party/0000755000176200001440000000000014746407342011424 5ustar liggesusersparty/tests/0000755000176200001440000000000014746402104012555 5ustar liggesusersparty/tests/Utils-regtest.R0000644000176200001440000000325514172231364015461 0ustar liggesusers set.seed(290875) library("party") if (!require("MASS", quietly = TRUE)) stop("cannot load package MASS") ### get rid of the NAMESPACE attach(list2env(as.list(asNamespace("party")))) ### ### ### Regression tests for utility functions ### ### functions defined in file ./src/Utils.c' ### ### ### tests for function C_kronecker for (i in 1:10) { A = matrix(rnorm(i*5), ncol = i, nrow = 5) B = matrix(rnorm(i*10), ncol = 10, nrow = i) Rkr = kronecker(A, B) mykr = .Call(R_kronecker, A, B) stopifnot(isequal(Rkr, mykr)) } ### test for function CR_svd (singular value decomposition) x <- matrix(rnorm(100), ncol = 10) x <- t(x) %*% x svdx <- qsvd(x) stopifnot(isequal(svd(x)$d, svdx$d)) stopifnot(isequal(svd(x)$u, svdx$u)) stopifnot(isequal(svd(x)$v, t(svdx$vt))) ### test for function R_MPinv (Moore-Penrose inverse) mpinvx <- MPinv(x) stopifnot(isequal(mpinvx, ginv(x))) ### test for function C_max y <- rnorm(1000) stopifnot(isequal(max(y), .Call(R_max, y))) ### test for function C_abs y <- rnorm(1000) stopifnot(isequal(abs(y), .Call(R_abs, y))) ### tests for function C_matprod{T} x <- matrix(rnorm(100), ncol = 4) y <- matrix(rnorm(40), nrow = 4) stopifnot(isequal(x %*% y, .Call(R_matprod, x, y))) x <- matrix(rnorm(100), ncol = 20) y <- matrix(rnorm(200), ncol = 20) stopifnot(isequal(x %*% t(y), .Call(R_matprodT, x, y))) ### test for function C_SampleNoReplace ### permutation case m <- 10000 storage.mode(m) <- "integer" perm <- .Call(R_permute, m) + 1 stopifnot(all(sort(perm) == (1:m))) ### the random subset case k <- 100 storage.mode(k) <- "integer" perm <- .Call(R_rsubset, m, k) + 1 stopifnot(all(perm %in% (1:m))) party/tests/Predict-regtest.R0000644000176200001440000000307314172231364015751 0ustar liggesusers 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(list2env(as.list(asNamespace("party")))) data(treepipit, package = "coin") ct <- ctree(counts ~ ., data = treepipit) stopifnot(isequal(predict(ct), predict(ct, newdata = treepipit))) data(GlaucomaM, package = "TH.data") ct <- ctree(Class ~ ., data = GlaucomaM) stopifnot(isequal(predict(ct), predict(ct, newdata = GlaucomaM))) stopifnot(isequal(predict(ct, type = "prob"), predict(ct, type = "prob", newdata = GlaucomaM))) stopifnot(isequal(predict(ct, type = "node"), predict(ct, type = "node", newdata = GlaucomaM))) stopifnot(isequal(predict(ct, type = "prob"), treeresponse(ct))) data("GBSG2", package = "TH.data") GBSG2tree <- ctree(Surv(time, cens) ~ ., data = GBSG2) stopifnot(isequal(GBSG2tree@predict_response(), GBSG2tree@predict_response(newdata = GBSG2))) stopifnot(isequal(GBSG2tree@cond_distr_response(), GBSG2tree@cond_distr_response(newdata = GBSG2))) data("mammoexp", package = "TH.data") attr(mammoexp$ME, "scores") <- 1:3 attr(mammoexp$SYMPT, "scores") <- 1:4 attr(mammoexp$DECT, "scores") <- 1:3 names(mammoexp)[names(mammoexp) == "SYMPT"] <- "symptoms" names(mammoexp)[names(mammoexp) == "PB"] <- "benefit" names(mammoexp) mtree <- ctree(ME ~ ., data = mammoexp) stopifnot(isequal(predict(mtree), predict(mtree, newdata = mammoexp))) stopifnot(isequal(predict(mtree), predict(mtree, newdata = mammoexp))) party/tests/bugfixes.R0000644000176200001440000010200614172231364014514 0ustar liggesusers RNGversion("3.5.2") set.seed(290875) library("party") library("survival") ### get rid of the NAMESPACE attach(list2env(as.list(asNamespace("party")))) ### check nominal level printing set.seed(290875) x <- gl(5, 50) df <- data.frame(y = c(rnorm(50, 0), rnorm(50, 1), rnorm(50, 2), rnorm(50, 3), rnorm(50, 4)), x = x, z = rnorm(250)) ctree(y ~ x, data = df) ### check asymptotic vs. MonteCarlo, especially categorical splits after ### MonteCarlo resampling a <- ctree(y ~ x + z, data = df, control = ctree_control(stump = TRUE)) b <- ctree(y ~ x + z, data = df, control = ctree_control(testtype = "Monte", stump = TRUE)) stopifnot(isequal(a@tree$psplit, b@tree$psplit)) stopifnot(isequal(a@tree$criterion$statistic, b@tree$criterion$statistic)) ### we did not check the hyper parameters try(cforest_control(minsplit = -1)) try(cforest_control(ntree = -1)) try(cforest_control(maxdepth = -1)) try(cforest_control(nresample = 10)) ### NA handling for factors and in random forest ### more than one (ordinal) response variable xo <- ordered(x) x[sample(1:length(x), 10)] <- NA cforest(y + xo ~ x + z, data = df, control = cforest_unbiased(ntree = 50)) ### make sure minsplit is OK in the presence of missing values ### spotted by Han Lee load("t1.RData") tr <- try(ctree(p ~., data = t1)) stopifnot(!inherits(tr, "try-error")) ### make sure number of surrogate splits exceeds number of inputs by 1 ### spotted by Henric Nilsson airq <- subset(airquality, !is.na(Ozone)) tr <- try(ctree(Ozone ~ Wind, data = airq, controls = ctree_control(maxsurrogate = 3))) stopifnot(inherits(tr, "try-error")) ### ctree() used only the first of a multivariate response ### spotted by Henric Nilsson airq <- subset(airquality, complete.cases(Ozone, Solar.R)) airOzoSol1 <- ctree(Ozone + Solar.R ~ Wind + Temp + Month + Day, data = airq) airOzoSol2 <- ctree(Solar.R + Ozone ~ Wind + Temp + Month + Day, data = airq) stopifnot(isequal(airOzoSol1@where, airOzoSol2@where)) ### one variable with all values missing dat <- data.frame(y = rnorm(100), x1 = runif(100), x2 = rep(NA, 100)) ctree(y ~ x1 + x2, data = dat) ### one factor with only one level dat$x2 <- factor(rep(0, 100)) try(ctree(y ~ x1 + x2, data = dat)) ### weights for sampling without replacement for cforest ### spotted by Carolin Strobl airq <- subset(airquality, !is.na(Ozone)) cctrl <- cforest_control(replace = FALSE, fraction = 0.5) n <- nrow(airq) w <- double(n) if (FALSE) { ### forest objects have weights remove in 0.9-13 ### case weights x <- runif(w) w[x > 0.5] <- 1 w[x > 0.9] <- 2 rf <- cforest(Ozone ~ .,data = airq, weights = w, control = cctrl) rfw <- sapply(rf@ensemble, function(x) x[[2]]) stopifnot(all(colSums(rfw) == ceiling(sum(w) / 2))) stopifnot(max(abs(rfw[w == 0,])) == 0) ### real weights w <- runif(n) w[1:10] <- 0 rf <- cforest(Ozone ~ .,data = airq, weights = w, control = cctrl) rfw <- sapply(rf@ensemble, function(x) x[[2]]) stopifnot(all(colSums(rfw) == ceiling(sum(w > 0) / 2))) stopifnot(max(abs(rfw[w == 0,])) == 0) } ### cforest with multivariate response df <- data.frame(y1 = rnorm(100), y2 = rnorm(100), x1 = runif(100), x2 = runif(100)) df$y1[df$x1 < 0.5] <- df$y1[df$x1 < 0.5] + 1 cf <- cforest(y1 + y2 ~ x1 + x2, data = df) pr <- predict(cf) stopifnot(length(pr) == nrow(df) || lengthl(pr[[1]]) != 2) ### varimp with ordered response ### spotted by Max Kuhn data("mammoexp", package = "TH.data") test <- cforest(ME ~ ., data = mammoexp, control = cforest_unbiased(ntree = 50)) stopifnot(sum(abs(varimp(test))) > 0) ### missing values in factors lead to segfaults on 64 bit systems ### spotted by Carolin Strobl y <- rnorm(100) x <- gl(2, 50) z <- gl(2, 50)[sample(1:100)] y <- y + (x == "1") * 3 xNA <- x xNA[1:2] <- NA ctree(y ~ xNA ) y <- rnorm(100) x <- y + rnorm(100, sd = 0.1) tmp <- data.frame(x, y) x[sample(1:100)[1:10]] <- NA ct1 <- ctree(y ~ x, data = tmp) ct2 <- ctree(y ~ x, data = tmp[complete.cases(tmp),]) w <- as.double(complete.cases(tmp)) ct3 <- ctree(y ~ x, data = tmp, weights = w) xx <- data.frame(x = rnorm(100)) t1 <- max(abs(predict(ct2, newdata = xx) - predict(ct3, newdata = xx))) == 0 t2 <- nterminal(ct1@tree) == nterminal(ct2@tree) t3 <- nterminal(ct3@tree) == nterminal(ct1@tree) t4 <- all.equal(ct2@tree$psplit, ct1@tree$psplit) stopifnot(t1 && t2 && t3 && t4) y <- rnorm(100) x <- cut(y, c(-Inf, -1, 0, 1, Inf)) tmp <- data.frame(x, y) x[sample(1:100)[1:10]] <- NA ct1 <- ctree(y ~ x, data = tmp) ct2 <- ctree(y ~ x, data = tmp[complete.cases(tmp),]) w <- as.double(complete.cases(tmp)) ct3 <- ctree(y ~ x, data = tmp, weights = w) stopifnot(all.equal(ct2@tree$psplit, ct1@tree$psplit)) stopifnot(all.equal(ct2@tree$psplit, ct3@tree$psplit)) ### predictions for obs with zero weights ### spotted by Mark Difford airq <- subset(airquality, !is.na(Ozone)) w <- rep(1, nrow(airq)) w[1:5] <- 0 ctw <- ctree(Ozone ~ ., data = airq, weights = w) stopifnot(all.equal(predict(ctw)[1:5], predict(ctw, newdata = airq)[1:5])) rfw <- cforest(Ozone ~ ., data = airq, weights = w) stopifnot(all.equal(predict(rfw)[1:5], predict(rfw, newdata = airq)[1:5])) ### more surrogate splits than available requested ### spotted by Henric Nilsson airq <- data.frame(airq, x1 = factor(ifelse(runif(nrow(airq)) < 0.5, 0, 1)), x2 = factor(ifelse(runif(nrow(airq)) < 0.5, 0, 1)), x3 = factor(ifelse(runif(nrow(airq)) < 0.5, 0, 1))) foo <- function(nm) ctree(Ozone ~ ., data = airq, controls = ctree_control(maxsurrogate = nm)) foo(4) try(foo(5)) try(foo(6)) ### variance = 0 due to constant variables ### spotted by Sebastian Wietzke v <- rep(0,20) w <- rep(0,20) x <- 1:20 y <- rep(1,20) z <- c(4,5,8,2,6,1,3,6,8,2,5,8,9,3,5,8,9,4,6,8) tmp <- ctree(z ~ v+w+x+y,controls = ctree_control(mincriterion = 0.80, minsplit = 2, minbucket = 1, testtype = "Univariate", teststat = "quad")) stopifnot(all(tmp@tree$criterion$criterion[c(1,2,4)] == 0)) ### optimal split in last observation lead to selection of suboptimal split data("GlaucomaM", package = "TH.data") tmp <- subset(GlaucomaM, vari <= 0.059) weights <- rep(1.0, nrow(tmp)) stopifnot(all.equal(Split(tmp$vasg, tmp$Class, weights, ctree_control()@splitctrl)[[1]], 0.066)) ### model.matrix.survReg was missing from modeltools data("GBSG2", package = "TH.data") nloglik <- function(x) -logLik(x) GBSG2$time <- GBSG2$time/365 mobGBSG2 <- mob(Surv(time, cens) ~ horTh + pnodes | progrec + menostat + estrec + menostat + age + tsize + tgrade, data = GBSG2, model = survReg, control = mob_control(objfun = nloglik, minsplit = 40)) plot(mobGBSG2, terminal = node_scatterplot, tp_args = list(yscale = c(-0.1, 11))) ### factors were evaluated for surrogate splits data("Ozone", package = "mlbench") Ozone$V2 <- ordered(Ozone$V2) Ozone <- subset(Ozone, !is.na(V4)) rf <- cforest(V4 ~ ., data = Ozone, control = cforest_unbiased(maxsurrogate = 7)) ### scores for response ### spotted and fixed by Silke Janitza tmp <- data.frame(y = gl(3, 10, ordered = TRUE), x = gl(3, 10, ordered = TRUE)) ct <- ctree(y ~ x, data = tmp, scores = list(y = c(0, 10, 11), x = c(1, 2, 5))) stopifnot(isTRUE(all.equal(ct@responses@scores, list(y = c(0, 10, 11))))) ### deal with empty levels for teststat = "quad" by ### removing elements of the teststatistic with zero variance ### reported by Wei-Yin Loh tdata <- structure(list(ytrain = structure(c(3L, 7L, 3L, 2L, 1L, 6L, 2L, 1L, 1L, 2L, 1L, 2L, 3L, 3L, 2L, 1L, 2L, 