\n qs::c_qsave(",gsub(".qs","",basename(k$l[i]))),harness_lines,fixed=TRUE) } harness.libFuzz <- file.path(libfuzzer.fun.path,basename(harness.path)) file.create(harness.libFuzz,recursive=TRUE) cat(harness_lines, file=harness.libFuzz, sep="\n") ##makefileupdate makefile_lines <- readLines(makefile.path,warn=FALSE) makefile_lines <- gsub(function.path,libfuzzer.fun.path,makefile_lines,fixed=TRUE) makefile_lines <- gsub("clang++ -g","clang++ -g -fsanitize=address,fuzzer",makefile_lines,fixed=TRUE) makefile_lines <- gsub("-ldeepstate","-ldeepstate -ldeepstate_LF",makefile_lines,fixed=TRUE) makefile_lines <- gsub("deepstate-master/build","deepstate-master/build_libfuzzer",makefile_lines,fixed=TRUE) makefile.libFuzz <- file.path(libfuzzer.fun.path,"Makefile") file.create(makefile.libFuzz,recursive=TRUE) cat(makefile_lines, file=makefile.libFuzz, sep="\n") compile_line <-paste0("rm -f *.o && make -f ",makefile.libFuzz) execution_line <- paste0("cd ",libfuzzer.fun.path," && ./",basename(executable)," -max_total_time=1800") if(exists_flag == 1){ print(compile_line) system(compile_line) print(execution_line) system(execution_line) } } } }

b1855867ac9233f8519cb914392f24b4abfad0c3

7ff90fda996b9b1da1048936749ad06c0e13da9a

/xgboost.R

af0a536a58066c03d5607dc431dd4d478886ccc8

[]

no_license

katieji737/Adtracking-XGBoost

08583a5a88d26c57b59557d2072be7b1f520a53e

8d795b50553190e3728bc4fcd01da3d7cb90a490

refs/heads/master

2020-04-03T18:29:48.251871

2018-10-31T02:32:08

155,486,519

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R

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4,317

r

xgboost.R

library(data.table) library(dplyr) library(xgboost) library(ggplot2) train <- fread("/Users/~/adtracking_dataset.csv", select =c("ip", "app", "device", "os", "channel", "click_time", "is_attributed"), showProgress=F, colClasses=c("ip"="numeric","app"="numeric","device"="numeric","os"="numeric","channel"="numeric","click_time"="character","is_attributed"="numeric")) #test <- fread("/Users/~/test.csv", #select =c("ip", "app", "device", "os", "channel", "click_time"), #showProgress=F, #colClasses=c("ip"="numeric","app"="numeric","device"="numeric","os"="numeric","channel"="numeric","click_time"="character")) set.seed(1234) train <- train[c(which(train$is_attributed == 1),sample(which(train$is_attributed == 0),9773,replace = F)), ] str(train) y <- train$is_attributed #write.csv(y,'/Users/katie/Desktop/train_y.csv') n_train = nrow(train) #dat_combined <- rbind(train,test[,-1],fill = T) #rm(train,test) #invisible(gc()) train[, ':='(hour = hour(click_time)) ][, ip_count := .N, by = "ip" ][, app_count := .N, by = "app" ][, channel_count := .N, by = "channel" ][, device_count := .N, by = "device" ][, os_count := .N, by = "os" ][, app_count := .N, by = "app" ][, ip_app := .N, by = "ip,app" ][, ip_dev := .N, by = "ip,device" ][, ip_os := .N, by = "ip,os" ][, ip_channel := .N, by = "ip,channel" ][,ip_hour := .N, by = "ip,hour" ][,app_device := .N, by = "app,device" ][,app_channel := .N, by = "app,channel" ][,channel_hour := .N, by = "channel,hour" ][,ip_app_channel := .N, by = "ip,app,channel" ][,app_channel_hour := .N, by = "app,channel,hour" ][,ip_app_hour := .N, by = "ip,app,hour" ][, c("ip","click_time", "is_attributed") := NULL] #write.csv(train, '/~/train_interaction.csv') invisible(gc()) train[, lapply(.SD, uniqueN), .SDcols = colnames(train)] %>% melt(variable.name = "features", value.name = "unique_values") %>% ggplot(aes(reorder(features, -unique_values), unique_values)) + geom_bar(stat = "identity", fill = "lightblue") + scale_y_log10(breaks = c(50,100,250, 500, 10000, 50000)) + geom_text(aes(label = unique_values), vjust = 1.6, color = "black", size=2) + theme_minimal() + labs(x = "features", y = "Number of unique values") within_train_index <- sample(c(1:n_train),0.7*n_train,replace = F) ## split the training dataset into train & validation processed_train_train = train[1:n_train,][within_train_index] y1 = y[1:n_train][within_train_index] processed_train_val = train[1:n_train,][-within_train_index] y2 = y[1:n_train][-within_train_index] processed_test = train[-c(1:n_train),] rm(train) rm(y) invisible(gc()) model_train <- xgb.DMatrix(data = data.matrix(processed_train_train), label = y1) rm(processed_train_train) invisible(gc()) model_val <- xgb.DMatrix(data = data.matrix(processed_train_val), label = y2) rm(processed_train_val) invisible(gc()) xgb_test <- xgb.DMatrix(data = data.matrix(processed_test)) rm(processed_test) invisible(gc()) params <- list(objective = "binary:logistic", booster = "gbtree", eval_metric = "auc", nthread = 7, eta = 0.05, max_depth = 10, gamma = 0.9, subsample = 0.8, colsample_bytree = 0.8, scale_pos_weight = 50, nrounds = 100) myxgb_model <- xgb.train(params, model_train, params$nrounds, list(val = model_val), print_every_n = 20, early_stopping_rounds = 50) imp <- xgb.importance(colnames(model_train), model=myxgb_model) xgb.plot.importance(imp, top_n = 15) cv <- xgb.cv(data = model_train, nrounds = 100, nthread = 7, nfold = 10, metrics = "auc", max_depth = 10, eta = 0.05, objective = "binary:logistic")

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/R/getENSEMBLGENOMES.Seq.R

8f434bade0e630a1111ffe5438f306c74fa4f097

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no_license

cran/biomartr

803c46a689f26cad60e675bf689e52c95eff975c

c5607e69575bd4d47c1003e308a719f8da78bccf

refs/heads/master

2023-07-10T20:27:51.927945

2023-06-20T12:50:07

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getENSEMBLGENOMES.Seq.R

#' @title Helper function for retrieving biological sequence files from #' ENSEMBLGENOMES #' @description This function downloads gff files of query organisms from #' ENSEMBLGENOMES #' @param organism scientific name of the organism of interest. #' @param release the ENSEMBLGENOMES release. Default is \code{release = NULL} meaning that the current (most recent) version is used. #' @param type biological sequence type. #' @param id.type id type. #' @param path location where file shall be stored. #' @author Hajk-Georg Drost #' @noRd getENSEMBLGENOMES.Seq <- function(organism, release = NULL, type = "dna", id.type = "toplevel", path) { if (!is.element(type, c("dna", "cds", "pep", "ncrna"))) stop("Please a 'type' argument supported by this function: 'dna', 'cds', 'pep', 'ncrna'.") name <- NULL # test if REST API is responding is.ensemblgenomes.alive() if (is.taxid(organism)) stop("Unfortunately, taxid retrieval is not yet implemented for ENSEMBLGENOMES...", call. = FALSE) if ( !suppressMessages(is.genome.available(organism = organism, db = "ensemblgenomes", details = FALSE)) ) { warning("Unfortunately organism '", organism, "' is not available at ENSEMBLGENOMES. ", "Please check whether or not the organism name is typed correctly or try db = 'ensembl'.", " Thus, download of this species has been omitted. ", call. = FALSE) return(FALSE) } else { taxon_id <- assembly <- accession <- NULL new.organism <- stringr::str_to_lower(stringr::str_replace_all(organism, " ", "_")) ensembl_summary <- suppressMessages(is.genome.available( organism = organism, db = "ensemblgenomes", details = TRUE )) if (nrow(ensembl_summary) == 0) { message("Unfortunately, organism '",organism,"' does not exist in this database. Could it be that the organism name is misspelled? Thus, download has been omitted.") return(FALSE) } if (nrow(ensembl_summary) > 1) { if (is.taxid(organism)) { ensembl_summary <- dplyr::filter(ensembl_summary, taxon_id == as.integer(organism), !is.na(assembly)) } else { ensembl_summary <- dplyr::filter(ensembl_summary, (name == stringr::str_to_lower(new.organism)) | (accession == organism), !is.na(assembly)) } message("Several entries were found for '", organism, "'.") # "... The first entry '", ensembl_summary$name[1],"' with accession id '",ensembl_summary$accession[1],"' was selected for download.") message("In case you wish to retrieve another genome version please consult is.genome.available(organism = '", organism,"', details = TRUE, db = 'ensemblgenomes') and specify another accession id as organism argument.") message("\n") # select only first entry } new.organism <- paste0( stringr::str_to_upper(stringr::str_sub(ensembl_summary$name[1], 1, 1)), stringr::str_sub(ensembl_summary$name[1], 2, nchar(ensembl_summary$name[1])) ) # retrieve detailed information for organism of interest } get.org.info <- ensembl_summary rest_url <- paste0( "http://rest.ensembl.org/info/assembly/", new.organism, "?content-type=application/json" ) rest_api_status <- test_url_status(url = rest_url, organism = organism) if (is.logical(rest_api_status)) { return(FALSE) } else { if (get.org.info$division == "EnsemblBacteria") { if (!file.exists(file.path(tempdir(), "EnsemblBacteria.txt"))) { tryCatch({ custom_download( "ftp://ftp.ensemblgenomes.org/pub/current/bacteria/species_EnsemblBacteria.txt", destfile = file.path(tempdir(), "EnsemblBacteria.txt"), mode = "wb" ) }, error = function(e) { message( "Something went wrong when accessing the API 'http://rest.ensemblgenomes.org'.", " Are you connected to the internet? ", "Is the homepage 'ftp://ftp.ensemblgenomes.org/pub/current/bacteria/species_EnsemblBacteria.txt' ", "currently available? Could it be that the scientific name is mis-spelled or includes special characters such as '.' or '('?" ) }) } suppressWarnings( bacteria.info <- readr::read_delim( file.path(tempdir(), "EnsemblBacteria.txt"), delim = "\t", quote = "\"", escape_backslash = FALSE, col_names = c( "name", "species", "division", "taxonomy_id", "assembly", "assembly_accession", "genebuild", "variation", "pan_compara", "peptide_compara", "genome_alignments", "other_alignments", "core_db", "species_id" ), col_types = readr::cols( name = readr::col_character(), species = readr::col_character(), division = readr::col_character(), taxonomy_id = readr::col_integer(), assembly = readr::col_character(), assembly_accession = readr::col_character(), genebuild = readr::col_character(), variation = readr::col_character(), pan_compara = readr::col_character(), peptide_compara = readr::col_character(), genome_alignments = readr::col_character(), other_alignments = readr::col_character(), core_db = readr::col_character(), species_id = readr::col_integer() ), comment = "#" ) ) # parse for wrong name conventions and fix them... organism <- stringr::str_replace_all(organism, " sp ", " sp. ") organism <- stringr::str_replace_all(organism, " pv ", " pv. ") organism <- stringr::str_replace_all(organism, " str ", " str. ") organism <- stringr::str_replace_all(organism, " subsp ", " subsp. ") organism <- stringr::str_replace_all(organism, "\$", "") organism <- stringr::str_replace_all(organism, "\$", "") assembly <- NULL bacteria.info <- dplyr::filter(bacteria.info, assembly == get.org.info$assembly) if (nrow(bacteria.info) == 0) { message( "Unfortunately organism '", ensembl_summary$display_name, "' could not be found. Have you tried another database yet? ", "E.g. db = 'ensembl'? Thus, download for this species is omitted." ) return(FALSE) } if (is.na(bacteria.info$core_db[1])) { message( "Unfortunately organism '", ensembl_summary$display_name, "' was not assigned to a bacteria collection. Thus download for this species is omitted." ) return(FALSE) } release_api <- jsonlite::fromJSON( "http://rest.ensembl.org/info/eg_version?content-type=application/json" ) if (!is.null(release)){ if (!is.element(release, seq_len(as.integer(release_api)))) stop("Please provide a release number that is supported by ENSEMBLGENOMES.", call. = FALSE) } # construct retrieval query if (is.null(release)) core_path <- "ftp://ftp.ensemblgenomes.org/pub/current/bacteria/fasta/" if (!is.null(release)) core_path <- paste0("ftp://ftp.ensemblgenomes.org/pub/release-", release ,"/bacteria/fasta/") ensembl.qry <- paste0(core_path, paste0(unlist( stringr::str_split(bacteria.info$core_db[1], "_") )[1:3], collapse = "_"), "/", stringr::str_to_lower(new.organism), "/", type, "/", paste0( new.organism, ".", rest_api_status$default_coord_system_version, ".", type, ifelse(id.type == "none", "", "."), ifelse(id.type == "none", "", id.type), ".fa.gz" ) ) } else { release_api <- jsonlite::fromJSON( "http://rest.ensembl.org/info/eg_version?content-type=application/json" ) if (!is.null(release)){ if (!is.element(release, seq_len(as.integer(release_api)))) stop("Please provide a release number that is supported by ENSEMBLGENOMES.", call. = FALSE) } # construct retrieval query if (is.null(release)) core_path <- "ftp://ftp.ensemblgenomes.org/pub/current/" if (!is.null(release)) core_path <- paste0("ftp://ftp.ensemblgenomes.org/pub/release-", release ,"/") # construct retrieval query ensembl.qry <- paste0( core_path, stringr::str_to_lower( stringr::str_replace(get.org.info$division[1], "Ensembl", "") ), "/fasta/", stringr::str_to_lower(new.organism), "/", type, "/", paste0( new.organism, ".", rest_api_status$default_coord_system_version, ".", type, ifelse(id.type == "none", "", "."), ifelse(id.type == "none", "", id.type), ".fa.gz" ) ) } # if (!exists.ftp.file(url = ensembl.qry, file.path = ensembl.qry)) { # message( # "Unfortunately no ", # type, # " file could be found for organism '", # organism, # "'. Thus, the download of this organism has been omitted." # ) # return(FALSE) # } if (file.exists(file.path( path, paste0( new.organism, ".", rest_api_status$default_coord_system_version, ".", type, ifelse(id.type == "none", "", "."), ifelse(id.type == "none", "", id.type), ".fa.gz" ) ))) { message( "File ", file.path( path, paste0( new.organism, ".", rest_api_status$default_coord_system_version, ".", type, ifelse(id.type == "none", "", "."), ifelse(id.type == "none", "", id.type), ".fa.gz" ) ), " exists already. Thus, download has been skipped." ) } else { custom_download(url = ensembl.qry, destfile = file.path( path, paste0( new.organism, ".", rest_api_status$default_coord_system_version, ".", type, ifelse(id.type == "none", "", "."), ifelse(id.type == "none", "", id.type), ".fa.gz" ) )) } return(c(file.path( path, paste0( new.organism, ".", rest_api_status$default_coord_system_version, ".", type, ifelse(id.type == "none", "", "."), ifelse(id.type == "none", "", id.type), ".fa.gz" ) ), ensembl.qry)) } }

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#' Calculate Capability Measures - Cpm #' #' Calculate Cpm, a measure that combines the measure of variability and targeting relative to nominal specifications. Higher is better. #' #' @param lower.specification Lower specification limit (if applicable) #' @param upper.specification Upper specification limit (if applicable) #' @param process.variability Estimate of process variability, expressed as variance #' @param process.center Estimate of process center #' @param nominal.center Nominal target for the process #' @param n.sigma The number of standard deviations to use in the denominator of the calculation. 6 is recommended, but 5.15 has also been historically used by Automotive Industry Action Group (AIAG). #' #' @return A scalar with computed Cpm. spc.capability.cpm.simple <- function( lower.specification ,upper.specification ,process.variability #Usually Expressed as Variance ,process.center ,nominal.center ,n.sigma = 6) { cpm <- NA if (!is.na(lower.specification) & !is.na(upper.specification)) { cpm <- (upper.specification - lower.specification)/(n.sigma*sqrt(process.variability + (process.center - nominal.center)^2)) } else if (is.na(lower.specification) | is.na(upper.specification)) { cpm <- (2*abs(process.center- na.omit(c(upper.specification,lower.specification))))/(n.sigma*sqrt(process.variability + (process.center - nominal.center)^2)) } cpm }

