CRAN Package Check Results for Package forestFloor

Last updated on 2020-02-19 14:48:15 CET.

Check Details

Version: 1.11.1
Check: tests
Result: ERROR
     Running 'allExamples.R' [0s/1s]
     Running 'rfPermute_supported.R' [0s/0s]
     Running 'stdUsage.R' [11s/12s]
     Running 'testBinaryClass.R' [3s/4s]
     Running 'testCaret.R' [18s/20s]
     Running 'testMultiClass.cpp.R' [6s/7s]
     Running 'test_Xtestmerger.R' [3s/3s]
    Running the tests in 'tests/stdUsage.R' failed.
    Complete output:
     > if(!interactive()) Sys.setenv(RGL_USE_NULL=TRUE) #disable RGL for headless machines
     > library(forestFloor)
     > library(randomForest)
     randomForest 4.6-14
     Type rfNews() to see new features/changes/bug fixes.
     > #simulate data
     > obs=2000
     > vars = 6
     >
     > X = data.frame(replicate(vars,rnorm(obs)))
     > Xtest = data.frame(replicate(vars,rnorm(obs)*3))
     > Y = with(X, X1^2 + sin(X2*pi) + 2 * X3 * X4 + .5 * rnorm(obs))
     >
     >
     > #grow a forest, remeber to include inbag
     > rf41=randomForest(X,Y,keep.inbag = TRUE,sampsize=499,ntree=100,importance = F)
     > #compute feature contributions
     > out = tryCatch({ff41 = forestFloor(rf41,X,bootstrapFC = TRUE,impType=1)},warning = function(w) w)
     > if(out$message != "found nothing of importance, revert to fallback") stop("wrong warning")
     >
     > #if(colnames(ff41$importance) != "%IncMSE") stop("wrong imp")
     > ff41 = forestFloor(rf41,X,bootstrapFC = TRUE,impType=2)
     > if(colnames(ff41$importance) != "IncNodePurity") stop("wrong imp")
     > ff41 = forestFloor(rf41,X,bootstrapFC = TRUE,impType=NULL)
     > if(colnames(ff41$importance) != "IncNodePurity") stop("wrong imp")
     >
     > #grow a forest, remeber to include inbag
     > rf42=randomForest(X,Y,keep.inbag = TRUE,sampsize=499,ntree=100,importance = T)
     > #compute feature contributions
     > ff42 = forestFloor(rf42,X,bootstrapFC = TRUE,impType=1)
     > if(colnames(ff42$importance) != "%IncMSE") stop("wrong imp")
     > ff42 = forestFloor(rf42,X,bootstrapFC = TRUE,impType=2)
     > if(colnames(ff42$importance) != "IncNodePurity") stop("wrong imp")
     > ff42 = forestFloor(rf42,X,bootstrapFC = TRUE,impType=NULL)
     > if(colnames(ff42$importance) != "%IncMSE") stop("wrong imp")
     >
     >
     >
     >
     > #test accuracy of feature contributions
     > #y_hat_OOB = row sum FC + Y_grandMean
     > FC.residuals = rf42$predicted - apply(ff42$FCmatrix[ff42$isTrain,],1,sum) - mean(Y)
     > if(max(abs(FC.residuals))>1E-12) stop(
     + paste0("When testing if: y_hat_OOB = row sum FCmatrix + Y_grandMean
     + one/some FCs error exceed allowed 1e-12, found.error=",max(abs(FC.residuals)))
     + )
     >
     > #test same results are reached with Xtest
     > ff43 = forestFloor(rf42,X,Xtest,bootstrapFC = TRUE)
     > if(max(abs(ff43$FCmatrix[ff43$isTrain,]-ff42$FCmatrix)) > 1E-12) stop(
     + "forestFloor with/without Xtest gives different feature contributions"
     + )
     >
     >
     > #print forestFloor
     > print(ff42)
     this is a forestFloor_regression object
    
     this object can be plotted in 2D with plot(x), see help(plot.forestFloor)
    
     this object can be plotted in 3D with show3d(x), see help(show3d)
    
    
    
     x contains following internal elements:
     FCmatrix X Y imp_ind importance isTrain>
     > #plot partial functions of most important variables first
     > plot(ff42,orderByImportance=TRUE)
     [1] "compute goodness-of-fit with leave-one-out k-nearest neighbor(guassian weighting), kknn package"
     > plot(ff43,orderByImportance=TRUE)
     [1] "compute goodness-of-fit with leave-one-out k-nearest neighbor(guassian weighting), kknn package"
     >
     > #Non interacting functions are well displayed, whereas X3 and X4 are not
     > #by applying different colourgradient, interactions reveal themself
     > #also a k-nearest neighbor fit is applied to evaluate goodness of fit
     > Col=fcol(ff43,3,orderByImportance=FALSE)
     > plot(ff43,col=Col,plot_GOF=TRUE,speed=T)
     [1] "compute goodness-of-fit with leave-one-out k-nearest neighbor(guassian weighting), kknn package"
     >
     >
     > ##make test set grey tone to show if point of test is extrapolated
     > Col=fcol(ff43,3,orderByImportance=FALSE,plotTest="andTrain",alpha=.2)
     > Col[ff43$isTrain] = "#000000FF"
     > plot(ff43,col=Col,speed=T,plotTest="andTrain",plot_GOF=F)
     >
     >
     > #if ever needed, k-nearest neighbor parameters for goodness-of-fit can be access through convolute_ff
     > #a new fit will be calculated and added to forstFloor object as ff42$FCfit
     > ff43 = convolute_ff(ff43,userArgs.kknn=alist(kernel="epanechnikov",kmax=5))
     > plot(ff43,col=Col,plot_GOF=TRUE)
     >
     > #in 3D the interaction between X3 and X reveals itself completely
     > show3d(ff43,3:4,col=Col,plot.rgl=list(size=5),orderByImportance=FALSE)
     > Col=fcol(ff43,1:2,orderByImportance=FALSE)
     > show3d(ff43,1:2,col=Col,plot.rgl=list(size=5),orderByImportance=FALSE)
     >
     >
     > Col=fcol(ff43,1:2,plotTest="andTrain",orderByImportance=FALSE)
     > show3d(ff43,1:2,col=Col,plot.rgl=list(size=5),orderByImportance=FALSE)
     >
     >
     >
     > #although no interaction, a joined additive effect of X1 and X2
     > #colour by FC-component FC1 and FC2 summed
     > Col = fcol(ff43,1:2,orderByImportance=FALSE,X.m=FALSE,RGB=TRUE,plotTest = "a")
     ----------- FAILURE REPORT --------------
     --- failure: the condition has length > 1 ---
     --- srcref ---
     :
     --- package (from environment) ---
     forestFloor
     --- call from context ---
     fcol(ff43, 1:2, orderByImportance = FALSE, X.m = FALSE, RGB = TRUE,
     plotTest = "a")
     --- call from argument ---
     if (!class(colM) %in% c("data.frame", "matrix")) {
     tryCatch({
     colM = matrix(colM, ncol = 1)
     }, error = function(e) stop(paste("input ff was neither data.frame or matrix and \ncould not be coerced to matrix:",
     e$message)))
     }
     --- R stacktrace ---
     where 1: fcol(ff43, 1:2, orderByImportance = FALSE, X.m = FALSE, RGB = TRUE,
     plotTest = "a")
    