6L, 2L, 4L, 6L, 1L, 2L, 3L, 7L, 6L, 4L, 6L, 2L, 2L, 1L, 2L, 6L, 1L, 7L, 1L, 3L, 6L, 2L, 1L, 7L, 2L, 7L, 2L, 3L, 2L, 1L, 1L, 3L, 1L, 6L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 6L, 6L, 7L, 2L, 2L, 2L, 2L, 2L, 1L, 3L, 6L, 5L, 1L, 1L, 4L, 7L, 2L, 3L, 3L, 3L, 1L, 8L, 1L, 6L, 2L, 8L, 3L, 4L, 6L, 2L, 7L, 3L, 6L, 6L, 1L, 1L, 2L, 6L, 3L, 3L, 1L, 2L, 3L, 1L, 2L, 7L, 2L, 3L, 6L, 2L, 5L, 2L, 2L, 2L, 1L, 3L, 3L, 7L, 3L, 2L, 3L, 3L, 1L, 6L, 1L, 1L, 1L, 7L, 1L, 3L, 7L, 6L, 1L, 3L, 3L, 6L, 4L, 2L, 3L, 2L, 8L, 3L, 4L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 3L, 4L, 6L, 4L, 8L, 2L, 2L, 3L, 3L, 2L, 3L, 6L, 2L, 1L, 2L, 2L, 7L, 2L, 1L, 1L, 7L, 2L, 7L, 6L, 6L, 6L), .Label = c("0", "1", "2", "3", "4", "5", "6", "7"), class = "factor"), landmass = c(5L, 3L, 4L, 6L, 3L, 4L, 1L, 2L, 2L, 6L, 3L, 1L, 5L, 5L, 1L, 3L, 1L, 4L, 1L, 5L, 4L, 2L, 1L, 5L, 3L, 4L, 5L, 4L, 4L, 1L, 4L, 1L, 4L, 2L, 5L, 2L, 4L, 4L, 6L, 1L, 1L, 3L, 3L, 3L, 4L, 1L, 1L, 2L, 4L, 1L, 4L, 4L, 3L, 2L, 6L, 3L, 3L, 2L, 4L, 4L, 3L, 3L, 3L, 3L, 1L, 6L, 1L, 4L, 4L, 2L, 1L, 1L, 5L, 3L, 3L, 6L, 5L, 5L, 3L, 5L, 3L, 4L, 1L, 5L, 5L, 5L, 4L, 6L, 5L, 5L, 4L, 4L, 3L, 3L, 4L, 4L, 5L, 5L, 3L, 6L, 4L, 1L, 6L, 5L, 1L, 4L, 4L, 6L, 5L, 3L, 1L, 6L, 1L, 4L, 4L, 5L, 5L, 3L, 5L, 5L, 2L, 6L, 2L, 2L, 6L, 3L, 1L, 5L, 3L, 4L, 4L, 5L, 4L, 4L, 5L, 6L, 4L, 4L, 5L, 5L, 5L, 1L, 1L, 1L, 4L, 2L, 3L, 3L, 5L, 5L, 4L, 5L, 4L, 6L, 2L, 4L, 5L, 1L, 5L, 4L, 3L, 2L, 1L, 1L, 5L, 6L, 3L, 2L, 5L, 6L, 3L, 4L, 4L, 4L), zone = c(1L, 1L, 1L, 3L, 1L, 2L, 4L, 3L, 3L, 2L, 1L, 4L, 1L, 1L, 4L, 1L, 4L, 1L, 4L, 1L, 2L, 3L, 4L, 1L, 1L, 4L, 1L, 2L, 1L, 4L, 4L, 4L, 1L, 3L, 1L, 4L, 2L, 2L, 3L, 4L, 4L, 1L, 1L, 1L, 1L, 4L, 4L, 3L, 1L, 4L, 1L, 1L, 4L, 3L, 2L, 1L, 1L, 4L, 2L, 4L, 1L, 1L, 4L, 1L, 4L, 1L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 4L, 2L, 1L, 1L, 4L, 1L, 1L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 4L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 4L, 4L, 1L, 1L, 4L, 4L, 2L, 2L, 1L, 1L, 4L, 2L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 4L, 2L, 3L, 3L, 1L, 1L, 4L, 1L, 1L, 2L, 1L, 1L, 4L, 4L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 4L, 4L, 4L, 1L, 4L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 4L, 1L, 1L, 4L, 1L, 1L, 4L, 3L, 4L, 4L, 1L, 2L, 1L, 4L, 1L, 3L, 1L, 2L, 2L, 2L), area = c(648L, 29L, 2388L, 0L, 0L, 1247L, 0L, 2777L, 2777L, 7690L, 84L, 19L, 1L, 143L, 0L, 31L, 23L, 113L, 0L, 47L, 600L, 8512L, 0L, 6L, 111L, 274L, 678L, 28L, 474L, 9976L, 4L, 0L, 623L, 757L, 9561L, 1139L, 2L, 342L, 0L, 51L, 115L, 9L, 128L, 43L, 22L, 0L, 49L, 284L, 1001L, 21L, 28L, 1222L, 1L, 12L, 18L, 337L, 547L, 91L, 268L, 10L, 108L, 249L, 0L, 132L, 0L, 0L, 109L, 246L, 36L, 215L, 28L, 112L, 1L, 93L, 103L, 1904L, 1648L, 435L, 70L, 21L, 301L, 323L, 11L, 372L, 98L, 181L, 583L, 0L, 236L, 10L, 30L, 111L, 0L, 3L, 587L, 118L, 333L, 0L, 0L, 0L, 1031L, 1973L, 1L, 1566L, 0L, 447L, 783L, 0L, 140L, 41L, 0L, 268L, 128L, 1267L, 925L, 121L, 195L, 324L, 212L, 804L, 76L, 463L, 407L, 1285L, 300L, 313L, 9L, 11L, 237L, 26L, 0L, 2150L, 196L, 72L, 1L, 30L, 637L, 1221L, 99L, 288L, 66L, 0L, 0L, 0L, 2506L, 63L, 450L, 41L, 185L, 36L, 945L, 514L, 57L, 1L, 5L, 164L, 781L, 0L, 84L, 236L, 245L, 178L, 0L, 9363L, 22402L, 15L, 0L, 912L, 333L, 3L, 256L, 905L, 753L, 391L), population = c(16L, 3L, 20L, 0L, 0L, 7L, 0L, 28L, 28L, 15L, 8L, 0L, 0L, 90L, 0L, 10L, 0L, 3L, 0L, 1L, 1L, 119L, 0L, 0L, 9L, 7L, 35L, 4L, 8L, 24L, 0L, 0L, 2L, 11L, 1008L, 28L, 0L, 2L, 0L, 2L, 10L, 1L, 15L, 5L, 0L, 0L, 6L, 8L, 47L, 5L, 0L, 31L, 0L, 0L, 1L, 5L, 54L, 0L, 1L, 1L, 17L, 61L, 0L, 10L, 0L, 0L, 8L, 6L, 1L, 1L, 6L, 4L, 5L, 11L, 0L, 157L, 39L, 14L, 3L, 4L, 57L, 7L, 2L, 118L, 2L, 6L, 17L, 0L, 3L, 3L, 1L, 1L, 0L, 0L, 9L, 6L, 13L, 0L, 0L, 0L, 2L, 77L, 0L, 2L, 0L, 20L, 12L, 0L, 16L, 14L, 0L, 2L, 3L, 5L, 56L, 18L, 9L, 4L, 1L, 84L, 2L, 3L, 3L, 14L, 48L, 36L, 3L, 0L, 22L, 5L, 0L, 9L, 6L, 3L, 3L, 0L, 5L, 29L, 39L, 2L, 15L, 0L, 0L, 0L, 20L, 0L, 8L, 6L, 10L, 18L, 18L, 49L, 2L, 0L, 1L, 7L, 45L, 0L, 1L, 13L, 56L, 3L, 0L, 231L, 274L, 0L, 0L, 15L, 60L, 0L, 22L, 28L, 6L, 8L), language = structure(c(10L, 6L, 8L, 1L, 6L, 10L, 1L, 2L, 2L, 1L, 4L, 1L, 8L, 6L, 1L, 6L, 1L, 3L, 1L, 10L, 10L, 6L, 1L, 10L, 5L, 3L, 10L, 10L, 3L, 1L, 6L, 1L, 10L, 2L, 7L, 2L, 3L, 10L, 1L, 2L, 2L, 6L, 5L, 6L, 3L, 1L, 2L, 2L, 8L, 2L, 10L, 10L, 6L, 1L, 1L, 9L, 3L, 3L, 10L, 1L, 4L, 4L, 1L, 6L, 1L, 1L, 2L, 3L, 6L, 1L, 3L, 2L, 7L, 9L, 6L, 10L, 6L, 8L, 1L, 10L, 6L, 3L, 1L, 9L, 8L, 10L, 10L, 1L, 10L, 8L, 10L, 10L, 4L, 4L, 10L, 10L, 10L, 10L, 10L, 10L, 8L, 2L, 10L, 10L, 1L, 8L, 10L, 10L, 10L, 6L, 6L, 1L, 2L, 3L, 10L, 10L, 8L, 6L, 8L, 6L, 2L, 1L, 2L, 2L, 10L, 5L, 2L, 8L, 6L, 10L, 6L, 8L, 3L, 1L, 7L, 1L, 10L, 6L, 10L, 8L, 10L, 1L, 1L, 1L, 8L, 6L, 6L, 4L, 8L, 7L, 10L, 10L, 3L, 10L, 1L, 8L, 9L, 1L, 8L, 10L, 1L, 2L, 1L, 1L, 5L, 6L, 6L, 2L, 10L, 1L, 6L, 10L, 10L, 10L), .Label = c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10"), class = "factor"), bars = c(0L, 0L, 2L, 0L, 3L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 3L, 3L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 3L, 2L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 3L, 3L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 3L, 3L, 1L, 0L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 3L, 0L, 3L, 3L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 2L, 0L, 0L, 3L, 0L, 3L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 3L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 3L, 0L, 0L, 0L, 0L, 3L, 3L, 0L, 0L, 3L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 5L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 0L, 0L, 0L, 3L, 0L), stripes = c(3L, 0L, 0L, 0L, 0L, 2L, 1L, 3L, 3L, 0L, 3L, 3L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 0L, 5L, 0L, 0L, 0L, 3L, 2L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 2L, 0L, 3L, 0L, 0L, 0L, 5L, 5L, 0L, 0L, 0L, 0L, 0L, 0L, 3L, 3L, 3L, 3L, 3L, 0L, 0L, 0L, 0L, 0L, 0L, 3L, 5L, 3L, 3L, 1L, 9L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 3L, 0L, 3L, 0L, 2L, 3L, 3L, 0L, 2L, 0L, 0L, 0L, 0L, 3L, 0L, 5L, 0L, 3L, 2L, 0L, 11L, 2L, 3L, 2L, 3L, 14L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 5L, 3L, 0L, 3L, 1L, 0L, 3L, 3L, 0L, 5L, 3L, 0L, 2L, 0L, 0L, 0L, 3L, 0L, 0L, 2L, 5L, 0L, 0L, 0L, 3L, 0L, 0L, 3L, 2L, 0L, 0L, 3L, 0L, 3L, 0L, 0L, 0L, 0L, 3L, 5L, 0L, 0L, 3L, 0L, 0L, 5L, 5L, 0L, 0L, 0L, 0L, 0L, 3L, 6L, 0L, 9L, 0L, 13L, 0L, 0L, 0L, 3L, 0L, 0L, 3L, 0L, 0L, 7L), colours = c(5L, 3L, 3L, 5L, 3L, 3L, 3L, 2L, 3L, 3L, 2L, 3L, 2L, 2L, 3L, 3L, 8L, 2L, 6L, 4L, 3L, 4L, 6L, 4L, 5L, 3L, 3L, 3L, 3L, 2L, 5L, 6L, 5L, 3L, 2L, 3L, 2L, 3L, 4L, 3L, 3L, 3L, 3L, 2L, 4L, 6L, 3L, 3L, 4L, 2L, 4L, 3L, 3L, 6L, 7L, 2L, 3L, 3L, 3L, 4L, 3L, 3L, 3L, 2L, 3L, 7L, 2L, 3L, 4L, 5L, 2L, 2L, 6L, 3L, 3L, 2L, 3L, 4L, 3L, 2L, 3L, 3L, 3L, 2L, 4L, 2L, 4L, 4L, 3L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 4L, 3L, 3L, 3L, 2L, 4L, 2L, 3L, 7L, 2L, 5L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 2L, 3L, 4L, 3L, 3L, 2L, 3L, 4L, 6L, 2L, 4L, 2L, 3L, 2L, 7L, 4L, 4L, 2L, 3L, 3L, 2L, 4L, 2L, 5L, 4L, 4L, 4L, 5L, 4L, 4L, 4L, 4L, 2L, 2L, 4L, 3L, 4L, 3L, 4L, 2L, 3L, 2L, 2L, 6L, 4L, 5L, 3L, 3L, 6L, 3L, 2L, 4L, 4L, 7L, 2L, 3L, 4L, 4L, 4L, 5L), red = c(1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), green = c(1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 1L), blue = c(0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L), gold = c(1L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 1L), white = c(1L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 1L), black = c(1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L), orange = c(0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L), mainhue = structure(c(5L, 7L, 5L, 2L, 4L, 7L, 8L, 2L, 2L, 2L, 7L, 2L, 7L, 5L, 2L, 4L, 2L, 5L, 7L, 6L, 2L, 5L, 2L, 4L, 7L, 7L, 7L, 7L, 4L, 7L, 4L, 2L, 4L, 7L, 7L, 4L, 5L, 7L, 2L, 2L, 2L, 8L, 8L, 7L, 2L, 5L, 2L, 4L, 1L, 2L, 5L, 5L, 8L, 2L, 2L, 8L, 8L, 8L, 5L, 7L, 4L, 1L, 8L, 2L, 4L, 2L, 2L, 4L, 4L, 5L, 1L, 2L, 2L, 7L, 2L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 5L, 8L, 1L, 7L, 7L, 7L, 7L, 7L, 2L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 2L, 5L, 5L, 2L, 7L, 2L, 7L, 4L, 2L, 3L, 7L, 8L, 2L, 2L, 6L, 5L, 2L, 7L, 7L, 7L, 5L, 7L, 1L, 7L, 7L, 2L, 8L, 7L, 3L, 7L, 7L, 5L, 5L, 5L, 5L, 8L, 5L, 2L, 6L, 8L, 7L, 4L, 5L, 2L, 5L, 7L, 7L, 2L, 7L, 7L, 7L, 5L, 7L, 5L, 7L, 7L, 7L, 7L, 2L, 5L, 4L, 7L, 8L, 8L, 8L, 7L, 7L, 4L, 7L, 7L, 7L, 7L, 5L, 5L, 5L), .Label = c("black", "blue", "brown", "gold", "green", "orange", "red", "white"), class = "factor"), circles = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 4L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L), crosses = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 2L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), saltires = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), quarters = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 4L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L), sunstars = c(1L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 6L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 22L, 0L, 0L, 1L, 1L, 14L, 3L, 1L, 0L, 1L, 4L, 1L, 1L, 5L, 0L, 4L, 1L, 15L, 0L, 1L, 0L, 0L, 0L, 1L, 10L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 7L, 0L, 0L, 0L, 1L, 0L, 0L, 5L, 0L, 0L, 0L, 0L, 0L, 3L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 4L, 1L, 0L, 1L, 1L, 1L, 2L, 0L, 6L, 4L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 2L, 5L, 1L, 0L, 4L, 0L, 1L, 0L, 2L, 0L, 2L, 0L, 1L, 0L, 5L, 5L, 1L, 0L, 0L, 1L, 0L, 2L, 0L, 0L, 0L, 1L, 0L, 0L, 2L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 50L, 1L, 0L, 0L, 7L, 1L, 5L, 1L, 0L, 0L, 1L), crescent = c(0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), triangle = c(0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L), icon = c(1L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 1L), animate = c(0L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 1L), text = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), topleft = structure(c(1L, 6L, 4L, 2L, 2L, 6L, 7L, 2L, 2L, 7L, 6L, 2L, 7L, 4L, 2L, 1L, 6L, 4L, 7L, 5L, 2L, 4L, 7L, 7L, 7L, 6L, 2L, 7L, 4L, 6L, 6L, 7L, 2L, 2L, 6L, 3L, 4L, 6L, 7L, 2L, 2L, 7L, 7L, 6L, 7L, 4L, 2L, 3L, 6L, 2L, 4L, 4L, 7L, 7L, 7L, 7L, 2L, 2L, 4L, 6L, 1L, 1L, 7L, 2L, 6L, 6L, 2L, 6L, 6L, 1L, 1L, 2L, 7L, 6L, 2L, 6L, 4L, 6L, 4L, 2L, 4L, 6L, 3L, 7L, 1L, 6L, 1L, 6L, 6L, 6L, 4L, 2L, 2L, 6L, 7L, 1L, 2L, 6L, 7L, 2L, 4L, 4L, 2L, 6L, 7L, 6L, 4L, 2L, 2L, 6L, 7L, 7L, 2L, 5L, 4L, 2L, 6L, 6L, 6L, 7L, 7L, 6L, 6L, 6L, 2L, 7L, 6L, 7L, 2L, 6L, 4L, 4L, 4L, 4L, 6L, 2L, 2L, 5L, 7L, 6L, 3L, 4L, 2L, 2L, 6L, 4L, 2L, 6L, 6L, 2L, 4L, 6L, 6L, 7L, 7L, 6L, 6L, 7L, 6L, 1L, 7L, 7L, 7L, 2L, 6L, 1L, 3L, 3L, 6L, 2L, 2L, 4L, 4L, 4L), .Label = c("black", "blue", "gold", "green", "orange", "red", "white"), class = "factor"), botright = structure(c(5L, 