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SizeMat_V6.R
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logistic_dir <- "C:/Users/cmundy/UTAS Research Dropbox/Craig Mundy/R_Stuff/Logistic"
library(tidyverse)
library(MASS)
library(gdata)
library(doBy)
library(openxlsx)
library(boot)
library(car)
library(lubridate)
library(snow)
source("C:/GitCode/AbResearch/SAM_Biplot.R")
#Outputs for Biplots
resdir <- 'C:/OneDrive - University of Tasmania/Fisheries Research/Abalone/SAM/SAM_plots'
setwd <- resdir
##-----------------------------
## Need to work out a way to combine the full sample with the top up sample (good grief)
## Currently combining all data collected at the same site in the same month as a 'sample'
##-----------------------------
## Consider trialling this form of bootstrap
## http://stackoverflow.com/questions/28053542/confidence-intervals-for-lethal-dose-ld-for-logistic-regression-in-r
# ##Load raw csv file
# infile <- "D:\\R_Stuff\\Logistic/BlacklipSAM.txt"
# BlckPop <- read.csv(infile, header=TRUE, sep=',', dec='.', as.is=TRUE)
# BlckPop <- BlckPop[order(BlckPop$SiteCode,BlckPop$SPC_ShellLength),]
## load new raw csv file. SAMExport2016.csv is an export from Access, where
## SEX = I, T, M, or F
infileNew <- file.path(logistic_dir, "SAMExport2016.csv")
BlckPopNew <- read.csv(infileNew, header=TRUE, sep=',', dec='.', as.is=TRUE)
BlckPopNew$SAM_Date <- as.Date(strptime(as.character(BlckPopNew$SAM_Date), "%d/%m/%Y", tz='AUSTRALIA/HOBART'))
BlckPopNew$SiteCode <- paste(BlckPopNew$SIT_Id,'_',year(BlckPopNew$SAM_Date),'_',month(BlckPopNew$SAM_Date), sep="")
BlckPopNew <- BlckPopNew[order(BlckPopNew$SiteCode,BlckPopNew$SPC_ShellLength),]
pick <- which(BlckPopNew$SPC_GonadStage == 4)
table(BlckPopNew$SiteCode[pick], BlckPopNew$SPC_GonadStage[pick])
pick2 <- which(BlckPopNew$SIT_Id %in% BlckPopNew$SIT_Id[pick])
table(BlckPopNew$SPC_GonadStage[pick2])
BlckPop <- BlckPopNew
## Note SiteCode is a single field that combines SiteNumber, Year & Month
## This effectively pools all data colelcted from a site in the same month,
## to address sites where sampling was collected over multiple days, but have
## different sample codes for some reason. May have some unintended side effects
Sex <- as.data.frame(table(BlckPop$SPC_Sex))
SAM_Type <- as.data.frame(table(BlckPop$SAM_Type))
GndStg <- as.data.frame(table(BlckPop$SPC_GonadStage))
Trem <- subset(BlckPop, BlckPop$SPC_Sex =="T") # Extract Trematode records
## Subset
samdata <- subset(BlckPop, SPC_Sex %in% c("I", "M", "F"))
table(BlckPop$SPC_Sex, BlckPop$SPC_GonadStage)
pick <- which(samdata$SPC_GonadStage == 4)
table(samdata$SIT_Id[pick])
## re-code Male and Female as M for Mature (I = Immature)
samdata$Mat <- ifelse(samdata$SPC_Sex=="I", c("I"), c("M"))
#samdata$Mat[samdata$SPC_GonadStage %in% c("0")] <- "I"
## Remove sites with no mature or no immature
SamAssess <- samdata %>%
group_by(SiteCode, Mat) %>%
summarise(N = n()) %>%
spread(Mat, N)
SamList <- subset(SamAssess, (I > 3 & M > 3))
## Remove sites with less than 150 records
SamList <- droplevels(subset(SamList, (I + M) > 150, drop=TRUE))