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##' Function to compute the Bayes factors from MCMC samples. ##' ##' Computes the Bayes factors using \code{method} with respect to ##' \code{reference}. ##' @title Computation of Bayes factors at the skeleton points ##' @param runs A list with outputs from the function ##' \code{\link{mcsglmm}} or \code{\link{mcstrga}}. ##' @param bfsize1 A scalar or vector of the same length as ##' \code{runs} with all integer values or all values in (0, 1]. How ##' many samples (or what proportion of the sample) to use for ##' estimating the Bayes factors at the first stage. The remaining ##' sample will be used for estimating the Bayes factors in the ##' second stage. Setting it to 1 will perform only the first stage. ##' @param method Which method to use to calculate the Bayes factors: ##' Reverse logistic or Meng-Wong. ##' @param reference Which model goes in the denominator. ##' @param transf Whether to use a transformed sample for the ##' computations. If \code{"no"} or \code{FALSE}, it doesn't. If ##' \code{"mu"} or \code{TRUE}, it uses the samples for the mean. If ##' \code{"wo"} it uses an alternative transformation. The latter ##' can be used only for the families indicated by ##' \code{.geoBayes_models$haswo}. ##' @return A list with components ##' \itemize{ ##' \item \code{logbf} A vector containing logarithm of the Bayes factors. ##' \item \code{logLik1} \code{logLik2} Matrices with the values of ##' the log-likelihood computed from the samples for each model at the ##' first and second stages. ##' \item \code{isweights} A vector with the importance sampling ##' weights for computing the Bayes factors at new points that will be ##' used at the second stage. Used internally in ##' \code{\link{bf2new}} and \code{\link{bf2optim}}. ##' \item \code{controlvar} A matrix with the control variates ##' computed at the samples that will be used in the second stage. ##' \item \code{sample2} The MCMC sample for mu or z that will be ##' used in the second stage. Used internally in ##' \code{\link{bf2new}} and \code{\link{bf2optim}}. ##' \item \code{N1}, \code{N2} Vectors containing the sample sizes ##' used in the first and second stages. ##' \item \code{distmat} Matrix of distances between locations. ##' \item \code{betm0}, \code{betQ0}, \code{ssqdf}, \code{ssqsc}, ##' \code{tsqdf}, \code{tsqsc}, \code{dispersion}, \code{response}, ##' \code{weights}, \code{modelmatrix}, \code{locations}, ##' \code{family}, \code{corrfcn}, \code{transf} Model parameters used ##' internally in. ##' \code{\link{bf2new}} and \code{\link{bf2optim}}. ##' \item \code{pnts} A list containing the skeleton points. Used ##' internally in \code{\link{bf2new}} and \code{\link{bf2optim}}. ##' } ##' @references Geyer, C. J. (1994). Estimating normalizing constants ##' and reweighting mixtures. Technical report, University of ##' Minnesota. ##' ##' Meng, X. L., & Wong, W. H. (1996). Simulating ratios of ##' normalizing constants via a simple identity: A theoretical ##' exploration. \emph{Statistica Sinica}, 6, 831-860. ##' ##' Roy, V., Evangelou, E., and Zhu, Z. (2015). Efficient estimation ##' and prediction for the Bayesian spatial generalized linear mixed ##' model with flexible link functions. \emph{Biometrics}, 72(1), 289-298. ##' @examples \dontrun{ ##' data(rhizoctonia) ##' ### Define the model ##' corrf <- "spherical" ##' kappa <- 0 ##' ssqdf <- 1 ##' ssqsc <- 1 ##' betm0 <- 0 ##' betQ0 <- .01 ##' family <- "binomial.probit" ##' ### Skeleton points ##' philist <- c(100, 140, 180) ##' omglist <- c(.5, 1) ##' parlist <- expand.grid(linkp=0, phi=philist, omg=omglist, kappa = kappa) ##' ### MCMC sizes ##' Nout <- 100 ##' Nthin <- 1 ##' Nbi <- 0 ##' ### Take MCMC samples ##' runs <- list() ##' for (i in 1:NROW(parlist)) { ##' runs[[i]] <- mcsglmm(Infected ~ 1, family, rhizoctonia, weights = Total, ##' atsample = ~ Xcoord + Ycoord, ##' Nout = Nout, Nthin = Nthin, Nbi = Nbi, ##' betm0 = betm0, betQ0 = betQ0, ##' ssqdf = ssqdf, ssqsc = ssqsc, ##' phi = parlist$phi[i], omg = parlist$omg[i], ##' linkp = parlist$linkp[i], kappa = parlist$kappa[i], ##' corrfcn = corrf, ##' corrtuning=list(phi = 0, omg = 0, kappa = 0)) ##' } ##' bf <- bf1skel(runs) ##' bf$logbf ##' } ##' @importFrom sp spDists ##' @useDynLib geoBayes bfsp_no bfsp_mu bfsp_wo bfsp_tr ##' @export bf1skel <- function(runs, bfsize1 = 0.80, method = c("RL", "MW"), reference = 1, transf = c("no", "mu", "wo")) { method <- match.arg(method) imeth <- match(method, eval(formals()$method)) if (!all(sapply(runs, inherits, what = "geomcmc"))) { stop ("Input runs is not a list with elements of class geomcmc.") } nruns <- length(runs) if (nruns == 0) stop ("No runs specified") reference <- as.integer(reference) if (isTRUE(reference < 1L | reference > nruns)) { stop("Argument reference does not correspond to a run in runs.") } Nout <- sapply(runs, function(x) x$MCMC$Nout) Nout1 <- getsize(bfsize1, Nout, "*") Ntot1 <- sum(Nout1) Nout2 <- Nout - Nout1 Ntot2 <- sum(Nout2) ## Check if fixed phi and omg if (!all(sapply(runs, function(x) length(x$FIXED$phi) == 1))) { stop("Each input runs must have a fixed value phi.") } if (!all(sapply(runs, function(x) length(x$FIXED$omg) == 1))) { stop("Each input runs must have a fixed value omg.") } ## Extract data and model nm_DATA <- c("response", "weights", "modelmatrix", "offset", "locations", "longlat") nm_MODEL <- c("family", "corrfcn", "betm0", "betQ0", "ssqdf", "ssqsc", "tsqdf", "tsqsc", "dispersion") DATA <- runs[[1]]$DATA[nm_DATA] MODEL <- runs[[1]]$MODEL[nm_MODEL] if (nruns > 1) { for (i in 2:nruns) { if (!identical(runs[[i]]$DATA[nm_DATA], DATA)) { stop("MCMC chains don't all correspond to the same data.") } if (!identical(runs[[i]]$MODEL[nm_MODEL], MODEL)) { stop("MCMC chains don't all correspond to the same model.") } } } y <- DATA$response n <- as.integer(length(y)) l <- DATA$weights F <- DATA$modelmatrix offset <- DATA$offset p <- NCOL(F) loc <- DATA$locations dm <- sp::spDists(loc, longlat = DATA$longlat) family <- MODEL$family ## ifam <- .geoBayes_family(family) corrfcn <- MODEL$corrfcn icf <- .geoBayes_correlation(corrfcn) betm0 <- MODEL$betm0 betQ0 <- MODEL$betQ0 ssqdf <- MODEL$ssqdf ssqsc <- MODEL$ssqsc tsqdf <- MODEL$tsqdf tsqsc <- MODEL$tsqsc dispersion <- MODEL$dispersion ## Choose sample getsample <- transfsample(runs, list(response = y, family = family), transf) sample <- matrix(unlist(getsample$sample), n) itr <- getsample$itr transf <- getsample$transf real_transf <- getsample$real_transf ifam <- getsample$ifam ## Skeleton points phi_pnts <- as.double(sapply(runs, function(r) r$FIXED$phi)) omg_pnts <- as.double(sapply(runs, function(r) r$FIXED$omg)) nu_pnts <- as.double(sapply(runs, function(r) r$FIXED$linkp_num)) if (.geoBayes_corrfcn$needkappa[icf]) { kappa_pnts <- sapply(runs, function(r) r$FIXED$kappa) kappa_pnts <- .geoBayes_getkappa(kappa_pnts, icf) } else { kappa_pnts <- rep(0, nruns) } bfroutine <- paste0("bfsp_", real_transf) if (nruns == 1) { MCMC <- runs[[1]]$MCMC out <- list(logbf = 1, logLik1 = MCMC$logLik[1:Ntot1], logLik2 = MCMC$logLik[-(1:Ntot1)], isweights = rep.int(0, Ntot2), controlvar = matrix(1, Ntot2, 1), z = sample[[1]][, -(1:Ntot1), drop = FALSE], N1 = Nout1, N2 = Nout2, betm0 = betm0, betQ0 = betQ0, ssqdf = ssqdf, ssqsc = ssqsc, tsqdf = tsqdf, tsqsc = tsqsc, dispersion = dispersion, response = y, weights = l, modelmatrix = F, offset = offset, locations = loc, longlat = DATA$longlat, distmat = dm, family = family, referencebf = 0, corrfcn = corrfcn, transf = transf, real_transf = real_transf, itr = itr, pnts = list(nu = nu_pnts, phi = phi_pnts, omg = omg_pnts, kappa = kappa_pnts)) return(out) } ## Split the sample sel1 <- rep(rep(c(TRUE, FALSE), nruns), rbind(Nout1, Nout2)) z1 <- sample[, sel1, drop = FALSE] z2 <- sample[, !sel1, drop = FALSE] logbf <- numeric(nruns) lglk1 <- matrix(0., Ntot1, nruns) lglk2 <- matrix(0., Ntot2, nruns) zcv <- matrix(0., Ntot2, nruns) weights <- numeric(Ntot2) if (ifam == 0) { tsq <- tsqsc } else { tsq <- dispersion } RUN <- .Fortran(bfroutine, weights = weights, zcv = zcv, logbf = logbf, lglk1 = lglk1, lglk2 = lglk2, as.double(phi_pnts), as.double(omg_pnts), as.double(nu_pnts), as.double(z1), as.integer(Nout1), as.integer(Ntot1), as.double(z2), as.integer(Nout2), as.integer(Ntot2), as.double(y), as.double(l), as.double(F), as.double(offset), as.double(dm), as.double(betm0), as.double(betQ0), as.double(ssqdf), as.double(ssqsc), max(tsqdf, 0), as.double(tsq), as.double(kappa_pnts), as.integer(icf), as.integer(n), as.integer(p), as.integer(nruns), as.integer(ifam), as.integer(imeth), as.integer(itr), PACKAGE = "geoBayes") refbf <- RUN$logbf[reference] logbf <- RUN$logbf - refbf if (Ntot2 > 0) { weights <- RUN$weights lglk2 <- RUN$lglk2 zcv <- RUN$zcv } else { weights <- lglk2 <- zcv <- NULL } out <- list(logbf = logbf, logLik1 = RUN$lglk1, logLik2 = lglk2, isweights = weights, controlvar = zcv, sample2 = z2, N1 = Nout1, N2 = Nout2, betm0 = betm0, betQ0 = betQ0, ssqdf = ssqdf, ssqsc = ssqsc, tsqdf = tsqdf, tsqsc = tsqsc, dispersion = dispersion, response = y, weights = l, modelmatrix = F, offset = offset, locations = loc, distmat = dm, family = family, corrfcn = corrfcn, transf = transf, real_transf = real_transf, itr = itr, pnts = list(nu = nu_pnts, phi = phi_pnts, omg = omg_pnts, kappa = kappa_pnts)) out }

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ec2_operations.R \name{ec2_register_transit_gateway_multicast_group_sources} \alias{ec2_register_transit_gateway_multicast_group_sources} \title{Registers sources (network interfaces) with the specified transit gateway multicast group} \usage{ ec2_register_transit_gateway_multicast_group_sources( TransitGatewayMulticastDomainId, GroupIpAddress = NULL, NetworkInterfaceIds, DryRun = NULL ) } \arguments{ \item{TransitGatewayMulticastDomainId}{[required] The ID of the transit gateway multicast domain.} \item{GroupIpAddress}{The IP address assigned to the transit gateway multicast group.} \item{NetworkInterfaceIds}{[required] The group sources' network interface IDs to register with the transit gateway multicast group.} \item{DryRun}{Checks whether you have the required permissions for the action, without actually making the request, and provides an error response. If you have the required permissions, the error response is \code{DryRunOperation}. Otherwise, it is \code{UnauthorizedOperation}.} } \description{ Registers sources (network interfaces) with the specified transit gateway multicast group. See \url{https://www.paws-r-sdk.com/docs/ec2_register_transit_gateway_multicast_group_sources/} for full documentation. } \keyword{internal}

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## run rwr ---------------------------------------------------------------------------- sigDG_test <- function(filepath, n) { setwd(filepath) load("F:/MeRIPSinglebase/review1/result/Pro_Dis.RData") load("ag0.RData") source("F:/MeRIPSinglebase/metdb2/process/rwrhFun.R") mmpath <- "rwrmm.RData" x <- read.table("F:/MeRIPSinglebase/review1/result/gene_id1_new.txt") siggene <- as.character(x$V1) siggene <- siggene[is.element(siggene, id)] sigdis <- sigdis[is.element(sigdis$gene, id),] re <- rwrh(siggene, mmpath, sigdis$doid) re0 <- list() filepath <- "disease/randomm" for(i in 1:100) { mmpath <- paste(filepath, "/rwrmm", i, ".RData", sep = "") re0[[i]] <- rwrhr(siggene, mmpath, sigdis$doid) print(i) } randomdis <- lapply(re0, "[[", 2) randomdis <- lapply(randomdis, "[", 1:n) randomdis <- unlist(randomdis) topdis <- re$topdis[1:n] pval <- lapply(as.list(topdis), function(x, y){sum(is.element(y,x))/100}, randomdis) re$topdis[1:n][unlist(pval) < 0.05] topdis <- re$topdis[1:n][unlist(pval) < 0.05] topdisgene <- sigdis[is.element(sigdis$disname, topdis),] topdisgene <- tapply(as.character(topdisgene$gene), as.character(topdisgene$disname), c) topdisgene <- lapply(topdisgene, function(x){paste(x, collapse = ",")}) topdisgene <- data.frame(dis = names(topdisgene), gene = unlist(topdisgene)) topdisgene <- data.frame(dis = topdis, gene = topdisgene$gene[match(topdis, topdisgene$dis)]) write.table(topdisgene, file = paste("disease/top_", n, "_disgene10.xls", sep = ""), quote = F, col.names = T, row.names = F, sep = "\t") return(topdisgene) } ## get con sig dis ---------------------------------------------------------------------------------------------- n <- 10 filepath <- "F:/MeRIPSinglebase/review1/data/random/biogrid" biogrid <- sigDG_test(filepath, n) filepath <- "F:/MeRIPSinglebase/review1/data/random/iRef" iRef <- sigDG_test(filepath, n) filepath <- "F:/MeRIPSinglebase/review1/data/random/hint" hint <- sigDG_test(filepath, n) filepath <- "F:/MeRIPSinglebase/review1/data/random/multinet" multinet <- sigDG_test(filepath, n) id <- c(as.character(biogrid$dis), as.character(iRef$dis), as.character(hint$dis), as.character(multinet$dis)) ind <- tapply(id, id, length) condis <- names(ind)[ind == 4] condis <- biogrid[is.element(biogrid$dis, condis),] write.table(condis, file = paste("F:/MeRIPSinglebase/review1/result/top_", n, "_disgene.xls", sep = ""), quote = F, col.names = T, row.names = F, sep = "\t")

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\name{sim.stpp} \alias{sim.stpp} \title{Generate spatio-temporal point patterns} \description{ Generate one (or several) realisation(s) of a spatio-temporal point process in a region \eqn{S\times T}{S x T}. } \usage{ sim.stpp(class="poisson", s.region, t.region, npoints=NULL, nsim=1, ...) } \arguments{ \item{class}{Must be chosen among "poisson", "cluster", "cox", "infectious" and "inhibition".} \item{s.region}{Two-column matrix specifying polygonal region containing all data locations. If \code{s.region} is missing, the unit square is considered.} \item{t.region}{Vector containing the minimum and maximum values of the time interval. If \code{t.region} is missing, the interval \eqn{[0,1]}{[0,1]} is considered.} \item{npoints}{Number of points to simulate.} \item{nsim}{Number of simulations to generate. Default is 1.} \item{...}{Additional parameters related to the \code{class} parameter. See \code{\link{rpp}} for the Poisson process; \code{\link{rpcp}} for the Poisson cluster process; \code{\link{rlgcp}} for the Log-Gaussian Cox process; \code{\link{rinter}} for the interaction (inhibition or contagious) process and \code{\link{rinfec}} for the infectious process.} } \value{ A list containing: \item{xyt}{Matrix (or list of matrices if \code{nsim}>1) containing the points \eqn{(x,y,t)}{(x,y,t)} of the simulated point pattern. \code{xyt} (or any element of the list if \code{nsim}>1) is an object of the class \code{stpp}.} \item{s.region, t.region}{Parameters passed in argument.} } \author{ Edith Gabriel <edith.gabriel@inrae.fr> } \seealso{ \code{\link{rpp}}, \code{\link{rinfec}}, \code{\link{rinter}}, \code{\link{rpcp}} and \code{\link{rlgcp}} for the simulation of Poisson, infectious, interaction, Poisson cluster and log-gaussian Cox processes respectively; and \code{\link{plot.stpp}}, \code{\link{animation}} and \code{\link{stan}} for plotting space-time point patterns. }

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/Frequentist-two-stage.R

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wwyoo/Bayes-two-stage

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Frequentist-two-stage.R

#This code implements the frequentist two-stage procedure by Belitser et al. (2012) Optimal #two-stage procedures for estimating location and size of the #maximum of a multivariate regression function, in the Annals of Statistics 44, number 6, pages 2850-2876 #The original code was supplied by one of the authors of the paper, and I have modified it for the purpose #of comparing their procedure with our proposed Bayesian two-stage method ################################################################################################################# #par(mai=c(0,0.1,0,0.1)) #choose optimal smoothning (bandwidth) of loess by leave-one-out cross validation optimalband <- function(W, bandwidth){ mse <- rep(0, nrow(W)) for(i in 1:nrow(W)){ testz <- W$z[i] testy <- W$y[i] testx <- W$x[i] trainz <- W$z[-i] #leave one out trainy <- W$y[-i] trainx <- W$x[-i] m <- loess(trainz ~trainx + trainy, degree = 1, span=bandwidth) testobs <- data.frame(trainx = testx, trainy = testy) fit <- predict(m, newdata = testobs) mse[i] <- (fit - testz) ^ 2 } return(mean(mse)) } ################################################################################################################## # error standard deviation: sigma <- 0.1 # true regression function, put in dataframe fframe: f <- function(x, y){ x <- 2*x - 1 y <- 2*y - 1 (1+exp(-5*(x)^2-2*(y)^4))*(cos(4*x)+cos(5*y)) } #true mu xfmax <- 0.5 yfmax <- 0.5 M0 <- f(xfmax, yfmax) # initial, uniformly spaced data, put in dataframe obsframe: # total n1*n1 observations nrep <- 1000 mseF2mu <- rep(0,nrep) mseF2M <- rep(0, nrep) n1 <- 30 x <- seq(0,1, length=n1) y <- seq(0,1, length=n1) obsframe <- expand.grid(x=x, y=y) band <- seq(0.01,0.5,by=0.01) mseband <- rep(0, length(band)) #set.seed(100) # obsframe$z <- f(obsframe$x, obsframe$y) + sigma*rnorm(n1*n1) #for(i in 1:length(band)){ # bandwidth <- band[i] # mseband[i] <- optimalband(W = obsframe, bandwidth = bandwidth) # print(i) #} deltacan1 <- seq(0, 0.2, by = 0.01)[-1] deltacan2 <- seq(0, 0.2, by = 0.01)[-1] #deltacan <- expand.grid(deltacan1, deltacan2) deltacan <- cbind(deltacan1, deltacan2) ndelta <- nrow(deltacan) msedeltaF2 <-rep(0, ndelta) n3 <- 96 #for(j in 1:ndelta){ #to choose optimal localization delta1 <- 0.06 delta2 <- 0.06 set.seed(100) for(i in 1:nrep){ obsframe$z <- f(obsframe$x, obsframe$y) + sigma*rnorm(n1*n1) # local linear regression to find stage one estimator: m <- loess(obsframe$z ~obsframe$x + obsframe$y, degree =1, span=0.02) fit <- predict(m) #persp(x,y, matrix(fit, n1, n1), theta = 50, phi = 20, expand = 0.5, zlab="", xlab="x", ylab="y", main="") -> res maxindex <- which(fit==max(fit)) xmutilde <- obsframe$x[maxindex] ymutilde <- obsframe$y[maxindex] #points(trans3d(xmutilde,ymutilde, f(xfmax, yfmax), pmat=res), col=2, type = "b", pch = 19) # new data # new designpoints: x0 <- xmutilde y0 <- ymutilde x1 <- x0+delta1 y1 <- y0 x2 <- x0 y2 <- y0+delta2 x3 <- x0-delta1 y3 <- y0 x4 <- x0 y4 <- y0-delta2 x5 <- x0+delta1 y5 <- y0-delta2 x6 <- x0+delta1 y6 <- y0+delta2 x7 <- x0-delta1 y7 <- y0+delta2 x8 <- x0-delta1 y8 <- y0-delta2 # regressors: p <- rep(c(x0, x1, x2, x3, x4, x5, x6, x7, x8), n3) q <- rep(c(y0, y1, y2, y3, y4, y5, y6, y7, y8), n3) r <- rep(c((x0)^2, (x1)^2, (x2)^2, (x3)^2, (x4)^2, (x5)^2, (x6)^2, (x7)^2, (x8)^2), n3) s <- rep(c((y0)^2, (y1)^2, (y2)^2, (y3)^2, (y4)^2, (y5)^2, (y6)^2, (y7)^2, (y8)^2), n3) t <- rep(c(x0*y0, x1*y1, x2*y2, x3*y3, x4*y4, x5*y5, x6*y6, x7*y7, x8*y8), n3) # new observations: newz <- f(p,q) + sigma*rnorm(9*n3) #points(trans3d(c(x0, x1, x2, x3, x4, x5, x6, x7, x8), c(y0, y1, y2, y3, y4, y5, y6, y7, y8), newz, pmat=res), col=8) a <- lm(newz ~ p+q+r+s+t)$coefficients # find muhat, the argmax of the quadratic surface A <- matrix(data=c(2*a[4], a[6], a[6], 2*a[5]), nrow=2, ncol=2, byrow=TRUE) b <- -c(a[2], a[3]) u <- solve(A,b) xmuhat <- as.numeric(u[1]) ymuhat <- as.numeric(u[2]) # plot of quadratic surface: #g <- function(k,l) a[1] + a[2]*k + a[3]*l + a[4]*k^2+a[5]*l^2+a[6]*k*l #gframe <- expand.grid(v=v, w=w) #gframe$g <- g(gframe$v, gframe$w) #persp(v,w, matrix(gframe$g, 51, 51), theta = 30, phi = 30, expand = 0.5, zlab="", xlab="x", ylab="y", main="") -> res # plot the final estimator: #points(trans3d(xmuhat,ymuhat, g(xmuhat, ymuhat), pmat=res), col="green", pch=19) mseF2mu[i] <- sqrt((xmuhat - xfmax)^2 + (ymuhat - yfmax)^2) #produce third box-plot in Figure 2 of our paper #mseF2M[i] <- abs(a[1] + a[2]*xmuhat + a[3]*ymuhat + a[4]*xmuhat^2 + a[5]*ymuhat^2 + a[6]*xmuhat*ymuhat - M0) print(i) } #msedeltaF2[j] <- mean(mseF2) #print(j) #}