     --- value of length: 2 type: logical ---
     [1] FALSE TRUE
     --- function from context ---
     function (ff, cols = NULL, orderByImportance = NULL, plotTest = NULL,
     X.matrix = TRUE, hue = NULL, saturation = NULL, brightness = NULL,
     hue.range = NULL, sat.range = NULL, bri.range = NULL, alpha = NULL,
     RGB = NULL, byResiduals = FALSE, max.df = 3, imp.weight = NULL,
     imp.exp = 1, outlier.lim = 3, RGB.exp = NULL)
     {
     if (!X.matrix)
     if (class(ff) == "forestFloor_multiClass")
     stop("cannot colour by feature contributions for object of class\n 'forestFloor_multiClass'. Set X.matrix=TRUE")
     ib <- function(x, low, high) (x - low) * (high - x) > 0
     span <- function(x, mid, width) if (min(x) != max(x)) {
     ((x - min(x))/(max(x) - min(x)) - 0.5) * width + mid
     }
     else {
     x[] = mid
     }
     auto.range = function(level, low = 0, high = 1) abs(min(level -
     low, high - level)) * 2
     contain = function(x, low = 0, high = 1) {
     x[x > high] = high
     x[x < low] = low
     x
     }
     if (class(ff) %in% c("forestFloor_regression", "forestFloor_multiClass")) {
     plotThese = checkPlotTest(plotTest, ff$isTrain)
     if (!(all(plotThese))) {
     if (class(ff) == "forestFloor_multiClass") {
     ff$FCarray = ff$FCarray[plotThese, , ]
     }
     else {
     if (class(ff) == "forestFloor_regression") {
     ff$FCmatrix = ff$FCmatrix[plotThese, ]
     }
     }
     ff$Y = ff$Y[plotThese]
     ff$X = ff$X[plotThese, ]
     }
     }
     if (class(ff) %in% c("forestFloor_regression", "forestFloor_multiClass")) {
     if (byResiduals) {
     if (is.null(ff$FCfit)) {
     print("no $FCfit found, computing tempoary LOO-kNN-gaussion fit to main affect")
     print("use ff = convolute_ff(ff) to compute a fixed fit")
     ff = convolute_ff(ff)
     }
     colM = ff$FCmatrix - ff$FCfit
     }
     else {
     if (X.matrix)
     colM = ff$X
     else colM = ff$FCmatrix
     }
     if (is.null(imp.weight))
     imp.weight = TRUE
     if (is.null(orderByImportance))
     orderByImportance = TRUE
     }
     else {
     colM = ff
     if (is.null(imp.weight))
     imp.weight = FALSE
     if (is.null(orderByImportance))
     orderByImportance = FALSE
     }
     if (orderByImportance)
     if (class(ff) %in% c("forestFloor_regression", "forestFloor_multiClass")) {
     colM = colM[, ff$imp_ind]
     }
     else {
     warning("orderByImportance=TRUE takes no effect for non 'forestFloor'-class. As if set to NULL or FALSE...")
     }
     if (!class(colM) %in% c("data.frame", "matrix")) {
     tryCatch({
     colM = matrix(colM, ncol = 1)
     }, error = function(e) stop(paste("input ff was neither data.frame or matrix and \ncould not be coerced to matrix:",
     e$message)))
     }
     colM = data.frame(colM)
     if (is.null(cols))
     cols = 1:dim(colM)[2]
     if (length(cols) < 1 || !is.numeric(cols) || any(!cols %in%
     1:dim(colM)[2])) {
     stop("no cols selected or is not integer/numeric or wrong coloumns")
     }
     sel.colM = data.frame(colM[, cols])
     sel.cols = 1:length(cols)
     if (is.null(RGB))
     if (length(cols) == 1)
     RGB = TRUE
     else RGB = FALSE
     if (!RGB) {
     if (is.null(saturation))
     saturation = 0.85
     if (is.null(brightness))
     brightness = 0.75
     if (is.null(hue))
     hue = 0.25
     }
     else {
     if (is.null(saturation))
     saturation = 1
     if (is.null(brightness))
     brightness = 0.75
     if (is.null(hue))
     hue = 0.66
     if (is.null(RGB.exp))
     RGB.exp = 1.2
     if (is.null(hue.range))
     hue.range = 2
     }
     as.numeric.factor <- function(x, rearrange = TRUE) {
     if (is.numeric(x))
     return(x)
     if (rearrange)
     x = match(x, levels(droplevels(x)))
     else x = match(x, levels(x))
     return(x)
     }
     for (i in 1:dim(sel.colM)[2]) {
     if (is.factor(sel.colM[, i])) {
     this.fac = as.numeric.factor(sel.colM[, i])
     sel.colM[, i] = this.fac
     }
     if (is.character(sel.colM[, i]))
     sel.colM[, i] = as.numeric(sel.colM[, i])
     }
     sel.colM = box.outliers(sel.colM, limit = outlier.lim)
     if (imp.weight && length(cols) > 1) {
     if (class(ff) %in% c("forestFloor_regression", "forestFloor_multiClass")) {
     sel.imp = ff$importance[cols]
     non.negative.imp = sel.imp + min(sel.imp)
     sumnorm.imp = non.negative.imp/sum(non.negative.imp)
     exp.imp = sumnorm.imp^imp.exp
     impM = t(replicate(dim(colM)[1], exp.imp))
     sel.colM = sel.colM * impM
     sel.colM = sel.colM/max(sel.colM)
     }
     else {
     warning("importance weighting only possible for class 'forestFloor'")
     }
     }
     if (any(!c(class(hue), class(saturation), class(brightness)) %in%
     c("numeric", "integer"))) {
     stop("hue, saturation and brightness must be of class numeric or integer")
     }
     hue = hue - floor(hue)
     saturation = max(min(saturation, 1), 0)
     brightness = max(min(brightness, 1), 0)
     if (RGB == TRUE) {
     if (is.null(bri.range))
     bri.range = 0.05
     if (is.null(alpha))
     alpha = 0.7
     len.colM = box.outliers(sel.colM, limit = Inf)
     if (dim(len.colM)[2] == 1)
     nX = as.numeric(len.colM[, 1])
     else nX = as.numeric(apply(len.colM, 1, mean))
     hsvcol = t(sapply(nX, function(x) rgb2hsv(x^RGB.exp,
     1 - x^RGB.exp - (1 - x)^RGB.exp, (1 - x)^RGB.exp)))
     hue.vec = hsvcol[, 1] * hue.range + hue
     hue.vec[hue.vec > 1] = hue.vec[hue.vec > 1] - floor(hue.vec[hue.vec >
     1])
     hsvcol[, 1] = hue.vec
     sat.range = auto.range(saturation)
     hsvcol[, 2] = span(hsvcol[, 2], saturation, sat.range)
     hsvcol[, 2] = contain(hsvcol[, 2])
     bri.range = auto.range(brightness)
     hsvcol[, 3] = span(hsvcol[, 3], brightness, bri.range)
     hsvcol[, 3] = contain(hsvcol[, 3])
     colours = apply(hsvcol, 1, function(x) hsv(x[1], x[2],
     x[3], alpha = alpha))
     return(colours)
     }
     col.df = length(cols)
     if (!max.df %in% c(1, 2, 3))
     stop("fcol input 'max.df' must be set to either 1, 2 or 3")
     if (col.df > max.df) {
     len.colM = box.outliers(prcomp(sel.colM)$x[, 1:max.df],
     limit = Inf)
     col.df = max.df
     }
     else {
     len.colM = box.outliers(sel.colM, limit = Inf)
     }
     if (is.null(hue.range)) {
     if (col.df == 1)
     hue.range = 0.85
     if (col.df == 2)
     hue.range = 1
     if (col.df == 3)
     hue.range = 1
     }
     if (is.null(sat.range)) {
     if (col.df == 1)
     sat.range = "not used"
     if (col.df == 2)
     sat.range = auto.range(saturation)
     if (col.df == 3)
     sat.range = auto.range(saturation)
     }
     if (is.null(bri.range)) {
     if (col.df == 1)
     bri.range = "not used"
     if (col.df == 2)
     bri.range = "not used"
     if (col.df == 3)
     bri.range = auto.range(brightness)
     }
     if (is.null(alpha))
     alpha = min(1, 400/dim(len.colM)[1])
     if (col.df == 1) {
     hue.vec = as.numeric(len.colM[, 1]) * hue.range + hue
     hue.vec[hue.vec > 1] = hue.vec[hue.vec > 1] - floor(hue.vec[hue.vec >
     1])
     colours = hsv(h = hue.vec, s = saturation, v = brightness,
     alpha = alpha)
     }
     if (col.df == 2) {
     hsvcol = t(rgb2hsv(len.colM[, 1], len.colM[, 2], 1 -
     apply(len.colM, 1, mean)))
     hue.vec = hsvcol[, 1] * hue.range + hue
     hue.vec[hue.vec > 1] = hue.vec[hue.vec > 1] - 1
     hsvcol[, 1] = hue.vec
     hsvcol[, 2] = ((len.colM[, 1] - mean(len.colM[, 1]))^2 +
     (len.colM[, 2] - mean(len.colM[, 2]))^2)^sat.range *
     saturation
     hsvcol[, 2] = hsvcol[, 2]/max(hsvcol[, 2])
     hsvcol[, 3] = brightness
     colours = hsv(hsvcol[, 1], hsvcol[, 2], hsvcol[, 3],
     alpha = alpha)
     }
     if (col.df == 3) {
     hsvcol = t(rgb2hsv(len.colM[, 1], len.colM[, 2], len.colM[,
     3]))
     hue.vec = hsvcol[, 1] * hue.range + hue
     hue.vec[hue.vec > 1] = hue.vec[hue.vec > 1] - 1
     hsvcol[, 1] = hue.vec
     span.sat = span(hsvcol[, 2], saturation, sat.range)
