R/myboot2.R

In casspants/MATH4753kran1018: MATH4753 Package

#' Bootstrap
#'
#' Runs a bootstrap analysis of the given data based on the function you would like to run.
#' Provides a histogram of the bootstrap analysis with confidence interval labels
#'
#' @param iter number of iterations
#' @param x the vector of data
#' @param fun the function to be used (e.g. "mean")
#' @param alpha alpha value; for the confidence interval
#' @param cx font size of histogram labels
#' @param ... other formatting options
#'
#' @return
#' @export
#'
#' @examples myboot2(iter=10000,x,fun="mean",alpha=0.05,cx=1.5,...)
#' With 10000 iterations of the data stored in x with the function
#' performing a bootstrap for the mean of the data stored in x.
#' This example uses an alpha level of 0.05, so it has a 95%
#' confidence interval. It has a font size setting of 1.5.
myboot2<-function(iter,x,fun,alpha,cx,...){  #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)

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