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matrices.r

c(4,2,-8) # Creación de un vector sin asignarlo a una variable ## [1] 4 2 -8 ## ---------------- ## Distintas formas de asignar un vector a una variable u <- c(4,2,-8) # Usando el operador <- c(4, 2, -8) -> v # Usando el operador -> # Usando la función assign: assign("w", c(4, 2, -8)) p = c(4, 2, -8) # Usando el operador = print(u); print(v); print(w); print(p) ## [1] 4 2 -8 ## [1] 4 2 -8 ## [1] 4 2 -8 ## [1] 4 2 -8

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/man/is.Date.Rd

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cran/popEpi

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is.Date.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/utility_functions.R \name{is.Date} \alias{is.Date} \title{Test if object is a \code{Date} object} \usage{ is.Date(obj) } \arguments{ \item{obj}{object to test on} } \value{ `TRUE` if `obj` is of class `"Date"` or `"IDate"`. } \description{ Tests if an object is a \code{Date} object and returns a logical vector of length 1. \code{IDate} objects are also \code{Date} objects, but \code{date} objects from package \pkg{date} are not. } \seealso{ \code{\link{get.yrs}}, \code{\link{is_leap_year}}, \code{\link{as.Date}} } \author{ Joonas Miettinen }

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/mpg-envinfosys-teams-2018-rs_18_mcfest-master/src/017_plots.R

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Muesgen/Mcfest_Remote_Sensing_GIS

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017_plots.R

root_folder <- envimaR::alternativeEnvi(root_folder = "~/edu/mpg-envinsys-plygrnd", alt_env_id = "COMPUTERNAME", alt_env_value = "PCRZP", alt_env_root_folder = "F:\\edu\\mpg-envinsys-plygrnd") source(paste0(root_folder, "/mpg-envinfosys-teams-2018-rs_18_mcfest/src/000_setup.R")) ####Confusion Matrix#### library(scales) library(ggplot2) ggplotConfusionMatrix <- function(m, mod){ mytitle <- paste("External Accuracy", percent_format()(m$overall[1]), "Internal Accuracy", percent_format()(max(mod$results$Accuracy)), "\n", "External Kappa", percent_format()(m$overall[2]), "Internal Kappa", percent_format() (max(mod$results$Kappa))) p <- ggplot(data = as.data.frame(m$table) , aes(x = Reference, y = Prediction)) + geom_tile(aes(fill = log(Freq)), colour = "white") + scale_fill_gradient(low = "white", high = "steelblue") + geom_text(aes(x = Reference, y = Prediction, label = Freq)) + theme(legend.position = "none", plot.title = element_text(hjust=0.5, face = "bold", size = 20), axis.text = element_text(size = 18), axis.title.x = element_text(size = 20, margin = margin(t = 10, r = 0, b = 0, l = 0)), axis.title.y = element_text(size = 20, margin = margin(t = 0, r = 20, b = 0, l = 0)), title = element_text(size = 24), panel.background = element_rect(fill = "#d8d8d8", colour = "#d8d8d8", size = 0.5, linetype = "solid"), plot.background = element_rect(fill = "#d8d8d8")) + ggtitle(mytitle) return(p) } names <- c("Beech", "Douglas fir", "Spruce", "Larch", "Oak") mod8 <- readRDS(paste0(envrmt$path_data_training, "mod8.rds")) conf8 <- readRDS(paste0(envrmt$path_data_training, "confmod8.rds")) colnames(conf8$table) <- names rownames(conf8$table) <- names ggplotConfusionMatrix(conf8, mod8) mod9 <- readRDS(paste0(envrmt$path_data_training, "mod9.rds")) conf9 <- readRDS(paste0(envrmt$path_data_training, "confmod9.rds")) colnames(conf9$table) <- names rownames(conf9$table) <- names x <- ggplotConfusionMatrix(conf9, mod9) png(paste0(envrmt$path_data_plots, "conf8.png"), res=200, width=10, height = 8, units = "in") print(ggplotConfusionMatrix(conf8, mod8)) dev.off() png(paste0(envrmt$path_data_plots, "conf9.png"), res=200, width=10, height = 8, units = "in") print(ggplotConfusionMatrix(conf9, mod9)) dev.off() ####Species Acc#### csegs8 <- raster::shapefile(paste0(envrmt$path_data_mof, "cseg_stats_mod8.shp")) csegs9 <- raster::shapefile(paste0(envrmt$path_data_mof, "cseg_stats_mod9.shp")) unique(csegs8@data[which(csegs8@data$spec %in% unique(csegs8@data$spec)[1:5]), c(14, 16)]) unique(csegs9@data[which(csegs9@data$spec %in% unique(csegs9@data$spec)[1:5]), c(16, 17)]) ####Var Importance#### mod8 <- readRDS(paste0(envrmt$path_data_training, "mod8.rds")) conf8 <- readRDS(paste0(envrmt$path_data_training, "confmod8.rds")) x <- caret::varImp(mod8) for (i in seq(nrow(x$importance))){ x$importance$mean[i] <- rowMeans(x$importance[i,1:5]) } write.table(x = x$importance, file = paste0(envrmt$path_data_training , "conf8imp.csv"), sep = ";", dec = ".") mod9 <- readRDS(paste0(envrmt$path_data_training, "mod9.rds")) conf9 <- readRDS(paste0(envrmt$path_data_training, "confmod9.rds")) x <- caret::varImp(mod9) for (i in seq(nrow(x$importance))){ x$importance$mean[i] <- rowMeans(x$importance[i,1:5]) } write.table(x = x$importance, file = paste0(envrmt$path_data_training , "conf9imp.csv"), sep = ";", dec = ".")

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/3_montecarlo/taskPath/BIN/R/analysis.R

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ynagae1tryeting/lab

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analysis.R

source("./function.R") library(lattice) library(tidyverse) percent <- 0.2 centers2percent <- function(x, d, percent, thr){ for(N in 0:(nrow(d)/2)){ #message(paste(N, " / ", nrow(d)/2, sep="")) # 該当の座標に入っている記号を抽出 i <- x[1] j <- x[2] i_1 <- seq(i-N, i+N) j_1 <- seq(j-N, j+N) i_2 <- seq(i-N-1, i+N+1) j_2 <- seq(j-N-1, j+N+1) # 周期境界を超える配置を規格化 if(sum(i_1[i_1 > nrow(d)]) != 0){ i_1[i_1 > nrow(d)] <- i_1[i_1 > nrow(d)] - nrow(d) } if(sum(j_1[j_1 > nrow(d)]) != 0){ j_1[j_1 > nrow(d)] <- j_1[j_1 > nrow(d)] - nrow(d) } if(sum(i_2[i_2 > nrow(d)]) != 0){ i_2[i_2 > nrow(d)] <- i_2[i_2 > nrow(d)] - nrow(d) } if(sum(j_2[j_2 > nrow(d)]) != 0){ j_2[j_2 > nrow(d)] <- j_2[j_2 > nrow(d)] - nrow(d) } index <- 0 for(I in i_1){ for(J in j_1){ index <- c(index, D[((D[,1]==c(I)) * (D[,2]==J))==1 ,3]) } } index <- index[-1] N_Sn_1 <- sum(index==0) * 1 + sum(index==1) * 2 N_Si_1 <- sum(index==-1) * 2 + sum(index==0) * 1 index <- 0 for(I in i_2){ for(J in j_2){ index <- c(index, D[((D[,1]==c(I)) * (D[,2]==J))==1 ,3]) } } index <- index[-1] N_Sn_2 <- sum(index==0) * 1 + sum(index==1) * 2 N_Si_2 <- sum(index==-1) * 2 + sum(index==0) * 1 # N_Sn_1とN_Sn2が同じ時、Siに囲まれていると判断してループを終了する if( (N_Sn_2 - N_Sn_1) <= thr ){ break } } # クラスタに含まれている原子数を返す res <- data.frame( "N_Si"=N_Si_1, "N_Sn"=N_Sn_1, "atoms"=N_Si_1 + N_Sn_1, "percent"=(N_Sn_1 / (N_Sn_1 + N_Si_1)) ) return(res) } for(N in seq(10, 200, 10)){ message(paste("STEP: ", N, sep="")) # d: MCSを経たcell matrix d <- as.matrix(read.csv(paste("./cell/",percent*100,"_percent/step",N,".csv", sep=""))) colnames(d) <- NULL D <- melt(d) # Si*2: -1 # SiSn: 0 # Sn*2: 1 # D[((D[,1]==c(1,2,3)) * (D[,2]==j))==1 ,3] D_ <- D[D[,3]!=-1,] # Snが入っているところだけを抽出 # クラスタ中心を探索 k <- kmeans( D_[,1:2], centers=round(nrow(d)*ncol(d)*percent,0)/2, iter.max = 10000, nstart = 1) f <- function(x, d){ i <- x[1] j <- x[2] return(d[i, j]) } centers <- unique(round(k$centers,0)[,1:2]) centers <- data.frame( centers, apply(centers, 1, f, d) ) centers <- centers[centers[,3]!=-1,1:2] r <- apply(centers, 1, centers2percent, d, percent, 3) label <- matrix(unlist(r), ncol=4, byrow=T) write.csv(label, paste("./ana/",percent*100,"_percent/step", N, ".csv", sep=""), quote=F, row.names=F) }

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Load_Data.R

library(ggplot2) library(plyr) #Load data if no exist if (!"neiData" %in% ls()) { neiData <- readRDS("data/summarySCC_PM25.rds") } if (!"sccData" %in% ls()) { sccData <- readRDS("./data/Source_Classification_Code.rds") } #show neiData head(neiData) #show sccData head(sccData) print(paste("Dimension of NEI Data: ",dim(neiData)[1],dim(neiData)[2] )) print(paste("Dimension of SCC Data: ",dim(sccData)[1],dim(sccData)[2] ))

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/RAEN.R \name{RAEN} \alias{RAEN} \alias{predict.RAEN} \title{Random Ensemble Variable Selection for High Dimensional Data} \usage{ RAEN( x, y, B, ngrp = floor(15 * ncol(x)/nrow(x)), parallel = TRUE, family = "competing", ncore = 2 ) \method{predict}{RAEN}(object, newdata, ...) } \arguments{ \item{x}{the predictor matrix} \item{y}{the time and status object for survival} \item{B}{times of bootstrap} \item{ngrp}{the number of blocks to separate variables into. Default is 15*p/N, where p is the number of predictors and N is the sample size.} \item{parallel}{Logical TRUE or FALSE. Whether to use multithread computing, which can save consideratable amount of time for high dimensional data. Default is TRUE.} \item{family}{what family of data types. Default is 'competing'. Quantile regression for competing risks will be available through the developmental version on github} \item{ncore}{Number of cores used for parallel computing, if parallel=TRUE} \item{object}{the RAEN object containing the variable selection results} \item{newdata}{the predictor matrix for prediction} \item{...}{other parameters to pass} } \value{ a dataframe with the variable names and the regression coefficients the linear predictor of the outcome risk } \description{ Perform variable selection for high dimensional data } \examples{ \donttest{ library(RAEN) data(toydata) x=toydata[,-c(1:2)] y=toydata[,1:2] fgrp<-deCorr(x, ngrp=20) } }

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toRemove<-setdiff(usSbagliati,usGiusti) #giusti sono pochi,sbagliati sono di piu resto<-setdiff(usSbagliati,toRemove) newUs<-us.state.names[us.state.names[,"name"] %in% resto,] #write.csv(newUs, file = "us-state-names.tsv",row.names=FALSE) write.table(newUs, file='us-state-names.tsv', quote=FALSE, sep='\t',row.names=FALSE)

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MyChoYS/K_TIL

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lab12.R

#문제1 v1 <- c("Happy","Birthday","to","you") length(v1)+sum(nchar(v1)) str_count() #문제2 v2 <- paste(v1[1],v1[2],v1[3],v1[4]) length(v2) + nchar(v2) #문제3 paste(LETTERS,1:10) paste(LETTERS,1:10,sep = "") #문제4 v4 <- "Good Morning" v4 <- list(strsplit(v4," ")[[1]][1],strsplit(v4," ")[[1]][2]) library (stringr) ### str_sub(v4,1,4) #문제5 v5 <- c("Yesterday is histroy, tommrrow is a mystery, today is a gift!", "That's why we call it the present - from kung fu panda") v5 <- gsub("[[:punct:]]","",v5) v5 <- unlist(strsplit(v5," ")) v5 #문제6 s1 <- "@^^@Have a nice day!! 좋은 하루!! 오늘도 100점 하루...." r1 <- gsub("[가-힣]", "", s1) r2 <- gsub("[[:punct:]]","",s1) r3 <- gsub("[[:punct:]가-힣]","",s1) r4 <- gsub("100","백",s1) #문제7***** library(KoNLP) hotel1 <- scan("output/hotel.txt", what="") #Filter(function(x) {nchar(x) >= 2}, hotel2) 단어 두글자 이상으로 제한 hotel2<- Filter(function(x) {nchar(x) >= 2}, gsub("[[:cntrl:][:punct:]]","",unlist(extractNoun(hotel1)))) wcount <- sort(table(hotel2),decreasing = T)[1:10] df <- data.frame(wcount ) colnames(df) <- c("wname","wcount") View(df) write.csv(df,file = "output/hotel_top_word.csv")

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/comprehend_operations.R \name{comprehend_delete_document_classifier} \alias{comprehend_delete_document_classifier} \title{Deletes a previously created document classifier} \usage{ comprehend_delete_document_classifier(DocumentClassifierArn) } \arguments{ \item{DocumentClassifierArn}{[required] The Amazon Resource Name (ARN) that identifies the document classifier.} } \value{ An empty list. } \description{ Deletes a previously created document classifier Only those classifiers that are in terminated states (IN_ERROR, TRAINED) will be deleted. If an active inference job is using the model, a \code{ResourceInUseException} will be returned. This is an asynchronous action that puts the classifier into a DELETING state, and it is then removed by a background job. Once removed, the classifier disappears from your account and is no longer available for use. } \section{Request syntax}{ \preformatted{svc$delete_document_classifier( DocumentClassifierArn = "string" ) } } \keyword{internal}

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Kmeans_FeatureEngineering_Deeplearning.R

library(onehot) library(caret) library(rpart) library(rpart.plot) library(fastICA) library(h2o) library(plyr) HR <- read.csv('D:/UTD MSBA/Fall 2017 (Sem 3)/Machine Learning/Project 4/HR_comma_sep.csv',header = TRUE) HR.f <- HR HR.f$left <- NULL #one hot encoding data_ <- onehot(HR.f,stringsAsFactors = FALSE,addNA = FALSE,max_levels = 20) data <- as.data.frame(predict(data_,HR.f)) head(data) #scaling maxs <- apply(data, 2, max) mins <- apply(data, 2, min) scaled <- as.data.frame(scale(data, center = mins, scale = maxs - mins)) dim(scaled) summary(scaled) set.seed(123) # Compute and plot within cluster Sum of squares for k = 1 to k = 15. k <- 15 wss <- sapply(2:k,function(k){kmeans(scaled, k, nstart=10,iter.max = 15 )$tot.withinss}) #tot.withinss or total within-cluster sum of square should be as small as possible plot(2:k, wss,type="b", pch = 19, frame = FALSE,xlab="Number of clusters K",ylab="Total within-clusters sum of squares") res = kmeans(scaled,9) table(HR$left,res$cluster) #(between_SS / total_SS) should be high - it explains the total variation #--------------------------Car---------------- set.seed(123) cardata <- read.csv("D:/UTD MSBA/Fall 2017 (Sem 3)/Machine Learning/Project 4/car_data.csv", header=TRUE) cardata.f <- cardata cardata.f$car <- NULL #one hot encoding one_hot_encoding = function(dat){ t = onehot(dat,stringsAsFactors = FALSE,addNA = FALSE,max_levels = 20) t = as.data.frame(predict(t,dat)) return(t) } #Scaling scaling_data = function(dat){ maxs <- apply(dat, 2, max) mins <- apply(dat, 2, min) dat <- as.data.frame(scale(dat, center = mins, scale = maxs - mins)) return(dat) } #Elbow Graph get_elbow_graph <- function(dat){ k <- 15 wss_car <- sapply(1:k,function(k){kmeans(dat, k)$tot.withinss}) wss_car plot(1:k, wss_car,type="b", pch = 19, frame = FALSE,xlab="Number of clusters K", ylab="Total within-clusters sum of squares") } data = one_hot_encoding(cardata.f) scaled_car = scaling_data(data) set.seed(123) sprintf("Initial K-Means") get_elbow_graph(scaled_car) k_means_org_features = kmeans(scaled_car,3) k_means_org_features #Nueral Networks with Cluster features clust_features = one_hot_encoding(as.data.frame(as.factor(k_means_org_features$cluster))) colnames(clust_features) = c("cluster 1","cluster 2","cluster 3","cluster 4", "cluster 5", "cluster 6", "cluster 7","cluster 8") h2o.init(ip = "localhost", port = 54321, max_mem_size = "4000m") clust_features$car <- as.factor(cardata$car) splits <- h2o.splitFrame(as.h2o(clust_features), c(0.6,0.19,0.2), seed=1234) train <- h2o.assign(splits[[1]], "train.hex") # 60% valid <- h2o.assign(splits[[2]], "valid.hex") # 19% test <- h2o.assign(splits[[3]], "test.hex") # 20% response <- "car" predictors <- setdiff(names(clust_features), response) predictors m1 <- h2o.deeplearning( training_frame=train, validation_frame=valid, ## validation dataset: used for scoring and early stopping x=predictors, y=response, activation="Rectifier", hidden=c(100,100,100), epochs=10, nfolds = 5, seed = 123, variable_importances=T, l2 = 6e-4, loss = "CrossEntropy", distribution = "bernoulli", stopping_metric = "misclassification" ) pred = h2o.predict(m1,train) accuracy = pred$predict == train$car err_rates = 1 - mean(accuracy) sprintf("Train Error: %f",err_rates) pred = h2o.predict(m1,test) accuracy = pred$predict == test$car test_err_rates = 1 - mean(accuracy) sprintf("Test Error: %f",test_err_rates) #PCA set.seed(123) pr = princomp(scaled_car,scores = TRUE) pr_cardata = pr$scores[,1:14] sprintf("After PCA") get_elbow_graph(pr_cardata) k_means_pca_features = kmeans(pr_cardata,3) k_means_pca_features #ICA set.seed(123) ic = fastICA(scaled_car, n.comp = 10, alg.typ = "parallel", fun = "logcosh", alpha = 1, method = "R", row.norm = FALSE, maxit = 200, tol = 0.0001, verbose = FALSE) ica_data = ic$S sprintf("After ICA") get_elbow_graph(ica_data) k_means_ica_features = kmeans(ica_data,12) k_means_ica_features #RCA random.component.selection <- function(d=2, d.original=10) { selected.features <- numeric(d); n.feat <- d.original+1; feat <- floor(runif(1,1,n.feat)); selected.features[1] <- feat; for (i in 2:d) { present <- TRUE; while(present) { feat <- floor(runif(1,1,n.feat)); for (j in 1:(i-1)) { if (selected.features[j] == feat) break; } if ((j==i-1) && (selected.features[j] != feat)) { present<-FALSE; selected.features[i] <- feat; } } } selected.features } random_projection <- function(d, m, scaling=FALSE){ d.original <- nrow(m); if (d >= d.original) stop("random.subspace: subspace dimension must be lower than space dimension", call.=FALSE); # generation of the vector selected.features containing the indices randomly selected selected.features <- random.component.selection(d, d.original); # random data projection if (scaling == TRUE) reduced.m <- sqrt(d.original/d) * m[selected.features,] else reduced.m <- m[selected.features,]; reduced.m } m = as.matrix(t(scaled_car)) rp_features = as.data.frame(t(random_projection(20, m))) sprintf("After RCA") get_elbow_graph(rp_features) k_means_rp_features = kmeans(rp_features,8) k_means_rp_features #Feature Selection (Decision Tree) projecttree <- rpart(car~.,data=cardata,method="class",parms = list(split = "information")) sig_vars = names(projecttree$variable.importance) data = one_hot_encoding(cardata[,sig_vars]) scaled_car = scaling_data(data) get_elbow_graph(scaled_car) k_means_f_select = kmeans(scaled_car,6) k_means_f_select #output column is appended again as it was dropped from original dataset #scaled_car$car <- car$car #head(scaled_car) #levels(scaled_car$car)[levels(scaled_car$car)%in%c("acc","good","vgood")] <- 0 #levels(scaled_car$car)[levels(scaled_car$car)%in%c("unacc")] <- 1 # Compute and plot wss for k = 1 to k = 15.