     hsvcol[, 2] = contain(span.sat)
     mean.bri = apply(len.colM, 1, mean)
     span.bri = span(mean.bri, brightness, bri.range)
     hsvcol[, 3] = contain(span.bri)
     colours = hsv(hsvcol[, 1], hsvcol[, 2], hsvcol[, 3],
     alpha = alpha)
     }
     return(colours)
     }
     <bytecode: 0x6e78750>
     <environment: namespace:forestFloor>
     --- function search by body ---
     Function fcol in namespace forestFloor has this body.
     ----------- END OF FAILURE REPORT --------------
     Error in if (!class(colM) %in% c("data.frame", "matrix")) { :
     the condition has length > 1
     Calls: fcol
     Execution halted
    Running the tests in 'tests/testMultiClass.cpp.R' failed.
    Complete output:
     > if(!interactive()) Sys.setenv(RGL_USE_NULL=TRUE) #disable RGL for headless machines
     > library(randomForest)
     randomForest 4.6-14
     Type rfNews() to see new features/changes/bug fixes.
     > library(forestFloor)
     > require(utils)
     >
     > data(iris)
     > iris
     Sepal.Length Sepal.Width Petal.Length Petal.Width Species
     1 5.1 3.5 1.4 0.2 setosa
     2 4.9 3.0 1.4 0.2 setosa
     3 4.7 3.2 1.3 0.2 setosa
     4 4.6 3.1 1.5 0.2 setosa
     5 5.0 3.6 1.4 0.2 setosa
     6 5.4 3.9 1.7 0.4 setosa
     7 4.6 3.4 1.4 0.3 setosa
     8 5.0 3.4 1.5 0.2 setosa
     9 4.4 2.9 1.4 0.2 setosa
     10 4.9 3.1 1.5 0.1 setosa
     11 5.4 3.7 1.5 0.2 setosa
     12 4.8 3.4 1.6 0.2 setosa
     13 4.8 3.0 1.4 0.1 setosa
     14 4.3 3.0 1.1 0.1 setosa
     15 5.8 4.0 1.2 0.2 setosa
     16 5.7 4.4 1.5 0.4 setosa
     17 5.4 3.9 1.3 0.4 setosa
     18 5.1 3.5 1.4 0.3 setosa
     19 5.7 3.8 1.7 0.3 setosa
     20 5.1 3.8 1.5 0.3 setosa
     21 5.4 3.4 1.7 0.2 setosa
     22 5.1 3.7 1.5 0.4 setosa
     23 4.6 3.6 1.0 0.2 setosa
     24 5.1 3.3 1.7 0.5 setosa
     25 4.8 3.4 1.9 0.2 setosa
     26 5.0 3.0 1.6 0.2 setosa
     27 5.0 3.4 1.6 0.4 setosa
     28 5.2 3.5 1.5 0.2 setosa
     29 5.2 3.4 1.4 0.2 setosa
     30 4.7 3.2 1.6 0.2 setosa
     31 4.8 3.1 1.6 0.2 setosa
     32 5.4 3.4 1.5 0.4 setosa
     33 5.2 4.1 1.5 0.1 setosa
     34 5.5 4.2 1.4 0.2 setosa
     35 4.9 3.1 1.5 0.2 setosa
     36 5.0 3.2 1.2 0.2 setosa
     37 5.5 3.5 1.3 0.2 setosa
     38 4.9 3.6 1.4 0.1 setosa
     39 4.4 3.0 1.3 0.2 setosa
     40 5.1 3.4 1.5 0.2 setosa
     41 5.0 3.5 1.3 0.3 setosa
     42 4.5 2.3 1.3 0.3 setosa
     43 4.4 3.2 1.3 0.2 setosa
     44 5.0 3.5 1.6 0.6 setosa
     45 5.1 3.8 1.9 0.4 setosa
     46 4.8 3.0 1.4 0.3 setosa
     47 5.1 3.8 1.6 0.2 setosa
     48 4.6 3.2 1.4 0.2 setosa
     49 5.3 3.7 1.5 0.2 setosa
     50 5.0 3.3 1.4 0.2 setosa
     51 7.0 3.2 4.7 1.4 versicolor
     52 6.4 3.2 4.5 1.5 versicolor
     53 6.9 3.1 4.9 1.5 versicolor
     54 5.5 2.3 4.0 1.3 versicolor
     55 6.5 2.8 4.6 1.5 versicolor
     56 5.7 2.8 4.5 1.3 versicolor
     57 6.3 3.3 4.7 1.6 versicolor
     58 4.9 2.4 3.3 1.0 versicolor
     59 6.6 2.9 4.6 1.3 versicolor
     60 5.2 2.7 3.9 1.4 versicolor
     61 5.0 2.0 3.5 1.0 versicolor
     62 5.9 3.0 4.2 1.5 versicolor
     63 6.0 2.2 4.0 1.0 versicolor
     64 6.1 2.9 4.7 1.4 versicolor
     65 5.6 2.9 3.6 1.3 versicolor
     66 6.7 3.1 4.4 1.4 versicolor
     67 5.6 3.0 4.5 1.5 versicolor
     68 5.8 2.7 4.1 1.0 versicolor
     69 6.2 2.2 4.5 1.5 versicolor
     70 5.6 2.5 3.9 1.1 versicolor
     71 5.9 3.2 4.8 1.8 versicolor
     72 6.1 2.8 4.0 1.3 versicolor
     73 6.3 2.5 4.9 1.5 versicolor
     74 6.1 2.8 4.7 1.2 versicolor
     75 6.4 2.9 4.3 1.3 versicolor
     76 6.6 3.0 4.4 1.4 versicolor
     77 6.8 2.8 4.8 1.4 versicolor
     78 6.7 3.0 5.0 1.7 versicolor
     79 6.0 2.9 4.5 1.5 versicolor
     80 5.7 2.6 3.5 1.0 versicolor
     81 5.5 2.4 3.8 1.1 versicolor
     82 5.5 2.4 3.7 1.0 versicolor
     83 5.8 2.7 3.9 1.2 versicolor
     84 6.0 2.7 5.1 1.6 versicolor
     85 5.4 3.0 4.5 1.5 versicolor
     86 6.0 3.4 4.5 1.6 versicolor
     87 6.7 3.1 4.7 1.5 versicolor
     88 6.3 2.3 4.4 1.3 versicolor
     89 5.6 3.0 4.1 1.3 versicolor
     90 5.5 2.5 4.0 1.3 versicolor
     91 5.5 2.6 4.4 1.2 versicolor
     92 6.1 3.0 4.6 1.4 versicolor
     93 5.8 2.6 4.0 1.2 versicolor
     94 5.0 2.3 3.3 1.0 versicolor
     95 5.6 2.7 4.2 1.3 versicolor
     96 5.7 3.0 4.2 1.2 versicolor
     97 5.7 2.9 4.2 1.3 versicolor
     98 6.2 2.9 4.3 1.3 versicolor
     99 5.1 2.5 3.0 1.1 versicolor
     100 5.7 2.8 4.1 1.3 versicolor
     101 6.3 3.3 6.0 2.5 virginica
     102 5.8 2.7 5.1 1.9 virginica
     103 7.1 3.0 5.9 2.1 virginica
     104 6.3 2.9 5.6 1.8 virginica
     105 6.5 3.0 5.8 2.2 virginica
     106 7.6 3.0 6.6 2.1 virginica
     107 4.9 2.5 4.5 1.7 virginica
     108 7.3 2.9 6.3 1.8 virginica
     109 6.7 2.5 5.8 1.8 virginica
     110 7.2 3.6 6.1 2.5 virginica
     111 6.5 3.2 5.1 2.0 virginica
     112 6.4 2.7 5.3 1.9 virginica
     113 6.8 3.0 5.5 2.1 virginica
     114 5.7 2.5 5.0 2.0 virginica
     115 5.8 2.8 5.1 2.4 virginica
     116 6.4 3.2 5.3 2.3 virginica
     117 6.5 3.0 5.5 1.8 virginica
     118 7.7 3.8 6.7 2.2 virginica
     119 7.7 2.6 6.9 2.3 virginica
     120 6.0 2.2 5.0 1.5 virginica
     121 6.9 3.2 5.7 2.3 virginica
     122 5.6 2.8 4.9 2.0 virginica
     123 7.7 2.8 6.7 2.0 virginica
     124 6.3 2.7 4.9 1.8 virginica
     125 6.7 3.3 5.7 2.1 virginica
     126 7.2 3.2 6.0 1.8 virginica
     127 6.2 2.8 4.8 1.8 virginica
     128 6.1 3.0 4.9 1.8 virginica
     129 6.4 2.8 5.6 2.1 virginica
     130 7.2 3.0 5.8 1.6 virginica
     131 7.4 2.8 6.1 1.9 virginica
     132 7.9 3.8 6.4 2.0 virginica
     133 6.4 2.8 5.6 2.2 virginica
     134 6.3 2.8 5.1 1.5 virginica
     135 6.1 2.6 5.6 1.4 virginica
     136 7.7 3.0 6.1 2.3 virginica
     137 6.3 3.4 5.6 2.4 virginica
     138 6.4 3.1 5.5 1.8 virginica
     139 6.0 3.0 4.8 1.8 virginica
     140 6.9 3.1 5.4 2.1 virginica
     141 6.7 3.1 5.6 2.4 virginica
     142 6.9 3.1 5.1 2.3 virginica
     143 5.8 2.7 5.1 1.9 virginica
     144 6.8 3.2 5.9 2.3 virginica
     145 6.7 3.3 5.7 2.5 virginica
     146 6.7 3.0 5.2 2.3 virginica
     147 6.3 2.5 5.0 1.9 virginica
     148 6.5 3.0 5.2 2.0 virginica
     149 6.2 3.4 5.4 2.3 virginica
     150 5.9 3.0 5.1 1.8 virginica
     > X = iris[,!names(iris) %in% "Species"]
     > Y = iris[,"Species"]
     > as.numeric(Y)
     [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
     [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
     [75] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3
     [112] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
     [149] 3 3
     > rf.42 = randomForest(X,Y,keep.forest=T,replace=F,keep.inbag=T,samp=15,ntree=100)
     > ff.42 = forestFloor(rf.42,X,calc_np = F,bootstrapFC = TRUE)
     >
     > #test accuracy of feature contributions
     > #y_hat_OOB = row sum FC + Y_grandMean
     > FCc = t(t(apply(ff.42$FCarray,c(1,3),sum))+as.vector(table(Y)/length(Y)))
     > FC.residuals = FCc-predict(rf.42,type="prob")
     > if(max(abs(FC.residuals))>1E-12) stop(
     + paste0("When testing if: y_hat_OOB = row sum FCmatrix + Y_grandMean
     + one/some FCs error exceeds allowed 1e-12, found.error=",max(abs(FC.residuals)))
     + )
     >
     > Xtest = iris[1:50,] #copy
     > Xtest = Xtest[,-5] #drop Species
     > Xtest[1:4] = lapply(iris[1:4],sample,50) #random resample 50 samples
     >
     >
     > #test same results are reached with Xtest
     > ff.43 = forestFloor(rf.42,X,Xtest,bootstrapFC = T)
     > if(max(abs(ff.43$FCarray[ff.43$isTrain,,]-ff.42$FCarray)) > 1E-12) stop(
     + "forestFloor with/without Xtest gives different feature contributions"
     + )
     > plot(ff.43,speedup_GOF = TRUE,plotTest = F)
     >
     >
     > pred = sapply(1:3,function(i) apply(ff.42$FCarray[,,i],1,sum))+1/3
     > rfPred = predict(rf.42,type="vote",norm.votes=T)
     > rfPred[is.nan(rfPred)] = 1/3
     > if(cor(as.vector(rfPred),as.vector(pred))^2<0.99) stop("fail testMultiClass")
     > attributes(ff.42)
     $names
     [1] "X" "Y" "importance" "imp_ind" "FCarray"
     [6] "sumOfInbags" "isTrain"
    