## Routine Outlier Removal: Unusual large immature animals
pick <- which((samdata$SPC_ShellLength > 139) & (samdata$Mat=="I"))
outlier <- samdata[pick,]
samdata <- samdata[-pick,]
## Routine Outlier Removal: Unusual small mature animals
pick <- which((samdata$SPC_ShellLength < 60) & (samdata$Mat=="M"))
outlier <- rbind(outlier,samdata[pick,])
samdata <- samdata[-pick,]
SamList$LD05 <- as.numeric(NA)
SamList$LD25 <- as.numeric(NA)
SamList$LD50 <- as.numeric(NA)
SamList$LD75 <- as.numeric(NA)
SamList$LD85 <- as.numeric(NA)
SamList$LD90 <- as.numeric(NA)
SamList$LD95 <- as.numeric(NA)
SamList$IQR <- as.numeric(NA)
SamList$N.underLD05 <- as.numeric(NA)
SamList$N.underLD50 <- as.numeric(NA)
SamList$N.overLD95 <- as.numeric(NA)
SamList$N.IQR <- as.numeric(NA)
SamList$Ld50BootU95 <- as.numeric(NA)
SamList$Ld50BootL95 <- as.numeric(NA)
SamList$Ld75BootU95 <- as.numeric(NA)
SamList$Ld75BootL95 <- as.numeric(NA)
SamList$Ld85BootU95 <- as.numeric(NA)
SamList$Ld85BootL95 <- as.numeric(NA)
SamList$Ld90BootU95 <- as.numeric(NA)
SamList$Ld90BootL95 <- as.numeric(NA)
SamList$Ld95BootU95 <- as.numeric(NA)
SamList$Ld95BootL95 <- as.numeric(NA)
SamList$a <- as.numeric(NA)
SamList$b <- as.numeric(NA)
NumSites <- nrow(SamList)
NumSites
#samdataT<-subset(samdata, SiteCode %in% KeepSits)
#setwd('D:/Fisheries Research/Abalone/SAM/SAM_Biplots')
# i <- 577
# i <- 5
# i <- "10_1988_8"
#Loop through unique DiveId's
for (i in 1:NumSites) {
subdat <- droplevels(subset(samdata, samdata$SiteCode == SamList$SiteCode[i]))
head(subdat)
SizeMat <- table(subdat$SPC_ShellLength, subdat$Mat)
SizeMat <- as.data.frame(rbind(SizeMat))
SizeMat$ShellLength <- as.numeric(rownames(SizeMat))
head(SizeMat)
SizeMat$Total <- SizeMat$I + SizeMat$M
SizeMat$MatRatio <- SizeMat$M/SizeMat$Total
r <- glm(MatRatio ~ ShellLength, family=binomial(link = "logit"), data = SizeMat, weights = Total)
ld05 <- dose.p(r, p = 0.05);
SamList$LD05[i] <- as.vector(ld05)
ld25 <- dose.p(r, p = 0.25);
SamList$LD25[i] <- as.vector(ld25)
ld50 <- dose.p(r, p = 0.5);
SamList$LD50[i] <- as.numeric(ld50)
ld75 <- dose.p(r, p = 0.75);
SamList$LD75[i] <- as.vector(ld75)
ld85 <- dose.p(r, p = 0.85);
SamList$LD85[i] <- as.vector(ld85)
ld90 <- dose.p(r, p = 0.90);
SamList$LD90[i] <- as.vector(ld90)
ld95 <- dose.p(r, p = 0.95);
SamList$LD95[i] <- as.numeric(ld95)
SamList$IQR[i] <- SamList$LD75[i] - SamList$LD25[i]
SamList$a[i] <- as.numeric (r$coef[1])
SamList$b[i] <- as.numeric (r$coef[2])
N.underLD05 <- nrow(droplevels(subset(subdat, subdat$SPC_ShellLength < as.integer(as.vector(ld05)))))
SamList$N.underLD05[i] <- as.numeric(N.underLD05)
N.underLD50 <- nrow(droplevels(subset(subdat, subdat$SPC_ShellLength < as.integer(as.vector(ld50)))))
SamList$N.underLD50[i] <- as.numeric(N.underLD50)
N.overLD95 <- nrow(droplevels(subset(subdat, subdat$SPC_ShellLength >= as.integer(as.vector(ld95)))))
SamList$N.overLD95[i] <- as.numeric(N.overLD95)
SamList$N.overLD95[i] <- as.numeric(N.overLD95)
N.overLD25 <- nrow(droplevels(subset(subdat, subdat$SPC_ShellLength >= as.integer(as.vector(ld25)))))