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#==============================================================================# # Autor(es): Eduard Martinez # Colaboradores: # Fecha creacion: 17/03/2019 # Fecha modificacion: 17/03/2021 # Version de R: 4.0.3. #==============================================================================# # intial configuration rm(list = ls()) # limpia el entorno de R pacman::p_load(tidyverse,data.table,readxl) # cargar y/o instalar paquetes a usar #----------------------------------# # Importar archivos usando un loop # #----------------------------------# # crear vector con ruta de objetos a cargar list.files(path = "data/input" , full.names = T) files = list.files(path = "data/input" , full.names = T) # lista para almacenar bases de datos lista_data = list() lista_data # Loop conteo = 1 # Para contar numero iterraciones for (i in files){ lista_data[[conteo]] = read_excel(path = i) conteo = conteo + 1 } # exportar lista saveRDS(lista_data,"data/output/lista siedco.rds") #----------------------------------# # Importar archivos usando un loop # #----------------------------------# # Importar archivo "lista siedco.rds" de data/output ldata = readRDS("data/output/lista siedco.rds") # Verificar visualmente los datos df1 = ldata[[1]] df10 = ldata[[10]] # Limpiar una base de datos df_i = ldata[[4]] df_i = subset(df_i,is.na(...2)==F) # elimino observaciones no relevantes colnames(df_i) = df_i[1,] %>% as.character() # Cambiar nombres df_i = df_i[-1,] # Generalizar el paso anterior en una funcion f_clean = function(i){ df_i = ldata[[i]] df_i = subset(df_i,is.na(...2)==F) # elimino observaciones no relevantes colnames(df_i) = df_i[1,] %>% as.character() # Cambiar nombres df_i = df_i[-1,] return(df_i) } # aplicar la funcion data = lapply(1:14, function(z) f_clean(i = z)) # veamos los elementos de la lista dc1 = data[[1]] dc10 = data[[10]] # apliar en un dataframe dataframe = rbindlist(l = data,use.names = ,fill = T)

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proj <- function() { v <- vector('list',length=10) names(v) <- c('status','proj','ID','fixed','target','wtf','wtt','wtr','olds','current') class(v) <- 'proj' return(v) } init.proj <- function(v) {

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import-restaurants.R

setwd('~/Projets/katossky.github.io/data/journey-planner') library(rvest) library(plyr) library(dplyr) library(tidyr) library(stringr) library(jsonlite) library(data.table) # SCRAPING --------------------------------------------------------------------- get_michelin_restaurants <- function(top, bottom, left, right){ url <- paste0( 'http://www.viamichelin.com/web/Recherche_Restaurants?', 'geoboundaries=', bottom, ',', left,':',top,',', right ) michelin <- read_html(url) page_tabs <- michelin %>% html_nodes('.pagination-container a') nb_pages <- page_tabs %>% html_text() %>% as.integer %>% max Restaurants <- vector(mode='list', length=nb_pages) Restaurants[[1]] <- michelin %>% html_nodes('li.poi-item') for(page in 2:nb_pages){ sleep <- runif(1, min = 0, max = 1) %>% round(2) Sys.sleep(sleep) cat('Slept', sleep, 'seconds. Treating page', page, 'of', nb_pages, fill=TRUE) michelin <- read_html(paste0(url, '&page=', page)) Restaurants[[page]] <- michelin %>% html_nodes('li.poi-item') } return(Restaurants) } Restaurants <- c( get_michelin_restaurants(70, 50, 0, 20), # Scandinavia + Germany + North France get_michelin_restaurants(50, 40,-10, 10) # France + North Spain ) Restaurants <- do.call(c, Restaurants) class(Restaurants) <- 'xml_nodeset' # FORMATTING ------------------------------------------------------------------- prices <- Restaurants %>% html_nodes(css='.poi-item-price em') %>% html_text %>% unlist %>% str_extract('^[0-9]+') %>% as.integer %>% matrix(ncol=2, byrow=TRUE) RESTAURANTS <- data.table( name = Restaurants %>% html_nodes(css='.poi-item-name') %>% html_text, url = Restaurants %>% html_nodes(css='.poi-item-name a') %>% xml_attr('href') %>% paste0('http://www.viamichelin.com', .), price_from = prices[,1], price_to = prices[,2], stars = Restaurants %>% html_nodes(css='.poi-item-stars') %>% as.character %>% str_extract_all('="star"') %>% lengths, bib_gourmand = Restaurants %>% html_nodes(css='.poi-item-stars') %>% as.character %>% str_extract_all('="bib-gourmand"') %>% lengths %>% as.logical, assiette_michelin = Restaurants %>% html_nodes(css='.poi-item-stars') %>% as.character %>% str_extract_all('="assiette"') %>% lengths %>% as.logical ) # guide year is only 2016... # Restaurants %>% html_nodes(css='.poi-item-stars') %>% html_text %>% # str_extract('[0123456789]{4}') %>% table # SCRAPING MORE INFORMATION ---------------------------------------------------- n <- nrow(RESTAURANTS) Restaurants <- vector(mode='list', length=n) r <- 1 r <- 6510 r <- 9633 while(r <= nrow(RESTAURANTS)){ Sys.sleep(sleep <- runif(1, min = 0, max = 1)) cat( 'Slept', round(sleep, 2), 'seconds.', 'Treating restaurant', r, 'of', n, ':', RESTAURANTS$name[r], '.', fill=TRUE ) Restaurants[[r]] <- read_html(RESTAURANTS$url[r]) %>% html_nodes('body') r <- r+1 } save(Restaurants, file=paste0(Sys.Date(),'michelin-detailed-restaurants.RData')) Restaurants2 <- do.call(c, Restaurants) class(Restaurants2) <- 'xml_nodeset' # FORMATTING ------------------------------------------------------------------- # cuisine type RESTAURANTS$cuisine <- Restaurants2 %>% html_nodes(css='.datasheet-cooking-type') %>% html_text(trim=TRUE) # citation RESTAURANTS$citation <- Restaurants2 %>% html_nodes(css='.datasheet') %>% as.character %>% str_extract_all('<blockquote>[\\s\\S]*</blockquote>') %>% laply(function(v) if(length(v)==0) NA else v) %>% str_sub(17,-19) # standard RESTAURANTS$standard_code <- Restaurants2 %>% html_nodes(css='.datasheet-quotation') %>% as.character %>% str_extract('standing-[0123456789]{2}') %>% str_sub(10,-1) %>% as.factor RESTAURANTS$standard <- RESTAURANTS$standard_code %>% revalue(replace=c( `12`='simple', `13`='good', `14`='very good', `15`='excellent', `16`='exceptionnal', `17`='simple', `18`='good', `19`='very good', `20`='excellent', `21`='exceptionnal' )) RESTAURANTS$best_addresses <- RESTAURANTS$standard_code %in% 17:21 # twenty_or_less RESTAURANTS$twenty_or_less <- Restaurants2 %>% html_nodes(css='.datasheet-quotation') %>% as.character %>% str_detect('good-value-menu') # address RESTAURANTS$address <- Restaurants2 %>% html_nodes(css=paste( '.datasheet', '.datasheet-item:not(.datasheet-name):not(.datasheet-description-container)' )) %>% html_text # phone RESTAURANTS$phone <- Restaurants2 %>% html_nodes(css='.datasheet .datasheet-more-info:last-child') %>% as.character %>% str_extract_all('href="tel:.*?"') %>% # lengths %>% table laply(function(v) if(length(v)==0) NA else v) %>% str_sub(11,-2) # mail RESTAURANTS$mail <- Restaurants2 %>% html_nodes(css='.datasheet .datasheet-more-info:last-child') %>% as.character %>% str_extract_all('href="mailto:.*?"') %>% # lengths %>% table laply(function(v) if(length(v)==0) NA else v) %>% str_sub(14,-2) # website RESTAURANTS$website <- Restaurants2 %>% html_nodes(css='.datasheet .datasheet-more-info:last-child') %>% as.character %>% str_extract_all('href="http://.*?"') %>% laply(function(v) if(length(v)==0) NA else v) %>% str_sub(7,-2) # other information # reading RESTAURANTS$good_to_know <- Restaurants %>% lapply(html_nodes, xpath="//p[text()='Good to know']/../ul/li/text()") %>% lapply(as.character) %>% laply(function(v) if(length(v)==0) NA else v) RESTAURANTS$additional_information <- Restaurants %>% lapply(html_nodes, xpath="//p[normalize-space(text())='Additional information']/../ul/li/text()") %>% lapply(as.character) # extracting variables 1 RESTAURANTS <- rbind( RESTAURANTS %>% unnest(additional_information) %>% mutate(place_holder=TRUE) %>% spread(additional_information, place_holder, fill=FALSE), RESTAURANTS %>% filter(lengths(additional_information)==0) %>% select(-additional_information), fill=TRUE ) # extracting variables 2 NA_to_F <- function(vect){vect[is.na(vect)]<- FALSE;vect} RESTAURANTS$dinner_only <- NA_to_F(str_detect(RESTAURANTS$good_to_know, 'dinner only')) RESTAURANTS$booking <- ifelse( NA_to_F(str_detect(RESTAURANTS$good_to_know, 'booking advisable')), yes='advisable', no=ifelse(NA_to_F(str_detect(RESTAURANTS$good_to_know, 'booking essential')), yes='essential', no ='not required' ) ) # coordinates RESTAURANTS <- Restaurants2 %>% html_nodes('div.poi_view') %>% xml_attr('data-fetch_summary') %>% lapply(function(item) fromJSON(item)$restaurants$id %>% str_match( '^(-?[0-9]+\\.[0-9]+)\\|(-?[0-9]+\\.[0-9]+)' ) %>% `colnames<-`(c('match','lat','lon')) %>% as.data.table) %>% rbind_all %>% select(-match) %>% mutate_each('as.numeric') %>% cbind(RESTAURANTS) save(RESTAURANTS, file=paste0(Sys.Date(),'-michelin-restaurants.RData')) write.csv(RESTAURANTS, file=paste0(Sys.Date(),'-michelin-restaurants.csv')) save(RESTAURANTS, file=paste0(Sys.Date(),'-michelin-restaurants2.RData'))

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##rankhospital("MD", "heart failure", 5) ##setwd("C:/COMMUNISM/coursera/Data Science/2/work/week4/rprog-data-ProgAssignment3-data") rankhospital <- function (state, outcome, num){ outcomeData <- read.csv("outcome-of-care-measures.csv", colClasses = "character") neededColumns <- grep("^Hospital.30.Day.Death..Mortality..Rates.from.", names(outcomeData)) figureOutOutcomesColumns <- function (outComeName) { neededColumns[grep(outComeName, names(outcomeData[neededColumns]))] } ## Check that state and outcome are valid ##outcome checkin if(outcome == "heart attack") { neededColumns <- figureOutOutcomesColumns("Heart.Attack") } else if (outcome == "heart failure") { neededColumns <- figureOutOutcomesColumns("Heart.Failure") } else if (outcome == "pneumonia") { neededColumns <- figureOutOutcomesColumns("Pneumonia") } else {stop("invalid outcome")} ##state checkin if(max(outcomeData[,"State"]==state) == 0) stop("invalid state") outcomeData[,neededColumns] <- sapply(outcomeData[,neededColumns], function(x) as.numeric(x)) outcomeDataClean <- outcomeData [!is.na(outcomeData [,neededColumns]),] outcomeState <- outcomeDataClean[outcomeDataClean[,"State"]==state, ] ##outcomeState <- transform (outcomeState, rnk = rank(outcomeState[, neededColumns], ties.method="min")) index <- with(outcomeState, order(outcomeState[,neededColumns], outcomeState[,"Hospital.Name"])) ##outcomeState res <- outcomeState[index, ] ##sorted by neededColumns and then by Hospital.Name ##num checkin if(num == "best") num <- 1 if(num == "worst") num <-length(res[,1]) name <- res[num, "Hospital.Name"] if(is.null(name)) NA else name }

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cor_nba.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/cor_nba.R \name{cor_nba} \alias{cor_nba} \title{A Correlation martix Function} \usage{ cor_nba(dataset, y) } \arguments{ \item{dataset}{is the dataset of NBA} \item{y}{is the Year that we are going to figure it out} } \description{ This function allows you to compute the correlation matrix of numeric variables in the datasets } \examples{ cor_nba() } \keyword{,matrix} \keyword{Correlatiin}

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kanikatiwari/Data-Analytics-R-

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2020-07-31T05:47:20.458938

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DT.R

#Decision Tree #Loading the package for decision tree library(party) #using readingSkills dataset head(readingSkills) readingSkills #creating input data frame data.input = readingSkills[c(1:105),] data.input a = c(1:50) cb = data.frame(cbind(a)) c = ctree(a~., data=cb) plot(c) #Creating the tree with one factor & plotting output.tree1 = ctree(nativeSpeaker ~ age, data= data.input) output.tree1 plot(output.tree1) #Creating the tree with two factor & plotting output.tree2 = ctree(nativeSpeaker ~ age+ shoeSize, data= data.input) output.tree2 plot(output.tree2) #Creating the tree with multi-factor & plotting output.tree = ctree(nativeSpeaker ~ age+ shoeSize+ score, data= data.input) output.tree plot(output.tree) ?decisiontree ?ctree

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/R/plot.R

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cran/wbs

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refs/heads/master

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plot.R

#' @title Plot for an 'sbs' object #' @description Plots the input vector used to generate 'sbs' object \code{x} with fitted piecewise constant function, equal to the mean #' between change-points specified in \code{cpt}. #' @details When \code{cpt} is omitted, the function automatically finds change-points #' using \code{changepoints} function with a default value of the threshold. #' @method plot sbs #' @importFrom stats ts.plot #' @importFrom graphics lines title #' @export #' @param x an object of class 'sbs', returned by \code{\link{sbs}} #' @param cpt a vector of integers with localisations of change-points #' @param ... other parameters which may be passed to \code{plot} and \code{changepoints} #' @seealso \code{\link{sbs}} \code{\link{changepoints}} plot.sbs <- function(x,cpt,...){ ts.plot(x$x,ylab="x",...) if(missing(cpt)){ w.cpt <- changepoints(x,...) print means <- means.between.cpt(x$x,w.cpt$cpt.th[[1]]) }else{ means <- means.between.cpt(x$x,cpt) } lines(x=means,type="l",col="red") title("Fitted piecewise constant function") } #' @title Plot for a 'wbs' object #' @description Plots the input vector used to generate 'wbs' object \code{x} with fitted piecewise constant function, equal to the mean #' between change-points specified in \code{cpt}. #' @details When \code{cpt} is omitted, the function automatically finds change-points #' using \code{changepoints} function with strengthened Schwarz Information Criterion as a stopping criterion for the WBS algorithm. #' @method plot wbs #' @export #' @param x an object of class 'wbs', returned by \code{\link{wbs}} #' @param cpt a vector of integers with localisations of change-points #' @param ... other parameters which may be passed to \code{plot} and \code{changepoints} #' @seealso \code{\link{wbs}} \code{\link{changepoints}} \code{\link{ssic.penalty}} plot.wbs <- function(x,cpt,...){ if(missing(cpt)) plot.sbs(x,cpt=changepoints(x,penalty="ssic.penalty")$cpt.ic[["ssic.penalty"]],...) else plot.sbs(x,cpt,...) }