     $class
     [1] "forestFloor_multiClass"
    
     > args(forestFloor:::plot.forestFloor_multiClass)
     function (x, plot_seq = NULL, label.seq = NULL, plotTest = NULL,
     limitY = TRUE, col = NULL, colLists = NULL, orderByImportance = TRUE,
     fig.columns = NULL, plot_GOF = TRUE, GOF_args = list(), speedup_GOF = TRUE,
     jitter_these_cols = NULL, jitter.factor = NULL, ...)
     NULL
     > plot(ff.42,plot_GOF=T,cex=.7,
     + colLists=list("#FF0000A5",
     + "#00FF0050",
     + "#0000FF35")
     + )
     >
     > plot(ff.43,plot_GOF=T,cex=.7,
     + colLists=list("#FF0000A5",
     + "#00FF0050",
     + "#0000FF35")
     + )
     >
     > #use col interface
     > plot(ff.43,plot_GOF=T,cex=.7,
     + col=list("#FF0000A5","#00FF0050","#0000FF35") #one colour per class
     + )
     >
     > plot(ff.43,plot_GOF=T,cex=.7,
     + col=c("#FF0000A5","#00FF0050","#0000FF35") #one colour per class
     + )
     >
     >
     > #try to alter std par
     > plot(ff.42,plot_GOF=T,cex=.7,
     + colLists=list("#FF0000A5",
     + "#00FF0050",
     + "#0000FF35"),
     + mfrow=c(4,3)
     + )
     >
     >
     > show3d(ff.42,1:2,1:2,plot_GOF=T)
     > show3d(ff.42,1:2,1,plot_GOF=T)#test plotting only one feature contribution
     >
     >
     > #plot all effect 2D only
     > pars = plot_simplex3(ff.42,Xi=c(1:3),restore_par=F,zoom.fit=NULL,var.col=NULL,fig.cols=2,fig.rows=1,
     + fig3d=F,includeTotal=T,auto.alpha=.4,set_pars=T)
     > pars = plot_simplex3(ff.42,Xi=0,restore_par=F,zoom.fit=NULL,var.col=alist(alpha=.3,cols=1:4),
     + fig3d=F,includeTotal=T,auto.alpha=.8,set_pars=F)
     ----------- FAILURE REPORT --------------
     --- failure: the condition has length > 1 ---
     --- srcref ---
     :
     --- package (from environment) ---
     forestFloor
     --- call from context ---
     box.outliers(prcomp(sel.colM)$x[, 1:max.df], limit = Inf)
     --- call from argument ---
     if (class(x) == "data.frame") {
     sx = as.data.frame(sx, row.names = row.names(x))
     names(sx) = names(x)
     }
     --- R stacktrace ---
     where 1: box.outliers(prcomp(sel.colM)$x[, 1:max.df], limit = Inf)
     where 2: (function (ff, cols = NULL, orderByImportance = NULL, plotTest = NULL,
     X.matrix = TRUE, hue = NULL, saturation = NULL, brightness = NULL,
     hue.range = NULL, sat.range = NULL, bri.range = NULL, alpha = NULL,
     RGB = NULL, byResiduals = FALSE, max.df = 3, imp.weight = NULL,
     imp.exp = 1, outlier.lim = 3, RGB.exp = NULL)
     {
     if (!X.matrix)
     if (class(ff) == "forestFloor_multiClass")
     stop("cannot colour by feature contributions for object of class\n 'forestFloor_multiClass'. Set X.matrix=TRUE")
     ib <- function(x, low, high) (x - low) * (high - x) > 0
     span <- function(x, mid, width) if (min(x) != max(x)) {
     ((x - min(x))/(max(x) - min(x)) - 0.5) * width + mid
     }
     else {
     x[] = mid
     }
     auto.range = function(level, low = 0, high = 1) abs(min(level -
     low, high - level)) * 2
     contain = function(x, low = 0, high = 1) {
     x[x > high] = high
     x[x < low] = low
     x
     }
     if (class(ff) %in% c("forestFloor_regression", "forestFloor_multiClass")) {
     plotThese = checkPlotTest(plotTest, ff$isTrain)
     if (!(all(plotThese))) {
     if (class(ff) == "forestFloor_multiClass") {
     ff$FCarray = ff$FCarray[plotThese, , ]
     }
     else {
     if (class(ff) == "forestFloor_regression") {
     ff$FCmatrix = ff$FCmatrix[plotThese, ]
     }
     }
     ff$Y = ff$Y[plotThese]
     ff$X = ff$X[plotThese, ]
     }
     }
     if (class(ff) %in% c("forestFloor_regression", "forestFloor_multiClass")) {
     if (byResiduals) {
     if (is.null(ff$FCfit)) {
     print("no $FCfit found, computing tempoary LOO-kNN-gaussion fit to main affect")
     print("use ff = convolute_ff(ff) to compute a fixed fit")
     ff = convolute_ff(ff)
     }
     colM = ff$FCmatrix - ff$FCfit
     }
     else {
     if (X.matrix)
     colM = ff$X
     else colM = ff$FCmatrix
     }
     if (is.null(imp.weight))
     imp.weight = TRUE
     if (is.null(orderByImportance))
     orderByImportance = TRUE
     }
     else {
     colM = ff
     if (is.null(imp.weight))
     imp.weight = FALSE
     if (is.null(orderByImportance))
     orderByImportance = FALSE
     }
     if (orderByImportance)
     if (class(ff) %in% c("forestFloor_regression", "forestFloor_multiClass")) {
     colM = colM[, ff$imp_ind]
     }
     else {
     warning("orderByImportance=TRUE takes no effect for non 'forestFloor'-class. As if set to NULL or FALSE...")
     }
     if (!class(colM) %in% c("data.frame", "matrix")) {
     tryCatch({
     colM = matrix(colM, ncol = 1)
     }, error = function(e) stop(paste("input ff was neither data.frame or matrix and \ncould not be coerced to matrix:",
     e$message)))
     }
     colM = data.frame(colM)
     if (is.null(cols))
     cols = 1:dim(colM)[2]
     if (length(cols) < 1 || !is.numeric(cols) || any(!cols %in%
     1:dim(colM)[2])) {
     stop("no cols selected or is not integer/numeric or wrong coloumns")
     }
     sel.colM = data.frame(colM[, cols])
     sel.cols = 1:length(cols)
     if (is.null(RGB))
     if (length(cols) == 1)
     RGB = TRUE
     else RGB = FALSE
     if (!RGB) {
     if (is.null(saturation))
     saturation = 0.85
     if (is.null(brightness))
     brightness = 0.75
     if (is.null(hue))
     hue = 0.25
     }
     else {
     if (is.null(saturation))
     saturation = 1
     if (is.null(brightness))
     brightness = 0.75
     if (is.null(hue))
     hue = 0.66
     if (is.null(RGB.exp))
     RGB.exp = 1.2
     if (is.null(hue.range))
     hue.range = 2
     }
     as.numeric.factor <- function(x, rearrange = TRUE) {
     if (is.numeric(x))
     return(x)
     if (rearrange)
     x = match(x, levels(droplevels(x)))
     else x = match(x, levels(x))
     return(x)
     }
     for (i in 1:dim(sel.colM)[2]) {
     if (is.factor(sel.colM[, i])) {
     this.fac = as.numeric.factor(sel.colM[, i])
     sel.colM[, i] = this.fac
     }
     if (is.character(sel.colM[, i]))
     sel.colM[, i] = as.numeric(sel.colM[, i])
     }
     sel.colM = box.outliers(sel.colM, limit = outlier.lim)
     if (imp.weight && length(cols) > 1) {
     if (class(ff) %in% c("forestFloor_regression", "forestFloor_multiClass")) {
     sel.imp = ff$importance[cols]
     non.negative.imp = sel.imp + min(sel.imp)
     sumnorm.imp = non.negative.imp/sum(non.negative.imp)
     exp.imp = sumnorm.imp^imp.exp
     impM = t(replicate(dim(colM)[1], exp.imp))
     sel.colM = sel.colM * impM
     sel.colM = sel.colM/max(sel.colM)
     }
     else {
     warning("importance weighting only possible for class 'forestFloor'")
     }
     }
     if (any(!c(class(hue), class(saturation), class(brightness)) %in%
     c("numeric", "integer"))) {
     stop("hue, saturation and brightness must be of class numeric or integer")
     }
     hue = hue - floor(hue)
     saturation = max(min(saturation, 1), 0)
     brightness = max(min(brightness, 1), 0)
     if (RGB == TRUE) {
     if (is.null(bri.range))
     bri.range = 0.05
     if (is.null(alpha))
     alpha = 0.7
