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))) }