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/R/rviewgraph-package.R

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cran/rviewgraph

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refs/heads/master

2023-06-01T08:24:19.029111

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rviewgraph-package.R

#' @name rviewgraph-package #' @aliases rviewgraph-package rviewgraph #' @docType package #' @title Animated Graph Layout Viewer #' @description #' for graph viewing, manipulation and plotting. #' #' @details #' \tabular{ll}{ #' Package: \tab rviewgraph\cr #' Type: \tab Package\cr #' Version: \tab 1.4.2\cr #' Date: \tab 2021-10-25\cr #' License: \tab GPL-2 \cr #' LazyLoad: \tab yes\cr #' SystemRequirements: \tab 'Java' >= 8\cr #' } #' Provides 'Java' graphical user interfaces (GUI) for viewing, manipulating #' and plotting graphs. Graphs may be directed or undirected. #' #' The original program, \code{rViewGraph} takes #' a graph specified as an incidence matrix, array of edges, or in \code{igraph} format #' and runs a graphical user interface that shows an #' animation of a force directed algorithm positioning the vertices in two dimensions. #' If run from a non-interactive R session, \code{rViewGraph} prints an #' error message and returns \code{NULL}. #' #' A new program, \code{vg}, is an alternative interface to the underlying #' 'Java' program that provides a more coherent way of specifying the graph, #' and more control over how the vertices appear in the GUI. Specifically, #' \code{vg} allows for arbitrary integer indices to identify the vertices, #' and allows changes to the graph's vertex and edge sets. The text labels, #' colours, shapes and sizes of the vertices can also be specified, either before #' or after vertices are added to the graph. These changes can be made while the #' vertex positioning animation is running. \code{vg} also provides #' functions for saving and restoring the state of the graph including #' vertices and edges, #' vertex positions, and vertex appearances. #' \code{vg()} can be run non-interactively without a GUI which allows a #' graph structure to be built and saved for a future interactive session. #' #' Both programs can also start a dialog box to print the current view of the graph. #' #' The underlying positioning methods works well for graphs of various structure #' of up to a few thousand vertices. It's not fazed by graphs that comprise several #' components. #' #' #' @seealso vg rViewGraph #' @seealso #' There is a vignette on 'Building a simple graph sampler'. #' #' @keywords internal "_PACKAGE" #> [1] "_PACKAGE"

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Deleetdk/test_bias_omitted_variable_bias

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refs/heads/master

2021-06-10T11:30:50.741214

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server.R

# This is the server logic for a Shiny web application. # You can find out more about building applications with Shiny here: # # http://shiny.rstudio.com # library(shiny) library(ggplot2) library(stringr) library(psych) library(plyr) library(DT) library(grid) theme_set(theme_bw()) n = 5000 shinyServer(function(input, output) { reac_d = reactive({ set.seed(74) #for reproducible results #encapsulate the data generation in {} to make them reproducible { A = data.frame(S_score = rnorm(n, input$S_adv), T_score = rnorm(n, input$T_adv), group = rep("A", n)) B = data.frame(S_score = rnorm(n), T_score = rnorm(n), group = rep("B", n)) d = as.data.frame(rbind(A, B)) error_size = sqrt(1 - (input$cor_S^2 + input$cor_T^2)) d$Y = d$S_score * input$cor_S + d$T_score * input$cor_T + error_size * rnorm(n * 2) } #rescale Y d$Y = d$Y * 100 + 500 #model fit = lm(as.formula(input$model), d) d$Y_hat = predict(fit) return(d) }) output$plot <- renderPlot({ #get reactive data d = reac_d() #text r = cor(d$Y, d$Y_hat) r_group = ddply(d, .(group), summarize, cor = cor(Y, Y_hat)) print(r_group) text = str_c("r of prediction with criteria:", "\nboth groups together: ", round(r, 2), "\nblue group: ", round(r_group[1, 2], 2), "\nred group: ", round(r_group[2, 2], 2)) text_object = grobTree(textGrob(text, x=.02, y=.98, hjust=0, vjust = 1), gp = gpar(fontsize=11)) #text size #plot ggplot(d, aes(Y_hat, Y, color = group)) + geom_point(alpha = .5) + geom_smooth(method = "lm", se = F, linetype = "dashed", size = .7) + geom_smooth(aes(color = NULL), method = "lm", se = F, linetype = "dashed", color = "black", size = .7) + xlab("Predicted criteria score") + ylab("Criteria score") + scale_color_manual(values = c("#4646ff", "#ff4646"), #, #change colors name = "Group", #change legend title labels = c("Blue", "Red")) + #change labels annotation_custom(text_object) }) output$table = DT::renderDataTable({ #fetch data d = reac_d() #desc. stats desc = ddply(d, .(group), summarize, mean_S = mean(S_score), mean_T = mean(T_score), mean_Y = mean(Y), mean_Y_hat = mean(Y_hat)) #table d2 = matrix(nrow = 4, ncol = 3) #S d2[1, 1] = desc[1, "mean_S"] d2[1, 2] = desc[2, "mean_S"] d2[1, 3] = desc[1, "mean_S"] - desc[2, "mean_S"] #T d2[2, 1] = desc[1, "mean_T"] d2[2, 2] = desc[2, "mean_T"] d2[2, 3] = desc[1, "mean_T"] - desc[2, "mean_S"] #Y d2[3, 1] = desc[1, "mean_Y"] d2[3, 2] = desc[2, "mean_Y"] d2[3, 3] = desc[1, "mean_Y"] - desc[2, "mean_Y"] #Y d2[4, 1] = desc[1, "mean_Y_hat"] d2[4, 2] = desc[2, "mean_Y_hat"] d2[4, 3] = desc[1, "mean_Y_hat"] - desc[2, "mean_Y_hat"] d2 = round(d2, 2) rownames(d2) = c("Trait S", "Trait T", "Criteria score", "Predicted criteria score") colnames(d2) = c("Blue group", "Red group", "Blue group's advantage") DT::datatable(d2, , options = list(searching = F, ordering = F, paging = F, info = F)) }) })

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/Clinical/clin.script/superceded/clinical_data_DW.R

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nmm199/MB_RNAseq

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refs/heads/master

2021-01-20T01:57:02.583285

2019-02-01T17:54:17

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clinical_data_DW.R

### File introduction ### File name: clinical_data_4.R ### Aim of file is to # 1. Run basic descriptive statistics on a cohort of children treated for medulloblastoma, whose details are contained within the local clinical database # 2. Analyse genes of interest in relation to univariate and multivariate risk prediction models for survival (overall, event-free and progression-free) # 3. This script covers analysis up to and including univariate risk factor analysis # 4. Multivariate analysis / AUC will be covered by a separate script ### Author: Dr Marion Mateos ### Date: July 3 2017 ### R version 3.4.0 (2017-04-21) ### Platform: x86_64-pc-linux-gnu (64-bit) ### Running under: Ubuntu 16.04.2 LTS # attached base packages: # [1] parallel stats graphics grDevices utils datasets methods base ### Packages and version numbers ## [1] survival_2.41-3 ## RColorBrewer_1.1-2 ## car_2.1-4 ## gplots_3.0.1 ## NMF_0.20.6 ## Biobase_2.36.2 ## BiocGenerics_0.22.0 ## cluster_2.0.6 ## rngtools_1.2.4 ## pkgmaker_0.22 ## registry_0.3 ### Libraries to be used # install.packages('gplots') # install.packages('survival') library(NMF) library(gplots) library(car) library(stats) library(survival) ### Functions used source(file = "/home/nmm199/R/MB_RNAseq/Clinical/clin.script/clinical_data_functions_DW.R") ### names of functions for info on function see source file ### "chi.sq" ### "cor.result" ### "lin.reg" ### "km.log.test" ### "km.log.test.OS" ### "cox.result.OS" ### "km.log.test.EFS" ### updatepData ### External files required ### clinical database "x.data" ### 7 molecular group data "meth.data" ### cytogenetic arm data "cytogen.data" ### RNA expression data "RNA.data" ############################################################################### ### deals with making a GOI.vsd vector this is the part that is going to change ### at the moment this is just any old expression data ### you will plug in your own goi - isoforms, novel genes, etc ### just for demonstration purposes at the moment ############################################################################### cat ("reading in expression data", sep ="\n") RNA.data <- "/home/dan/mygit/rna_seq_mb/paper/MB.vsd.txt" mb.vsd <- read.delim(RNA.data) #### providing a putative biomarker goi <- "ENSG00000136997" goi.vsd <- as.numeric(mb.vsd[goi,]) ### the output would be a vector with a continuous variable, names equal NMB numbers names(goi.vsd) <- gsub("T","",names(mb.vsd)) ##################################################################################### ### update your pData object x.data <- "/home/nmm199/R/MB_RNAseq/Input data/database270617.csv" cat ("reading in clinical database", sep ="\n") ### add in row names to original database file pData <- read.csv(x.data, row.names = 1) meth.data <- "/home/nmm199/R/MB_RNAseq/Input data/all7subgroupCalls.csv" meth7 <- read.csv(meth.data, header=TRUE, sep=",", quote="\"", dec=".", row.names=1) cytogen.data <- "/home/nmm199/R/MB_RNAseq/Input data/arm_calls_clean280617.txt" cytogen <- read.table (cytogen.data, header=T, sep="\t") test.pData <- updatepData(pData, meth7, cytogen, pdf.file = "./temp.pdf", log.file = "./temp.log.txt") save(test.pData, file = "/home/nmm199/R/MB_RNAseq/Clinical/test.pData") log.file = "pDatalog.txt" ################################################################################ pdf.file <- "marker.results.pdf" results <- clinPathAssess(test.pData,goi.vsd,pdf.file = "marker.results.pdf",log.file = "marker.results.txt")

