Nothing
`chisqClass2` <-
function(data,cl,n.cat,same.ncat=FALSE,compPval=TRUE){
if(any(is.na(cl)))
stop("No missing values allowed in cl.")
if(missing(n.cat))
n.cat<-max(data,na.rm=TRUE)
uni.cl<-sort(unique(cl))
n.lev<-length(uni.cl)
if(length(n.lev)>10)
stop("cl contains more than 10 different values.")
if(any(uni.cl!=1:n.lev))
stop("The labels of the classes must be 1 to ",n.lev,".")
n.obs<-ncol(data)
if(length(cl)!=n.obs)
stop("The length of cl must be equal to the number of observations.")
CL<-matrix(0,n.obs,n.lev)
for(i in 1:n.lev)
CL[cl==i,i]<-1
if(any(colSums(CL)<2))
stop("There must be at least two observations per class.")
listCells<-vector("list",n.cat)
stats<-numeric(nrow(data))
if(any(is.na(data))){
anyna<-TRUE
mat.na<-!is.na(data)
vec.ncl<-mat.na%*%CL
n.obs<-rowSums(mat.na)
}
else{
mat.na<-TRUE
vec.ncl<-colSums(CL)
anyna<-FALSE
}
if(compPval)
df<-numeric(nrow(data))
for(i in 1:n.cat){
mat.i<-mat.na & data==i
N<-mat.i%*%CL
rS<-rowSums(N)
if(same.ncat && any(rS==0))
stop("All rows of data must show the same number of categories.",call.=FALSE)
tmp<-if(anyna) rS*vec.ncl/n.obs else rS%*%t(vec.ncl)/n.obs
tmp[tmp==0]<-1
tmp2<-rowSums(N*N/tmp)
stats<-tmp2+stats
if(compPval)
df <- df + (rowSums(mat.i)>0)
}
names(stats)<-rownames(data)
stats<-stats-n.obs
if(!compPval)
return(stats)
df<-(df-1)*(n.lev-1)
rawp<-pchisq(stats,df,lower.tail=FALSE)
rawp[df==0] <- 1
structure(list(stats=stats,df=df,rawp=rawp))
}
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