N.overLD75 <- nrow(droplevels(subset(subdat, subdat$SPC_ShellLength >= as.integer(as.vector(ld75)))))
SamList$N.IQR[i]<-as.numeric(N.overLD25-N.overLD75)
SamList$N.overLD95[i] <- as.numeric(N.overLD95)
##
fname <- paste("logregplot",SamList$SiteCode[i], sep='_')
writeName <- paste(resdir,"/",fname, sep ="")
pdfName <- paste(writeName,".pdf", sep ="")
wmfName <- paste(writeName,".wmf", sep ="")
# logistic.graph <- plotgraph(SizeMat,SamList$LD50[i],SamList$SiteCode[i],SamList)#,savefile=T)
#
# # ggsave(wmfName, plot=logistic.graph, units="cm",width=16,height=18)
# # ggsave(pdfName, plot=logistic.graph, units="cm",width=16,height=18)
# #
# pdf(pdfName,width=6.5,height=6.5)
# plotgraph(SizeMat,SamList$LD50[i],SamList$SiteCode[i],SamList)#,savefile=T)
# dev.off()
#
# win.metafile(wmfName,width=6.5,height=6.5)
# plotgraph(SizeMat,SamList$LD50[i],SamList$SiteCode[i],SamList)#,savefile=T)
# dev.off()
## Bootstrap the ld50 paramater ####
BootSAM50 <- function(data, indices) {
require(MASS)
bootdata <- data[indices, ]
SizeMat <- table(bootdata$SPC_ShellLength, bootdata$Mat)
SizeMat <- as.data.frame(rbind(SizeMat))
SizeMat$ShellLength <- as.numeric(rownames(SizeMat))
SizeMat$Total <- SizeMat$I + SizeMat$M
SizeMat$MatRatio <- SizeMat$M/SizeMat$Total
model <- glm(MatRatio ~ ShellLength, family=binomial(link = "logit"), data = SizeMat, weights = Total)
#conRD <- -coef(model)[1]/coef(model)[2]
ld50 <- dose.p(model, p = 0.5)
return(ld50)
}
try({
booted.SAM <- boot(subdat,statistic=BootSAM50, R= 1000, parallel = "snow", ncpus = 8)
booted.SAM.CI <- boot.ci(booted.SAM,type="bca",conf = c(0.95))
boot95.lower <- format(booted.SAM.CI$bca[4],nsmall = 2)
boot95.upper <- format(booted.SAM.CI$bca[5],nsmall = 2)
SamList$Ld50BootU95[i] <- as.numeric(boot95.upper)
SamList$Ld50BootL95[i] <- as.numeric(boot95.lower)
})
## Bootstrap the ld75 paramater ####
BootSAM75 <- function(data, indices) {
require(MASS)
bootdata <- data[indices, ]
SizeMat <- table(bootdata$SPC_ShellLength, bootdata$Mat)
SizeMat <- as.data.frame(rbind(SizeMat))
SizeMat$ShellLength <- as.numeric(rownames(SizeMat))
SizeMat$Total <- SizeMat$I + SizeMat$M
SizeMat$MatRatio <- SizeMat$M/SizeMat$Total
model <- glm(MatRatio ~ ShellLength, family=binomial(link = "logit"), data = SizeMat, weights = Total)
#conRD <- -coef(model)[1]/coef(model)[2]
ld75 <- dose.p(model, p = 0.75)
return(ld75)
}
try({
booted.SAM <- boot(subdat,statistic=BootSAM75, R= 1000, parallel = "snow", ncpus = 8)
booted.SAM.CI <- boot.ci(booted.SAM,type="bca",conf = c(0.95))
boot95.lower <- format(booted.SAM.CI$bca[4],nsmall = 2)
boot95.upper <- format(booted.SAM.CI$bca[5],nsmall = 2)
SamList$Ld75BootU95[i] <- as.numeric(boot95.upper)
SamList$Ld75BootL95[i] <- as.numeric(boot95.lower)
})
## Bootstrap the ld85 paramater ####
BootSAM85 <- function(data, indices) {
require(MASS)
bootdata <- data[indices, ]
SizeMat <- table(bootdata$SPC_ShellLength, bootdata$Mat)
SizeMat <- as.data.frame(rbind(SizeMat))
SizeMat$ShellLength <- as.numeric(rownames(SizeMat))
SizeMat$Total <- SizeMat$I + SizeMat$M
