set.seed(290875)

library("party")

if (!require("mvtnorm"))

    stop("cannot load package mvtnorm")

### get rid of the NAMESPACE

attach(asNamespace("party"))

### 

###

###    Regression tests for conditional distributions

###    

###    functions defined in file `./src/Distributions.c'

### chisq-distribution of quadratic forms

t <- 2.1

df <- 2

storage.mode(t) <- "double"

storage.mode(df) <- "double"

stopifnot(isequal(1 - pchisq(t, df = df), ### P-values!!!

          .Call("R_quadformConditionalPvalue", t, df, PACKAGE = "party")))

stopifnot(isequal(2*pnorm(-t), 

          .Call("R_maxabsConditionalPvalue", t, matrix(1), as.integer(1), 0.0, 0.0, 0.0, PACKAGE = "party")))

maxpts <- 25000

storage.mode(maxpts) <- "integer"

abseps <- 0.0001

releps <- 0

tol <- 1e-10

a <- 1.96

b <- diag(2)

p1 <- .Call("R_maxabsConditionalPvalue", a, b, maxpts, abseps, releps, tol, PACKAGE = "party")

p2 <- pmvnorm(lower = rep(-a,2), upper = rep(a,2), corr = b)

stopifnot(isequal(round(p1, 3), round(1 - p2, 3)))

b <- diag(4)

p1 <- .Call("R_maxabsConditionalPvalue", a, b, maxpts, abseps, releps, tol, PACKAGE = "party")

p2 <- pmvnorm(lower = rep(-a,4), upper = rep(a,4), corr = b)

stopifnot(isequal(round(p1, 3), round(1 - p2, 3)))

b <- diag(4)

b[upper.tri(b)] <- c(0.1, 0.2, 0.3)

b[lower.tri(b)] <- t(b)[lower.tri(b)]

p1 <- .Call("R_maxabsConditionalPvalue", a, b, maxpts, abseps, releps, tol, PACKAGE = "party")

p2 <- pmvnorm(lower = rep(-a,4), upper = rep(a,4), corr = b)

stopifnot(isequal(round(p1, 3), round(1 - p2, 3)))

### Monte-Carlo approximation of P-Values, univariate

mydata = data.frame(y = gl(2, 50), x1 = rnorm(100),  

                    x2 = rnorm(100), x3 = rnorm(100))

inp <- initVariableFrame(mydata[,"x1",drop = FALSE], trafo = function(data)

ptrafo(data, numeric_trafo = rank))

resp <- initVariableFrame(mydata[,"y",drop = FALSE], trafo = NULL, response = TRUE)

ls <- new("LearningSample", inputs = inp, responses = resp,

          weights = rep(1, inp@nobs), nobs = nrow(mydata),

          ninputs = inp@ninputs)

tm <- ctree_memory(ls)

varctrl <- new("VariableControl")

varctrl@teststat <- factor("max", levels = c("max", "quad"))

varctrl@pvalue <- FALSE

gtctrl <- new("GlobalTestControl")

gtctrl@testtype <- factor("MonteCarlo", levels = levels(gtctrl@testtype))

gtctrl@nresample <- as.integer(19999)

pvals <- .Call("R_GlobalTest", ls, ls@weights, tm, varctrl, gtctrl, PACKAGE = "party")

wstat <- abs(qnorm(wilcox.test(x1 ~ y, data = mydata, 

             exact = FALSE, correct = FALSE)$p.value/2))

wpval <- wilcox.test(x1 ~ y, data = mydata, exact = TRUE)$p.value

stopifnot(isequal(wstat, pvals[[1]]))

stopifnot(abs(wpval - (1 - pvals[[2]])) < 0.01)

### Monte-Carlo approximations of P-Values, multiple inputs

mydata = data.frame(y = gl(2, 50), x1 = rnorm(100),  

                    x2 = rnorm(100), x3 = rnorm(100))

inp <- initVariableFrame(mydata[,c("x1", "x2", "x3"),

                                drop = FALSE], trafo = function(data)

ptrafo(data, numeric_trafo = rank))

resp <- initVariableFrame(mydata[,"y",drop = FALSE], trafo = NULL, response = TRUE)

ls <- new("LearningSample", inputs = inp, responses = resp,

          weights = rep(1, inp@nobs), nobs = nrow(mydata),

          ninputs = inp@ninputs)

tm <- ctree_memory(ls)

varctrl <- new("VariableControl")

varctrl@teststat <- factor("max", levels = c("max", "quad"))

varctrl@pvalue <- TRUE

gtctrl <- new("GlobalTestControl")

gtctrl@testtype <- factor("Univariate", levels = levels(gtctrl@testtype))

gtctrl@nresample <- as.integer(19999)

pvals <- .Call("R_GlobalTest", ls, ls@weights, tm, varctrl, gtctrl, PACKAGE = "party")

wstat <- c(abs(qnorm(wilcox.test(x1 ~ y, data = mydata, 

               exact = FALSE, correct = FALSE)$p.value/2)),

           abs(qnorm(wilcox.test(x2 ~ y, data = mydata, 

               exact = FALSE, correct = FALSE)$p.value/2)),

           abs(qnorm(wilcox.test(x3 ~ y, data = mydata, 

               exact = FALSE, correct = FALSE)$p.value/2)))

wpval <- c(wilcox.test(x1 ~ y, data = mydata, 

               exact = FALSE, correct = FALSE)$p.value,

           wilcox.test(x2 ~ y, data = mydata, 

               exact = FALSE, correct = FALSE)$p.value,

           wilcox.test(x3 ~ y, data = mydata, 

               exact = FALSE, correct = FALSE)$p.value)

stopifnot(isequal(wstat, pvals[[1]]))

stopifnot(isequal(wpval, 1 - pvals[[2]]))

### Monte-Carlo approximations of P-Values, min-P approach

gtctrl@testtype <- factor("MonteCarlo", levels = levels(gtctrl@testtype))

gtctrl@nresample <- as.integer(19999)

pvals <- .Call("R_GlobalTest", ls, ls@weights, tm, varctrl, gtctrl, PACKAGE = "party")

stopifnot(isequal(wstat, pvals[[1]]))

stopifnot(all(wpval < (1 - pvals[[2]])))