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% \VignetteIndexEntry{Validation of choplump R package}
% \VignetteKeyword{validation}
% \VignetteKeyword{htest}

<<PreliminaryCalculations,echo=FALSE,hide=TRUE>>=
#source("H:\\main\\methods\\choplump\\r\\chop.functions.R")
library(choplump)
SEED<-1:200
@

\begin{document}
\baselineskip 12pt




{\Large Validation of choplump R package}

by Michael P. Fay \\
\today



\section*{Summary}

This document outlines several ways we have tested the choplump R package. 

\begin{itemize}
\item In Section~\ref{sec-exact.two.ways} we calculate the exact choplump tests in two ways. One method is a crude 
and slower method that is easier to program, and the other method is the faster method which is used for the final
\texttt{choplump} function. We test both methods with small sample sizes and get the same answers. 
\item In Section~\ref{sec-wilcox.compare} we show that when we use the methods outlined in the \texttt{computation}  
vignette to calculate the usual Wilcoxon Rank sum test (but with many tied zeros), we obtain the same answer
as from the previously developed \texttt{coin} package (Hothorn,  Hornik,
  van de Wiel and  Zeileis, 2006).
\item In Section~\ref{sec-approx.vs.exact} we show that the asymptotic approximation to the p-value 
closely matches the exact p-value for sample sizes as small as $10$ 
total non-zero responses in both groups. 
\end{itemize}



\section{Calculate Exact Test Two Ways}
\label{sec-exact.two.ways}

In this section, we introduce the \texttt{choplumpGeneral} function which is a slow version of the \texttt{choplump} test. 
It's structure closely matches the general description of the choplump test.  The key to this function is the \texttt{chooseMatrix} 
function. The call \texttt{chooseMatrix(N,M)} produces a \texttt{choose(N,M)} by $N$ matrix with each row a different permutation of 
 $M$ ones and $N-M$ zeros. 
 
For example:
<<>>=
chooseMatrix(5,2)
@

Here is the general chopping function and the general choplump function:
<<echo=TRUE>>=
chopGeneral
choplumpGeneral
@

Now we calculate some p-values from some small data sets  using both the \texttt{choplumpGeneral} function and the final \texttt{choplump} 
function. We show that both give the same answers.

<<>>=
make.data<-function(N,M,SEED){
    set.seed(SEED)
    Z<-rep(0,N)
    Z[sample(1:N,N/2,replace=FALSE)]<-1
    test<-data.frame(W=c(rep(0,N-M),abs(rnorm(M))),Z=Z)
    return(test)
}

test<-make.data(10,6,SEED[1])
test
choplumpGeneral(test$W,test$Z,testfunc=testfunc.wilcox.ties.general)
cout<-choplump(W~Z,data=test,use.ranks=TRUE,exact=TRUE)
cout
cout$p.values
@

Now we show the equivalence of the two functions for the difference in means test. Note to match directions we use the 
negative of the \texttt{TDiM} function. 

<<>>= 
testfunc.DiM.general<-function(d){
    -TDiM(d$W,d$Z)
}
choplumpGeneral(test$W,test$Z,testfunc=testfunc.DiM.general)
choplump(W~Z,data=test,use.ranks=FALSE,exact=TRUE)$p.values
@


\section{Calculate Exact Wilcoxon Rank sum test using Similar methods}
\label{sec-wilcox.compare}

In this section we show how we can use similar methods to those used in the choplump package 
to calculate either exact or approximate Wilcoxon rank sum test p-values.
Then we can check out that the exact  p-values match those output from \texttt{wilcox}\_\texttt{test} in the \texttt{coin}
package. In the process, we show how our algorithm can be faster in some cases when there are very many zeros.  

<<echo=TRUE>>=
library(coin)
test<-make.data(20,12,SEED[1])
test
wilcox.manyzeros.exact(W=test$W,Z=test$Z)
test2<-data.frame(W=test$W,Z=as.factor(test$Z))
wilcox_test(W~Z,data=test2,distribution="exact",alternative="less")
wilcox_test(W~Z,data=test2,distribution="exact",alternative="greater")
wilcox_test(W~Z,data=test2,distribution="exact",alternative="two.sided")

test<-make.data(1000,12,SEED[2])
t0<-proc.time()
wilcox.manyzeros.exact(W=test$W,Z=test$Z)
## time for our algorithm
proc.time()-t0
test2<-data.frame(W=test$W,Z=as.factor(test$Z))
t1<-proc.time()
wilcox_test(W~Z,data=test2,distribution="exact",alternative="two.sided")
## time for coin algorithm
proc.time()-t1
@


\section{Compare Asymptotic Approximation to Exact Calculation} 
\label{sec-approx.vs.exact}