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/R/univregs.R

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cran/MXM

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refs/heads/master

2022-09-12T12:14:29.564720

2022-08-25T07:52:40

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univregs.R

univregs <- function(target, dataset, targetID = -1, test = NULL, user_test = NULL, wei = NULL, ncores = 1) { univariateModels <- list(); dm <- dim(dataset) rows <- dm[1] cols <- dm[2] if (targetID != -1 ) { target <- dataset[, targetID] dataset[, targetID] <- rbinom(rows, 1, 0.5) } id <- NULL if ( !identical(test, testIndFisher) & !identical(test, testIndSpearman) ) { ina <- NULL id <- Rfast::check_data(dataset) if ( sum(id > 0) ) dataset[, id] <- rnorm(rows * length(id) ) } la <- length( unique(target) ) if ( !is.null(user_test) ) { univariateModels <- univariateScore(target, dataset, test = user_test, wei, targetID) } else if ( identical(test, testIndFisher) ) { ## Pearson's correlation a <- as.vector( cor(target, dataset) ) dof <- rows - 3; #degrees of freedom wa <- 0.5 * log( (1 + a) / (1 - a) ) * sqrt(dof) id <- which( is.na(a) ) if ( length(id) > 0) wa[id] <- 0 univariateModels$stat <- wa; univariateModels$pvalue <- log(2) + pt( abs(wa), dof, lower.tail = FALSE, log.p = TRUE) ; } else if ( identical(test, testIndSpearman) ) { ## Spearman's correlation a <- as.vector( cor(target, dataset) ) dof <- rows - 3; #degrees of freedom wa <- 0.5 * log( (1 + a) / (1 - a) ) * sqrt(dof) / 1.029563 id <- which( is.na(a) ) if ( length(id) > 0) wa[id] <- 0 univariateModels$stat <- wa univariateModels$pvalue <- log(2) + pt( abs(wa), dof, lower.tail = FALSE, log.p = TRUE); } else if ( identical(test, gSquare) ) { ## G^2 test z <- cbind(dataset, target) if ( !is.matrix(z) ) z <- as.matrix(z) dc <- Rfast::colrange(z, cont = FALSE) a <- Rfast::g2tests(data = z, x = 1:cols, y = cols + 1, dc = dc) stat <- a$statistic univariateModels$stat <- stat univariateModels$pvalue <- pchisq(stat, a$df, lower.tail = FALSE, log.p = TRUE) } else if ( identical(test, testIndBeta) ) { ## Beta regression mod <- beta.regs(target, dataset, wei, logged = TRUE, ncores = ncores) univariateModels$stat <- mod[, 1] univariateModels$pvalue <- mod[, 2] } else if ( identical(test, testIndMMReg) ) { ## M (Robust) linear regression fit1 <- MASS::rlm(target ~ 1, maxit = 2000, method = "MM") lik1 <- as.numeric( logLik(fit1) ) lik2 <- numeric(cols) dof <- numeric(cols) ina <- 1:cols if ( ncores <= 1 | is.null(ncores) ) { for ( i in 1:cols ) { fit2 <- MASS::rlm(target ~ dataset[, i], maxit = 2000, method = "MM" ) lik2[i] <- as.numeric( logLik(fit2) ) dof[i] <- length( coef(fit2) ) - 1 } stat <- 2 * (lik2 - lik1) univariateModels$stat <- stat univariateModels$pvalue <- pchisq(stat, dof, lower.tail = FALSE, log.p = TRUE) } else { cl <- parallel::makePSOCKcluster(ncores) doParallel::registerDoParallel(cl) mod <- foreach(i = 1:cols, .combine = rbind, .packages = "MASS") %dopar% { fit2 <- MASS::rlm(target ~ dataset[, i], maxit = 2000, method = "MM" ) lik2 <- as.numeric( logLik(fit2) ) return( c(lik2, length( coef(fit2) ) ) ) } parallel::stopCluster(cl) stat <- as.vector( 2 * abs(lik1 - mod[, 1]) ) dof <- as.vector( mod[, 2] ) - 1 univariateModels$stat <- stat univariateModels$pvalue <- pchisq(stat, dof, lower.tail = FALSE, log.p = TRUE) } } else if ( identical(test, testIndReg) & !is.null(wei) ) { ## Weighted linear regression univariateModels <- list(); stat <- pval <- numeric(cols) if ( ncores <= 1 | is.null(ncores) ) { for ( i in 1:cols ) { fit2 <- lm( target ~ dataset[, i], weights = wei, y = FALSE, model = FALSE ) tab <- anova(fit2) stat[i] <- tab[1, 4] df1 <- tab[1, 1] ; df2 = tab[2, 1] pval[i] <- pf( stat[i], df1, df2, lower.tail = FALSE, log.p = TRUE ) } univariateModels$stat <- stat univariateModels$pvalue <- pval } else { cl <- parallel::makePSOCKcluster(ncores) doParallel::registerDoParallel(cl) mod <- foreach(i = 1:cols, .combine = rbind) %dopar% { ww <- lm( target ~ dataset[, i], weights = wei, y = FALSE, model = FALSE ) tab <- anova( ww ) stat <- tab[1, 4] df1 <- tab[1, 1] ; df2 = tab[2, 1] pval <- pf( stat, df1, df2, lower.tail = FALSE, log.p = TRUE ) return( c(stat, pval) ) } parallel::stopCluster(cl) univariateModels$stat <- mod[, 1] univariateModels$pvalue <- mod[, 2] } } else if ( identical(test, testIndReg) & is.null(wei) ) { ## linear regression mod <- Rfast::regression(dataset, target, logged = TRUE) univariateModels$stat <- mod[, 1] univariateModels$pvalue <- mod[, 2] } else if ( identical(test, testIndMVreg) ) { ## Weighted linear regression univariateModels = list(); stat = pval = numeric(cols) if ( ncores <= 1 | is.null(ncores) ) { for ( i in 1:cols ) { fit2 = lm( target ~ dataset[, i], weights = wei, y = FALSE, model = FALSE ) tab = anova(fit2) stat[i] = tab[2, 3] df1 = tab[2, 4] ; df2 = tab[2, 5] pval[i] = pf( stat[i], df1, df2, lower.tail = FALSE, log.p = TRUE ) } univariateModels$stat <- stat univariateModels$pvalue <- pval } else { cl <- parallel::makePSOCKcluster(ncores) doParallel::registerDoParallel(cl) mod <- foreach(i = 1:cols, .combine = rbind) %dopar% { ww <- lm( target ~ dataset[, i], weights = wei, y = FALSE, model = FALSE ) tab <- anova( ww ) stat <- tab[2, 3] df1 <- tab[2, 4] ; df2 = tab[2, 5] pval <- pf( stat, df1, df2, lower.tail = FALSE, log.p = TRUE ) return( c(stat, pval) ) } parallel::stopCluster(cl) univariateModels$stat <- mod[, 1] univariateModels$pvalue <- mod[, 2] } } else if ( identical(test, testIndOrdinal) ) { ## ordinal regression lik2 <- numeric(cols) dof <- numeric(cols) fit1 <- ordinal::clm(target ~ 1, weights = wei) lik1 <- as.numeric( logLik(fit1) ) df1 <- length( coef(fit1) ) if ( ncores <= 1 | is.null(ncores) ) { for ( i in 1:cols ) { mat <- model.matrix(target ~ dataset[, i] ) fit2 <- ordinal::clm.fit(target, mat, weights = wei) lik2[i] <- as.numeric( fit2$logLik ) dof[i] <- length( coef(fit2) ) - df1 } stat = 2 * (lik2 - lik1) univariateModels$stat <- stat univariateModels$pvalue <- pchisq(stat, dof, lower.tail = FALSE, log.p = TRUE) } else { cl <- parallel::makePSOCKcluster(ncores) doParallel::registerDoParallel(cl) mod <- foreach(i = 1:cols, .combine = rbind, .packages = "ordinal") %dopar% { mat <- model.matrix(target ~ dataset[, i] ) fit2 <- ordinal::clm.fit(target, mat, weights = wei) lik2 <- as.numeric( fit2$logLik ) return( c(lik2, length( coef(fit2) ) ) ) } parallel::stopCluster(cl) stat = 2 * (mod[, 1] - lik1) univariateModels$stat <- stat univariateModels$pvalue <- pchisq(stat, mod[, 2] - df1, lower.tail = FALSE, log.p = TRUE) } } else if ( identical(test, testIndMultinom) ) { ## multinomial regression target = as.factor( as.numeric( target ) ) lik2 = numeric(cols) dof = numeric(cols) fit1 = nnet::multinom(target ~ 1, trace = FALSE, weights = wei) lik1 = as.numeric( logLik(fit1) ) df1 = length( coef(fit1) ) if ( ncores <= 1 | is.null(ncores) ) { for ( i in 1:cols ) { fit2 = nnet::multinom(target ~ dataset[, i], trace = FALSE, weights = wei ) lik2[i] = as.numeric( logLik(fit2) ) dof[i] = length( coef(fit2) ) - df1 } stat = 2 * (lik2 - lik1) univariateModels$stat <- stat univariateModels$pvalue <- pchisq(stat, dof, lower.tail = FALSE, log.p = TRUE) } else { cl <- parallel::makePSOCKcluster(ncores) doParallel::registerDoParallel(cl) mod <- foreach(i = 1:cols, .combine = rbind, .packages = "nnet") %dopar% { fit2 = nnet::multinom(target ~ dataset[, i], weights = wei) lik2 = as.numeric( logLik(fit2 ) ) return( c(lik2, length( coef(fit2) ) ) ) } parallel::stopCluster(cl) stat = 2 * (mod[, 1] - lik1) univariateModels$stat <- stat univariateModels$pvalue <- pchisq(stat, mod[, 2] - df1, lower.tail = FALSE, log.p = TRUE) } } else if ( identical(test, testIndLogistic) & is.matrix(dataset) & is.null(wei) ) { ## logistic regression if ( is.factor(target) ) target <- as.numeric(target) - 1 mod <- Rfast::univglms( target, dataset, oiko = "binomial", logged = TRUE ) univariateModels$stat <- mod[, 1] univariateModels$pvalue <- mod[, 2] } else if ( identical(test, testIndLogistic) & is.data.frame(dataset) & is.null(wei) ) { ## logistic regression if ( is.factor(target) ) target <- as.numeric(target) - 1 mod <- Rfast::univglms2( target, dataset, oiko = "binomial", logged = TRUE ) univariateModels$stat <- mod[, 1] univariateModels$pvalue <- mod[, 2] } else if ( identical(test, testIndLogistic) & !is.null(wei) ) { ## Logistic regression fit1 = glm(target ~ 1, binomial, weights = wei) lik1 = fit1$deviance lik2 = numeric(cols) dof = numeric(cols) if ( ncores <= 1 | is.null(ncores) ) { for ( i in 1:cols ) { fit2 = glm( target ~ dataset[, i], binomial, weights = wei ) lik2[i] = fit2$deviance dof[i] = length( coef(fit2) ) - 1 } stat = lik1 - lik2 univariateModels$stat <- stat univariateModels$pvalue <- pchisq(stat, dof, lower.tail = FALSE, log.p = TRUE) } else { cl <- parallel::makePSOCKcluster(ncores) doParallel::registerDoParallel(cl) mod <- foreach(i = 1:cols, .combine = rbind) %dopar% { fit2 = glm( target ~ dataset[, i], binomial, weights = wei ) lik2 = fit2$deviance return( c(lik2, length( coef(fit2) ) - 1 ) ) } parallel::stopCluster(cl) stat = lik1 - mod[, 1] univariateModels$stat <- stat univariateModels$pvalue <- pchisq(stat, mod[, 2], lower.tail = FALSE, log.p = TRUE) } } else if ( identical(test, testIndBinom) ) { ## Binomial regression wei <- target[, 2] y <- target[, 1] / wei fit1 = glm(y ~ 1, binomial, weights = wei) lik1 = fit1$deviance lik2 = numeric(cols) dof = numeric(cols) if ( ncores <= 1 | is.null(ncores) ) { for ( i in 1:cols ) { fit2 = glm( y ~ dataset[, i], binomial, weights = wei ) lik2[i] = fit2$deviance dof[i] = length( coef(fit2) ) - 1 } stat = lik1 - lik2 univariateModels$stat <- stat univariateModels$pvalue <- pchisq(stat, dof, lower.tail = FALSE, log.p = TRUE) } else { cl <- parallel::makePSOCKcluster(ncores) doParallel::registerDoParallel(cl) wei <- target[, 2] y <- target[, 1] / wei mod <- foreach(i = ina, .combine = rbind) %dopar% { fit2 = glm( y ~ dataset[, i], binomial, weights = wei ) lik2 = as.numeric( logLik(fit2) ) return( c(lik2, length( coef(fit2) ) - 1 ) ) } parallel::stopCluster(cl) stat = as.vector( lik1 - mod[, 1] ) univariateModels$stat <- stat univariateModels$pvalue <- pchisq(stat, mod[, 2], lower.tail = FALSE, log.p = TRUE) } } else if ( identical(test, testIndPois) & is.matrix(dataset) & is.null(wei) ) { ## Poisson regression mod <- Rfast::univglms( target, dataset, oiko = "poisson", logged = TRUE ) univariateModels$stat <- mod[, 1] univariateModels$pvalue <- mod[, 2] } else if ( identical(test, testIndPois) & is.data.frame(dataset) & is.null(wei) ) { ## Poisson regression mod <- Rfast::univglms2( target, dataset, oiko = "poisson", logged = TRUE ) univariateModels$stat <- mod[, 1] univariateModels$pvalue <- mod[, 2] } else if ( identical(test, testIndPois) & !is.null(wei) ) { ## Poisson regression fit1 = glm(target ~ 1, poisson, weights = wei) lik1 = fit1$deviance lik2 = numeric(cols) dof = numeric(cols) ina <- 1:cols if ( ncores <= 1 | is.null(ncores) ) { for ( i in ina ) { fit2 = glm( target ~ dataset[, i], poisson, weights = wei ) lik2[i] = fit2$deviance dof[i] = length( coef(fit2) ) - 1 } stat = lik1 - lik2 univariateModels$stat <- stat univariateModels$pvalue <- pchisq(stat, dof, lower.tail = FALSE, log.p = TRUE) } else { cl <- parallel::makePSOCKcluster(ncores) doParallel::registerDoParallel(cl) mod <- foreach(i = ina, .combine = rbind) %dopar% { fit2 = glm( target ~ dataset[, i], poisson, weights = wei ) return( c(fit2$deviance, length( coef(fit2) ) - 1 ) ) } parallel::stopCluster(cl) stat = as.vector( lik1 - mod[, 1] ) univariateModels$stat <- stat univariateModels$pvalue <- pchisq(stat, mod[, 2], lower.tail = FALSE, log.p = TRUE) } } else if ( identical(test, testIndNB) ) { ## Negative binomial regression lik1 <- MASS::glm.nb( target ~ 1, weights = wei )$twologlik if ( ncores <= 1 | is.null(ncores) ) { lik2 <- dof <- numeric(cols) for ( i in 1:cols ) { fit2 = MASS::glm.nb( target ~ dataset[, i], weights = wei ) lik2[i] = fit2$twologlik dof[i] = length( coef(fit2) ) - 1 } stat = lik2 - lik1 univariateModels$stat <- stat univariateModels$pvalue <- pchisq(stat, dof, lower.tail = FALSE, log.p = TRUE) } else { cl <- parallel::makePSOCKcluster(ncores) doParallel::registerDoParallel(cl) mod <- foreach(i = ina, .combine = rbind, .packages = "MASS") %dopar% { fit2 = MASS::glm.nb( target ~ dataset[, i], weights = wei ) return( c(fit2$twologlik, length( coef(fit2) ) - 1 ) ) } parallel::stopCluster(cl) stat <- as.vector(mod[, 1]) - lik1 univariateModels$stat <- stat univariateModels$pvalue <- pchisq(stat, mod[, 2], lower.tail = FALSE, log.p = TRUE) } } else if ( identical(test, testIndNormLog) ) { ## Normal log link regression fit1 = glm(target ~ 1, family = gaussian(link = log), weights = wei) lik1 = fit1$deviance lik2 = numeric(cols) dof = numeric(cols) ina <- 1:cols phi <- numeric(cols) if ( ncores <= 1 | is.null(ncores) ) { for ( i in ina ) { fit2 = glm( target ~ dataset[, i], family = gaussian(link = log), weights = wei ) lik2[i] = fit2$deviance phi[i] <- summary(fit2)[[14]] dof[i] = length( fit2$coefficients ) } stat = (lik1 - lik2 ) / (dof - 1) / phi univariateModels$stat <- stat univariateModels$pvalue <- pf(stat, dof - 1, rows - dof, lower.tail = FALSE, log.p = TRUE) } else { cl <- parallel::makePSOCKcluster(ncores) doParallel::registerDoParallel(cl) mod <- foreach(i = ina, .combine = rbind) %dopar% { fit2 = glm( target ~ dataset[, i], family = gaussian(link = log), weights = wei ) return( c(fit2$deviance, length( fit2$coefficients ), summary(fit2)[[14]] ) ) } parallel::stopCluster(cl) stat = as.vector( lik1 - mod[, 1] ) / (mod[, 2] - 1) / mod[, 3] univariateModels$stat <- stat univariateModels$pvalue <- pf(stat, mod[, 2] - 1, rows - mod[, 2], lower.tail = FALSE, log.p = TRUE) } } else if ( identical(test, testIndGamma) ) { ## Gamma regression fit1 = glm(target ~ 1, family = Gamma(link = log), weights = wei) lik1 = fit1$deviance lik2 = numeric(cols) dof = numeric(cols) ina <- 1:cols phi <- numeric(cols) if ( ncores <= 1 | is.null(ncores) ) { for ( i in ina ) { fit2 = glm( target ~ dataset[, i], family = Gamma(link = log), weights = wei ) lik2[i] = fit2$deviance phi[i] = summary(fit2)[[ 14 ]] dof[i] = length( fit2$coefficients) } stat = (lik1 - lik2) / (dof - 1) / phi univariateModels$stat <- stat univariateModels$pvalue <- pf(stat, dof - 1, rows - dof, lower.tail = FALSE, log.p = TRUE) } else { cl <- parallel::makePSOCKcluster(ncores) doParallel::registerDoParallel(cl) mod <- foreach(i = ina, .combine = rbind) %dopar% { fit2 = glm( target ~ dataset[, i], family = Gamma(link = log), weights = wei ) return( c(fit2$deviance, length( fit2$coefficients ), summary(fit2)[[14]] ) ) } parallel::stopCluster(cl) stat = as.vector( lik1 - mod[, 1] ) / (mod[, 2] - 1) / mod[, 3] univariateModels$stat <- stat univariateModels$pvalue <- pf(stat, mod[, 2] - 1, rows - mod[, 2], lower.tail = FALSE, log.p = TRUE) } } else if ( identical(test, testIndZIP) ) { ## Zero-inflated Poisson regression moda <- zip.regs(target, dataset, wei, logged = TRUE, ncores = ncores) univariateModels$stat <- moda[, 1] univariateModels$pvalue <- moda[, 2] } else if ( identical(test, testIndRQ) ) { ## Median (quantile) regression fit1 = quantreg::rq(target ~ 1, weights = wei) stat = pval = numeric(cols) if ( ncores <= 1 | is.null(ncores) ) { for ( i in 1:cols ) { fit2 = quantreg::rq(target ~ dataset[, i], weights = wei ) ww = anova(fit1, fit2, test = "rank") df1 = as.numeric( ww[[1]][1] ) df2 = as.numeric( ww[[1]][2] ) stat[i] = as.numeric( ww[[1]][3] ) pval[i] = pf(stat[i], df1, df2, lower.tail = FALSE, log.p = TRUE) } univariateModels$stat <- stat univariateModels$pvalue <- pval } else { cl <- parallel::makePSOCKcluster(ncores) doParallel::registerDoParallel(cl) mod <- foreach(i = ina, .combine = rbind, .packages = "quantreg") %dopar% { fit2 = quantreg::rq(target ~ dataset[, i], weights = wei ) ww = anova(fit1, fit2, test = "rank") df1 = as.numeric( ww[[1]][1] ) df2 = as.numeric( ww[[1]][2] ) stat = as.numeric( ww[[1]][3] ) pval = pf(stat, df1, df2, lower.tail = FALSE, log.p = TRUE) return( c(stat, pval ) ) } parallel::stopCluster(cl) univariateModels$stat <- as.vector( mod[, 1] ) univariateModels$pvalue <- as.vector( mod[, 2] ) } } else if ( identical(test, testIndIGreg) ) { ## Inverse Gaussian regression fit1 = glm(target ~ 1, family = inverse.gaussian(link = log), weights = wei) lik1 = as.numeric( logLik(fit1) ) lik2 = numeric(cols) dof = numeric(cols) if ( ncores <= 1 | is.null(ncores) ) { for ( i in 1:cols ) { fit2 = glm( target ~ dataset[, i], family = inverse.gaussian(link = log), weights = wei ) lik2[i] = as.numeric( logLik(fit2) ) dof[i] = length( coef(fit2) ) - 1 } stat = 2 * (lik2 - lik1) univariateModels$stat <- stat univariateModels$pvalue <- pchisq(stat, dof, lower.tail = FALSE, log.p = TRUE) } else { cl <- parallel::makePSOCKcluster(ncores) doParallel::registerDoParallel(cl) mod <- foreach(i = 1:cols, .combine = rbind) %dopar% { fit2 = glm( target ~ dataset[, i], family = inverse.gaussian(link = log), weights = wei ) lik2 = as.numeric( logLik(fit2) ) return( c(lik2, length( coef(fit2) ) - 1 ) ) } parallel::stopCluster(cl) stat = as.vector( 2 * (mod[, 1] - lik1) ) univariateModels$stat <- stat univariateModels$pvalue <- pchisq(stat, mod[, 2], lower.tail = FALSE, log.p = TRUE) } } else if ( identical(test, censIndCR) ) { ## Cox regression stat = numeric(cols) dof = numeric(cols) if ( ncores <= 1 | is.null(ncores) ) { for ( i in 1:cols ) { fit2 = survival::coxph( target ~ dataset[, i], weights = wei) res <- anova(fit2) dof[i] <- res[2, 3] stat[i] <- res[2, 2] } univariateModels$stat <- stat univariateModels$pvalue <- pchisq(stat, dof, lower.tail = FALSE, log.p = TRUE) } else { cl <- parallel::makePSOCKcluster(ncores) doParallel::registerDoParallel(cl) mod <- foreach(i = 1:cols, .combine = rbind, .packages = "survival") %dopar% { fit2 = survival::coxph( target ~ dataset[, i], weights = wei ) res <- anova(fit2) return( c(res[2, 2], res[2, 3] ) ) } parallel::stopCluster(cl) univariateModels$stat <- mod[, 1] univariateModels$pvalue <- pchisq(mod[, 1], mod[, 2], lower.tail = FALSE, log.p = TRUE) } } else if ( identical(test, censIndWR) ) { ## Weibull regression fit1 <- survival::survreg(target ~ 1, weights = wei) lik1 <- as.numeric( logLik(fit1) ) lik2 <- numeric(cols) dof <- numeric(cols) if ( ncores <= 1 | is.null(ncores) ) { for ( i in 1:cols ) { fit2 <- survival::survreg( target ~ dataset[, i], weights = wei, control=list(iter.max = 5000) ) lik2[i] <- as.numeric( logLik(fit2) ) dof[i] <- length( coef(fit2) ) - 1 } stat <- 2 * (lik2 - lik1) univariateModels$stat <- stat univariateModels$pvalue <- pchisq(stat, dof, lower.tail = FALSE, log.p = TRUE) } else { cl <- parallel::makePSOCKcluster(ncores) doParallel::registerDoParallel(cl) mod <- foreach(i = 1:cols, .combine = rbind, .packages = "survival") %dopar% { fit2 <- survival::survreg( target ~ dataset[, i], weights = wei, control=list(iter.max = 5000) ) lik2 <- as.numeric( logLik(fit2) ) return( c(lik2, length( coef(fit2) ) - 1 ) ) } parallel::stopCluster(cl) stat <- as.vector( 2 * (mod[, 1] - lik1) ) univariateModels$stat <- stat univariateModels$pvalue <- pchisq(stat, mod[, 2], lower.tail = FALSE, log.p = TRUE) } } else if ( identical(test, censIndLLR) ) { ## Weibull regression fit1 <- survival::survreg(target ~ 1, weights = wei, dist = "loglogistic") lik1 <- as.numeric( logLik(fit1) ) lik2 <- numeric(cols) dof <- numeric(cols) if ( ncores <= 1 | is.null(ncores) ) { for ( i in 1:cols ) { fit2 <- survival::survreg( target ~ dataset[, i], weights = wei, control = list(iter.max = 5000), dist = "loglogistic" ) lik2[i] <- as.numeric( logLik(fit2) ) dof[i] <- length( coef(fit2) ) - 1 } stat <- 2 * (lik2 - lik1) univariateModels$stat <- stat univariateModels$pvalue <- pchisq(stat, dof, lower.tail = FALSE, log.p = TRUE) } else { cl <- parallel::makePSOCKcluster(ncores) doParallel::registerDoParallel(cl) mod <- foreach(i = 1:cols, .combine = rbind, .packages = "survival") %dopar% { fit2 <- survival::survreg( target ~ dataset[, i], weights = wei, control = list(iter.max = 5000), dist = "loglogistic" ) lik2 <- as.numeric( logLik(fit2) ) return( c(lik2, length( coef(fit2) ) - 1 ) ) } parallel::stopCluster(cl) stat <- as.vector( 2 * (mod[, 1] - lik1) ) univariateModels$stat <- stat univariateModels$pvalue <- pchisq(stat, mod[, 2], lower.tail = FALSE, log.p = TRUE) } } else if ( identical(test, testIndTobit) ) { ## Tobit regression fit1 = survival::survreg(target ~ 1, weights = wei, dist = "gaussian") lik1 = as.numeric( logLik(fit1) ) lik2 = numeric(cols) dof = numeric(cols) if ( ncores <= 1 | is.null(ncores) ) { for ( i in 1:cols ) { fit2 = survival::survreg( target ~ dataset[, i], weights = wei, dist = "gaussian" ) lik2[i] = as.numeric( logLik(fit2) ) dof[i] = length( coef(fit2) ) - 1 } stat = 2 * (lik2 - lik1) univariateModels$stat <- stat univariateModels$pvalue <- pchisq(stat, dof, lower.tail = FALSE, log.p = TRUE) } else { cl <- parallel::makePSOCKcluster(ncores) doParallel::registerDoParallel(cl) mod <- foreach(i = 1:cols, .combine = rbind, .packages = "survival") %dopar% { fit2 = survival::survreg( target ~ dataset[, i], weights = wei, dist = "gaussian" ) lik2 = as.numeric( logLik(fit2) ) return( c(lik2, length( coef(fit2) ) - 1 ) ) } parallel::stopCluster(cl) stat = as.vector( 2 * (mod[, 1] - lik1) ) univariateModels$stat <- stat univariateModels$pvalue <- pchisq(stat, mod[, 2], lower.tail = FALSE, log.p = TRUE) } } else if ( identical(test, testIndClogit) ) { ## Conditional logistic regression case <- as.logical(target[, 1]); ## case subject <- target[, 2] #the patient id stat <- numeric(cols) dof <- numeric(cols) if ( ncores <= 1 | is.null(ncores) ) { for ( i in 1:cols ) { fit2 <- survival::clogit( case ~ dataset[, i] + strata(subject) ) dof[i] <- length( fit2$coefficients ) stat[i] <- diff( fit2$loglik ) } univariateModels$stat <- 2 * stat univariateModels$pvalue <- pchisq(stat, dof, lower.tail = FALSE, log.p = TRUE) } else { cl <- parallel::makePSOCKcluster(ncores) doParallel::registerDoParallel(cl) mod <- foreach(i = 1:cols, .combine = rbind, .packages = "survival") %dopar% { fit2 <- survival::clogit(case ~ dataset[, i] + strata(subject) ) return( c( diff(fit2$loglik) , length( fit2$coefficients ) ) ) } parallel::stopCluster(cl) univariateModels$stat <- 2 * as.vector( mod[, 1] ) univariateModels$pvalue <- pchisq(mod[, 1], mod[, 2], lower.tail = FALSE, log.p = TRUE) } } else if ( identical(test, censIndER) ) { ## Exponential regression fit1 <- survival::survreg(target ~ 1, dist = "exponential", weights = wei) lik1 <- as.numeric( logLik(fit1) ) lik2 <- numeric(cols) dof <- numeric(cols) if ( ncores <= 1 | is.null(ncores) ) { for ( i in 1:cols ) { fit2 <- survival::survreg( target ~ dataset[, i], dist = "exponential", weights = wei ) lik2[i] <- as.numeric( logLik(fit2) ) dof[i] <- length( coef(fit2) ) - 1 } stat <- 2 * (lik2 - lik1) univariateModels$stat <- stat univariateModels$pvalue <- pchisq(stat, dof, lower.tail = FALSE, log.p = TRUE) } else { cl <- parallel::makePSOCKcluster(ncores) doParallel::registerDoParallel(cl) mod <- foreach(i = 1:cols, .combine = rbind, .packages = "survival") %dopar% { fit2 <- survival::survreg( target ~ dataset[, i], dist = "exponential", weights = wei ) return( c(as.numeric( logLik(fit2) ), length( coef(fit2) ) - 1 ) ) } parallel::stopCluster(cl) stat = as.vector( 2 * (mod[, 1] - lik1) ) univariateModels$stat <- stat univariateModels$pvalue <- pchisq(stat, mod[ ,2], lower.tail = FALSE, log.p = TRUE) } } else if ( identical(test, testIndQBinom) ) { ## Quasi Binomial regression fit1 <- glm(target ~ 1, family = quasibinomial(link = logit), weights = wei) lik1 <- fit1$deviance lik2 <- numeric(cols) dof <- numeric(cols) ina <- 1:cols phi <- numeric(cols) if ( ncores <= 1 | is.null(ncores) ) { for ( i in ina ) { fit2 <- glm( target ~ dataset[, i], family = quasibinomial(link = logit), weights = wei ) lik2[i] <- fit2$deviance phi[i] <- summary(fit2)[[ 14 ]] dof[i] <- length( fit2$coefficients) } stat <- (lik1 - lik2) / (dof - 1) / phi univariateModels$stat <- stat univariateModels$pvalue <- pf(stat, dof - 1, rows - dof, lower.tail = FALSE, log.p = TRUE) } else { cl <- parallel::makePSOCKcluster(ncores) doParallel::registerDoParallel(cl) mod <- foreach(i = ina, .combine = rbind) %dopar% { fit2 <- glm( target ~ dataset[, i], family = quasibinomial(link = logit), weights = wei ) return( c(fit2$deviance, length( fit2$coefficients ), summary(fit2)[[14]] ) ) } parallel::stopCluster(cl) stat <- as.vector( lik1 - mod[, 1] ) / (mod[, 2] - 1) / mod[, 3] univariateModels$stat <- stat univariateModels$pvalue <- pf(stat, mod[, 2] - 1, rows - mod[, 2], lower.tail = FALSE, log.p = TRUE) } } else if ( identical(test, testIndQPois) ) { ## Quasi Poisson regression fit1 <- glm(target ~ 1, family = quasipoisson(link = log), weights = wei) lik1 <- fit1$deviance lik2 <- numeric(cols) dof <- numeric(cols) ina <- 1:cols phi <- numeric(cols) if ( ncores <= 1 | is.null(ncores) ) { for ( i in ina ) { fit2 <- glm( target ~ dataset[, i], family = quasipoisson(link = log), weights = wei ) lik2[i] <- fit2$deviance phi[i] <- summary(fit2)[[ 14 ]] dof[i] <- length( fit2$coefficients) } stat <- (lik1 - lik2) / (dof - 1) / phi univariateModels$stat <- stat univariateModels$pvalue <- pf(stat, dof - 1, rows - dof, lower.tail = FALSE, log.p = TRUE) } else { cl <- parallel::makePSOCKcluster(ncores) doParallel::registerDoParallel(cl) mod <- foreach(i = ina, .combine = rbind) %dopar% { fit2 <- glm( target ~ dataset[, i], family = quasipoisson(link = log), weights = wei ) return( c(fit2$deviance, length( fit2$coefficients ), summary(fit2)[[14]] ) ) } parallel::stopCluster(cl) stat <- as.vector( lik1 - mod[, 1] ) / (mod[, 2] - 1) / mod[, 3] univariateModels$stat <- stat univariateModels$pvalue <- pf(stat, mod[, 2] - 1, rows - mod[, 2], lower.tail = FALSE, log.p = TRUE) } } else if ( identical(test, testIndSPML) ) { ## Circular regression if ( !is.matrix(dataset) ) dataset <- as.matrix(dataset) mod <- Rfast::spml.regs(target, dataset, logged = TRUE, parallel = (ncores > 1) ) univariateModels$stat <- mod[, 1] univariateModels$pvalue <- mod[, 2] } else univariateModels <- NULL if ( !is.null(univariateModels) ) { if (targetID != - 1) { univariateModels$stat[targetID] <- 0 univariateModels$pvalue[targetID] <- log(1) } if ( sum( id > 0 ) > 0 ) { univariateModels$stat[id] <- 0 univariateModels$pvalue[id] <- log(1) } } univariateModels }