SizeMat$MatRatio <- SizeMat$M/SizeMat$Total
model <- glm(MatRatio ~ ShellLength, family=binomial(link = "logit"), data = SizeMat, weights = Total)
#conRD <- -coef(model)[1]/coef(model)[2]
ld85 <- dose.p(model, p = 0.85)
return(ld85)
}
try({
booted.SAM <- boot(subdat,statistic=BootSAM85, R= 1000, parallel = "snow", ncpus = 8)
booted.SAM.CI <- boot.ci(booted.SAM,type="bca",conf = c(0.95))
boot95.lower <- format(booted.SAM.CI$bca[4],nsmall = 2)
boot95.upper <- format(booted.SAM.CI$bca[5],nsmall = 2)
SamList$Ld85BootU95[i] <- as.numeric(boot95.upper)
SamList$Ld85BootL95[i] <- as.numeric(boot95.lower)
})
## Bootstrap the ld90 paramater ####
BootSAM90 <- function(data, indices) {
require(MASS)
bootdata <- data[indices, ]
SizeMat <- table(bootdata$SPC_ShellLength, bootdata$Mat)
SizeMat <- as.data.frame(rbind(SizeMat))
SizeMat$ShellLength <- as.numeric(rownames(SizeMat))
SizeMat$Total <- SizeMat$I + SizeMat$M
SizeMat$MatRatio <- SizeMat$M/SizeMat$Total
model <- glm(MatRatio ~ ShellLength, family=binomial(link = "logit"), data = SizeMat, weights = Total)
#conRD <- -coef(model)[1]/coef(model)[2]
ld90 <- dose.p(model, p = 0.9)
return(ld90)
}
try({
booted.SAM <- boot(subdat,statistic=BootSAM90, R= 1000, parallel = "snow", ncpus = 8)
booted.SAM.CI <- boot.ci(booted.SAM,type="bca",conf = c(0.95))
boot95.lower <- format(booted.SAM.CI$bca[4],nsmall = 2)
boot95.upper <- format(booted.SAM.CI$bca[5],nsmall = 2)
SamList$Ld90BootU95[i] <- as.numeric(boot95.upper)
SamList$Ld90BootL95[i] <- as.numeric(boot95.lower)
})
## Bootstrap the ld95 paramater ####
BootSAM95 <- function(data, indices) {
require(MASS)
bootdata <- data[indices, ]
SizeMat <- table(bootdata$SPC_ShellLength, bootdata$Mat)
SizeMat <- as.data.frame(rbind(SizeMat))
SizeMat$ShellLength <- as.numeric(rownames(SizeMat))
SizeMat$Total <- SizeMat$I + SizeMat$M
SizeMat$MatRatio <- SizeMat$M/SizeMat$Total
model <- glm(MatRatio ~ ShellLength, family=binomial(link = "logit"), data = SizeMat, weights = Total)
#conRD <- -coef(model)[1]/coef(model)[2]
ld95 <- dose.p(model, p = 0.95)
return(ld95)
}
try({
booted.SAM <- boot(subdat,statistic=BootSAM95, R= 1000, parallel = "snow", ncpus = 8)
booted.SAM.CI <- boot.ci(booted.SAM,type="bca",conf = c(0.95))
boot95.lower <- format(booted.SAM.CI$bca[4],nsmall = 2)
boot95.upper <- format(booted.SAM.CI$bca[5],nsmall = 2)
SamList$Ld95BootU95[i] <- as.numeric(boot95.upper)
SamList$Ld95BootL95[i] <- as.numeric(boot95.lower)
})
}
SiteNames <- unique(samdata[,c(1:7,14)])
SamResults <- inner_join(SamList, SiteNames, by="SiteCode")
write.xlsx(SamResults, "C:/CloudStor/R_Stuff/Logistic/SamResultsBoot2019.xlsx")
##Size at Emergence ####
Shell <- as.data.frame(table(BlckPop$SPC_ShellCover))
shelldata <- subset(BlckPop, BlckPop$SPC_ShellCover %in% c("A","P","0","1","2","3"))
shelldata$Shell <- ifelse(shelldata$SPC_ShellCover %in% c("A","0"), c("C"), c("E"))
ShellAssess <- shelldata %>%
group_by(SiteCode, Shell) %>%
summarise(N = n()) %>%
spread(Shell, N)
ShellList <- subset(ShellAssess, (C >3 & E > 3))
