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pigeon prefdir bootstrap.R
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pigeon prefdir bootstrap.R
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########################################################################
########################################################################
# Last edited by B.Goller on 06 June 2016
# To do:
########################################################################
########################################################################
#Clear the workspace
rm(list = ls())
########################################################################
########################################################################
if(! "circular" %in% .packages(all=TRUE))
install.packages("circular")
library(circular)
########################################################################
########################################################################
#DEFINE FUNCTIONS
########################################################################
########################################################################
quick.mean <- function(x){return(mean(x,na.rm = TRUE))}
########################################################################
########################################################################
quick.stdev <- function(x){return(sd(x,na.rm = TRUE))}
########################################################################
########################################################################
angle.diff <- function(theta1, theta2){
delta.theta <- abs(theta1-theta2)
if(delta.theta > 180){delta.theta <- 360-delta.theta}
return(delta.theta)
}
########################################################################
########################################################################
diff.grid.mean <- function(x){
grid <- matrix(NA,length(x),length(x))
for(i in 1:length(x)){
for(j in 1:length(x)){
grid[i,j] <- angle.diff(x[i],x[j])
}
}
return(mean(grid[upper.tri(grid)]))
}
########################################################################
########################################################################
response.to.density <- function(dir, firing) {
#Use the firing to create repeats of the direction value
output <- NULL
num.dirs <- unique(dir)
rep.min <- min(as.numeric(lapply(num.dirs, FUN = function(x) length(which(dir == x)))))
for(i in 1:(length(num.dirs)*rep.min)) {
output <- c(output,rep(dir[i],round(10*firing[i])))
}
return(output)
}
########################################################################
########################################################################
dir.by.mean <- function(dir, firing){
v <- rep(0,8)
h <- rep(0,8)
unique.dir <- unique(dir)
rep.min <- min(as.numeric(lapply(unique.dir, FUN = function(x) length(which(dir == x)))))
#print(rep.min)
for(i in 1:length(unique.dir)){
#Vertical component of firing vector
v[i] <- sum(firing[which(dir == unique.dir[i])[1:rep.min]]*sin(-unique.dir[i]*pi/180))
#Horizontal component of firing vector
h[i] <- sum(firing[which(dir == unique.dir[i])[1:rep.min]]*cos(-unique.dir[i]*pi/180))
#rep.test[i] <- length(firing[which(dir == unique.dir[i])])
}
#calculate preferred direction as arctangent of the sums of vertical and horizontal components
pd <- atan(sum(v)/sum(h))*(180/pi)
ifelse(sum(v)>0, ifelse(sum(h)>0, pd.adj<-360-pd, pd.adj <- 180+abs(pd)),
ifelse(sum(h)>0,pd.adj <- abs(pd),pd.adj <- 180-abs(pd)))
return(c(sum(v),sum(h),pd, pd.adj))
}
########################################################################
########################################################################
simulate.cell.response <- function(file.name, reps){
raw.data <- read.csv(file.name, header = FALSE)
mean.data <- cbind(raw.data[,1:2],apply(raw.data[,3:22],1,quick.mean))
dirs <- unique(mean.data[!is.na(mean.data[,1]),1])
spds <- unique(mean.data[!is.na(mean.data[,2]),2])
print(length(dirs))
mean.base <- quick.mean(mean.data[is.na(mean.data[,2]),3])
mean.data <- cbind(mean.data,mean.data[,3]-mean.base)
grabber <- matrix(NA, length(dirs)*length(spds),3+reps)
grabber[,1] <- dirs
grabber[,2] <- spds
#check the sampling function arguments in other bootstrapping scripts...