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/R/Nancy rF.R

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GillisLabAtMoffitt/myelosuppression_CHIP

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c96d7b7783bf98579e5c945e33bd2c0d45c2db5c

refs/heads/master

2022-11-28T09:00:44.392125

2020-08-07T14:08:21

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r

Nancy rF.R

rf.data = read.csv('CHIP_CRASH_data_for_stats_v07_randomforest_2.csv',stringsAsFactors = F,header=T) # calculate random forest to get variable importance rf.data <- data.frame(rf.data) head(rf.data) rf.data <- rf.data %>% select("Case_Control", "CHIP", "Age", "Gender", "Race", "Ethnicity", "Smoking", "Mets", "Prior_chemo", "Prior_rad", "BaseANC", "BaseHGB", "BasePLT", "BaseWBC"# , # "MAX2heme" ) rf.data$Ethnicity[rf.data$Ethnicity==''] = 'unknown' rf.data <- rf.data %>% mutate(Case_Control = factor(Case_Control, labels = c("Cases", "Controls"), levels= c(1, 0))) rf.data = rf.data %>% mutate_if(is.character, as.factor) for(i in 11:14){ rf.data[is.na(rf.data[,i]),i] = median(rf.data[,i],na.rm=T) } set.seed(1485) # rf <- randomForest(rf.data, as.factor(rf.data$Case_Control),ntree=2000,importance=TRUE)#,sampsize=c(samp.size,samp.size)) rf <- randomForest(Case_Control ~ ., data=rf.data,ntree=2000,importance=TRUE) print(rf) rf.feat <- rf$importance ###################################################################### # Plotting Classification Trees with the plot.rpart and rattle pckages cat("Getting Features...") feats <- rf.feat[order(rf.feat[,"MeanDecreaseGini"],decreasing=TRUE),"MeanDecreaseGini"] write.csv(rf.feat,"output/RF_features_gini_scores.csv") png("output/RF_features_gini_plot.png",width=600,height=600,units='px') plot(feats,type="l",ylab="Mean Decrease Gini Score",xlab="Genes") abline(h=.2,col="red") #cutoff feats <- feats[feats>.2] abline(v=length(feats),col="red") graphics.off() probe.feats <- names(feats) tree.data <- rf.data[,probe.feats] tree.data <- cbind(as.character(rf.data$Case_Control),tree.data) colnames(tree.data)[1] <- "Response" tree.data <- data.frame(tree.data) head(tree.data) # Make big tree form <- as.formula(Response ~ .) # Leave one out cross-validation LOOCV library(rpart) set.seed(1485) leave.out <- sample(1:nrow(tree.data),replace=FALSE) loocv.results <- NULL for(i in 1:nrow(tree.data)){ train <- tree.data[-leave.out[i],] test <- tree.data[leave.out[i],] sample.id <- rownames(tree.data)[leave.out[i]] all.tree <- rpart(form,train,control=rpart.control(minsplit=2,minbucket=1)) # cat("Pruning Tree...") # prune.val <- all.tree$cptable[which.min(all.tree$cptable[,"xerror"])[1],"CP"] # train.pfit <- prune(all.tree,cp=prune.val) # cat("Done.\n") #don't prune train.pfit <- all.tree #cat("Plotting Tree...") #fancyRpartPlot(all.tree,type=1,extra=2) #pdf(paste0("Decision_Tree_CVfold-",fold,"_trainData_",format(Sys.Date(),"%d%b%Y"),".pdf"),width=18,height=12) #fancyRpartPlot_DR(all.tree) #graphics.off() #cat("Done.\n") cat("Predicting using test data for calls...LOOCV:",i,"/",length(leave.out),"\n") pred.tree <- predict(train.pfit,test) calls <- colnames(pred.tree)[apply(pred.tree,1,which.max)] features <- unlist(unique(train.pfit$frame[1])) features <- paste(features[grep("<leaf>",features,invert=TRUE)],collapse=";") temp <- c(i,sample.id,calls,as.character(test[["Response"]]),features) #calls <- cbind(rownames(pred.tree),calls) #actual <- phenotype #calls <- cbind(calls,actual) loocv.results <- rbind(loocv.results,temp) } colnames(loocv.results) <- c("iteration","sample_id_left_out","calls","actual","features") head(loocv.results) #Calculate percent accuracy score <- apply(loocv.results,1,function(x) if(x[3]==x[4]){return(1)}else{return(0)}) loocv.results <- cbind(loocv.results,score) perc.correct <- sum(score)/length(score) for(i in unique(loocv.results[,"calls"])){ print(i) print(sum(as.numeric(loocv.results[which(loocv.results[,"calls"]==i),"score"]))/length(which(loocv.results[,"calls"]==i))) } actual <- loocv.results[,"actual"] cv.call.mat <- matrix(0,nrow=length(unique(actual)),ncol=length(unique(actual))) colnames(cv.call.mat) <- unique(actual) rownames(cv.call.mat) <- unique(actual) cat("Calculing Model Performance...") for(i in 1:nrow(loocv.results)){ act <- loocv.results[i,"actual"] pred <- loocv.results[i,"calls"] cv.call.mat[pred,act] <- cv.call.mat[pred,act]+1 } ### create final tree based on loocv features final.feats <- unique(unlist(strsplit(loocv.results[,"features"],split=";"))) final.tree.data <- tree.data[c("Response",final.feats)] final.tree <- rpart(form,final.tree.data,control=rpart.control(minsplit=2,minbucket=1)) # cat("Pruning Tree...") # prune.val <- final.tree$cptable[which.min(final.tree$cptable[,"xerror"])[1],"CP"] # final.pfit <- prune(final.tree,cp=prune.val) # cat("Done.\n") # don't prune final.pfit <- final.tree #cat("Plotting Tree...") #fancyRpartPlot(all.tree,type=1,extra=2) pdf(paste0("output/Decision_Tree_ALLData.pdf"),width=18,height=12) fancyRpartPlot(final.pfit) graphics.off() #cat("Done.\n") cat("Predicting back on data for calls...") pred.tree <- predict(final.pfit,final.tree.data) calls <- colnames(pred.tree)[apply(pred.tree,1,which.max)] calls <- cbind(rownames(pred.tree),calls) actual <- as.character(final.tree.data[,"Response"]) calls <- cbind(calls,actual) score <- apply(calls,1,function(x) if(x[2]==x[3]){return(1)}else{return(0)}) calls <- cbind(calls,score) perc.correct <- sum(score)/length(score) cat("Done.\n") final.features <- unlist(unique(final.pfit$frame[1])) final.features <- paste(final.features[grep("<leaf>",final.features,invert=TRUE)],collapse=";") final.call.mat <- matrix(0,nrow=length(unique(actual)),ncol=length(unique(actual))) colnames(final.call.mat) <- unique(actual) rownames(final.call.mat) <- unique(actual) cat("Calculing Model Performance...") for(i in 1:nrow(calls)){ act <- calls[i,"actual"] pred <- calls[i,"calls"] final.call.mat[pred,act] <- final.call.mat[pred,act]+1 } summary.file <- NULL #calculate summary statistics for(resp in unique(colnames(cv.call.mat))){ j <- which(rownames(cv.call.mat)==resp) tp <- cv.call.mat[j,j] fp <- sum(cv.call.mat[j,-j]) tn <- sum(cv.call.mat[-j,-j]) fn <- sum(cv.call.mat[-j,j]) sens <- round(tp/(tp+fn),digits = 3) spec <- round(tn/(tn+fp), digits = 3) ba <- mean(c(sens,spec)) OR <- (tp/fp)/(fn/tn) OR.se <- sqrt((1/cv.call.mat[1,1])+(1/cv.call.mat[1,2])+(1/cv.call.mat[2,1])+(1/cv.call.mat[2,2])) OR.ci.l <- exp(log(OR)-(1.96*OR.se)) OR.ci.h <- exp(log(OR)+(1.96*OR.se)) RR <- (tp/(tp+fp)) / (fn/(tn+fn)) TPR <- sens FPR <- 1-spec PPV <- tp/(tp+fp) NPV <- tn/(tn+fn) p.val <- fisher.test(matrix(c(tp,fp,tn,fn),nrow=2))[[1]] temp.out <- c("LOOCV",resp,sens,spec,ba,OR,OR.ci.l,OR.ci.h,RR,TPR,FPR,PPV,NPV,p.val,features) summary.file <- rbind(summary.file,temp.out) } for(resp in unique(colnames(final.call.mat))){ j <- which(rownames(final.call.mat)==resp) tp <- final.call.mat[j,j] fp <- sum(final.call.mat[j,-j]) tn <- sum(final.call.mat[-j,-j]) fn <- sum(final.call.mat[-j,j]) test.n <- "NA"#nrow(test.tree.data) train.n <-nrow(tree.data) n.cancer.train <- length(which(tree.data[,"Response"]==resp)) n.cancer.test <- "NA"#length(which(test.tree.data[,"Class"]==j)) sens <- round(tp/(tp+fn),digits = 3) spec <- round(tn/(tn+fp), digits = 3) ba <- mean(c(sens,spec)) OR <- (tp/fp)/(fn/tn) OR.se <- sqrt((1/final.call.mat[1,1])+(1/final.call.mat[1,2])+(1/final.call.mat[2,1])+(1/final.call.mat[2,2])) OR.ci.l <- exp(log(OR)-(1.96*OR.se)) OR.ci.h <- exp(log(OR)+(1.96*OR.se)) RR <- (tp/(tp+fp)) / (fn/(tn+fn)) TPR <- sens FPR <- 1-spec PPV <- tp/(tp+fp) NPV <- tn/(tn+fn) p.val <- fisher.test(matrix(c(tp,fp,tn,fn),nrow=2))[[1]] temp.out <- c("ALL",resp,sens,spec,ba,OR,OR.ci.l,OR.ci.h,RR,TPR,FPR,PPV,NPV,p.val,final.features) summary.file <- rbind(summary.file,temp.out) } #out.path <- "Data/Predictive_Model/DecisionTree/ALL_AML/noCNV/" write.csv(calls,"output/Decision_Tree_Calls_ALL_Data.csv",row.names=FALSE) write.csv(loocv.results,"output/Decision_Tree_Calls_LOOCV_Data.csv",row.names=FALSE) ## Counts for LOOCV and ALL write("#rows=predicted;cols=actual","output/Decision_Tree_CallCounts.csv",sep="") write("LOOCV","output/Decision_Tree_CallCounts.csv",sep="",append = TRUE) cv.call.mat.out <- cbind(rownames(cv.call.mat),cv.call.mat) write.table(cv.call.mat.out,"output/Decision_Tree_CallCounts.csv",sep=",",append=TRUE,row.names=FALSE) write("\nAll Data","output/Decision_Tree_CallCounts.csv",sep="",append = TRUE) final.call.mat.out <- cbind(rownames(final.call.mat),final.call.mat) write.table(final.call.mat.out,"output/Decision_Tree_CallCounts.csv",sep=",",append=TRUE,row.names=FALSE) ##write out summary file colnames(summary.file) <-c("Model","Response","sens","spec","ba","OR","OR_95ci_l","OR_95ci_h","RR","TPR","FPR","PPV","NPV","Fisher_p","features") write.csv(summary.file,"output/Decision_Tree_SummaryResults.csv",row.names=FALSE) # For Baseline prdiction---- rf.data = read.csv('CHIP_CRASH_data_for_stats_v07_randomforest_2.csv',stringsAsFactors = F,header=T) # calculate random forest to get variable importance rf.data <- data.frame(rf.data) head(rf.data) rf.data$BaseANC median(rf.data$BaseANC, na.rm = TRUE) rf.data <- rf.data %>% select("Case_Control", "CHIP", "Age", "Gender", "Race", "Ethnicity", "Smoking", "Mets", "Prior_chemo", "Prior_rad", "BaseANC", "VAF"# , # "MAX2heme" ) for(i in 11:14){ rf.data[is.na(rf.data[,i]),i] = median(rf.data[,i],na.rm=T) } rf.data$Ethnicity[rf.data$Ethnicity==''] = 'unknown' rf.data <- rf.data %>% mutate(Case_Control = factor(Case_Control, labels = c("Cases", "Controls"), levels= c(1, 0))) %>% mutate(Base_ANC_grp = case_when( BaseANC >= median(rf.data$BaseANC, na.rm = TRUE) ~ "ANC_high", BaseANC < median(rf.data$BaseANC, na.rm = TRUE) ~ "ANC_low" )) %>% mutate_if(is.character, as.factor) %>% select(-BaseANC) set.seed(1485) # rf <- randomForest(rf.data, as.factor(rf.data$Case_Control),ntree=2000,importance=TRUE)#,sampsize=c(samp.size,samp.size)) rf <- randomForest(Base_ANC_grp ~ ., data=rf.data,ntree=2000,importance=TRUE) print(rf) rf.feat <- rf$importance ###################################################################### # Plotting Classification Trees with the plot.rpart and rattle pckages cat("Getting Features...") feats <- rf.feat[order(rf.feat[,"MeanDecreaseGini"],decreasing=TRUE),"MeanDecreaseGini"] write.csv(rf.feat,"output/RF_features_gini_scores.csv") png("output/RF_features_gini_plot.png",width=600,height=600,units='px') plot(feats,type="l",ylab="Mean Decrease Gini Score",xlab="Genes") abline(h=.2,col="red") #cutoff feats <- feats[feats>.2] abline(v=length(feats),col="red") graphics.off() probe.feats <- names(feats) tree.data <- rf.data[,probe.feats] tree.data <- cbind(as.character(rf.data$Base_ANC_grp),tree.data) colnames(tree.data)[1] <- "Response" tree.data <- data.frame(tree.data) head(tree.data) # Make big tree form <- as.formula(Response ~ .) # Leave one out cross-validation LOOCV library(rpart) set.seed(1485) leave.out <- sample(1:nrow(tree.data),replace=FALSE) loocv.results <- NULL for(i in 1:nrow(tree.data)){ train <- tree.data[-leave.out[i],] test <- tree.data[leave.out[i],] sample.id <- rownames(tree.data)[leave.out[i]] all.tree <- rpart(form,train,control=rpart.control(minsplit=2,minbucket=1)) # cat("Pruning Tree...") # prune.val <- all.tree$cptable[which.min(all.tree$cptable[,"xerror"])[1],"CP"] # train.pfit <- prune(all.tree,cp=prune.val) # cat("Done.\n") #don't prune train.pfit <- all.tree #cat("Plotting Tree...") #fancyRpartPlot(all.tree,type=1,extra=2) #pdf(paste0("Decision_Tree_CVfold-",fold,"_trainData_",format(Sys.Date(),"%d%b%Y"),".pdf"),width=18,height=12) #fancyRpartPlot_DR(all.tree) #graphics.off() #cat("Done.\n") cat("Predicting using test data for calls...LOOCV:",i,"/",length(leave.out),"\n") pred.tree <- predict(train.pfit,test) calls <- colnames(pred.tree)[apply(pred.tree,1,which.max)] features <- unlist(unique(train.pfit$frame[1])) features <- paste(features[grep("<leaf>",features,invert=TRUE)],collapse=";") temp <- c(i,sample.id,calls,as.character(test[["Response"]]),features) #calls <- cbind(rownames(pred.tree),calls) #actual <- phenotype #calls <- cbind(calls,actual) loocv.results <- rbind(loocv.results,temp) } colnames(loocv.results) <- c("iteration","sample_id_left_out","calls","actual","features") head(loocv.results) #Calculate percent accuracy score <- apply(loocv.results,1,function(x) if(x[3]==x[4]){return(1)}else{return(0)}) loocv.results <- cbind(loocv.results,score) perc.correct <- sum(score)/length(score) for(i in unique(loocv.results[,"calls"])){ print(i) print(sum(as.numeric(loocv.results[which(loocv.results[,"calls"]==i),"score"]))/length(which(loocv.results[,"calls"]==i))) } actual <- loocv.results[,"actual"] cv.call.mat <- matrix(0,nrow=length(unique(actual)),ncol=length(unique(actual))) colnames(cv.call.mat) <- unique(actual) rownames(cv.call.mat) <- unique(actual) cat("Calculing Model Performance...") for(i in 1:nrow(loocv.results)){ act <- loocv.results[i,"actual"] pred <- loocv.results[i,"calls"] cv.call.mat[pred,act] <- cv.call.mat[pred,act]+1 } ### create final tree based on loocv features final.feats <- unique(unlist(strsplit(loocv.results[,"features"],split=";"))) final.tree.data <- tree.data[c("Response",final.feats)] final.tree <- rpart(form,final.tree.data,control=rpart.control(minsplit=2,minbucket=1)) # cat("Pruning Tree...") # prune.val <- final.tree$cptable[which.min(final.tree$cptable[,"xerror"])[1],"CP"] # final.pfit <- prune(final.tree,cp=prune.val) # cat("Done.\n") # don't prune final.pfit <- final.tree #cat("Plotting Tree...") #fancyRpartPlot(all.tree,type=1,extra=2) pdf(paste0("output/Decision_Tree_ALLData.pdf"),width=18,height=12) fancyRpartPlot(final.pfit) graphics.off() #cat("Done.\n") cat("Predicting back on data for calls...") pred.tree <- predict(final.pfit,final.tree.data) calls <- colnames(pred.tree)[apply(pred.tree,1,which.max)] calls <- cbind(rownames(pred.tree),calls) actual <- as.character(final.tree.data[,"Response"]) calls <- cbind(calls,actual) score <- apply(calls,1,function(x) if(x[2]==x[3]){return(1)}else{return(0)}) calls <- cbind(calls,score) perc.correct <- sum(score)/length(score) cat("Done.\n") final.features <- unlist(unique(final.pfit$frame[1])) final.features <- paste(final.features[grep("<leaf>",final.features,invert=TRUE)],collapse=";") final.call.mat <- matrix(0,nrow=length(unique(actual)),ncol=length(unique(actual))) colnames(final.call.mat) <- unique(actual) rownames(final.call.mat) <- unique(actual) cat("Calculing Model Performance...") for(i in 1:nrow(calls)){ act <- calls[i,"actual"] pred <- calls[i,"calls"] final.call.mat[pred,act] <- final.call.mat[pred,act]+1 } summary.file <- NULL #calculate summary statistics for(resp in unique(colnames(cv.call.mat))){ j <- which(rownames(cv.call.mat)==resp) tp <- cv.call.mat[j,j] fp <- sum(cv.call.mat[j,-j]) tn <- sum(cv.call.mat[-j,-j]) fn <- sum(cv.call.mat[-j,j]) sens <- round(tp/(tp+fn),digits = 3) spec <- round(tn/(tn+fp), digits = 3) ba <- mean(c(sens,spec)) OR <- (tp/fp)/(fn/tn) OR.se <- sqrt((1/cv.call.mat[1,1])+(1/cv.call.mat[1,2])+(1/cv.call.mat[2,1])+(1/cv.call.mat[2,2])) OR.ci.l <- exp(log(OR)-(1.96*OR.se)) OR.ci.h <- exp(log(OR)+(1.96*OR.se)) RR <- (tp/(tp+fp)) / (fn/(tn+fn)) TPR <- sens FPR <- 1-spec PPV <- tp/(tp+fp) NPV <- tn/(tn+fn) p.val <- fisher.test(matrix(c(tp,fp,tn,fn),nrow=2))[[1]] temp.out <- c("LOOCV",resp,sens,spec,ba,OR,OR.ci.l,OR.ci.h,RR,TPR,FPR,PPV,NPV,p.val,features) summary.file <- rbind(summary.file,temp.out) } for(resp in unique(colnames(final.call.mat))){ j <- which(rownames(final.call.mat)==resp) tp <- final.call.mat[j,j] fp <- sum(final.call.mat[j,-j]) tn <- sum(final.call.mat[-j,-j]) fn <- sum(final.call.mat[-j,j]) test.n <- "NA"#nrow(test.tree.data) train.n <-nrow(tree.data) n.cancer.train <- length(which(tree.data[,"Response"]==resp)) n.cancer.test <- "NA"#length(which(test.tree.data[,"Class"]==j)) sens <- round(tp/(tp+fn),digits = 3) spec <- round(tn/(tn+fp), digits = 3) ba <- mean(c(sens,spec)) OR <- (tp/fp)/(fn/tn) OR.se <- sqrt((1/final.call.mat[1,1])+(1/final.call.mat[1,2])+(1/final.call.mat[2,1])+(1/final.call.mat[2,2])) OR.ci.l <- exp(log(OR)-(1.96*OR.se)) OR.ci.h <- exp(log(OR)+(1.96*OR.se)) RR <- (tp/(tp+fp)) / (fn/(tn+fn)) TPR <- sens FPR <- 1-spec PPV <- tp/(tp+fp) NPV <- tn/(tn+fn) p.val <- fisher.test(matrix(c(tp,fp,tn,fn),nrow=2))[[1]] temp.out <- c("ALL",resp,sens,spec,ba,OR,OR.ci.l,OR.ci.h,RR,TPR,FPR,PPV,NPV,p.val,final.features) summary.file <- rbind(summary.file,temp.out) } #out.path <- "Data/Predictive_Model/DecisionTree/ALL_AML/noCNV/" write.csv(calls,"output/Decision_Tree_Calls_ALL_Data.csv",row.names=FALSE) write.csv(loocv.results,"output/Decision_Tree_Calls_LOOCV_Data.csv",row.names=FALSE) ## Counts for LOOCV and ALL write("#rows=predicted;cols=actual","output/Decision_Tree_CallCounts.csv",sep="") write("LOOCV","output/Decision_Tree_CallCounts.csv",sep="",append = TRUE) cv.call.mat.out <- cbind(rownames(cv.call.mat),cv.call.mat) write.table(cv.call.mat.out,"output/Decision_Tree_CallCounts.csv",sep=",",append=TRUE,row.names=FALSE) write("\nAll Data","output/Decision_Tree_CallCounts.csv",sep="",append = TRUE) final.call.mat.out <- cbind(rownames(final.call.mat),final.call.mat) write.table(final.call.mat.out,"output/Decision_Tree_CallCounts.csv",sep=",",append=TRUE,row.names=FALSE) ##write out summary file colnames(summary.file) <-c("Model","Response","sens","spec","ba","OR","OR_95ci_l","OR_95ci_h","RR","TPR","FPR","PPV","NPV","Fisher_p","features") write.csv(summary.file,"output/Decision_Tree_SummaryResults.csv",row.names=FALSE)