## Remove sites with less than 150 records
ShellList <- droplevels(subset(ShellList, (C + E)> 150, drop=TRUE))
## Outlier Removal: Unusual large cryptic animals
pick <- which((shelldata$SPC_ShellLength >150) & (shelldata$Shell=="C"))
outlier <- shelldata[pick,]
shelldata <- shelldata[-pick,]
## Outlier Removal: Unusual small emergent animals
pick <- which((shelldata$SPC_ShellLength <60) & (shelldata$Shell=="E"))
outlier <- rbind(outlier,shelldata[pick,])
shelldata <- shelldata[-pick,]
ShellList$LD05 <- as.numeric(NA)
ShellList$LD25 <- as.numeric(NA)
ShellList$LD50 <- as.numeric(NA)
ShellList$LD75 <- as.numeric(NA)
ShellList$LD95 <- as.numeric(NA)
ShellList$IQR <- as.numeric(NA)
ShellList$N.underLD05 <- as.numeric(NA)
ShellList$N.underLD50 <- as.numeric(NA)
ShellList$N.overLD95 <- as.numeric(NA)
ShellList$Ld50BootU95 <- as.numeric(NA)
ShellList$Ld50BootL95 <- as.numeric(NA)
ShellList$Ld95BootU95 <- as.numeric(NA)
ShellList$Ld95BootL95 <- as.numeric(NA)
NumSites <- nrow(ShellList)
NumSites
i <- 324
#Loop through unique DiveId's
for (i in 1:NumSites) {
subdat <- droplevels(subset(shelldata, shelldata$SiteCode == ShellList$SiteCode[i]))
SizeShell <- table(subdat$SPC_ShellLength, subdat$Shell)
SizeShell <- as.data.frame(rbind(SizeShell))
SizeShell$ShellLength <- as.numeric(rownames(SizeShell))
head(SizeShell)
SizeShell$Total <- SizeShell$C + SizeShell$E
SizeShell$ShellRatio <- SizeShell$E/SizeShell$Total
r <- glm(ShellRatio ~ ShellLength, family=binomial(link = "logit"), data = SizeShell, weights = Total)
ld05 <- dose.p(r, p = 0.05)
ShellList$LD05[i] <- as.vector(ld05)
ld25 <- dose.p(r, p = 0.25)
ShellList$LD25[i] <- as.vector(ld25)
ld50 <- dose.p(r, p = 0.5)
ShellList$LD50[i] <- as.numeric(ld50)
ld75 <- dose.p(r, p = 0.75)
ShellList$LD75[i] <- as.vector(ld75)
ld95 <- dose.p(r, p = 0.95)
ShellList$LD95[i] <- as.numeric(ld95)
ShellList$IQR[i] <- ShellList$LD75[i] - ShellList$LD25[i]
N.underLD05 <- nrow(droplevels(subset(subdat, subdat$SPC_ShellLength < as.integer(as.vector(ld05)))))
ShellList$N.underLD05[i] <- as.numeric(N.underLD05)
N.underLD50 <- nrow(droplevels(subset(subdat, subdat$SPC_ShellLength < as.integer(as.vector(ld50)))))
ShellList$N.underLD50[i] <- as.numeric(N.underLD50)
N.overLD95 <- nrow(droplevels(subset(subdat, subdat$SPC_ShellLength >= as.integer(as.vector(ld95)))))
ShellList$N.overLD95[i] <- as.numeric(N.overLD95)
##
# fname <- paste("logregplot_em",SamList$SiteCode[i], sep='_')
# writeName <- paste(resdir,"/",fname, sep ="")
# pdfName <- paste(writeName,".pdf", sep ="")
# wmfName <- paste(writeName,".wmf", sep ="")
#
# ggsave(wmfName, plot=logistic.graph, units="cm",width=16,height=18)
# ggsave(pdfName, plot=logistic.graph, units="cm",width=16,height=18)
#
# pdf(pdfName,width=6.5,height=6.5)
# plotgraph(SizeShell,ShellList$LD50[i],ShellList$SiteCode[i],ShellList)#,savefile=T)
# dev.off()
#
# win.metafile(wmfName,width=6.5,height=6.5)
# plotgraph(SizeShell,ShellList$LD50[i],ShellList$SiteCode[i],ShellList)#,savefile=T)
# dev.off()
#
## Bootstrap the ld50 paramater
BootSEM50 <- function(data, indices) {
require(MASS)
bootdata <- data[indices, ]
