Nothing
discstep<-function(formula, data, nboot = 500, alpha = 0.05, ...){
#
# Step-down multiple comparison procedure for comparing
# J independent discrete random variables.
# The method is based on a generalization of the Storer--Kim method
# comparing independent binomials; it can be sensitive to differences
# not detected by measures of location.
#
# x is a matrix with n rows and J columns
# or it can have list mode
#
if (missing(data)) {
mf <- model.frame(formula)
} else {
mf <- model.frame(formula, data)
}
cl <- match.call()
x <- split(model.extract(mf, "response"), mf[,2])
vals=lapply(x,unique)
vals=sort(elimna(list2vec(vals)))
K=length(unique(vals))
n=lapply(x,length)
n=list2vec(n)
J=length(x)
if(J==2)stop('For 2 groups use disc2com')
if(J>5)stop('Designed for 5 groups or less')
com <- com1 <- modgen(J)
## insert group labels
lnames <- levels(mf[,2])
com <- lapply(com, function(xx) lnames[xx])
ntest=length(com)
jp1=J+1
com=com[jp1:length(com)]; com1 <- com1[jp1:length(com1)]
ntest=length(com)
mout= as.data.frame(matrix(NA,nrow=ntest,ncol=3))
colnames(mout)=c('Groups','p-value','p.crit')
test=NULL
for(i in 1:ntest){
test[i]=discANOVA.sub(x[com[[i]]])$test #$
nmod=length(com[[i]])-1
temp=c(nmod:0)
#mout[i,1]=sum(com[[i]]*10^temp)
mout[i,1] <- paste(com[[i]], collapse = "-")
}
mout[,3]=alpha
xx=list()
pv=NA
jm2=J-2
mout[,3]=alpha
TB=matrix(NA,nrow=nboot,ncol=ntest)
step1=discANOVA.sub(x)
C1=step1$C1
HT=NULL
for(i in 1:K)HT[i]=mean(C1[i,])
for(ib in 1:nboot){
xx=list()
for(j in 1:J){
temp=rmultinomial(n[j],1,HT)
xx[[j]]=which(temp[1,]==1)
for(i in 2:n[j])xx[[j]][i]=which(temp[i,]==1)
}
for(k in 1:ntest)TB[ib,k]=discANOVA.sub(xx[com1[[k]]])$test #$
}
for(k in 1:ntest){
mout[k,2]=1-mean(test[k]>TB[,k])-.5*mean(test[k]==TB[,k])
pnum=length(com[[k]])
pe=1-(1-alpha)^(pnum/J)
if(length(com[[k]])<=jm2)mout[k,3]=pe
}
outtable <- mout[nrow(mout):1,]
rownames(outtable) <- NULL
result <- list(partable = outtable, alpha = alpha, call = cl)
class(result) <- "robtab"
result
}
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