7L, 8L, 7L, 7L, 1L, 2L, 2L, 2L, 2L, 7L, 2L, 7L, 5L, 2L, 7L, 7L, 5L, 7L, 7L, 2L, 5L, 2L, 4L, 7L, 5L, 7L, 8L, 4L, 7L, 5L, 2L, 4L, 7L, 7L, 7L, 5L, 7L, 2L, 2L, 2L, 8L, 7L, 7L, 5L, 5L, 2L, 7L, 1L, 2L, 7L, 7L, 8L, 2L, 2L, 8L, 7L, 7L, 2L, 5L, 4L, 4L, 7L, 2L, 7L, 7L, 2L, 5L, 5L, 5L, 7L, 2L, 2L, 5L, 2L, 8L, 7L, 1L, 6L, 2L, 7L, 5L, 4L, 8L, 5L, 7L, 5L, 2L, 7L, 7L, 2L, 7L, 7L, 2L, 5L, 5L, 8L, 7L, 7L, 2L, 5L, 7L, 2L, 7L, 2L, 7L, 4L, 2L, 2L, 2L, 8L, 2L, 2L, 5L, 5L, 2L, 1L, 7L, 5L, 5L, 8L, 1L, 2L, 7L, 7L, 7L, 7L, 3L, 7L, 5L, 5L, 5L, 7L, 2L, 8L, 5L, 2L, 2L, 8L, 1L, 4L, 7L, 2L, 5L, 1L, 5L, 2L, 7L, 1L, 7L, 2L, 7L, 5L, 7L, 8L, 7L, 7L, 2L, 1L, 7L, 7L, 8L, 8L, 7L, 7L, 5L, 8L, 7L, 7L, 7L, 7L, 5L, 3L, 5L), .Label = c("black", "blue", "brown", "gold", "green", "orange", "red", "white"), class = "factor")), .Names = c("ytrain", "landmass", "zone", "area", "population", "language", "bars", "stripes", "colours", "red", "green", "blue", "gold", "white", "black", "orange", "mainhue", "circles", "crosses", "saltires", "quarters", "sunstars", "crescent", "triangle", "icon", "animate", "text", "topleft", "botright"), row.names = c(NA, -174L), class = "data.frame") tdata$language <- factor(tdata$language) tdata$ytrain <- factor(tdata$ytrain) library("coin") m <- ctree(ytrain ~ language, data = subset(tdata, language != "8"), control = ctree_control(testtype = "Univariate", maxdepth = 1L)) it <- independence_test(ytrain ~ language, data = subset(tdata, language != "8"), teststat = "quad") stopifnot(isTRUE(all.equal(m@tree$criterion$statistic, statistic(it), check.attributes = FALSE))) ### easier example levels(tdata$language) <- c(1, 1, 1, 1, 1, 1, 2, 8, 1, 1) levels(tdata$ytrain) <- c(1, 1, 2, 2, 3, 3, 4, 4, 5, 6) m <- ctree(ytrain ~ language, data = subset(tdata, language != "8"), control = ctree_control(testtype = "Univariate", maxdepth = 1L)) it <- independence_test(ytrain ~ language, data = subset(tdata, language != "8"), teststat = "quad") stopifnot(isTRUE(all.equal(m@tree$criterion$statistic, statistic(it), check.attributes = FALSE))) ## the whole exercise manually Y <- model.matrix(~ language - 1, data = subset(tdata, language != "8")) X <- model.matrix(~ ytrain -1, data = subset(tdata, language != "8")) w <- rep(1, nrow(X)) ### coin:::LinearStatistic and coin:::ExpectCovarLinearStatistic ### have been removed from coin as of 2.0-0 ### use libcoin to compare with if (FALSE) { ### (require("libcoin")) { lstec <- LinStatExpCov(X = X, Y = Y, weights = as.integer(w)) tmp <- new("LinStatExpectCovar", ncol(Y), ncol(X)) tmp@linearstatistic <- lstec$LinearStatistic tmp@expectation <- lstec$Expectation tmp@covariance <- matrix(0, nrow = length(lstec$LinearStatistic), ncol = length(lstec$LinearStatistic)) tmp@covariance[lower.tri(tmp@covariance, diag = TRUE)] <- lstec$Covariance tmp@covariance <- tmp@covariance + t(tmp@covariance) diag(tmp@covariance) <- diag(tmp@covariance) / 2 a <- .Call(R_linexpcovReduce, tmp) u <- matrix(tmp@linearstatistic - tmp@expectation, nc = 1) d <- tmp@dimension u <- matrix(tmp@linearstatistic - tmp@expectation, nc = 1)[1:d,,drop = FALSE] S <- coin:::MPinv(matrix(as.vector(tmp@covariance[1:d^2]), ncol = d)) stat <- t(u) %*% S$MPinv %*% u stopifnot(isTRUE(all.equal(stat[1,1], statistic(it), check.attributes = FALSE))) x <- matrix(as.vector(tmp@covariance[1:d^2]), ncol = d) s <- svd(x) m <- new("svd_mem", 18L) m@p <- as.integer(d) s2 <- .Call(R_svd, x, m) stopifnot(max(abs(s$d - m@s[1:d])) < sqrt(.Machine$double.eps)) stopifnot(max(abs(s$v - t(matrix(m@v[1:d^2], nrow = d)))) < sqrt(.Machine$double.eps)) stopifnot(max(abs(s$u - matrix(m@u[1:d^2], nrow = d))) < sqrt(.Machine$double.eps)) s2 <- .Call(R_svd, tmp@covariance, m) stopifnot(max(abs(s$d - m@s[1:d])) < sqrt(.Machine$double.eps)) stopifnot(max(abs(s$v - t(matrix(m@v[1:d^2], nrow = d)))) < sqrt(.Machine$double.eps)) stopifnot(max(abs(s$u - matrix(m@u[1:d^2], nrow = d))) < sqrt(.Machine$double.eps)) a <- .Call(R_MPinv, tmp@covariance, sqrt(.Machine$double.eps), m) stat <- t(u) %*% matrix(a@MPinv[1:d^2], ncol = d) %*% u stopifnot(isTRUE(all.equal(stat[1,1], statistic(it), check.attributes = FALSE))) } party/tests/TreeGrow-regtest.Rout.save0000644000176200001440000002466514172231364017614 0ustar liggesusers R version 3.3.2 (2016-10-31) -- "Sincere Pumpkin Patch" Copyright (C) 2016 The R Foundation for Statistical Computing Platform: x86_64-pc-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: mvtnorm Loading required package: modeltools Loading required package: stats4 Loading required package: strucchange Loading required package: zoo Attaching package: 'zoo' The following objects are masked from 'package:base': as.Date, as.Date.numeric Loading required package: sandwich > if (!require("TH.data")) + stop("cannot load package TH.data") Loading required package: TH.data Loading required package: survival Loading required package: MASS Attaching package: 'TH.data' The following object is masked from 'package:MASS': geyser > if (!require("coin")) + stop("cannot load package coin") Loading required package: coin > > ### get rid of the NAMESPACE > attach(list2env(as.list(asNamespace("party")))) The following objects are masked from package:party: cforest, cforest_classical, cforest_control, cforest_unbiased, conditionalTree, ctree, ctree_control, edge_simple, initVariableFrame, mob, mob_control, node_barplot, node_bivplot, node_boxplot, node_density, node_hist, node_inner, node_scatterplot, node_surv, node_terminal, nodes, party_intern, prettytree, proximity, ptrafo, response, reweight, sctest.mob, treeresponse, varimp, varimpAUC, where > > 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) [1] normal normal normal normal normal normal normal normal [9] normal normal normal glaucoma normal normal normal normal [17] normal normal normal normal normal normal normal normal [25] normal normal normal normal normal normal normal normal [33] normal normal glaucoma normal normal normal normal normal [41] normal normal glaucoma normal normal normal normal normal [49] normal normal normal normal normal normal normal normal [57] normal normal normal normal normal normal normal normal [65] normal normal normal normal normal glaucoma normal normal [73] normal normal normal normal normal normal normal normal [81] glaucoma normal normal normal normal normal normal normal [89] normal normal normal normal normal normal normal normal [97] normal normal glaucoma glaucoma glaucoma glaucoma normal normal [105] normal normal normal glaucoma glaucoma normal glaucoma glaucoma [113] glaucoma glaucoma glaucoma glaucoma glaucoma normal normal glaucoma [121] glaucoma glaucoma glaucoma glaucoma glaucoma glaucoma normal glaucoma [129] normal glaucoma normal glaucoma glaucoma glaucoma glaucoma glaucoma [137] glaucoma glaucoma glaucoma glaucoma glaucoma glaucoma glaucoma glaucoma [145] glaucoma glaucoma normal glaucoma glaucoma glaucoma glaucoma normal [153] glaucoma glaucoma glaucoma glaucoma normal glaucoma glaucoma glaucoma [161] glaucoma glaucoma normal normal glaucoma glaucoma normal glaucoma [169] glaucoma glaucoma glaucoma glaucoma normal glaucoma glaucoma glaucoma [177] normal glaucoma normal glaucoma glaucoma glaucoma normal glaucoma [185] glaucoma glaucoma normal glaucoma glaucoma normal glaucoma normal [193] glaucoma glaucoma glaucoma glaucoma Levels: glaucoma normal > > # print(tree) > > stump <- ctree(Class ~ ., data = GlaucomaM, + control = ctree_control(stump = TRUE)) > print(stump) Conditional inference tree with 2 terminal nodes Response: Class Inputs: ag, at, as, an, ai, eag, eat, eas, ean, eai, abrg, abrt, abrs, abrn, abri, hic, mhcg, mhct, mhcs, mhcn, mhci, phcg, phct, phcs, phcn, phci, hvc, vbsg, vbst, vbss, vbsn, vbsi, vasg, vast, vass, vasn, vasi, vbrg, vbrt, vbrs, vbrn, vbri, varg, vart, vars, varn, vari, mdg, mdt, mds, mdn, mdi, tmg, tmt, tms, tmn, tmi, mr, rnf, mdic, emd, mv Number of observations: 196 1) vari <= 0.059; criterion = 1, statistic = 71.475 2)* weights = 87 1) vari > 0.059 3)* weights = 109 > > data(treepipit, package = "coin") > > tr <- ctree(counts ~ ., data = treepipit) > tr Conditional inference tree with 2 terminal nodes Response: counts Inputs: age, coverstorey, coverregen, meanregen, coniferous, deadtree, cbpiles, ivytree, fdist Number of observations: 86 1) coverstorey <= 40; criterion = 0.998, statistic = 13.678 2)* weights = 24 1) coverstorey > 40 3)* weights = 62 > plot(tr) > > > data(GlaucomaM, package = "TH.data") > > tr <- ctree(Class ~ ., data = GlaucomaM) > tr Conditional inference tree with 4 terminal nodes Response: Class Inputs: ag, at, as, an, ai, eag, eat, eas, ean, eai, abrg, abrt, abrs, abrn, abri, hic, mhcg, mhct, mhcs, mhcn, mhci, phcg, phct, phcs, phcn, phci, hvc, vbsg, vbst, vbss, vbsn, vbsi, vasg, vast, vass, vasn, vasi, vbrg, vbrt, vbrs, vbrn, vbri, varg, vart, vars, varn, vari, mdg, mdt, mds, mdn, mdi, tmg, tmt, tms, tmn, tmi, mr, rnf, mdic, emd, mv Number of observations: 196 1) vari <= 0.059; criterion = 1, statistic = 71.475 2) vasg <= 0.066; criterion = 1, statistic = 29.265 3)* weights = 79 2) vasg > 0.066 4)* weights = 8 1) vari > 0.059 5) tms <= -0.066; criterion = 0.951, statistic = 11.221 6)* weights = 65 5) tms > -0.066 7)* weights = 44 > plot(tr) > > data(GBSG2, package = "TH.data") > > GBSG2tree <- ctree(Surv(time, cens) ~ ., data = GBSG2) > GBSG2tree Conditional inference tree with 4 terminal nodes Response: Surv(time, cens) Inputs: horTh, age, menostat, tsize, tgrade, pnodes, progrec, estrec Number of observations: 686 1) pnodes <= 3; criterion = 1, statistic = 56.156 2) horTh == {yes}; criterion = 0.965, statistic = 8.113 3)* weights = 128 2) horTh == {no} 4)* weights = 248 1) pnodes > 3 5) progrec <= 20; criterion = 0.999, statistic = 14.941 6)* weights = 144 5) progrec > 20 7)* weights = 166 > plot(GBSG2tree) > plot(GBSG2tree, terminal_panel = node_surv(GBSG2tree)) > survfit(Surv(time, cens) ~ as.factor(GBSG2tree@where), data = GBSG2) Call: survfit(formula = Surv(time, cens) ~ as.factor(GBSG2tree@where), data = GBSG2) n events median 0.95LCL 0.95UCL as.factor(GBSG2tree@where)=3 128 31 NA 2372 NA as.factor(GBSG2tree@where)=4 248 88 2093 1814 NA as.factor(GBSG2tree@where)=6 144 103 624 525 797 as.factor(GBSG2tree@where)=7 166 77 1701 1174 2018 > names(GBSG2) [1] "horTh" "age" "menostat" "tsize" "tgrade" "pnodes" [7] "progrec" "estrec" "time" "cens" > > tr <- ctree(Surv(time, cens) ~ ., data = GBSG2, + control = ctree_control(teststat = "max", + testtype = "Univariate")) There were 18 warnings (use warnings() to see them) > tr Conditional inference tree with 10 terminal nodes Response: Surv(time, cens) Inputs: horTh, age, menostat, tsize, tgrade, pnodes, progrec, estrec Number of observations: 686 1) pnodes <= 3; criterion = 1, statistic = 7.494 2) horTh == {yes}; criterion = 