set.seed(290875)

library("party")

if (!require("TH.data"))

    stop("cannot load package TH.data")

if (!require("coin"))

    stop("cannot load package coin")

data("GlaucomaM", package = "TH.data")

rf <- cforest(Class ~ ., data = GlaucomaM, control = cforest_unbiased(ntree = 30))

stopifnot(mean(GlaucomaM$Class != predict(rf)) < 

          mean(GlaucomaM$Class != predict(rf, OOB = TRUE)))

data("GBSG2", package = "TH.data")

rfS <- cforest(Surv(time, cens) ~ ., data = GBSG2, control = cforest_unbiased(ntree = 30))

treeresponse(rfS, newdata = GBSG2[1:2,])

### give it a try, at least

varimp(rf, pre1.0_0 = TRUE)

P <- proximity(rf)

stopifnot(max(abs(P - t(P))) == 0)

P[1:10,1:10]

### variable importances

a <- cforest(Species ~ ., data = iris,

             control = cforest_unbiased(mtry = 2, ntree = 10))

varimp(a, pre1.0_0 = TRUE)

varimp(a, conditional = TRUE)

airq <- subset(airquality, complete.cases(airquality))

a <- cforest(Ozone ~ ., data = airq,

             control = cforest_unbiased(mtry = 2, ntree = 10))

varimp(a, pre1.0_0 = TRUE)   

varimp(a, conditional = TRUE)

data("mammoexp", package = "TH.data")

a <- cforest(ME ~ ., data = mammoexp, control = cforest_classical(ntree = 10))

varimp(a, pre1.0_0 = TRUE)   

varimp(a, conditional = TRUE)

stopifnot(all.equal(unique(sapply(a@weights, sum)), nrow(mammoexp)))

### check user-defined weights

nobs <- nrow(GlaucomaM)

i <- rep(0.0, nobs)

i[1:floor(.632 * nobs)] <- 1

folds <- replicate(100, sample(i))

rf2 <- cforest(Class ~ ., data = GlaucomaM, control = cforest_unbiased(ntree = 100), weights = folds)

table(predict(rf), predict(rf2))