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+++ b/partyMod/tests/Distributions.Rout.save
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+
+R Under development (unstable) (2014-06-29 r66051) -- "Unsuffered Consequences"
+Copyright (C) 2014 The R Foundation for Statistical Computing
+Platform: x86_64-unknown-linux-gnu (64-bit)
+
+R is free software and comes with ABSOLUTELY NO WARRANTY.
+You are welcome to redistribute it under certain conditions.
+Type 'license()' or 'licence()' for distribution details.
+
+R is a collaborative project with many contributors.
+Type 'contributors()' for more information and
+'citation()' on how to cite R or R packages in publications.
+
+Type 'demo()' for some demos, 'help()' for on-line help, or
+'help.start()' for an HTML browser interface to help.
+Type 'q()' to quit R.
+
+> 
+> set.seed(290875)
+> library("party")
+Loading required package: grid
+Loading required package: zoo
+
+Attaching package: 'zoo'
+
+The following objects are masked from 'package:base':
+
+    as.Date, as.Date.numeric
+
+Loading required package: sandwich
+Loading required package: strucchange
+Loading required package: modeltools
+Loading required package: stats4
+> if (!require("mvtnorm"))
++     stop("cannot load package mvtnorm")
+Loading required package: mvtnorm
+> 
+> 
+> ### get rid of the NAMESPACE
+> attach(asNamespace("party"))
+The following objects are masked from package:party:
+
+    cforest, cforest_classical, cforest_control, cforest_unbiased,
+    conditionalTree, ctree, ctree_control, ctree_memory, edge_simple,
+    mob, mob_control, node_barplot, node_bivplot, node_boxplot,
+    node_density, node_hist, node_inner, node_scatterplot, node_surv,
+    node_terminal, proximity, ptrafo, reweight, sctest.mob, varimp,
+    varimpAUC
+
+> 
+> ### 
+> ###
+> ###    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]])))
+> 
+> proc.time()
+   user  system elapsed 
+  0.956   0.040   0.996