0.996, statistic = 2.848 3) progrec <= 74; criterion = 0.975, statistic = 2.241 4)* weights = 73 3) progrec > 74 5)* weights = 55 2) horTh == {no} 6) menostat == {Pre}; criterion = 0.978, statistic = 2.286 7) age <= 37; criterion = 1, statistic = 3.858 8)* weights = 21 7) age > 37 9)* weights = 115 6) menostat == {Post} 10)* weights = 112 1) pnodes > 3 11) progrec <= 20; criterion = 1, statistic = 3.865 12) pnodes <= 9; criterion = 0.991, statistic = 2.612 13)* weights = 87 12) pnodes > 9 14)* weights = 57 11) progrec > 20 15) horTh == {yes}; criterion = 0.976, statistic = 2.251 16) menostat == {Pre}; criterion = 0.965, statistic = 2.105 17)* weights = 20 16) menostat == {Post} 18)* weights = 45 15) horTh == {no} 19)* weights = 101 > 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) [1] "ME" "symptoms" "benefit" "HIST" "BSE" "DECT" > tr <- ctree(ME ~ ., data = mammoexp) > tr Conditional inference tree with 3 terminal nodes Response: ME Inputs: symptoms, benefit, HIST, BSE, DECT Number of observations: 412 1) symptoms <= Agree; criterion = 1, statistic = 29.933 2)* weights = 113 1) symptoms > Agree 3) benefit <= 8; criterion = 0.988, statistic = 9.17 4)* weights = 208 3) benefit > 8 5)* weights = 91 > plot(tr) > > treeresponse(tr, newdata = mammoexp[1:5,]) [[1]] [1] 0.3990385 0.3798077 0.2211538 [[2]] [1] 0.84070796 0.05309735 0.10619469 [[3]] [1] 0.3990385 0.3798077 0.2211538 [[4]] [1] 0.6153846 0.2087912 0.1758242 [[5]] [1] 0.3990385 0.3798077 0.2211538 > > ### 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)) > > proc.time() user system elapsed 2.364 0.044 2.404 party/tests/LinearStatistic-regtest.R0000644000176200001440000000761714172231364017471 0ustar liggesusers set.seed(290875) library("party") ### get rid of the NAMESPACE attach(list2env(as.list(asNamespace("party")))) ### ### ### Regression tests for linear statistics, expectations and covariances ### ### functions defined in file `./src/LinearStatistics.c' ### tests for function C_LinearStatistic ### Linear Statistics x = matrix(c(rep.int(1,4), rep.int(0,6)), ncol = 1) y = matrix(1:10, ncol = 1) weights = rep(1, 10) linstat = LinearStatistic(x, y, weights) stopifnot(isequal(linstat, sum(1:4))) weights[1] = 0 linstat = LinearStatistic(x, y, weights) stopifnot(isequal(linstat, sum(2:4))) xf <- gl(3, 10) yf <- gl(3, 10)[sample(1:30)] x <- sapply(levels(xf), function(l) as.numeric(xf == l)) colnames(x) <- NULL y <- sapply(levels(yf), function(l) as.numeric(yf == l)) colnames(y) <- NULL weights <- sample(1:30) linstat <- LinearStatistic(x, y, weights) stopifnot(isequal(linstat, as.vector(t(x) %*% diag(weights) %*% y))) xf <- factor(cut(rnorm(6000), breaks = c(-Inf, -2, 0.5, Inf))) x <- sapply(levels(xf), function(l) as.numeric(xf == l)) yf <- factor(cut(rnorm(6000), breaks = c(-Inf, -0.5, 1.5, Inf))) y <- sapply(levels(yf), function(l) as.numeric(yf == l)) weights <- rep(1, nrow(x)) colnames(x) <- NULL colnames(y) <- NULL weights <- rep(1, 6000) linstat <- LinearStatistic(x, y, weights) stopifnot(isequal(as.vector(table(xf, yf)), linstat)) stopifnot(isequal(as.vector(t(x)%*%y), linstat)) ### tests for function C_ExpectCovarInfluence eci <- ExpectCovarInfluence(y, weights) isequal(eci@sumweights, sum(weights)) isequal(eci@expectation, drop(weights %*% y / sum(weights))) ys <- t(t(y) - eci@expectation) stopifnot(isequal(eci@covariance, (t(ys) %*% (weights * ys)) / sum(weights))) ### tests for function C_ExpectCovarLinearStatistic ### Conditional Expectation and Variance (via Kruskal-Wallis statistic) ### case 1: p > 1, q = 1 group <- gl(3, 5) x <- sapply(levels(group), function(l) as.numeric(group == l)) y <- matrix(1:15, ncol = 1) weights <- rep(1, 15) linstat <- LinearStatistic(x, y, weights) expcov <- ExpectCovarLinearStatistic(x, y, weights) KW <- quadformTestStatistic(linstat, expcov@expectation, expcov@covariance) kts <- kruskal.test(y ~ group)$statistic stopifnot(isequal(KW, kts)) ### case 2: p = 1, q > 1 linstat <- LinearStatistic(y, x, weights) expcov <- ExpectCovarLinearStatistic(y, x, weights) KW <- quadformTestStatistic(linstat, expcov@expectation, expcov@covariance) kts <- kruskal.test(y ~ group)$statistic stopifnot(isequal(KW, kts)) ### case 3: p = 1, q = 1 x <- x[,1,drop = FALSE] linstat <- LinearStatistic(x, y, weights) expcov <- ExpectCovarLinearStatistic(x, y, weights) KW <- quadformTestStatistic(linstat, expcov@expectation, expcov@covariance) kts <- kruskal.test(y ~ as.factor(x))$statistic stopifnot(isequal(KW, kts)) ### case 4: p > 1, q > 1 via chisq.test n <- 900 xf <- gl(3, n / 3) yf <- gl(3, n / 3)[sample(1:n)] x <- sapply(levels(xf), function(l) as.numeric(xf == l)) colnames(x) <- NULL y <- sapply(levels(yf), function(l) as.numeric(yf == l)) colnames(y) <- NULL weights <- rep(1, n) linstat <- LinearStatistic(x, y, weights) expcov <- ExpectCovarLinearStatistic(x, y, weights) chi <- quadformTestStatistic(linstat, expcov@expectation, expcov@covariance) chis <- chisq.test(table(xf, yf))$statistic stopifnot(isequal(round(chi, 1), round(chis, 1))) ### tests for function C_PermutedLinearStatistic ### Linear Statistics with permuted indices x <- matrix(rnorm(100), ncol = 2) y <- matrix(rnorm(100), ncol = 2) weights <- rep(1, 50) indx <- 1:50 perm <- 1:50 stopifnot(isequal(LinearStatistic(x, y, weights), PermutedLinearStatistic(x, y, indx, perm))) x <- matrix(1:10000, ncol = 2) y <- matrix(1:10000, ncol = 2) for (i in 1:100) { indx <- sample(1:ncol(y), replace = TRUE) perm <- sample(1:ncol(y), replace = TRUE) stopifnot(isequal(as.vector(t(x[indx,]) %*% y[perm, ]), PermutedLinearStatistic(x, y, indx, perm))) } party/tests/Distributions.R0000644000176200001440000001072014172231364015543 0ustar liggesusers set.seed(290875) library("party") if (!require("mvtnorm")) stop("cannot load package mvtnorm") ### get rid of the NAMESPACE attach(list2env(as.list(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))) stopifnot(isequal(2*pnorm(-t), .Call(R_maxabsConditionalPvalue, t, matrix(1), as.integer(1), 0.0, 0.0, 0.0))) 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) 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) 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) p2 <- pmvnorm(lower = rep(-a,4), upper = rep(a,4), corr = b) stopifnot(isequal(round(p1, 3), round(1 - p2, 3))) if (FALSE) { ### 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) 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) 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) stopifnot(isequal(wstat, pvals[[1]])) stopifnot(all(wpval < (1 - pvals[[2]]))) }party/tests/Predict-regtest.Rout.save0000644000176200001440000000667214172231364017446 0ustar liggesusers R version 3.3.2 (2016-10-31) -- "Sincere Pumpkin Patch" Copyright (C) 2016 The R Foundation for Statistical Computing Platform: x86_64-pc-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: mvtnorm Loading required package: modeltools Loading required package: stats4 Loading required package: strucchange Loading required package: zoo Attaching package: 'zoo' The following objects are masked from 'package:base': as.Date, as.Date.numeric Loading required package: sandwich > if (!require("TH.data")) + stop("cannot load package TH.data") Loading required package: TH.data Loading required package: survival Loading required package: MASS Attaching package: 'TH.data' The following object is masked from 'package:MASS': geyser > if (!require("coin")) + stop("cannot load package coin") Loading required package: coin > > ### get rid of the NAMESPACE > attach(list2env(as.list(asNamespace("party")))) The following objects are masked from package:party: cforest, cforest_classical, cforest_control, cforest_unbiased, conditionalTree, ctree, ctree_control, edge_simple, initVariableFrame, mob, mob_control, node_barplot, node_bivplot, node_boxplot, node_density, node_hist, node_inner, node_scatterplot, node_surv, node_terminal, nodes, party_intern, prettytree, proximity, ptrafo, response, reweight, sctest.mob, treeresponse, varimp, varimpAUC, where > > data(treepipit, package = "coin") > ct <- ctree(counts ~ ., data = treepipit) > stopifnot(isequal(predict(ct), predict(ct, newdata = treepipit))) > > > data(GlaucomaM, package = "TH.data") > ct <- ctree(Class ~ ., data = GlaucomaM) > stopifnot(isequal(predict(ct), predict(ct, newdata = GlaucomaM))) > stopifnot(isequal(predict(ct, type = "prob"), predict(ct, type = "prob", + newdata = GlaucomaM))) > stopifnot(isequal(predict(ct, type = "node"), predict(ct, type = "node", + newdata = GlaucomaM))) > stopifnot(isequal(predict(ct, type = "prob"), treeresponse(ct))) > > data("GBSG2", package = "TH.data") > > GBSG2tree <- ctree(Surv(time, cens) ~ ., data = GBSG2) > stopifnot(isequal(GBSG2tree@predict_response(), + GBSG2tree@predict_response(newdata = GBSG2))) > stopifnot(isequal(GBSG2tree@cond_distr_response(), + GBSG2tree@cond_distr_response(newdata = GBSG2))) > > 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) [1] "ME" "symptoms" "benefit" "HIST" "BSE" "DECT" > mtree <- ctree(ME ~ ., data = mammoexp) > stopifnot(isequal(predict(mtree), predict(mtree, newdata = mammoexp))) > stopifnot(isequal(predict(mtree), predict(mtree, newdata = mammoexp))) > > proc.time() user system elapsed 1.536 0.036 1.567 party/tests/RandomForest-regtest.Rout.save0000644000176200001440000001600414172231364020445 0ustar liggesusers R version 4.1.1 (2021-08-10) -- "Kick Things" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: x86_64-pc-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. > > RNGversion("3.5.2") Warning message: In RNGkind("Mersenne-Twister", "Inversion", "Rounding") : non-uniform 'Rounding' sampler used > set.seed(290875) > library("party") Loading required package: grid Loading required package: mvtnorm Loading required package: modeltools Loading required package: stats4 Loading required package: strucchange Loading required package: zoo Attaching package: 'zoo' The following objects are masked from 'package:base': as.Date, as.Date.numeric Loading required package: sandwich > if (!require("TH.data")) + stop("cannot load package TH.data") Loading required package: TH.data Loading required package: survival Loading required package: MASS Attaching package: 'TH.data' The following object is masked from 'package:MASS': geyser > if (!require("coin")) + stop("cannot load package coin") Loading required 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,]) $`1` Call: survfit(formula = y ~ 1, weights = weights) records n events median 0.95LCL 0.95UCL [1,] 146 30 15.9 1753 1481 NA $`2` Call: survfit(formula = y ~ 1, weights = weights) records n events median 0.95LCL 0.95UCL [1,] 148 30 13.4 1975 1343 2018 > > ### give it a try, at least > varimp(rf, pre1.0_0 = TRUE) ag at as an ai 0.0000000000 -0.0023148148 0.0009259259 