Calculate & plot bootstrapped confidence intervals of each generation in a simulation

Question

I am running a simple genetic drift simulation in R.

# Population size
N<-5000
# Number with focal allele
X1<-(N/2)
# Number of generations
ngens<-(2000)
# Number of replicates
nreps<-10

# Drift function
drift <- function(N, X1, ngens, nreps) {
# Makes a matrix of NA's of nreps columns, and ngen rows
       p <- matrix(NA, nrow=ngens, ncol=nreps)
  # Set base population
       p[1,] <- X1/N
  # Repetitive sampling function, each generation sample 10 times from the generation before (gen-1)  
       for(gen in 2:ngens)
         p[gen,] <- rbinom(n=nreps, size=N, prob=p[gen-1,]) / N
       p
}
# Run function "drift" & output as data frame
p <- data.frame(drift(N, X1, ngens, nreps))
# Plot
matplot(p, type="l", ylim=c(0,1), lty=1, xlab="Generations", ylab="Proportion Focal",col="grey")
# Mean value
p$mean<-apply(p[,c(1:10)],1,mean)
matplot(p$mean, type="l", ylim=c(0,1), lty=1, xlab="Generations", ylab="Proportion Focal",col="black",add=T)

I would like to:

1. Calculate confidence intervals ( by bootstrapping ) around the mean for each generation
2. Add a pair of columns to my dataframe with the upper and lower confidence interval estimates which can then be plotted on the matplot just like I have done with the mean value

Can anyone suggest a way to do this? I am aware I need the Boot package and know roughly how to use this but guidance would be good.

The problem (for me) is getting a loop which generates a CI for each generation of the simulation and pasting that to the "p" dataframe

EDIT:

I have tried this as partly suggested by @bakyaw and the "for" loop adapted from an old script I used once.

myBootFunction<-function(x){
  b <- boot(x, function(u,i) mean(u[i]), R = 999)
  boot.ci(b, type = c("norm"))
}

meanList<-apply(p[c(2:ngens),c(1:nreps)],1,function(x)myBootFunction(x))
for(i in 1:49) {
  low<-meanList[[i]][[4]][[2]]
  high<-meanList[[i]][[4]][[3]]
  CIMatrix<-cbind(high,low)}

Note the addition of c(2:ngens), without out it this error comes up.

[1] "All values of t are equal to 0.5 \\n Cannot calculate confidence intervals"

However, this still only creates the CIMatrix as a 1x2 double matrix, rather than one with every generation in it.

Answer 1

This can stay as the answer if you have solved your problem. Boot function:

    library(boot)

    myBootFunction<-function(x){
        b <- boot(x, function(u,i) mean(u[i]), R = 999)
        boot.ci(b, type = c("norm", "basic", "perc"))
    }

then instead of this line in your code: p$mean<-apply(p[,c(1:10)],1,mean)

you can use :

meanList<-apply(p[,c(1:10)],1,function(x)myBootFunction(x))

After having the list with the confidence intervals you may convert it to a data frame and then work on it.