Nothing

```
discANOVA<-function(formula, data, nboot = 500){
#
# Test the global hypothesis that for two or more independent groups,
# the corresponding discrete distributions are identical.
# That is, test the hypothesis that independent groups have identical
# multinomial distributions. A generalization of the Storer--Kim method is used.
#
# Could also use a chi-squared test via the function: disc2.chi.sq
#
# The method is designed for situations where the cardinality of the
# sample space is relatively small. The method can be sensitive to
# differences that are missed using a measure of location.
#
# Control over the Type I error probability is excellent, even when n=10
#
# 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])
#if(is.matrix(x) || is.data.frame(x))x=listm(x)
vals=lapply(x,unique)
vals=sort(elimna(list2vec(vals)))
K=length(unique(vals))
n=lapply(x,length)
n=list2vec(n)
J=length(x)
step1=discANOVA.sub(x)
test=step1$test
C1=step1$C1
HT=NULL
for(i in 1:K)HT[i]=mean(C1[i,])
tv=NULL
TB=NA
VP=NA
B1hat=NA
xx=list()
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)
}
TB[ib]=discANOVA.sub(xx)$test
}
pv=1-mean(test>TB)-.5*mean(test==TB)
result <- list(test = test, crit.val = NA, p.value = pv, call = cl)
class(result) <- "med1way"
result
}
```

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