# R/discmcp.R In WRS2: A Collection of Robust Statistical Methods

```discmcp <- function(formula, data, alpha = 0.05, nboot = 500){
#
#   Multiple comparisons for  J independent groups
#   having discrete distributions.
#   The method is based on a chi-squared test for each pair of groups to be compared
#
#   The data are assumed to be stored in x
#   which either has list mode or is a matrix.  In the first case
#   x[[1]] contains the data for the first group, x[[2]] the data
#   for the second group, etc. Length(x)=the number of groups = J.
#   If stored in a matrix, the columns of the matrix correspond
#   to groups.
#
#   Missing values are allowed.
#
# Probability of one or more Type I errors controlled using Hochberg's method.
#

if (missing(data)) {
mf <- model.frame(formula)
} else {
mf <- model.frame(formula, data)
}
cl <- match.call()

x <- split(model.extract(mf, "response"), mf[,2])

J<-length(x)
ncon=(J^2-J)/2
Jm<-J-1
#
# Determine critical values
dvec=alpha/c(1:ncon)

output<-as.data.frame(matrix(NA,nrow=ncon,ncol=4))
colnames(output)<-c('Group 1','Group 2','p.value','p.crit')
ic=0
for(j in 1:J){
for(k in 1:J){
if(j<k){
ic=ic+1
#output[ic,1]=j
output[ic,1] <- levels(mf[,2])[j]
#output[ic,2]=k
output[ic,2] <- levels(mf[,2])[k]
output[ic,3]=disc2com(x[[j]],x[[k]],simulate.p.value = TRUE, B=nboot)\$p.value
}}}
temp2<-order(0-output[,3])
zvec<-dvec[1:ncon]
#sigvec<-(test[temp2]>=zvec)
output[temp2,4]<-zvec
num.sig<-sum(output[,3]<=output[,4])

outtable <- output
result <- list(partable = outtable, call = cl)
class(result) <- "robtab"
result

## FIXME output
}
```

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WRS2 documentation built on May 2, 2019, 4:46 p.m.