0.0009259259 0.0078703704 eag eat eas ean eai 0.0000000000 0.0000000000 0.0000000000 0.0013888889 -0.0009259259 abrg abrt abrs abrn abri 0.0000000000 0.0000000000 0.0032407407 0.0027777778 0.0041666667 hic mhcg mhct mhcs mhcn 0.0060185185 0.0000000000 0.0013888889 -0.0004629630 0.0027777778 mhci phcg phct phcs phcn 0.0078703704 0.0060185185 0.0000000000 0.0004629630 0.0018518519 phci hvc vbsg vbst vbss 0.0166666667 0.0032407407 0.0032407407 0.0013888889 0.0000000000 vbsn vbsi vasg vast vass 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000 vasn vasi vbrg vbrt vbrs 0.0000000000 0.0046296296 0.0000000000 0.0018518519 0.0004629630 vbrn vbri varg vart vars 0.0032407407 0.0004629630 0.0351851852 0.0000000000 0.0245370370 varn vari mdg mdt mds 0.0129629630 0.0481481481 0.0000000000 0.0000000000 -0.0013888889 mdn mdi tmg tmt tms 0.0000000000 0.0000000000 0.0273148148 0.0000000000 0.0097222222 tmn tmi mr rnf mdic -0.0023148148 0.0226851852 0.0000000000 0.0037037037 0.0055555556 emd mv 0.0000000000 -0.0009259259 > > P <- proximity(rf) > stopifnot(max(abs(P - t(P))) == 0) > > P[1:10,1:10] 2 43 25 65 70 16 6 2 1.0000000 0.26666667 0.7666667 0.20000000 0.10000000 0.13333333 0.70000000 43 0.2666667 1.00000000 0.2000000 0.03333333 0.06666667 0.36666667 0.23333333 25 0.7666667 0.20000000 1.0000000 0.26666667 0.10000000 0.10000000 0.76666667 65 0.2000000 0.03333333 0.2666667 1.00000000 0.00000000 0.03333333 0.33333333 70 0.1000000 0.06666667 0.1000000 0.00000000 1.00000000 0.23333333 0.06666667 16 0.1333333 0.36666667 0.1000000 0.03333333 0.23333333 1.00000000 0.10000000 6 0.7000000 0.23333333 0.7666667 0.33333333 0.06666667 0.10000000 1.00000000 5 0.5333333 0.06666667 0.6000000 0.46666667 0.10000000 0.06666667 0.63333333 12 0.5000000 0.06666667 0.5000000 0.50000000 0.10000000 0.06666667 0.53333333 63 0.4666667 0.23333333 0.5000000 0.23333333 0.16666667 0.13333333 0.56666667 5 12 63 2 0.53333333 0.50000000 0.4666667 43 0.06666667 0.06666667 0.2333333 25 0.60000000 0.50000000 0.5000000 65 0.46666667 0.50000000 0.2333333 70 0.10000000 0.10000000 0.1666667 16 0.06666667 0.06666667 0.1333333 6 0.63333333 0.53333333 0.5666667 5 1.00000000 0.83333333 0.4333333 12 0.83333333 1.00000000 0.5000000 63 0.43333333 0.50000000 1.0000000 > > ### variable importances > a <- cforest(Species ~ ., data = iris, + control = cforest_unbiased(mtry = 2, ntree = 10)) > varimp(a, pre1.0_0 = TRUE) Sepal.Length Sepal.Width Petal.Length Petal.Width 0.06181818 0.00000000 0.20727273 0.33636364 > varimp(a, conditional = TRUE) Sepal.Length Sepal.Width Petal.Length Petal.Width 0.007272727 0.000000000 0.103636364 0.243636364 > > airq <- subset(airquality, complete.cases(airquality)) > a <- cforest(Ozone ~ ., data = airq, + control = cforest_unbiased(mtry = 2, ntree = 10)) > varimp(a, pre1.0_0 = TRUE) Solar.R Wind Temp Month Day 137.76700 550.19004 295.40387 16.21802 5.42690 > varimp(a, conditional = TRUE) Solar.R Wind Temp Month Day 67.713060 341.413307 227.670123 4.257196 3.204209 > > data("mammoexp", package = "TH.data") > a <- cforest(ME ~ ., data = mammoexp, control = cforest_classical(ntree = 10)) > varimp(a, pre1.0_0 = TRUE) SYMPT PB HIST BSE DECT 0.02466021 0.01046237 0.01607246 0.01045324 0.00133305 > varimp(a, conditional = TRUE) SYMPT PB HIST BSE DECT 0.019882337 0.009532482 0.006163146 0.007732481 0.003382481 > > 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)) glaucoma normal glaucoma 89 4 normal 1 102 > > proc.time() user system elapsed 2.769 0.094 2.847 party/tests/TestStatistic-regtest.R0000644000176200001440000000176714172231364017176 0ustar liggesusers set.seed(290875) library("party") ### get rid of the NAMESPACE attach(list2env(as.list(asNamespace("party")))) ### ### ### Regression tests for test statistics ### ### functions defined in file `./src/TestStatistic.c' ### tests for function C_maxabsTeststatistic xf <- gl(3, 10) yf <- gl(3, 10)[sample(1:30)] x <- sapply(levels(xf), function(l) as.numeric(xf == l)) colnames(x) <- NULL y <- sapply(levels(yf), function(l) as.numeric(yf == l)) colnames(y) <- NULL weights <- sample(1:30) linstat <- LinearStatistic(x, y, weights) expcov <- ExpectCovarLinearStatistic(x, y, weights) maxabs <- max(abs(linstat - expcov@expectation) / sqrt(diag(expcov@covariance))) stopifnot(isequal(maxabs, maxabsTestStatistic(linstat, expcov@expectation, expcov@covariance, 1e-10))) expcov@covariance[1,1] <- 1e-12 stopifnot(isequal(maxabs, maxabsTestStatistic(linstat, expcov@expectation, expcov@covariance, 1e-10))) ### tests for function C_quadformTeststatistic ### -> see LinearStatistic-regtest.R party/tests/t1.RData0000644000176200001440000061042314172231364014025 0ustar liggesusers‹ìÜuTÑÿ/|°@A:T@AiQ ñƒŠ" ‚ˆJ+¥tˆ”twwwwnº[F¤Ë‡ß³îÖºþý}î³îï°Öáu&Îì™=µß3sŽ”°ì3²g°°°Ža;Ž}ð:x{âØÁ?l,,¢ÓÿÕßè:ÖqÒƒwD/,¬3îB¸XÿïFŒ1bü¿\"Œ1bĈ#FŒ1bĈã\2Œ1bĈ#FŒ1bĈãÌs1bĈ#FŒ1bĈ#Æÿ¸ç1bĈ#FŒ1bĈ#FŒÿqé0bĈ#FŒ1bĈ#FŒÿq/ýÏë¿gôÿôó1bĈñÿÿO_ïÆˆ#FŒ1bĈ#FŒÿ'H‡#FŒ1bĈ#FŒ1büùÿ“ç®0bĈ#FŒ1bĈ#Æÿ«eÀˆ#FŒ1bĈ#FŒ1þǽ‚#FŒ1bĈ#FŒ1büËô?S¬ÿõ;£¨¨,ðrËcëßÿÕ '}x²]ÞÎo [Å5"¬^°±h—y%gœµ»Çk‘þ™•=§Û¦ðµ7­æ¢ñò”„ƒê\)ÿ»û°i%A¼vÑa÷0ÍÈcX›‡B±Ëï÷¿„L5Â@\!øò”UTvvB]Ì6Lcü!Ø%’¨–â„O¶CPvÁ†¯"Äj rHˆÔ¡ðA»Ì¨ûÆ$¥ù-´!Â#ÎY¯'T.‡øå»üÖË>úP·ã^a&´N¡ŸÅ Öà?(³jò˜ ¬×%·!1¾Ö–oC&}Ä£ÐùËPô»^ˆõ¯ŽÊ3ˆAšÃYË…FÍÃéÊÙid, á#÷wQ.—OÖ«ênB$Ÿ.ÕCѽ´4Å0äè ûѧ‚ÇÃ&îuÖm±èqჯZ&'¡ ÕÛOáŽ9þõk‰@Ü’?ò#Ü9×ÁåÕW胚¢ }×ÇfÈ~é|Í:êpüŒ·x”³ÆÃGÝx×êöµlàix¥Î’œD{Ç-Ú#CåmdS³› ÆLö‹D!yÿ¾ÇÓB¼€&JÚÊ|kžý³þRíˆkœ¨sÁýÁF0â[˜•<™ö¤Ã$Ôßæ í{r“Œ!$½;õá\Ï9H8æ¨6L@òÏt^ã+ ÆB“Ä ²7Wè.V9ÍÕ³ÍÂéËÖDOP„JR–Å›nàR¡WéЇDOÅ(3ˆ®g kÊÕEoó2 îjZN/Ÿyù~&ã ½éN“MsØ?}À C~'‹v'VëQ.7xò:÷§)—d¢øŠrBÉË<ü cÛSê…ª-ê¾ãZ{âš>û.YA™ø(絫jPgªéÚê9åò‘ÚÑÛíŸ-ëäñrïæŒTº¿¬Ž¹Q Ñíú¹ï˜Õ•ƒI|£.|á Õ w{¡ŠjújHùnß_S”€Â+/ÜYyà^cÇätÃPÛÅôNò)òÃáÜCÛ”¸ôá΃W‡ë=qíâq€4Cã­mÃÃåȾ~«ê÷-ˆ ¬÷ûBv R]å/p”݃<¯©•õ ÈwÃ2‹·„è™ \ë~È#»k#H‘ †¡9çýrñÖ»pîKžÐ2¬OÞ†S/R?‰p–ëÝw®äÃKþcÔ[î‰1J ²×j×4 IÞÇ}NýÍü톸24¼¦o7øõª²ƒ¼¢'E )åž8ió{Ðÿz»d}"%ͤçÍ.£Té›~]í7Àë‰ò9Ö%),ªÃ›®ùµË¿‹ØUjƒèl;ýn²¿(ø»Ÿzˆ¾éχ×>Ñk×<0Û¢*”v6E¿….T‹1¾‡”’|_»Lä¿–—̪Àú>•t]hƒâë gØ”ÃðM‰=!¾2z¥ñ½ÖI›1voÖžBá&ë(N;/r)¤hÛu_hll–š¤h å”Ö¹Êônà ^"Þ=œÊš…ÊAß,"³Ï¨‚^Л yó3Ù4jˆMŸ¦4LØBJý‰dEŸ¸ô³…ÐU†Oÿà&Æx«8¿¹XT`#wÐ]"aܧ÷ r.Z]-† gÊõ8aËël˜!x5ö_­gäQFîßüçoã3!%=[Ù×/¡ô”€ÀvP)Š>;aI™¼e®›Æ?ˆ„ì–À1MñE4Jçµ²q²zÒ6TÂUÅP“‰eÝùW¡b?ÿ«Ð¥©Ãí¦˜òÂ9£±AäOäV@—%8Öòú•ÐA§Ò–Uö2ÇåýAÖ]A·5iˆN³k}9ò¶f7å !ã¼ôeêS¨\I. 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Óî„iw´;aÚ0íN˜vçÿ˜öÿë/¬ÏÒ{HÏÑ£ýþç/«ÿçÿósôé9¦gõ~£zí›þ½ÿ|ûÿ–Üb\BÆparty/tests/RandomForest-regtest.R0000644000176200001440000000313614172231364016762 0ustar liggesusers RNGversion("3.5.2") 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)) party/tests/mob.Rout.save0000644000176200001440000003440314531101500015132 0ustar liggesusers R version 4.3.2 (2023-10-31) -- "Eye Holes" Copyright (C) 2023 The R Foundation for Statistical Computing Platform: x86_64-pc-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. > library("party") Loading required package: grid Loading required package: mvtnorm Loading required package: modeltools Loading required package: stats4 Loading required package: strucchange Loading required package: zoo Attaching package: 'zoo' The following objects are masked from 'package:base': as.Date, as.Date.numeric Loading required package: sandwich > > data("BostonHousing", package = "mlbench") > BostonHousing$lstat <- log(BostonHousing$lstat) > BostonHousing$rm <- BostonHousing$rm^2 > BostonHousing$chas <- factor(BostonHousing$chas, levels = 0:1, labels = c("no", "yes")) > BostonHousing$rad <- factor(BostonHousing$rad, ordered = TRUE) > fmBH <- mob(medv ~ lstat + rm | zn + indus + chas + nox + age + dis + rad + tax + crim + b + ptratio, + control = mob_control(minsplit = 40, verbose = TRUE), + data = BostonHousing, model = linearModel) ------------------------------------------- Fluctuation tests of splitting variables: zn indus chas nox age statistic 3.363356e+01 6.532322e+01 2.275635e+01 8.136281e+01 3.675850e+01 p.value 1.023987e-04 1.363602e-11 4.993053e-04 3.489797e-15 2.263798e-05 dis rad tax crim b statistic 6.848533e+01 1.153641e+02 9.068440e+01 8.655065e+01 3.627629e+01 p.value 2.693904e-12 7.087680e-13 2.735524e-17 2.356348e-16 2.860686e-05 ptratio statistic 7.221524e+01 p.value 3.953623e-13 Best splitting variable: tax Perform split? yes ------------------------------------------- Node properties: tax <= 432; criterion = 1, statistic = 115.364 ------------------------------------------- Fluctuation tests of splitting variables: zn indus chas nox age statistic 27.785009791 21.3329346 8.0272421 23.774323202 11.9204284 p.value 0.001494064 0.0285193 0.4005192 0.009518732 0.7666366 dis rad tax crim b statistic 24.268011081 50.481593270 3.523250e+01 3.276813e+01 9.0363245 p.value 0.007601532 0.003437763 4.275527e-05 1.404487e-04 0.9871502 ptratio statistic 4.510680e+01 p.value 3.309747e-07 Best splitting variable: ptratio Perform split? yes ------------------------------------------- Node properties: ptratio <= 15.2; criterion = 1, statistic = 50.482 ------------------------------------------- Fluctuation tests of splitting variables: zn indus chas nox age statistic 3.233350e+01 22.26864036 12.93407112 22.10510234 20.41295354 p.value 1.229678e-04 0.01504788 0.05259509 0.01622098 0.03499731 dis rad tax crim b statistic 17.7204735 5.526565e+01 2.879128e+01 20.28503194 6.5549665 p.value 0.1091769 7.112214e-04 6.916307e-04 0.03706934 0.9999522 ptratio statistic 4.789850e+01 p.value 4.738855e-08 Best splitting variable: ptratio Perform split? yes ------------------------------------------- Node properties: ptratio <= 19.6; criterion = 1, statistic = 55.266 ------------------------------------------- Fluctuation tests of splitting variables: zn indus chas nox age dis statistic 14.971474 14.6477733 7.1172962 14.3455158 8.2176363 16.1112185 p.value 0.280361 0.3134649 0.5405005 0.3467974 0.9906672 0.1847818 rad tax crim b ptratio statistic 43.17824350 3.447271e+01 9.340075 8.7773142 10.8469969 p.value 0.03281124 4.281939e-05 0.952996 0.9772696 0.8202694 Best splitting variable: tax Perform split? yes ------------------------------------------- Node properties: tax <= 265; criterion = 1, statistic = 43.178 ------------------------------------------- Fluctuation tests of splitting variables: zn indus chas nox age dis statistic 11.998039 7.3971233 7.227770 9.2936189 14.3023962 8.9239826 p.value 0.574642 0.9931875 0.522447 0.9119621 0.2886603 0.9389895 rad tax crim b ptratio statistic 33.1746444 16.6666129 11.7143758 9.9050903 11.5927528 p.value 0.3926249 0.1206412 0.6153455 0.8539893 0.6328381 Best splitting variable: tax Perform split? no ------------------------------------------- ------------------------------------------- Fluctuation tests of splitting variables: zn indus chas nox age dis statistic 10.9187926 9.0917078 2.754081e+01 17.39203006 4.6282349 11.9581600 p.value 0.7091039 0.9172303 4.987667e-05 0.08922543 0.9999992 0.5607267 rad tax crim b ptratio statistic 0.2557803 