     len.colM = box.outliers(sel.colM, limit = Inf)
     if (dim(len.colM)[2] == 1)
     nX = as.numeric(len.colM[, 1])
     else nX = as.numeric(apply(len.colM, 1, mean))
     hsvcol = t(sapply(nX, function(x) rgb2hsv(x^RGB.exp,
     1 - x^RGB.exp - (1 - x)^RGB.exp, (1 - x)^RGB.exp)))
     hue.vec = hsvcol[, 1] * hue.range + hue
     hue.vec[hue.vec > 1] = hue.vec[hue.vec > 1] - floor(hue.vec[hue.vec >
     1])
     hsvcol[, 1] = hue.vec
     sat.range = auto.range(saturation)
     hsvcol[, 2] = span(hsvcol[, 2], saturation, sat.range)
     hsvcol[, 2] = contain(hsvcol[, 2])
     bri.range = auto.range(brightness)
     hsvcol[, 3] = span(hsvcol[, 3], brightness, bri.range)
     hsvcol[, 3] = contain(hsvcol[, 3])
     colours = apply(hsvcol, 1, function(x) hsv(x[1], x[2],
     x[3], alpha = alpha))
     return(colours)
     }
     col.df = length(cols)
     if (!max.df %in% c(1, 2, 3))
     stop("fcol input 'max.df' must be set to either 1, 2 or 3")
     if (col.df > max.df) {
     len.colM = box.outliers(prcomp(sel.colM)$x[, 1:max.df],
     limit = Inf)
     col.df = max.df
     }
     else {
     len.colM = box.outliers(sel.colM, limit = Inf)
     }
     if (is.null(hue.range)) {
     if (col.df == 1)
     hue.range = 0.85
     if (col.df == 2)
     hue.range = 1
     if (col.df == 3)
     hue.range = 1
     }
     if (is.null(sat.range)) {
     if (col.df == 1)
     sat.range = "not used"
     if (col.df == 2)
     sat.range = auto.range(saturation)
     if (col.df == 3)
     sat.range = auto.range(saturation)
     }
     if (is.null(bri.range)) {
     if (col.df == 1)
     bri.range = "not used"
     if (col.df == 2)
     bri.range = "not used"
     if (col.df == 3)
     bri.range = auto.range(brightness)
     }
     if (is.null(alpha))
     alpha = min(1, 400/dim(len.colM)[1])
     if (col.df == 1) {
     hue.vec = as.numeric(len.colM[, 1]) * hue.range + hue
     hue.vec[hue.vec > 1] = hue.vec[hue.vec > 1] - floor(hue.vec[hue.vec >
     1])
     colours = hsv(h = hue.vec, s = saturation, v = brightness,
     alpha = alpha)
     }
     if (col.df == 2) {
     hsvcol = t(rgb2hsv(len.colM[, 1], len.colM[, 2], 1 -
     apply(len.colM, 1, mean)))
     hue.vec = hsvcol[, 1] * hue.range + hue
     hue.vec[hue.vec > 1] = hue.vec[hue.vec > 1] - 1
     hsvcol[, 1] = hue.vec
     hsvcol[, 2] = ((len.colM[, 1] - mean(len.colM[, 1]))^2 +
     (len.colM[, 2] - mean(len.colM[, 2]))^2)^sat.range *
     saturation
     hsvcol[, 2] = hsvcol[, 2]/max(hsvcol[, 2])
     hsvcol[, 3] = brightness
     colours = hsv(hsvcol[, 1], hsvcol[, 2], hsvcol[, 3],
     alpha = alpha)
     }
     if (col.df == 3) {
     hsvcol = t(rgb2hsv(len.colM[, 1], len.colM[, 2], len.colM[,
     3]))
     hue.vec = hsvcol[, 1] * hue.range + hue
     hue.vec[hue.vec > 1] = hue.vec[hue.vec > 1] - 1
     hsvcol[, 1] = hue.vec
     span.sat = span(hsvcol[, 2], saturation, sat.range)
     hsvcol[, 2] = contain(span.sat)
     mean.bri = apply(len.colM, 1, mean)
     span.bri = span(mean.bri, brightness, bri.range)
     hsvcol[, 3] = contain(span.bri)
     colours = hsv(hsvcol[, 1], hsvcol[, 2], hsvcol[, 3],
     alpha = alpha)
     }
     return(colours)
     })(list(Sepal.Length = c(5.1, 4.9, 4.7, 4.6, 5, 5.4, 4.6, 5,
     4.4, 4.9, 5.4, 4.8, 4.8, 4.3, 5.8, 5.7, 5.4, 5.1, 5.7, 5.1, 5.4,
     5.1, 4.6, 5.1, 4.8, 5, 5, 5.2, 5.2, 4.7, 4.8, 5.4, 5.2, 5.5,
     4.9, 5, 5.5, 4.9, 4.4, 5.1, 5, 4.5, 4.4, 5, 5.1, 4.8, 5.1, 4.6,
     5.3, 5, 7, 6.4, 6.9, 5.5, 6.5, 5.7, 6.3, 4.9, 6.6, 5.2, 5, 5.9,
     6, 6.1, 5.6, 6.7, 5.6, 5.8, 6.2, 5.6, 5.9, 6.1, 6.3, 6.1, 6.4,
     6.6, 6.8, 6.7, 6, 5.7, 5.5, 5.5, 5.8, 6, 5.4, 6, 6.7, 6.3, 5.6,
     5.5, 5.5, 6.1, 5.8, 5, 5.6, 5.7, 5.7, 6.2, 5.1, 5.7, 6.3, 5.8,
     7.1, 6.3, 6.5, 7.6, 4.9, 7.3, 6.7, 7.2, 6.5, 6.4, 6.8, 5.7, 5.8,
     6.4, 6.5, 7.7, 7.7, 6, 6.9, 5.6, 7.7, 6.3, 6.7, 7.2, 6.2, 6.1,
     6.4, 7.2, 7.4, 7.9, 6.4, 6.3, 6.1, 7.7, 6.3, 6.4, 6, 6.9, 6.7,
     6.9, 5.8, 6.8, 6.7, 6.7, 6.3, 6.5, 6.2, 5.9), Sepal.Width = c(3.5,
     3, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1, 3.7, 3.4, 3, 3, 4,
     4.4, 3.9, 3.5, 3.8, 3.8, 3.4, 3.7, 3.6, 3.3, 3.4, 3, 3.4, 3.5,
     3.4, 3.2, 3.1, 3.4, 4.1, 4.2, 3.1, 3.2, 3.5, 3.6, 3, 3.4, 3.5,
     2.3, 3.2, 3.5, 3.8, 3, 3.8, 3.2, 3.7, 3.3, 3.2, 3.2, 3.1, 2.3,
     2.8, 2.8, 3.3, 2.4, 2.9, 2.7, 2, 3, 2.2, 2.9, 2.9, 3.1, 3, 2.7,
     2.2, 2.5, 3.2, 2.8, 2.5, 2.8, 2.9, 3, 2.8, 3, 2.9, 2.6, 2.4,
     2.4, 2.7, 2.7, 3, 3.4, 3.1, 2.3, 3, 2.5, 2.6, 3, 2.6, 2.3, 2.7,
     3, 2.9, 2.9, 2.5, 2.8, 3.3, 2.7, 3, 2.9, 3, 3, 2.5, 2.9, 2.5,
     3.6, 3.2, 2.7, 3, 2.5, 2.8, 3.2, 3, 3.8, 2.6, 2.2, 3.2, 2.8,
     2.8, 2.7, 3.3, 3.2, 2.8, 3, 2.8, 3, 2.8, 3.8, 2.8, 2.8, 2.6,
     3, 3.4, 3.1, 3, 3.1, 3.1, 3.1, 2.7, 3.2, 3.3, 3, 2.5, 3, 3.4,
     3), Petal.Length = c(1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5,
     1.4, 1.5, 1.5, 1.6, 1.4, 1.1, 1.2, 1.5, 1.3, 1.4, 1.7, 1.5, 1.7,
     1.5, 1, 1.7, 1.9, 1.6, 1.6, 1.5, 1.4, 1.6, 1.6, 1.5, 1.5, 1.4,
     1.5, 1.2, 1.3, 1.4, 1.3, 1.5, 1.3, 1.3, 1.3, 1.6, 1.9, 1.4, 1.6,
     1.4, 1.5, 1.4, 4.7, 4.5, 4.9, 4, 4.6, 4.5, 4.7, 3.3, 4.6, 3.9,
     3.5, 4.2, 4, 4.7, 3.6, 4.4, 4.5, 4.1, 4.5, 3.9, 4.8, 4, 4.9,
     4.7, 4.3, 4.4, 4.8, 5, 4.5, 3.5, 3.8, 3.7, 3.9, 5.1, 4.5, 4.5,
     4.7, 4.4, 4.1, 4, 4.4, 4.6, 4, 3.3, 4.2, 4.2, 4.2, 4.3, 3, 4.1,
     6, 5.1, 5.9, 5.6, 5.8, 6.6, 4.5, 6.3, 5.8, 6.1, 5.1, 5.3, 5.5,
     5, 5.1, 5.3, 5.5, 6.7, 6.9, 5, 5.7, 4.9, 6.7, 4.9, 5.7, 6, 4.8,
     4.9, 5.6, 5.8, 6.1, 6.4, 5.6, 5.1, 5.6, 6.1, 5.6, 5.5, 4.8, 5.4,
     5.6, 5.1, 5.1, 5.9, 5.7, 5.2, 5, 5.2, 5.4, 5.1), Petal.Width = c(0.2,
     0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, 0.2, 0.2, 0.1, 0.1,
     0.2, 0.4, 0.4, 0.3, 0.3, 0.3, 0.2, 0.4, 0.2, 0.5, 0.2, 0.2, 0.4,
     0.2, 0.2, 0.2, 0.2, 0.4, 0.1, 0.2, 0.2, 0.2, 0.2, 0.1, 0.2, 0.2,
     0.3, 0.3, 0.2, 0.6, 0.4, 0.3, 0.2, 0.2, 0.2, 0.2, 1.4, 1.5, 1.5,
     1.3, 1.5, 1.3, 1.6, 1, 1.3, 1.4, 1, 1.5, 1, 1.4, 1.3, 1.4, 1.5,
     1, 1.5, 1.1, 1.8, 1.3, 1.5, 1.2, 1.3, 1.4, 1.4, 1.7, 1.5, 1,
     1.1, 1, 1.2, 1.6, 1.5, 1.6, 1.5, 1.3, 1.3, 1.3, 1.2, 1.4, 1.2,
     1, 1.3, 1.2, 1.3, 1.3, 1.1, 1.3, 2.5, 1.9, 2.1, 1.8, 2.2, 2.1,
     1.7, 1.8, 1.8, 2.5, 2, 1.9, 2.1, 2, 2.4, 2.3, 1.8, 2.2, 2.3,
     1.5, 2.3, 2, 2, 1.8, 2.1, 1.8, 1.8, 1.8, 2.1, 1.6, 1.9, 2, 2.2,
     1.5, 1.4, 2.3, 2.4, 1.8, 1.8, 2.1, 2.4, 2.3, 1.9, 2.3, 2.5, 2.3,
     1.9, 2, 2.3, 1.8)), 0, alpha = 0.3, cols = 1:4)
     where 3: do.call(fcol, c(list(ff$X, i), var.col))
     where 4: plot.xy(xy.coords(x, y), type = type, ...)
     where 5: points.default(apply(xd1 * t(FC[, ]), 2, sum) + cent2[1], apply(xd2 *
     t(FC[, ]), 2, sum) + cent2[2], cex = 0.3, col = if (Col[1] ==
     "var.col") do.call(fcol, c(list(ff$X, i), var.col)) else Col)
     where 6: points(apply(xd1 * t(FC[, ]), 2, sum) + cent2[1], apply(xd2 *
     t(FC[, ]), 2, sum) + cent2[2], cex = 0.3, col = if (Col[1] ==
     "var.col") do.call(fcol, c(list(ff$X, i), var.col)) else Col)
     where 7: plot_simplex3(ff.42, Xi = 0, restore_par = F, zoom.fit = NULL,
     var.col = alist(alpha = 0.3, cols = 1:4), fig3d = F, includeTotal = T,
     auto.alpha = 0.8, set_pars = F)
    
