R/myBoot2.R

In cbain1/MATH4753BAIN:

#' @title myboot2
#'
#' @param iter The number of iterations you want to run
#' @param x The data
#' @param fun The function you wish to use
#' @param alpha The alpha level
#'
#' @return A plot containing the bootstrap information as well as the confidence interval around your given statistic at the 1-alpha level.
#' @export
#'
myboot2<-function(iter=10000,x,fun="mean",alpha=0.05,cx=1.5,...){  #Notice where the ... is repeated in the code
	n=length(x)   #sample size

	y=sample(x,n*iter,replace=TRUE)
	rs.mat=matrix(y,nr=n,nc=iter,byrow=TRUE)
	xstat=apply(rs.mat,2,fun) # xstat is a vector and will have iter values in it
	ci=quantile(xstat,c(alpha/2,1-alpha/2))# Nice way to form a confidence interval
	# A histogram follows
	# The object para will contain the parameters used to make the histogram
	para=hist(xstat,freq=FALSE,las=1,
			  main=paste("Histogram of Bootstrap sample statistics","\n","alpha=",alpha," iter=",iter,sep=""),
			  ...)

	#mat will be a matrix that contains the data, this is done so that I can use apply()
	mat=matrix(x,nr=length(x),nc=1,byrow=TRUE)

	#pte is the point estimate
	#This uses whatever fun is
	pte=apply(mat,2,fun)
	abline(v=pte,lwd=3,col="Black")# Vertical line
	segments(ci[1],0,ci[2],0,lwd=4)      #Make the segment for the ci
	text(ci[1],0,paste("(",round(ci[1],2),sep=""),col="Red",cex=cx)
	text(ci[2],0,paste(round(ci[2],2),")",sep=""),col="Red",cex=cx)

	# plot the point estimate 1/2 way up the density
	text(pte,max(para$density)/2,round(pte,2),cex=cx)

	invisible(list(ci=ci,fun=fun,x=x,xstat=xstat))# Some output to use if necessary
}