R/myboot2.R

In calwatson40/wats0079MATH4753: Compilation of MATH4753 functions

#' @title Boostrap Confidence Intervals for Sampling
#'
#' @param iter The number of iterations taken
#' @param x The vector of statistics
#' @param fun The function used to manipulate statistics
#' @param alpha The percentage from 0 or 100 to determine the interval
#' @param cx The x-axis coordinate of a center point
#' @param ... Any additional number of named or unnamed arguments
#'
#' @return A graphical representation of a bootstrap confidence interval
#' @importFrom stats quantile
#' @importFrom graphics hist abline segments text
#' @export
#'
#' @examples
#' \dontrun{myboot2(x=ddt$DDT)}
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,nrow=n,ncol=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,nrow=length(x),ncol=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))# Some output to use if necessary
}