SizeShell <- table(bootdata$SPC_ShellLength, bootdata$Shell)
SizeShell <- as.data.frame(rbind(SizeShell))
SizeShell$ShellLength <- as.numeric(rownames(SizeShell))
SizeShell$Total <- SizeShell$C + SizeShell$E
SizeShell$ShellRatio <- SizeShell$E/SizeShell$Total
model <- glm(ShellRatio ~ ShellLength, family=binomial(link = "logit"), data = SizeShell, weights = Total)
#conRD <- -coef(model)[1]/coef(model)[2]
ld50 <- dose.p(model, p = 0.5)
return(ld50)
}
try({
booted.SEM <- boot(subdat,statistic=BootSEM50, R= 1000, parallel = "snow", ncpus = 8)
booted.SEM.CI <- boot.ci(booted.SEM,type="bca",conf = c(0.95))
boot95.lower <- format(booted.SEM.CI$bca[4],nsmall = 2)
boot95.upper <- format(booted.SEM.CI$bca[5],nsmall = 2)
ShellList$Ld50BootU95[i] <- as.numeric(boot95.upper)
ShellList$Ld50BootL95[i] <- as.numeric(boot95.lower)
})
## Bootstrap the ld95 paramater
BootSEM95 <- function(data, indices) {
require(MASS)
bootdata <- data[indices, ]
SizeShell <- table(bootdata$SPC_ShellLength, bootdata$Shell)
SizeShell <- as.data.frame(rbind(SizeShell))
SizeShell$ShellLength <- as.numeric(rownames(SizeShell))
SizeShell$Total <- SizeShell$C + SizeShell$E
SizeShell$ShellRatio <- SizeShell$E/SizeShell$Total
model <- glm(ShellRatio ~ ShellLength, family=binomial(link = "logit"), data = SizeShell, weights = Total)
#conRD <- -coef(model)[1]/coef(model)[2]
ld95 <- dose.p(model, p = 0.95)
return(ld95)
}
try({
booted.SEM <- boot(subdat,statistic=BootSEM95, R= 1000, parallel = "snow", ncpus = 8)
booted.SEM.CI <- boot.ci(booted.SEM,type="bca",conf = c(0.95))
boot95.lower <- format(booted.SEM.CI$bca[4],nsmall = 2)
boot95.upper <- format(booted.SEM.CI$bca[5],nsmall = 2)
ShellList$Ld95BootU95[i] <- as.numeric(boot95.upper)
ShellList$Ld95BootL95[i] <- as.numeric(boot95.lower)
})
}
SiteNames <- unique(shelldata[,c(1:7,14)])
ShellResults <- merge(ShellList, SiteNames, by.x="SiteCode", by.Y="SiteCode", all.y=FALSE)
write.xlsx(ShellResults, "C:/CloudStor/R_Stuff/Logistic/SemResultsBoot2018.xlsx", sheetName="SEM", col.names=TRUE, row.names=TRUE, append=TRUE)
##Filter datasets to include only sites where there is a good spread of data
samfilt <- subset(SamList, N.underLD05 >15 & N.overLD95 > 15)
#shellfilt <- subset(ShellList, N.underLD05 >15 & N.overLD95 > 15)
colnames(samfilt) <- paste("SAM", colnames(samfilt), sep = ".")
shllListCols <- colnames(ShellList)
colnames(ShellList) <- paste("SEM", colnames(ShellList), sep = ".")
samfilt <- rename(samfilt, c("SAM.SiteCode"="SiteCode"))
ShellList <- rename(ShellList, c("SEM.SiteCode"="SiteCode"))
matched <- join(samfilt,ShellList,by="SiteCode",type="inner")
plot(matched$SAM.IQR ~ matched$SEM.IQR)
plot(matched$SAM.LD95 ~ matched$SEM.LD95)
write.xlsx(matched, "C:/CloudStor/R_Stuff/Logistic/SamResultsBoot.xlsx", sheet="SAM_SEM")
hist(SamList$LD50,breaks=10)
hist(SamList$LD95,breaks=10)
plot(SamList$LD50 ~ SamList$IQR)
plot(SamList$LD95 ~ SamList$IQR)
plot(SamList$N.underLD05 ~ SamList$IQR)
plot(SamList$N.overLD95 ~ SamList$IQR)
plot(SamList$LD50CR ~ SamList$IQR)
SamList$LD50CR <- SamList$Ld50BootU95 - SamList$Ld50BootL95