10.9076165 3.711175 3.158329 9.8865054 p.value 1.0000000 0.7106612 1.000000 1.000000 0.8410064 Best splitting variable: chas Perform split? yes ------------------------------------------- Splitting factor variable, objective function: no Inf No admissable split found in 'chas' > fmBH 1) tax <= 432; criterion = 1, statistic = 115.364 2) ptratio <= 15.2; criterion = 1, statistic = 50.482 3)* weights = 72 Terminal node model Linear model with coefficients: (Intercept) lstat rm 9.2349 -4.9391 0.6859 2) ptratio > 15.2 4) ptratio <= 19.6; criterion = 1, statistic = 55.266 5) tax <= 265; criterion = 1, statistic = 43.178 6)* weights = 63 Terminal node model Linear model with coefficients: (Intercept) lstat rm 3.9637 -2.7663 0.6881 5) tax > 265 7)* weights = 162 Terminal node model Linear model with coefficients: (Intercept) lstat rm -1.7984 -0.2677 0.6539 4) ptratio > 19.6 8)* weights = 56 Terminal node model Linear model with coefficients: (Intercept) lstat rm 17.5865 -4.6190 0.3387 1) tax > 432 9)* weights = 153 Terminal node model Linear model with coefficients: (Intercept) lstat rm 68.2971 -16.3540 -0.1478 > summary(fmBH) $`3` Call: NULL Weighted Residuals: Min 1Q Median 3Q Max -7.910 0.000 0.000 0.000 6.632 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 9.23488 3.95128 2.337 0.0223 * lstat -4.93910 0.88285 -5.595 4.14e-07 *** rm 0.68591 0.05136 13.354 < 2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 3.413 on 69 degrees of freedom Multiple R-squared: 0.922, Adjusted R-squared: 0.9197 F-statistic: 407.8 on 2 and 69 DF, p-value: < 2.2e-16 $`6` Call: NULL Weighted Residuals: Min 1Q Median 3Q Max -4.614 0.000 0.000 0.000 12.473 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 3.96372 5.00781 0.792 0.43177 lstat -2.76629 1.00406 -2.755 0.00776 ** rm 0.68813 0.07716 8.918 1.36e-12 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 3.2 on 60 degrees of freedom Multiple R-squared: 0.8176, Adjusted R-squared: 0.8115 F-statistic: 134.5 on 2 and 60 DF, p-value: < 2.2e-16 $`7` Call: NULL Weighted Residuals: Min 1Q Median 3Q Max -9.092 0.000 0.000 0.000 10.236 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.79839 2.84702 -0.632 0.529 lstat -0.26771 0.69581 -0.385 0.701 rm 0.65389 0.03757 17.404 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 2.652 on 159 degrees of freedom Multiple R-squared: 0.8173, Adjusted R-squared: 0.815 F-statistic: 355.6 on 2 and 159 DF, p-value: < 2.2e-16 $`8` Call: NULL Weighted Residuals: Min 1Q Median 3Q Max -8.466 0.000 0.000 0.000 4.947 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 17.58649 4.21666 4.171 0.000113 *** lstat -4.61897 0.84025 -5.497 1.13e-06 *** rm 0.33867 0.07574 4.472 4.13e-05 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 2.197 on 53 degrees of freedom Multiple R-squared: 0.6446, Adjusted R-squared: 0.6312 F-statistic: 48.07 on 2 and 53 DF, p-value: 1.238e-12 $`9` Call: NULL Weighted Residuals: Min 1Q Median 3Q Max -10.56 0.00 0.00 0.00 24.28 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 68.29709 3.83284 17.819 < 2e-16 *** lstat -16.35401 0.96577 -16.934 < 2e-16 *** rm -0.14779 0.05047 -2.928 0.00394 ** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 4.689 on 150 degrees of freedom Multiple R-squared: 0.6649, Adjusted R-squared: 0.6604 F-statistic: 148.8 on 2 and 150 DF, p-value: < 2.2e-16 > > ### check for one-node tree > fmBH <- try(mob(medv ~ lstat + rm | zn, control = mob_control(minsplit = 4000, verbose = TRUE), + data = BostonHousing, model = linearModel)) > stopifnot(class(fmBH) != "try-error") > > > data("PimaIndiansDiabetes", package = "mlbench") > fmPID <- mob(diabetes ~ glucose | pregnant + pressure + triceps + insulin + mass + pedigree + age, + control = mob_control(verbose = TRUE), + data = PimaIndiansDiabetes, model = glinearModel, family = binomial()) ------------------------------------------- Fluctuation tests of splitting variables: pregnant pressure triceps insulin mass pedigree statistic 2.988542e+01 7.5024235 15.94095417 6.5969297 4.880982e+01 18.33476114 p.value 9.778517e-05 0.9104325 0.06660773 0.9701412 8.316815e-09 0.02275017 age statistic 4.351412e+01 p.value 1.182811e-07 Best splitting variable: mass Perform split? yes ------------------------------------------- Node properties: mass <= 26.3; criterion = 1, statistic = 48.81 ------------------------------------------- Fluctuation tests of splitting variables: pregnant pressure triceps insulin mass pedigree age statistic 10.3924070 4.353740 5.911229 3.7855726 10.4748907 3.6263026 6.0978662 p.value 0.4903221 0.999824 0.986895 0.9999888 0.4785454 0.9999958 0.9817742 Best splitting variable: mass Perform split? no ------------------------------------------- ------------------------------------------- Fluctuation tests of splitting variables: pregnant pressure triceps insulin mass pedigree statistic 2.673912e+01 6.1757583 7.346804 7.8963977 9.1545915 17.96438828 p.value 4.434356e-04 0.9845137 0.922646 0.8700398 0.7033477 0.02677105 age statistic 3.498466e+01 p.value 8.098640e-06 Best splitting variable: age Perform split? yes ------------------------------------------- Node properties: age <= 30; criterion = 1, statistic = 34.985 ------------------------------------------- Fluctuation tests of splitting variables: pregnant pressure triceps insulin mass pedigree age statistic 4.3749991 9.4006532 7.661457 9.0583568 5.4287861 5.640420 6.3088818 p.value 0.9998989 0.6656073 0.893893 0.7168659 0.9967316 0.994611 0.9804133 Best splitting variable: pressure Perform split? no ------------------------------------------- ------------------------------------------- Fluctuation tests of splitting variables: pregnant pressure triceps insulin mass pedigree statistic 7.7282903 1.935271 3.6078314 4.9703223 10.136944 11.9004129 p.value 0.8882324 1.000000 0.9999987 0.9991162 0.555382 0.3205095 age statistic 10.1330698 p.value 0.5559631 Best splitting variable: pedigree Perform split? no ------------------------------------------- > fmPID 1) mass <= 26.3; criterion = 1, statistic = 48.81 2)* weights = 167 Terminal node model Binomial GLM with coefficients: (Intercept) glucose -9.95151 0.05871 1) mass > 26.3 3) age <= 30; criterion = 1, statistic = 34.985 4)* weights = 304 Terminal node model Binomial GLM with coefficients: (Intercept) glucose -6.70559 0.04684 3) age > 30 5)* weights = 297 Terminal node model Binomial GLM with coefficients: (Intercept) glucose -2.77095 0.02354 > summary(fmPID) $`2` Call: NULL Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -9.95151 1.74013 -5.719 1.07e-08 *** glucose 0.05871 0.01211 4.846 1.26e-06 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 96.202 on 166 degrees of freedom Residual deviance: 60.502 on 165 degrees of freedom AIC: 64.502 Number of Fisher Scoring iterations: 6 $`4` Call: NULL Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -6.705586 0.800193 -8.380 < 2e-16 *** glucose 0.046837 0.006208 7.544 4.54e-14 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 364.01 on 303 degrees of freedom Residual deviance: 280.98 on 302 degrees of freedom AIC: 284.98 Number of Fisher Scoring iterations: 5 $`5` Call: NULL Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -2.770954 0.548241 -5.054 4.32e-07 *** glucose 0.023536 0.004202 5.601 2.13e-08 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 407.11 on 296 degrees of freedom Residual deviance: 369.43 on 295 degrees of freedom AIC: 373.43 Number of Fisher Scoring iterations: 4 > > > proc.time() user system elapsed 1.371 0.072 1.438 party/tests/Examples/0000755000176200001440000000000014172231364014334 5ustar liggesusersparty/tests/Examples/party-Ex.Rout.save0000644000176200001440000007032014172231364017657 0ustar liggesusers R version 4.1.1 (2021-08-10) -- "Kick Things" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: x86_64-pc-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. Natural language support but running in an English locale 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. > pkgname <- "party" > source(file.path(R.home("share"), "R", "examples-header.R")) > options(warn = 1) > library('party') Loading required package: grid Loading required package: mvtnorm Loading required package: modeltools Loading required package: stats4 Loading required package: strucchange Loading required package: zoo Attaching package: ‘zoo’ The following objects are masked from ‘package:base’: as.Date, as.Date.numeric Loading required package: sandwich > > base::assign(".oldSearch", base::search(), pos = 'CheckExEnv') > base::assign(".old_wd", base::getwd(), pos = 'CheckExEnv') > cleanEx() > nameEx("BinaryTree-class") > ### * BinaryTree-class > > flush(stderr()); flush(stdout()) > > ### Name: BinaryTree Class > ### Title: Class "BinaryTree" > ### Aliases: BinaryTree-class weights weights-methods > ### weights,BinaryTree-method show,BinaryTree-method where where-methods > ### where,BinaryTree-method response response-methods > ### response,BinaryTree-method nodes nodes-methods > ### nodes,BinaryTree,integer-method nodes,BinaryTree,numeric-method > ### treeresponse treeresponse-methods treeresponse,BinaryTree-method > ### Keywords: classes > > ### ** Examples > > > set.seed(290875) > > airq <- subset(airquality, !is.na(Ozone)) > airct <- ctree(Ozone ~ ., data = airq, + controls = ctree_control(maxsurrogate = 3)) > > ### distribution of responses in the terminal nodes > plot(airq$Ozone ~ as.factor(where(airct))) > > ### get all terminal nodes from the tree > nodes(airct, unique(where(airct))) [[1]] 5)* weights = 48 [[2]] 3)* weights = 10 [[3]] 6)* weights = 21 [[4]] 9)* weights = 7 [[5]] 8)* weights = 30 > > ### extract weights and compute predictions > pmean <- sapply(weights(airct), function(w) weighted.mean(airq$Ozone, w)) > > ### the same as > drop(Predict(airct)) [1] 18.47917 18.47917 18.47917 18.47917 18.47917 18.47917 18.47917 18.47917 [9] 55.60000 18.47917 18.47917 18.47917 18.47917 18.47917 18.47917 18.47917 [17] 18.47917 18.47917 18.47917 18.47917 18.47917 18.47917 18.47917 31.14286 [25] 55.60000 18.47917 31.14286 48.71429 48.71429 31.14286 18.47917 18.47917 [33] 18.47917 18.47917 18.47917 81.63333 81.63333 31.14286 81.63333 48.71429 [41] 81.63333 81.63333 81.63333 81.63333 18.47917 31.14286 31.14286 55.60000 [49] 31.14286 81.63333 81.63333 48.71429 55.60000 81.63333 81.63333 31.14286 [57] 48.71429 81.63333 81.63333 81.63333 31.14286 55.60000 31.14286 31.14286 [65] 81.63333 81.63333 81.63333 81.63333 81.63333 81.63333 48.71429 31.14286 [73] 31.14286 18.47917 55.60000 18.47917 31.14286 31.14286 18.47917 18.47917 [81] 31.14286 55.60000 81.63333 81.63333 81.63333 81.63333 81.63333 81.63333 [89] 81.63333 81.63333 81.63333 81.63333 48.71429 31.14286 31.14286 18.47917 [97] 18.47917 31.14286 18.47917 55.60000 18.47917 