     --- value of length: 2 type: logical ---
     [1] FALSE FALSE
     --- function from context ---
     function (x, limit = 1.5, normalize = TRUE)
     {
     sx = scale(x)
     if (limit != FALSE) {
     sx[sx > limit] = limit
     sx[-sx > limit] = -limit
     }
     if (normalize) {
     sx.span = max(sx) - min(sx)
     sx = sx - min(sx)
     sx = sx/sx.span
     }
     else {
     obs = attributes(sx)$dim[1]
     if (dim(sx)[2] > 1) {
     sx = sx * t(replicate(obs, attributes(sx)$"scaled:scale")) +
     t(replicate(obs, attributes(sx)$"scaled:center"))
     }
     else {
     sx = sx * attributes(sx)$"scaled:scale" + attributes(sx)$"scaled:center"
     }
     }
     if (class(x) == "data.frame") {
     sx = as.data.frame(sx, row.names = row.names(x))
     names(sx) = names(x)
     }
     return(sx)
     }
     <bytecode: 0x90bf048>
     <environment: namespace:forestFloor>
     --- function search by body ---
     Function box.outliers in namespace forestFloor has this body.
     ----------- END OF FAILURE REPORT --------------
     Error in if (class(x) == "data.frame") { : the condition has length > 1
     Calls: plot_simplex3 ... points.default -> plot.xy -> do.call -> <Anonymous> -> box.outliers
     Execution halted
Flavor: r-devel-linux-x86_64-debian-clang