for(i in 1:reps){
for(j in 1:nrow(grabber)){
subset <- mean.data[which(mean.data[,1]==grabber[j,1] & mean.data[,2]==grabber[j,2]),4]
grabber[j,3] <- length(subset)
grabber[j,i+3] <- mean(subset[sample(1:length(subset),length(subset),replace = TRUE)])
}
}
return(grabber)
}
########################################################################
########################################################################
dir <- INSERT PATH TO "PIGEON-PSTH-direction/"
setwd(dir)
set.seed(100)
pigeon.file.list <- list.files(pattern = "\\.csv$", ignore.case = TRUE)
repeats <- 1000
pigeon.sim.prefdir <- matrix(NA,length(pigeon.file.list),repeats)
pigeon.sim.prefdir2 <- matrix(NA,length(pigeon.file.list),repeats)
pigeon.nosim.prefdir <- matrix(NA,length(pigeon.file.list),4)
for(g in 1:length(pigeon.file.list)){
print(g)
#Get the overall value for the cell
raw.data <- read.csv(pigeon.file.list[g], header = FALSE)
mean.data <- cbind(raw.data[,1:2],apply(raw.data[,3:22],1,quick.mean))
mean.base <- quick.mean(mean.data[is.na(mean.data[,2]),3])
mean.data <- cbind(mean.data,mean.data[,3]-mean.base)
colnames(mean.data) <- c("dir", "pattern", "mean", "relmean")
cellfire <- aggregate(relmean~dir,mean.data,quick.mean)
cellfire$relfire <- cellfire$relmean + abs(min(cellfire$relmean))
onecell.circ <- response.to.density(cellfire$dir+270, cellfire$relfire)
ray.test <- rayleigh.test(circular(onecell.circ,type = "angles",units = "degrees",rotation = "clock"))
pigeon.nosim.prefdir[g,1] <- ray.test$p.value
if(ray.test$p.value<0.05){
pigeon.nosim.prefdir[g,3:4] <- as.numeric(mle.vonmises(circular(onecell.circ,type = "angles",units = "degrees",rotation = "clock"))[c("mu","kappa")])
pigeon.nosim.prefdir[g,2] <- dir.by.mean(cellfire$dir+270,cellfire$relmean)[4]
}
#####################
#Bootstrap
pigeon.sim.cell <- simulate.cell.response(pigeon.file.list[g],repeats)
#The plus-270 degrees rotates D. Wylie's coordinate system (up = 0, forward = 90)
#to match ours (forward = 0, up = 270)
for(h in 1:repeats){
cell.relfire <- pigeon.sim.cell[,3+h] + abs(min(pigeon.sim.cell[,3+h]))
cell.fire.circ <- response.to.density(pigeon.sim.cell[,1]+270, cell.relfire)
ray.test <- rayleigh.test(circular(cell.fire.circ,type = "angles",units = "degrees",rotation = "clock"))
if(as.numeric(ray.test["p.value"])<0.05){
pigeon.sim.prefdir2[g,h] <- as.numeric(mle.vonmises(circular(cell.fire.circ,type = "angles",units = "degrees",rotation = "clock"))["mu"])
prefdir.by.sum <- dir.by.mean((pigeon.sim.cell[,1]+270),pigeon.sim.cell[,2+h])
ifelse(prefdir.by.sum[4]>180,
pigeon.sim.prefdir[g,h] <- prefdir.by.sum[4]-360,
pigeon.sim.prefdir[g,h] <- prefdir.by.sum[4])
}
}
}
########################################################################
########################################################################
pigeon.circ <- circular(pigeon.nosim.prefdir[,2],type = "angles",units = "degrees",rotation = "clock")
pigeon.ray.test <- rayleigh.test(pigeon.circ)
nosim.pop <- mle.vonmises(circular(pigeon.circ,type = "angles",units = "degrees",rotation = "clock"))
plot(pigeon.circ, shrink = 1, stack = TRUE, main = sprintf("PIGEON nosim pref dir (n = %i)",length(pigeon.circ[!is.na(pigeon.circ)])))
rose.diag(pigeon.circ, bins = 24, add = TRUE)
########################################################################
########################################################################
pigeon.sim.pops <- matrix(NA,repeats,4)
for(k in 1:repeats){
pigeon.sim.pops[k,] <- c(k,as.numeric(rayleigh.test(circular(pigeon.sim.prefdir[,k],type = "angles",units = "degrees",rotation = "clock"))["p.value"]),
as.numeric(mle.vonmises(circular(pigeon.sim.prefdir[,k],type = "angles",units = "degrees",rotation = "clock"))[c("mu","kappa")]))
}
pigeon.pop.prefdirs.boot <- circular(pigeon.sim.pops[which(pigeon.sim.pops[,2] < 0.05),3],type = "angles",units = "degrees",rotation = "clock")
pigeon.pop.mle <- mle.vonmises(pigeon.pop.prefdirs.boot)
plot(pigeon.pop.prefdirs.boot, shrink = 1.5, bins = 24, stack = TRUE, main = sprintf("pigeon Bootstrapped populations: preferred direction (%i/%i were non-uniform)",length(which(pigeon.sim.pops[,2] < 0.05)),repeats))
rose.diag(pigeon.pop.prefdirs.boot, bins = 24, add = TRUE)
sorted.prefdir <- sort(pigeon.sim.pops[,3])
sorted.prefdir[c(51,949)]