math4753cofe0003:

#' @Title boostrap 2 functiuon
#'
#' @param iter number of iterations
#' @param x sample size
#' @param fun function being calculated
#' @param alpha the confidence
#' @param xstat the size of the interval
#' @param ... addtion of extra params
#'
#' @return a histogram with the point of estimate
#' @export
#'
#' @examples
myboot2<-function(iter=10000,x,fun="mean",alpha=0.05,xstat,...){

  #Notice where the ... is repeated in the code
  n=length(x)   #sample size

  #Now sample with replacement
  y=sample(x,n*iter,replace=TRUE) #this takes a sample given the passed parameters

  # Make a matrix with all the resampled values
  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)) #it saves the resuly of the quantile into the object ci
  # 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="Histogram of Bootstrap sample statistics",...)

  #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=3)
  text(ci[2],0,paste(round(ci[2],2),")",sep=""),col="Red",cex=3)

  # plot the point estimate 1/2 way up the density
  text(pte,max(para$density)/2,round(pte,2),cex=3)

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