Version: 1.11.1
Check: whether package can be installed
Result: WARN
    Found the following significant warnings:
     testcpp_rec6.cpp:197:37: warning: value computed is not used [-Wunused-value]
     testcpp_rec6.cpp:551:37: warning: value computed is not used [-Wunused-value]
Flavors: r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-ix86+x86_64, r-devel-windows-ix86+x86_64-gcc8

Version: 1.11.1
Check: tests
Result: ERROR
     Running ‘allExamples.R’ [0s/1s]
     Running ‘rfPermute_supported.R’ [0s/1s]
     Running ‘stdUsage.R’ [9s/13s]
     Running ‘testBinaryClass.R’ [3s/4s]
     Running ‘testCaret.R’ [14s/22s]
     Running ‘testMultiClass.cpp.R’ [4s/7s]
     Running ‘test_Xtestmerger.R’ [2s/4s]
    Running the tests in ‘tests/stdUsage.R’ failed.
    Complete output:
     > if(!interactive()) Sys.setenv(RGL_USE_NULL=TRUE) #disable RGL for headless machines
     > library(forestFloor)
     > library(randomForest)
     randomForest 4.6-14
     Type rfNews() to see new features/changes/bug fixes.
     > #simulate data
     > obs=2000
     > vars = 6
     >
     > X = data.frame(replicate(vars,rnorm(obs)))
     > Xtest = data.frame(replicate(vars,rnorm(obs)*3))
     > Y = with(X, X1^2 + sin(X2*pi) + 2 * X3 * X4 + .5 * rnorm(obs))
     >
     >
     > #grow a forest, remeber to include inbag
     > rf41=randomForest(X,Y,keep.inbag = TRUE,sampsize=499,ntree=100,importance = F)
     > #compute feature contributions
     > out = tryCatch({ff41 = forestFloor(rf41,X,bootstrapFC = TRUE,impType=1)},warning = function(w) w)
     > if(out$message != "found nothing of importance, revert to fallback") stop("wrong warning")
     >
     > #if(colnames(ff41$importance) != "%IncMSE") stop("wrong imp")
     > ff41 = forestFloor(rf41,X,bootstrapFC = TRUE,impType=2)
     > if(colnames(ff41$importance) != "IncNodePurity") stop("wrong imp")
     > ff41 = forestFloor(rf41,X,bootstrapFC = TRUE,impType=NULL)
     > if(colnames(ff41$importance) != "IncNodePurity") stop("wrong imp")
     >
     > #grow a forest, remeber to include inbag
     > rf42=randomForest(X,Y,keep.inbag = TRUE,sampsize=499,ntree=100,importance = T)
     > #compute feature contributions
     > ff42 = forestFloor(rf42,X,bootstrapFC = TRUE,impType=1)
     > if(colnames(ff42$importance) != "%IncMSE") stop("wrong imp")
     > ff42 = forestFloor(rf42,X,bootstrapFC = TRUE,impType=2)
     > if(colnames(ff42$importance) != "IncNodePurity") stop("wrong imp")
     > ff42 = forestFloor(rf42,X,bootstrapFC = TRUE,impType=NULL)
     > if(colnames(ff42$importance) != "%IncMSE") stop("wrong imp")
     >
     >
     >
     >
     > #test accuracy of feature contributions
     > #y_hat_OOB = row sum FC + Y_grandMean
     > FC.residuals = rf42$predicted - apply(ff42$FCmatrix[ff42$isTrain,],1,sum) - mean(Y)
     > if(max(abs(FC.residuals))>1E-12) stop(
     + paste0("When testing if: y_hat_OOB = row sum FCmatrix + Y_grandMean
     + one/some FCs error exceed allowed 1e-12, found.error=",max(abs(FC.residuals)))
     + )
     >
     > #test same results are reached with Xtest
     > ff43 = forestFloor(rf42,X,Xtest,bootstrapFC = TRUE)
     > if(max(abs(ff43$FCmatrix[ff43$isTrain,]-ff42$FCmatrix)) > 1E-12) stop(
     + "forestFloor with/without Xtest gives different feature contributions"
     + )
     >
     >
     > #print forestFloor
     > print(ff42)
     this is a forestFloor_regression object
    
