#' @Title bootpvalue
#'
#' @param x values vector
#' @param conf.level level of confidence
#' @param iter
#' @param mu0 mean
#' @param test
#'
#' @return
#' @export
#'
#'
bootpval<-function(x,conf.level=0.95,iter=3000,mu0=0, test="two"){
n=length(x)
y=x-mean(x)+mu0 # transform the data so that it is centered at the NULL
rs.mat<-c() #rs.mat will become a resample matrix -- now it is an empty vector
xrs.mat<-c()
for(i in 1:iter){ # for loop - the loop will go around iter times
rs.mat<-cbind(rs.mat,sample(y,n,replace=TRUE)) #sampling from y cbind -- column bind -- binds the vectors together by columns
xrs.mat<-cbind(xrs.mat,sample(x,n,replace=TRUE)) #sampling from x cbind -- column bind -- binds the vectors together by columns
}
tstat<-function(z){ # The value of t when the NULL is assumed true (xbar-muo)/z/sqrt(n)
sqrt(n)*(mean(z)-mu0)/sd(z)
}
tcalc=tstat(x) # t for the data collected
ytstat=apply(rs.mat,2,tstat) # tstat of resampled y's, ytstat is a vector and will have iter values in it
xstat=apply(xrs.mat,2,mean) # mean of resampled x's
alpha=1-conf.level # calculating alpha
ci=quantile(xstat,c(alpha/2,1-alpha/2))# Nice way to form a confidence interval
pvalue=ifelse(test=="two",length(ytstat[ytstat>abs(tcalc) | ytstat < -abs(tcalc)])/iter,
ifelse(test=="upper",length(ytstat[ytstat>tcalc])/iter,
length(ytstat[ytstat<xstat])/iter))
h=hist(ytstat,plot=FALSE)
mid=h$mid
if(test=="two"){
ncoll=length(mid[mid<= -abs(tcalc)])
ncolr=length(mid[mid>= abs(tcalc)])
col=c(rep("Green",ncoll),rep("Gray",length(mid)-ncoll-ncolr),rep("Green",ncolr))
}
if(test=="upper"){
ncolr=length(mid[mid>= abs(tcalc)])
col=c(rep("Gray",length(mid)-ncolr),rep("Green",ncolr))
}
if(test=="lower"){
ncoll=length(mid[mid<= -abs(tcalc)])
col=c(rep("Green",ncoll),rep("Gray",length(mid)-ncoll))
}
hist(ytstat,col=col,freq=FALSE,las=1,main="",xlab=expression(T[stat]))
#segments(ci[1],0,ci[2],0,lwd=2)
pround=round(pvalue,4)
title(substitute(paste(P[value],"=",pround)))
return(list(pvalue=pvalue,tcalc=tcalc,n=n,x=x,test=test,ci=ci))
}
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