18.47917 55.60000 18.47917 [105] 18.47917 18.47917 31.14286 18.47917 18.47917 31.14286 18.47917 18.47917 [113] 55.60000 18.47917 18.47917 18.47917 > > ### or > unlist(treeresponse(airct)) [1] 18.47917 18.47917 18.47917 18.47917 18.47917 18.47917 18.47917 18.47917 [9] 55.60000 18.47917 18.47917 18.47917 18.47917 18.47917 18.47917 18.47917 [17] 18.47917 18.47917 18.47917 18.47917 18.47917 18.47917 18.47917 31.14286 [25] 55.60000 18.47917 31.14286 48.71429 48.71429 31.14286 18.47917 18.47917 [33] 18.47917 18.47917 18.47917 81.63333 81.63333 31.14286 81.63333 48.71429 [41] 81.63333 81.63333 81.63333 81.63333 18.47917 31.14286 31.14286 55.60000 [49] 31.14286 81.63333 81.63333 48.71429 55.60000 81.63333 81.63333 31.14286 [57] 48.71429 81.63333 81.63333 81.63333 31.14286 55.60000 31.14286 31.14286 [65] 81.63333 81.63333 81.63333 81.63333 81.63333 81.63333 48.71429 31.14286 [73] 31.14286 18.47917 55.60000 18.47917 31.14286 31.14286 18.47917 18.47917 [81] 31.14286 55.60000 81.63333 81.63333 81.63333 81.63333 81.63333 81.63333 [89] 81.63333 81.63333 81.63333 81.63333 48.71429 31.14286 31.14286 18.47917 [97] 18.47917 31.14286 18.47917 55.60000 18.47917 18.47917 55.60000 18.47917 [105] 18.47917 18.47917 31.14286 18.47917 18.47917 31.14286 18.47917 18.47917 [113] 55.60000 18.47917 18.47917 18.47917 > > ### don't use the mean but the median as prediction in each terminal node > pmedian <- sapply(weights(airct), function(w) + median(airq$Ozone[rep(1:nrow(airq), w)])) > > plot(airq$Ozone, pmean, col = "red") > points(airq$Ozone, pmedian, col = "blue") > > > > cleanEx() > nameEx("RandomForest-class") > ### * RandomForest-class > > flush(stderr()); flush(stdout()) > > ### Name: RandomForest-class > ### Title: Class "RandomForest" > ### Aliases: RandomForest-class treeresponse,RandomForest-method > ### weights,RandomForest-method where,RandomForest-method > ### show,RandomForest-method > ### Keywords: classes > > ### ** Examples > > > set.seed(290875) > > ### honest (i.e., out-of-bag) cross-classification of > ### true vs. predicted classes > data("mammoexp", package = "TH.data") > table(mammoexp$ME, predict(cforest(ME ~ ., data = mammoexp, + control = cforest_unbiased(ntree = 50)), + OOB = TRUE)) Never Within a Year Over a Year Never 189 29 16 Within a Year 58 43 3 Over a Year 56 18 0 > > > > cleanEx() > nameEx("Transformations") > ### * Transformations > > flush(stderr()); flush(stdout()) > > ### Name: Transformations > ### Title: Function for Data Transformations > ### Aliases: ptrafo ff_trafo > ### Keywords: manip > > ### ** Examples > > > ### rank a variable > ptrafo(data.frame(y = 1:20), + numeric_trafo = function(x) rank(x, na.last = "keep")) [1,] 1 [2,] 2 [3,] 3 [4,] 4 [5,] 5 [6,] 6 [7,] 7 [8,] 8 [9,] 9 [10,] 10 [11,] 11 [12,] 12 [13,] 13 [14,] 14 [15,] 15 [16,] 16 [17,] 17 [18,] 18 [19,] 19 [20,] 20 attr(,"assign") [1] 1 > > ### dummy coding of a factor > ptrafo(data.frame(y = gl(3, 9))) 1 2 3 1 1 0 0 2 1 0 0 3 1 0 0 4 1 0 0 5 1 0 0 6 1 0 0 7 1 0 0 8 1 0 0 9 1 0 0 10 0 1 0 11 0 1 0 12 0 1 0 13 0 1 0 14 0 1 0 15 0 1 0 16 0 1 0 17 0 1 0 18 0 1 0 19 0 0 1 20 0 0 1 21 0 0 1 22 0 0 1 23 0 0 1 24 0 0 1 25 0 0 1 26 0 0 1 27 0 0 1 attr(,"assign") [1] 1 1 1 > > > > > cleanEx() > nameEx("cforest") > ### * cforest > > flush(stderr()); flush(stdout()) > > ### Name: cforest > ### Title: Random Forest > ### Aliases: cforest proximity > ### Keywords: tree > > ### ** Examples > > > set.seed(290875) > > ### honest (i.e., out-of-bag) cross-classification of > ### true vs. predicted classes > data("mammoexp", package = "TH.data") > table(mammoexp$ME, predict(cforest(ME ~ ., data = mammoexp, + control = cforest_unbiased(ntree = 50)), + OOB = TRUE)) Never Within a Year Over a Year Never 189 29 16 Within a Year 58 43 3 Over a Year 56 18 0 > > ### fit forest to censored response > if (require("TH.data") && require("survival")) { + + data("GBSG2", package = "TH.data") + bst <- cforest(Surv(time, cens) ~ ., data = GBSG2, + control = cforest_unbiased(ntree = 50)) + + ### estimate conditional Kaplan-Meier curves + treeresponse(bst, newdata = GBSG2[1:2,], OOB = TRUE) + + ### if you can't resist to look at individual trees ... + party:::prettytree(bst@ensemble[[1]], names(bst@data@get("input"))) + } Loading required package: TH.data Loading required package: survival Loading required package: MASS Attaching package: ‘TH.data’ The following object is masked from ‘package:MASS’: geyser 1) pnodes <= 3; criterion = 1, statistic = 37.638 2) horTh == {}; criterion = 0.986, statistic = 6.053 3) pnodes <= 2; criterion = 0.905, statistic = 2.788 4) progrec <= 16; criterion = 0.761, statistic = 1.384 5)* weights = 0 4) progrec > 16 6) pnodes <= 1; criterion = 0.857, statistic = 2.149 7) progrec <= 154; criterion = 0.295, statistic = 0.143 8)* weights = 0 7) progrec > 154 9)* weights = 0 6) pnodes > 1 10)* weights = 0 3) pnodes > 2 11) age <= 54; criterion = 0.99, statistic = 6.605 12)* weights = 0 11) age > 54 13)* weights = 0 2) horTh == {} 14) menostat == {}; criterion = 0.895, statistic = 2.635 15) tsize <= 19; criterion = 0.541, statistic = 0.548 16) age <= 45; criterion = 0.979, statistic = 5.301 17)* weights = 0 16) age > 45 18)* weights = 0 15) tsize > 19 19) age <= 37; criterion = 0.943, statistic = 3.631 20)* weights = 0 19) age > 37 21) pnodes <= 2; criterion = 0.951, statistic = 3.866 22) age <= 49; criterion = 0.913, statistic = 2.922 23) tsize <= 23; criterion = 0.606, statistic = 0.728 24)* weights = 0 23) tsize > 23 25)* weights = 0 22) age > 49 26)* weights = 0 21) pnodes > 2 27)* weights = 0 14) menostat == {} 28) tgrade <= 1; criterion = 0.58, statistic = 0.65 29)* weights = 0 28) tgrade > 1 30) progrec <= 206; criterion = 0.874, statistic = 2.337 31) tsize <= 30; criterion = 0.847, statistic = 2.04 32) tgrade <= 2; criterion = 0.788, statistic = 1.558 33) pnodes <= 1; criterion = 0.141, statistic = 0.032 34)* weights = 0 33) pnodes > 1 35) tsize <= 23; criterion = 0.756, statistic = 1.356 36)* weights = 0 35) tsize > 23 37)* weights = 0 32) tgrade > 2 38)* weights = 0 31) tsize > 30 39)* weights = 0 30) progrec > 206 40)* weights = 0 1) pnodes > 3 41) horTh == {}; criterion = 0.981, statistic = 5.458 42) pnodes <= 13; criterion = 0.982, statistic = 5.549 43) progrec <= 19; criterion = 0.918, statistic = 3.019 44) tgrade <= 2; criterion = 0.887, statistic = 2.518 45)* weights = 0 44) tgrade > 2 46)* weights = 0 43) progrec > 19 47) menostat == {}; criterion = 0.977, statistic = 5.147 48)* weights = 0 47) menostat == {} 49) pnodes <= 6; criterion = 0.6, statistic = 3.518 50)* weights = 0 49) pnodes > 6 51)* weights = 0 42) pnodes > 13 52)* weights = 0 41) horTh == {} 53) estrec <= 79; criterion = 0.997, statistic = 8.922 54) progrec <= 132; criterion = 0.981, statistic = 5.529 55) estrec <= 38; criterion = 0.484, statistic = 0.422 56) age <= 59; criterion = 0.943, statistic = 3.615 57) tsize <= 20; criterion = 0.473, statistic = 0.399 58)* weights = 0 57) tsize > 20 59) progrec <= 0; criterion = 0.552, statistic = 0.576 60)* weights = 0 59) progrec > 0 61) estrec <= 2; criterion = 0.481, statistic = 0.416 62)* weights = 0 61) estrec > 2 63) progrec <= 20; criterion = 0.637, statistic = 1.917 64)* weights = 0 63) progrec > 20 65)* weights = 0 56) age > 59 66)* weights = 0 55) estrec > 38 67)* weights = 0 54) progrec > 132 68)* weights = 0 53) estrec > 79 69) tsize <= 21; criterion = 0.641, statistic = 0.875 70)* weights = 0 69) tsize > 21 71)* weights = 0 > > ### proximity, see ?randomForest > iris.cf <- cforest(Species ~ ., data = iris, + control = cforest_unbiased(mtry = 2)) > iris.mds <- cmdscale(1 - proximity(iris.cf), eig = TRUE) > op <- par(pty="s") > pairs(cbind(iris[,1:4], iris.mds$points), cex = 0.6, gap = 0, + col = c("red", "green", "blue")[as.numeric(iris$Species)], + main = "Iris Data: Predictors and MDS of Proximity Based on cforest") > par(op) > > > > > graphics::par(get("par.postscript", pos = 'CheckExEnv')) > cleanEx() detaching ‘package:TH.data’, ‘package:MASS’, ‘package:survival’ > nameEx("ctree") > ### * ctree > > flush(stderr()); flush(stdout()) > > ### Name: Conditional Inference Trees > ### Title: Conditional Inference Trees > ### Aliases: ctree conditionalTree > ### Keywords: tree > > ### ** Examples > > > set.seed(290875) > > ### regression > airq <- subset(airquality, !is.na(Ozone)) > airct <- ctree(Ozone ~ ., data = airq, + controls = ctree_control(maxsurrogate = 3)) > airct Conditional inference tree with 5 terminal nodes Response: Ozone Inputs: Solar.R, Wind, Temp, Month, Day Number of observations: 116 1) Temp <= 82; criterion = 1, statistic = 56.086 2) Wind <= 6.9; criterion = 0.998, statistic = 12.969 3)* weights = 10 2) Wind > 6.9 4) Temp <= 77; criterion = 0.997, statistic = 11.599 5)* weights = 48 4) Temp > 77 6)* weights = 21 1) Temp > 82 7) Wind <= 10.3; criterion = 0.997, statistic = 11.712 8)* weights = 30 7) Wind > 10.3 9)* weights = 7 > plot(airct) > mean((airq$Ozone - predict(airct))^2) [1] 403.6668 > ### extract terminal node ID, two ways > all.equal(predict(airct, type = "node"), where(airct)) [1] TRUE > > ### classification > irisct <- ctree(Species ~ .,data = iris) > irisct Conditional inference tree with 4 terminal nodes Response: Species Inputs: Sepal.Length, Sepal.Width, Petal.Length, Petal.Width Number of observations: 150 1) Petal.Length <= 1.9; criterion = 1, statistic = 140.264 2)* weights = 50 1) Petal.Length > 1.9 3) Petal.Width <= 1.7; criterion = 1, statistic = 67.894 4) Petal.Length <= 4.8; criterion = 0.999, statistic = 13.865 5)* weights = 46 4) Petal.Length > 4.8 6)* weights = 8 3) Petal.Width > 1.7 7)* weights = 46 > plot(irisct) > table(predict(irisct), iris$Species) setosa versicolor virginica setosa 50 0 0 versicolor 0 49 5 virginica 0 1 45 > > ### estimated class probabilities, a list > tr <- treeresponse(irisct, newdata = iris[1:10,]) > > ### ordinal regression > data("mammoexp", package = "TH.data") > mammoct <- ctree(ME ~ ., data = mammoexp) > plot(mammoct) > > ### estimated class probabilities > treeresponse(mammoct, newdata = mammoexp[1:10,]) [[1]] [1] 0.3990385 0.3798077 0.2211538 [[2]] [1] 0.84070796 0.05309735 0.10619469 [[3]] [1] 0.3990385 0.3798077 0.2211538 [[4]] [1] 0.6153846 0.2087912 0.1758242 [[5]] [1] 0.3990385 0.3798077 0.2211538 [[6]] [1] 0.3990385 0.3798077 0.2211538 [[7]] [1] 0.3990385 0.3798077 0.2211538 [[8]] [1] 0.3990385 0.3798077 0.2211538 [[9]] [1] 0.84070796 0.05309735 0.10619469 [[10]] [1] 0.3990385 0.3798077 0.2211538 > > ### survival analysis > if (require("TH.data") && require("survival")) { + data("GBSG2", package = "TH.data") + GBSG2ct <- ctree(Surv(time, cens) ~ .,data = GBSG2) + plot(GBSG2ct) + treeresponse(GBSG2ct, newdata = GBSG2[1:2,]) + } Loading required package: TH.data Loading required package: survival