To see how well the approximation performs, 
we simulate 200 data sets with $N=100$ and $M=10$. 
For this small sample size we can calculate the exact p-values. 
We randomly assign $N/2$ of the $Z_i$ values to 1 and the others are 0. 
We take pseudo-random numbers for the $X_i$, where  $X_i = |X_i^{\dagger}|$ and $X_i^{\dagger} \sim N(0,1)$.
We plot the bias (approximate p-value minus exact p-value) by the exact p-values in Figure~\ref{fig:approx.vs.exact}, together with 
the 95\% interquantile ranges of the bias.  
<<echo=FALSE>>=
test<-function(N,M,SEED,Use.Ranks=TRUE){
    out.approx<-out.exact<-rep(NA,length(SEED))
    for (i in 1:length(SEED)){
        set.seed(SEED[i])
        test<-make.data(N,M,SEED[i])
        out.approx[i]<-choplump(W~Z,data=test,use.ranks=Use.Ranks,method="approx")$p.values[1]
        out.exact[i]<-choplump(W~Z,data=test,use.ranks=Use.Ranks,method="exact",printNumCalcs=FALSE)$p.values[1]
    }
    out<-data.frame(plower.approx=out.approx,plower.exact=out.exact)
    return(out)
}
tout.ranks<-test(100,10,1:200,Use.Ranks=TRUE)
#tout.ranks
tout.noranks<-test(100,10,1:200,Use.Ranks=FALSE)
#tout.noranks
qrank<-quantile(tout.ranks[,1]-tout.ranks[,2],probs=c(.025,.975))
qnor<-quantile(tout.noranks[,1]-tout.noranks[,2],probs=c(.025,.975))
@
We see that even when $M$ is as small as $10$, 
the approximation does fairly well, with the 95\% interquantile range of the bias for the rank
 tests equal to (\Sexpr{round(qrank[1],4)},\Sexpr{round(qrank[2],4)}), and the similar statistic for the 
difference in means tests equal to (\Sexpr{round(qnor[1],4)},\Sexpr{round(qnor[2],4)}).



\begin{figure}
\caption{Comparison of Asymptotic Approximation and Exact P-values, Circles (solid lines) are 
Rank Tests and Triangles (dotted lines) are Difference in Means Tests.
Lines enclose middle 95\% of bias (asymptotic-exact).
 \label{fig:approx.vs.exact} }
<<figureApproxVsExact,fig=TRUE,echo=FALSE>>=
#plot(tout.ranks[,1],tout.ranks[,2],xlim=c(0,1),ylim=c(0,1),xlab="Approximate p-value",ylab="Exact p-value",pch=1,cex=1.2)
#points(tout.noranks[,1],tout.noranks[,2],pch=15,cex=1.2)
#lines(c(0,1),c(0,1),lty=2,col="gray")

plot(c(tout.ranks[,2],tout.noranks[,2]),c(tout.noranks[,1]-tout.noranks[,2],tout.ranks[,1]-tout.ranks[,2]),xlim=c(0,1),type="n",
    ylab="(Approximate p-value) - (Exact p-value)",xlab="Exact p-value")
points(tout.ranks[,2],tout.ranks[,1]-tout.ranks[,2],pch=1,cex=1.0)
points(tout.noranks[,2],tout.noranks[,1]-tout.noranks[,2],pch=2,cex=1.0)

lines(c(-1,2),rep(qrank[1],2),lty=1,col="gray")
lines(c(-1,2),rep(qrank[2],2),lty=1,col="gray")
lines(c(-1,2),rep(qnor[1],2),lty=2,col="gray")
lines(c(-1,2),rep(qnor[2],2),lty=2,col="gray")


#plot(c(.5*tout.ranks[,2]+.5*tout.ranks[,1],.5*tout.noranks[,2]+.5*tout.noranks[,1]),c(tout.noranks[,1]-tout.noranks[,2],tout.ranks[,1]-tout.ranks[,2]),xlim=c(0,1),type="n",ylab="Approximate p value - Exact p value",xlab="Average of Approimate and Exact p value")
#points(.5*tout.ranks[,2]+.5*tout.ranks[,1],tout.ranks[,1]-tout.ranks[,2],pch=1,cex=1.2)
#points(.5*tout.noranks[,2]+.5*tout.noranks[,1],tout.noranks[,1]-tout.noranks[,2],pch=2,cex=1.2)
#qrank<-quantile(tout.ranks[,1]-tout.ranks[,2],probs=c(.025,.975))
#qnor<-quantile(tout.noranks[,1]-tout.noranks[,2],probs=c(.025,.975))
#lines(c(-1,2),rep(qrank[1],2),lty=1,col="gray")
#lines(c(-1,2),rep(qrank[2],2),lty=1,col="gray")
#lines(c(-1,2),rep(qnor[1],2),lty=2,col="gray")
#lines(c(-1,2),rep(qnor[2],2),lty=2,col="gray")


@
\end{figure}


\section*{References}

\begin{description}
\item  Torsten Hothorn, Kurt Hornik, Mark A.
  van de Wiel and Achim Zeileis (2006). A
  Lego System for Conditional Inference.
  {\it The American Statistician} {\bf 60:} 
  257-263.
  
\end{description}
\end{document}
