set.seed(290875)

library("party")

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

    stop("cannot load package TH.data")

if (!require("coin"))

    stop("cannot load package coin")

### get rid of the NAMESPACE

attach(asNamespace("party"))

gtctrl <- new("GlobalTestControl")

tlev <- levels(gtctrl@testtype)

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

gtree <- ctree(Class ~ ., data = GlaucomaM)

tree <- gtree@tree

stopifnot(isequal(tree[[5]][[3]], 0.059))

predict(gtree)

# print(tree)

stump <- ctree(Class ~ ., data = GlaucomaM, 

               control = ctree_control(stump = TRUE))

print(stump)

data(treepipit, package = "coin")

tr <- ctree(counts ~ ., data = treepipit)

tr

plot(tr)

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

tr <- ctree(Class ~ ., data = GlaucomaM)

tr

plot(tr)

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

GBSG2tree <- ctree(Surv(time, cens) ~ ., data = GBSG2)

GBSG2tree

plot(GBSG2tree)

plot(GBSG2tree, terminal_panel = node_surv(GBSG2tree))

survfit(Surv(time, cens) ~ as.factor(GBSG2tree@where), data = GBSG2)

names(GBSG2)

tr <- ctree(Surv(time, cens) ~ ., data = GBSG2, 

            control = ctree_control(teststat = "max", 

                                    testtype = "Univariate"))

tr

plot(tr)

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

attr(mammoexp$ME, "scores") <- 1:3   

attr(mammoexp$SYMPT, "scores") <- 1:4

attr(mammoexp$DECT, "scores") <- 1:3 

names(mammoexp)[names(mammoexp) == "SYMPT"] <- "symptoms"

names(mammoexp)[names(mammoexp) == "PB"] <- "benefit"

names(mammoexp)

tr <- ctree(ME ~ ., data = mammoexp)

tr

plot(tr)

treeresponse(tr, newdata = mammoexp[1:5,])

### check different user interfaces

data("iris")

x <- as.matrix(iris[,colnames(iris) != "Species"])

y <- iris[,"Species"]

newx <- x

ls <- LearningSample(x, y)

p1 <- unlist(treeresponse(ctree(Species ~ ., data = iris), newdata = as.data.frame(newx)))

p2 <- unlist(treeresponse(ctreefit(ls, control = ctree_control()), newdata = as.matrix(newx)))

stopifnot(identical(max(abs(p1 - p2)), 0))

set.seed(29)

p1 <- unlist(treeresponse(cforestfit(ls, control = cforest_unbiased(mtry = 1)), newdata = as.matrix(newx)))

set.seed(29)

p2 <- unlist(treeresponse(cforest(Species ~ ., data = iris, control = cforest_unbiased(mtry = 1)), 

             newdata = as.data.frame(newx)))

stopifnot(identical(max(abs(p1 - p2)), 0))