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ccp=function(y,d,method="multinom",...){
# transform to numeric&matrix
y=factor(y)
y_levels=levels(y)
y=as.numeric(y)
d=data.matrix(d)
#determine label vector pv from y, d and method
if(method=="multinom"){
#require(nnet)
fit = nnet::multinom(y~d,...)
pv=predict(fit,type='class')
}else if(method=="tree"){
#require(rpart)
y = as.factor(y)
fit = rpart::rpart(y~d,...)
pv = predict(fit,type="class")
}else if(method=="svm"){
#require(e1071)
y = as.factor(y)
fit = e1071::svm(y~d,...)
pv = predict(fit)
}else if(method=="lda"){
#require(MASS)
fit = MASS::lda(y~d,...)
predict.test.fit = predict(fit)
pv = predict.test.fit$class
}else if(method=="label"){
temp = sum(d %in% 1:length(unique(y)))
if (temp!=length(d)){
cat("ERROR: The input value \"d\" should be a label vector encoded same as y.")
return(NULL)
}
pv=d
}else if(method=="prob"){
l=max.col(d)
for(i in 1:nrow(d)){
if (length(which(max(d[i,])==d[i,]))>1){
cat("WARNING: there exists two same max probability in one sample.\n")
break
}
}
pv=l
}
k=length(unique(y))
ns=sapply(1:k, function(i) sum(y==i) )
nn=sum(ns)
ros=ns/nn
ccps=sapply(1:k, function(i) sum(y==i & pv==i)/ns[i] )
ccp=sum(ccps*ros)
df=data.frame(CATEGORIES=sapply(1:k, function(i) y_levels[i]),VALUES=ccps,PREVALENCE=ros)
result=list(call=match.call(),measure=ccp,table=df)
class(result)="mcca.ccp"
return(result)
}
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