Loading required package: MASS Attaching package: ‘TH.data’ The following object is masked from ‘package:MASS’: geyser [[1]] Call: survfit(formula = y ~ 1, weights = weights) n events median 0.95LCL 0.95UCL [1,] 248 88 2093 1814 NA [[2]] Call: survfit(formula = y ~ 1, weights = weights) n events median 0.95LCL 0.95UCL [1,] 166 77 1701 1174 2018 > > ### if you are interested in the internals: > ### generate doxygen documentation > ## Not run: > ##D > ##D ### download src package into temp dir > ##D tmpdir <- tempdir() > ##D tgz <- download.packages("party", destdir = tmpdir)[2] > ##D ### extract > ##D untar(tgz, exdir = tmpdir) > ##D wd <- setwd(file.path(tmpdir, "party")) > ##D ### run doxygen (assuming it is there) > ##D system("doxygen inst/doxygen.cfg") > ##D setwd(wd) > ##D ### have fun > ##D browseURL(file.path(tmpdir, "party", "inst", > ##D "documentation", "html", "index.html")) > ##D > ## End(Not run) > > > > cleanEx() detaching ‘package:TH.data’, ‘package:MASS’, ‘package:survival’ > nameEx("mob") > ### * mob > > flush(stderr()); flush(stdout()) > > ### Name: mob > ### Title: Model-based Recursive Partitioning > ### Aliases: mob mob-class coef.mob deviance.mob fitted.mob logLik.mob > ### predict.mob print.mob residuals.mob sctest.mob summary.mob > ### weights.mob > ### Keywords: tree > > ### ** Examples > > > set.seed(290875) > > if(require("mlbench")) { + + ## recursive partitioning of a linear regression model + ## load data + data("BostonHousing", package = "mlbench") + ## and transform variables appropriately (for a linear regression) + BostonHousing$lstat <- log(BostonHousing$lstat) + BostonHousing$rm <- BostonHousing$rm^2 + ## as well as partitioning variables (for fluctuation testing) + BostonHousing$chas <- factor(BostonHousing$chas, levels = 0:1, + labels = c("no", "yes")) + BostonHousing$rad <- factor(BostonHousing$rad, ordered = TRUE) + + ## partition the linear regression model medv ~ lstat + rm + ## with respect to all remaining variables: + fmBH <- mob(medv ~ lstat + rm | zn + indus + chas + nox + age + + dis + rad + tax + crim + b + ptratio, + control = mob_control(minsplit = 40), data = BostonHousing, + model = linearModel) + + ## print the resulting tree + fmBH + ## or better visualize it + plot(fmBH) + + ## extract coefficients in all terminal nodes + coef(fmBH) + ## look at full summary, e.g., for node 7 + summary(fmBH, node = 7) + ## results of parameter stability tests for that node + sctest(fmBH, node = 7) + ## -> no further significant instabilities (at 5% level) + + ## compute mean squared error (on training data) + mean((BostonHousing$medv - fitted(fmBH))^2) + mean(residuals(fmBH)^2) + deviance(fmBH)/sum(weights(fmBH)) + + ## evaluate logLik and AIC + logLik(fmBH) + AIC(fmBH) + ## (Note that this penalizes estimation of error variances, which + ## were treated as nuisance parameters in the fitting process.) + + + ## recursive partitioning of a logistic regression model + ## load data + data("PimaIndiansDiabetes", package = "mlbench") + ## partition logistic regression diabetes ~ glucose + ## wth respect to all remaining variables + fmPID <- mob(diabetes ~ glucose | pregnant + pressure + triceps + + insulin + mass + pedigree + age, + data = PimaIndiansDiabetes, model = glinearModel, + family = binomial()) + + ## fitted model + coef(fmPID) + plot(fmPID) + plot(fmPID, tp_args = list(cdplot = TRUE)) + } Loading required package: mlbench > > > > cleanEx() detaching ‘package:mlbench’ > nameEx("panelfunctions") > ### * panelfunctions > > flush(stderr()); flush(stdout()) > > ### Name: Panel Generating Functions > ### Title: Panel-Generators for Visualization of Party Trees > ### Aliases: node_inner node_terminal edge_simple node_surv node_barplot > ### node_boxplot node_hist node_density node_scatterplot node_bivplot > ### Keywords: hplot > > ### ** Examples > > > set.seed(290875) > > airq <- subset(airquality, !is.na(Ozone)) > airct <- ctree(Ozone ~ ., data = airq) > > ## default: boxplots > plot(airct) > > ## change colors > plot(airct, tp_args = list(col = "blue", fill = hsv(2/3, 0.5, 1))) > ## equivalent to > plot(airct, terminal_panel = node_boxplot(airct, col = "blue", + fill = hsv(2/3, 0.5, 1))) > > ### very simple; the mean is given in each terminal node > plot(airct, type = "simple") > > ### density estimates > plot(airct, terminal_panel = node_density) > > ### histograms > plot(airct, terminal_panel = node_hist(airct, ymax = 0.06, + xscale = c(0, 250))) > > > > cleanEx() > nameEx("plot.BinaryTree") > ### * plot.BinaryTree > > flush(stderr()); flush(stdout()) > > ### Name: Plot BinaryTree > ### Title: Visualization of Binary Regression Trees > ### Aliases: plot.BinaryTree > ### Keywords: hplot > > ### ** Examples > > > set.seed(290875) > > airq <- subset(airquality, !is.na(Ozone)) > airct <- ctree(Ozone ~ ., data = airq) > > ### regression: boxplots in each node > plot(airct, terminal_panel = node_boxplot, drop_terminal = TRUE) > > if(require("TH.data")) { + ## classification: barplots in each node + data("GlaucomaM", package = "TH.data") + glauct <- ctree(Class ~ ., data = GlaucomaM) + plot(glauct) + plot(glauct, inner_panel = node_barplot, + edge_panel = function(ctreeobj, ...) { function(...) invisible() }, + tnex = 1) + + ## survival: Kaplan-Meier curves in each node + data("GBSG2", package = "TH.data") + library("survival") + gbsg2ct <- ctree(Surv(time, cens) ~ ., data = GBSG2) + plot(gbsg2ct) + plot(gbsg2ct, type = "simple") + } Loading required package: TH.data Loading required package: survival Loading required package: MASS Attaching package: ‘TH.data’ The following object is masked from ‘package:MASS’: geyser > > > > > cleanEx() detaching ‘package:TH.data’, ‘package:MASS’, ‘package:survival’ > nameEx("plot.mob") > ### * plot.mob > > flush(stderr()); flush(stdout()) > > ### Name: plot.mob > ### Title: Visualization of MOB Trees > ### Aliases: plot.mob > ### Keywords: hplot > > ### ** Examples > > > set.seed(290875) > > if(require("mlbench")) { + + ## recursive partitioning of a linear regression model + ## load data + data("BostonHousing", package = "mlbench") + ## and transform variables appropriately (for a linear regression) + BostonHousing$lstat <- log(BostonHousing$lstat) + BostonHousing$rm <- BostonHousing$rm^2 + ## as well as partitioning variables (for fluctuation testing) + BostonHousing$chas <- factor(BostonHousing$chas, levels = 0:1, + labels = c("no", "yes")) + BostonHousing$rad <- factor(BostonHousing$rad, ordered = TRUE) + + ## partition the linear regression model medv ~ lstat + rm + ## with respect to all remaining variables: + fm <- mob(medv ~ lstat + rm | zn + indus + chas + nox + age + dis + + rad + tax + crim + b + ptratio, + control = mob_control(minsplit = 40), data = BostonHousing, + model = linearModel) + + ## visualize medv ~ lstat and medv ~ rm + plot(fm) + + ## visualize only one of the two regressors + plot(fm, tp_args = list(which = "lstat"), tnex = 2) + plot(fm, tp_args = list(which = 2), tnex = 2) + + ## omit fitted mean lines + plot(fm, tp_args = list(fitmean = FALSE)) + + ## mixed numerical and categorical regressors + fm2 <- mob(medv ~ lstat + rm + chas | zn + indus + nox + age + + dis + rad, + control = mob_control(minsplit = 100), data = BostonHousing, + model = linearModel) + plot(fm2) + + ## recursive partitioning of a logistic regression model + data("PimaIndiansDiabetes", package = "mlbench") + fmPID <- mob(diabetes ~ glucose | pregnant + pressure + triceps + + insulin + mass + pedigree + age, + data = PimaIndiansDiabetes, model = glinearModel, + family = binomial()) + ## default plot: spinograms with breaks from five point summary + plot(fmPID) + ## use the breaks from hist() instead + plot(fmPID, tp_args = list(fivenum = FALSE)) + ## user-defined breaks + plot(fmPID, tp_args = list(breaks = 0:4 * 50)) + ## CD plots instead of spinograms + plot(fmPID, tp_args = list(cdplot = TRUE)) + ## different smoothing bandwidth + plot(fmPID, tp_args = list(cdplot = TRUE, bw = 15)) + + } Loading required package: mlbench > > > > cleanEx() detaching ‘package:mlbench’ > nameEx("readingSkills") > ### * readingSkills > > flush(stderr()); flush(stdout()) > > ### Name: readingSkills > ### Title: Reading Skills > ### Aliases: readingSkills > ### Keywords: datasets > > ### ** Examples > > > set.seed(290875) > readingSkills.cf <- cforest(score ~ ., data = readingSkills, + control = cforest_unbiased(mtry = 2, ntree = 50)) > > # standard importance > varimp(readingSkills.cf) nativeSpeaker age shoeSize 12.69213 82.26737 13.60017 > # the same modulo random variation > varimp(readingSkills.cf, pre1.0_0 = TRUE) nativeSpeaker age shoeSize 12.88414 79.09714 15.37933 > > # conditional importance, may take a while... > varimp(readingSkills.cf, conditional = TRUE) nativeSpeaker age shoeSize 11.466498 51.125596 1.521413 > > > > > cleanEx() > nameEx("reweight") > ### * reweight > > flush(stderr()); flush(stdout()) > > ### Name: reweight > ### Title: Re-fitting Models with New Weights > ### Aliases: reweight reweight.linearModel reweight.glinearModel > ### Keywords: regression > > ### ** Examples > > ## fit cars regression > mf <- dpp(linearModel, dist ~ speed, data = cars) > fm <- fit(linearModel, mf) > fm Linear model with coefficients: (Intercept) speed -17.579 3.932 > > ## re-fit, excluding the last 4 observations > ww <- c(rep(1, 46), rep(0, 4)) > reweight(fm, ww) Linear model with coefficients: (Intercept) speed -8.723 3.210 > > > > cleanEx() > nameEx("varimp") > ### * varimp > > flush(stderr()); flush(stdout()) > > ### Name: varimp > ### Title: Variable Importance > ### Aliases: varimp varimpAUC > ### Keywords: tree > > ### ** Examples > > > set.seed(290875) > readingSkills.cf <- cforest(score ~ ., data = readingSkills, + control = cforest_unbiased(mtry = 2, ntree = 50)) > > # standard importance > varimp(readingSkills.cf) nativeSpeaker age shoeSize 12.69213 82.26737 13.60017 > # the same modulo random variation > varimp(readingSkills.cf, pre1.0_0 = TRUE) nativeSpeaker age shoeSize 12.88414 79.09714 15.37933 > > # conditional importance, may take a while... > varimp(readingSkills.cf, conditional = TRUE) nativeSpeaker age shoeSize 11.466498 51.125596 1.521413 > > ## Not run: > ##D data("GBSG2", package = "TH.data") > ##D ### add a random covariate for sanity check > ##D set.seed(29) > ##D GBSG2$rand <- runif(nrow(GBSG2)) > ##D object <- cforest(Surv(time, cens) ~ ., data = GBSG2, > ##D control = cforest_unbiased(ntree = 20)) > ##D vi <- varimp(object) > ##D ### compare variable importances and absolute z-statistics > ##D layout(matrix(1:2)) > ##D barplot(vi) > ##D barplot(abs(summary(coxph(Surv(time, cens) ~ ., data = GBSG2))$coeff[,"z"])) > ##D ### looks more or less the same > ##D > ## End(Not run) > > > > ### *