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2021-04-27T02:22:07

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covid_data.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/data.R \docType{data} \name{covid_data} \alias{covid_data} \title{COVID-19 Geographic data} \format{ A dataframe with 6 variables: \describe{ \item{date}{Date} \item{state_name_2016}{State name} \item{indicator}{COVID-19 indicators} \item{value}{Value} \item{statistical_area}{Number of digits in statistical area} \item{statistical_area_code}{Statistical area code} } } \usage{ covid_data } \description{ A dataset containing the jobkeeper applications, jobseeker payments, and derived impact from COVID-19 at a geographical level. Jobkeeper and Jobseeker data is based on the SA2 classiciation, and employment impact is based on the SA4 classification. This dataset combines data from jobseeker_sa2, jobkeeper_sa2, payroll_sa4, and small_area_labour_market datasets and is intended for use on the AITI Economic Indicators dashboard. } \keyword{datasets}

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/R/loonGrob_l_layer_graph.R

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r

loonGrob_l_layer_graph.R

#' @rdname loonGrob #' #' @examples #' #' \dontrun{ #' ## graph examples #' #' G <- completegraph(names(iris[,-5])) #' LG <- linegraph(G) #' g <- l_graph(LG) #' #' nav0 <- l_navigator_add(g) #' l_configure(nav0, label = 0) #' con0 <- l_context_add_geodesic2d(navigator=nav0, data=iris[,-5]) #' #' nav1 <- l_navigator_add(g, from = "Sepal.Length:Petal.Width", #' to = "Petal.Length:Petal.Width", proportion = 0.6) #' l_configure(nav1, label = 1) #' con1 <- l_context_add_geodesic2d(navigator=nav1, data=iris[,-5]) #' #' nav2 <- l_navigator_add(g, from = "Sepal.Length:Petal.Length", #' to = "Sepal.Width:Petal.Length", proportion = 0.5) #' l_configure(nav2, label = 2) #' con2 <- l_context_add_geodesic2d(navigator=nav2, data=iris[,-5]) #' #' # To print directly use either #' plot(g) #' # or #' grid.loon(g) #' # or to save structure #' library(grid) #' lgrob <- loonGrob(g) #' grid.newpage(); grid.draw(lgrob) #' } #' #' @export loonGrob.l_layer_graph <- function(target, name = NULL, gp = NULL, vp = NULL) { widget <- l_create_handle(attr(target, "widget")) states <- get_layer_states(widget) active <- states$active if (!any(active)) { grob(name = name, gp = gp, vp = vp) } else { edgesGrob <- edgesGrob(states) nodeGlyphGrob <- nodeGlyphGrob(states) labelGrob <- labelGrob(states) # add navigators nav_ids <- l_navigator_ids(widget) if(length(nav_ids) == 0){ # No navigator, just return the graph gTree(children = gList( edgesGrob, nodeGlyphGrob, labelGrob), name = name, gp = gp, vp = vp ) } else { # have navigator, need path and navigator as well activeNavigator <- widget["activeNavigator"] gTree( children = gList( edgesGrob, do.call(gList, lapply(nav_ids, function(nav_id){ navPathGrob(states, navigator = l_create_handle(c(widget, nav_id)), name = paste0("navigation path edges", nav_id)) }) ), nodeGlyphGrob, labelGrob, do.call(gList, lapply(nav_ids, function(nav_id){ navPointsGrob(activeNavigator, states, navigator = l_create_handle(c(widget, nav_id)), name = paste0("navigation points edges", nav_id)) }) ) ), name = if (is.null(name)) "graph" else name, gp = gp, vp = vp ) } } } edgesGrob <- function(states = NULL, name = NULL){ active <- states$active activeNode <- states$nodes[active] activeX <- states$x[active] activeY <- states$y[active] isActiveEdge <- states$activeEdge gTree(children = do.call( gList, lapply(seq_len(length(activeNode)), function(i) { nodeFrom <- activeNode[i] nodeFrom_EdgeId <- which(states$from[isActiveEdge] == nodeFrom) if (length(nodeFrom_EdgeId) != 0){ nodeTo <- states$to[isActiveEdge][nodeFrom_EdgeId] nodeTo_CoordId <- which (activeNode %in% nodeTo == TRUE) numNodesTo <- length(nodeTo_CoordId) cols <- states$colorEdge[isActiveEdge][nodeFrom_EdgeId] x <- unit(c(rep(activeX[i], numNodesTo), activeX[nodeTo_CoordId]), "native") y <- unit(c(rep(activeY[i], numNodesTo), activeY[nodeTo_CoordId]), "native") polylineGrob(x, y, id=rep(1:numNodesTo, 2), gp=gpar(col= cols, lwd=1), name = paste("edge", i)) } else { condGrob(test = FALSE, grobFun = polylineGrob, name = paste("edge", i, "missing") ) } } ) ), name = if (is.null(name)) "graph edges" else name ) } labelGrob <- function(states = NULL, name = NULL){ active <- states$active activeNode <- states$nodes[active] activeX <- states$x[active] activeY <- states$y[active] activeAngle <- states$orbitAngle[active] orbitDistance <- states$orbitDistance gTree(children = do.call( gList, lapply(seq_len(length(activeNode)), function(i) { condGrob(test = states$showOrbit, grobFun = textGrob, name = paste("label", i), label = activeNode[i], x = unit(activeX[i], "native") + unit(orbitDistance * cos(activeAngle[i]), "mm" ), y = unit(activeY[i], "native") + unit(orbitDistance * sin(activeAngle[i]), "mm" ), gp=gpar(fontsize= 8, # TODO find this somewhere col= l_getOption("foreground"))) } ) ), name = if (is.null(name)) "graph labels" else name ) } nodeGlyphGrob <- function(states = NULL, name = NULL){ active <- states$active cex <- as_r_point_size(states$size[active]) selected <- states$selected[active] col <- get_display_color(states$color[active], selected) pch <- glyph_to_pch(states$glyph[active]) # is there a fill colour? filled <- pch %in% 21:24 activeX <- states$x[active] activeY <- states$y[active] gTree( children = do.call(gList, lapply(seq_len(length(filled)), function(i){ gp <- if (filled[i]) { gpar(fill = col[i], col = l_getOption("foreground"), cex = cex[i]) } else { gpar(col = col[i], cex = cex[i]) } pointsGrob(x = activeX[i], y = activeY[i], pch = pch[i], gp = gp, name = paste("node", i) ) } )), name = if (is.null(name)) "graph nodes" else name ) } navPathGrob <- function(states, navigator, name = NULL){ x <- as.numeric(states$x) y <- as.numeric(states$y) node <- states$nodes color <- as_hex6color(navigator['color']) from <- navigator['from'] to <- navigator['to'] prop <- navigator['proportion'] fromId <- sapply(1:length(from), function(i){which(node %in% from[i] == T)}) toId <- sapply(1:length(to), function(i){which(node %in% to[i] == T)}) if(length(from) == 0 || length(to) == 0) { grob(name = name) } else { visitedLinesGrob <- if(length(from) < 2) { grob(name = name) } else { do.call(gList, lapply(1:(length(from) - 1), function(i){ linesGrob(unit(c(x[fromId[i]], x[fromId[i+1]]), "native"), unit( c(y[fromId[i]], y[fromId[i+1]]), "native"), gp = gpar(col = color, lwd = 9), #TODO find the line widths name = paste("line", i, "(visited)") ) } ) ) } unvisitedLinesGrob <- if(length(to) < 2){ grob(name = name) } else { do.call(gList, lapply(1:(length(to) - 1), function(i){ linesGrob(unit( c(x[toId[i]], x[toId[i+1]]), "native"), unit( c(y[toId[i]], y[toId[i+1]]), "native"), gp = gpar(col = color, lwd = 3), #TODO find the line widths name = paste("line", i, "(unvisited)") ) } ) ) } xn <- (1 - prop) * x[fromId[length(fromId)]] + prop * x[toId[1]] yn <- (1 - prop) * y[fromId[length(fromId)]] + prop * y[toId[1]] betweenLinesGrob <- gList(linesGrob(unit(c(x[fromId[length(fromId)]], xn), "native"), unit(c(y[fromId[length(fromId)]], yn), "native"), gp = gpar(col = color, lwd = 9)), #TODO find the line widths linesGrob(unit(c(xn, x[toId[1]]), "native"), unit(c(yn, y[toId[1]]), "native"), gp = gpar(col = color, lwd = 3)) #TODO find the line widths ) gTree(children = gList(unvisitedLinesGrob, visitedLinesGrob, betweenLinesGrob), name = if (is.null(name)) "navigation path" else name ) } } # size of navigator is arbitrary, just as close as loon object. navPointsGrob <- function(activeNavigator, states, navigator, name){ x <- as.numeric(states$x) y <- as.numeric(states$y) node <- states$nodes color <- as_hex6color(navigator['color']) from <- navigator['from'] to <- navigator['to'] prop <- navigator['proportion'] label <- navigator['label'] fromId <- sapply(1:length(from), function(i){which(node %in% from[i] == TRUE)}) toId <- sapply(1:length(to), function(i){which(node %in% to[i] == TRUE)}) sel_color <- as.character(l_getOption("select-color")) if (grepl("^#", sel_color) && nchar(sel_color) == 13) { sel_color <- hex12tohex6(sel_color) } circleGp <- if(length(activeNavigator) != 0) { if(activeNavigator == navigator) { gpar(fill = color, lwd = 4, col = sel_color) # TODO line width? } else { gpar(fill = color) } } else {gpar(fill = color)} fromRadius <- unit(5.5, "mm") if(length(from) == 0){ xx <- unit(0.1, "npc") yy <- unit(0.9, "npc") gTree(children = gList(circleGrob(xx, yy, r = fromRadius, gp = circleGp, name = "navigator circle"), if(length(label) != 0) { textGrob(paste(label, collapse = " "), xx, yy, gp = gpar(fill = "black", fontsize = 9), name = "navigator label") # font size? } ), name = if (is.null(name)) "navigator" else name ) } else if(length(from) == 1 & length(to) == 0) { xx <- unit(x[fromId], "native") yy <- unit(y[fromId], "native") gTree(children = gList( circleGrob(x = xx, y = yy, r = fromRadius, gp = circleGp, name = "navigator circle" ), if(length(label) != 0) { textGrob(paste(label, collapse = " "), xx, yy, gp = gpar(fill = "black", fontsize = 9), name = "navigator label") } ), name = if (is.null(name)) "navigator" else name ) } else { xx <- unit( (1 - prop) * x[fromId[length(fromId)]] + prop * x[toId[1]], "native") yy <- unit( (1 - prop) * y[fromId[length(fromId)]] + prop * y[toId[1]], "native") toRadius <- unit(1, "mm") gTree(children = gList( # 'to' dot circleGrob(unit(x[toId[length(toId)]], "native"), unit(y[toId[length(toId)]], "native"), r = toRadius, gp = gpar(fill = color), name = "to dot" ), # 'from' navigator circleGrob(xx, yy, r = fromRadius, gp = circleGp, name = "navigator circle" ), # 'text' on the navigator condGrob( test = length(label) != 0, grobFun = textGrob, name = "navigator label", label = paste(label, collapse = " "), x = xx, y = yy, gp = gpar(fill = "black", fontsize = 9) ) ), name = if (is.null(name)) "navigator" else name ) } }

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/rSPACE/R/UtilityFunctions.R

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Jordan-Heiman/Full_rSPACE

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refs/heads/master

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UtilityFunctions.R

# Little functions to make things work setDefault<-function(x,val) ifelse(is.null(x),val,x)

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/man/bic.join.strings.Rd

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caitlinjones/bicrnaseq

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refs/heads/master

2021-05-23T05:46:24.178527

2018-04-28T00:00:27

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rd

bic.join.strings.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/bic_util.R \name{bic.join.strings} \alias{bic.join.strings} \title{Convert a character vector to a delimited string} \usage{ bic.join.strings(x, delim) } \arguments{ \item{x}{character vector} \item{delim}{delimiter to join vector elements} } \value{ a string } \description{ Given a character vector and a delimiter, join all items in the vector, separated by the given delimiter }