     this object can be plotted in 2D with plot(x), see help(plot.forestFloor)
    
     this object can be plotted in 3D with show3d(x), see help(show3d)
    
    
    
     x contains following internal elements:
     FCmatrix X Y imp_ind importance isTrain>
     > #plot partial functions of most important variables first
     > plot(ff42,orderByImportance=TRUE)
     [1] "compute goodness-of-fit with leave-one-out k-nearest neighbor(guassian weighting), kknn package"
     > plot(ff43,orderByImportance=TRUE)
     [1] "compute goodness-of-fit with leave-one-out k-nearest neighbor(guassian weighting), kknn package"
     >
     > #Non interacting functions are well displayed, whereas X3 and X4 are not
     > #by applying different colourgradient, interactions reveal themself
     > #also a k-nearest neighbor fit is applied to evaluate goodness of fit
     > Col=fcol(ff43,3,orderByImportance=FALSE)
     > plot(ff43,col=Col,plot_GOF=TRUE,speed=T)
     [1] "compute goodness-of-fit with leave-one-out k-nearest neighbor(guassian weighting), kknn package"
     >
     >
     > ##make test set grey tone to show if point of test is extrapolated
     > Col=fcol(ff43,3,orderByImportance=FALSE,plotTest="andTrain",alpha=.2)
     > Col[ff43$isTrain] = "#000000FF"
     > plot(ff43,col=Col,speed=T,plotTest="andTrain",plot_GOF=F)
     >
     >
     > #if ever needed, k-nearest neighbor parameters for goodness-of-fit can be access through convolute_ff
     > #a new fit will be calculated and added to forstFloor object as ff42$FCfit
     > ff43 = convolute_ff(ff43,userArgs.kknn=alist(kernel="epanechnikov",kmax=5))
     > plot(ff43,col=Col,plot_GOF=TRUE)
     >
     > #in 3D the interaction between X3 and X reveals itself completely
     > show3d(ff43,3:4,col=Col,plot.rgl=list(size=5),orderByImportance=FALSE)
     > Col=fcol(ff43,1:2,orderByImportance=FALSE)
     > show3d(ff43,1:2,col=Col,plot.rgl=list(size=5),orderByImportance=FALSE)
     >
     >
     > Col=fcol(ff43,1:2,plotTest="andTrain",orderByImportance=FALSE)
     > show3d(ff43,1:2,col=Col,plot.rgl=list(size=5),orderByImportance=FALSE)
     >
     >
     >
     > #although no interaction, a joined additive effect of X1 and X2
     > #colour by FC-component FC1 and FC2 summed
     > Col = fcol(ff43,1:2,orderByImportance=FALSE,X.m=FALSE,RGB=TRUE,plotTest = "a")
     ----------- FAILURE REPORT --------------
     --- failure: the condition has length > 1 ---
     --- srcref ---
     :
     --- package (from environment) ---
     forestFloor
     --- call from context ---
     fcol(ff43, 1:2, orderByImportance = FALSE, X.m = FALSE, RGB = TRUE,
     plotTest = "a")
     --- call from argument ---
     if (!class(colM) %in% c("data.frame", "matrix")) {
     tryCatch({
     colM = matrix(colM, ncol = 1)
     }, error = function(e) stop(paste("input ff was neither data.frame or matrix and \ncould not be coerced to matrix:",
     e$message)))
     }
     --- R stacktrace ---
     where 1: fcol(ff43, 1:2, orderByImportance = FALSE, X.m = FALSE, RGB = TRUE,
     plotTest = "a")
    
     --- value of length: 2 type: logical ---
     [1] FALSE TRUE
     --- function from context ---
     function (ff, cols = NULL, orderByImportance = NULL, plotTest = NULL,
     X.matrix = TRUE, hue = NULL, saturation = NULL, brightness = NULL,
     hue.range = NULL, sat.range = NULL, bri.range = NULL, alpha = NULL,
     RGB = NULL, byResiduals = FALSE, max.df = 3, imp.weight = NULL,
     imp.exp = 1, outlier.lim = 3, RGB.exp = NULL)
     {
     if (!X.matrix)
     if (class(ff) == "forestFloor_multiClass")
     stop("cannot colour by feature contributions for object of class\n 'forestFloor_multiClass'. Set X.matrix=TRUE")
     ib <- function(x, low, high) (x - low) * (high - x) > 0
     span <- function(x, mid, width) if (min(x) != max(x)) {
     ((x - min(x))/(max(x) - min(x)) - 0.5) * width + mid
     }
     else {
     x[] = mid
     }
     auto.range = function(level, low = 0, high = 1) abs(min(level -
     low, high - level)) * 2
     contain = function(x, low = 0, high = 1) {
     x[x > high] = high
     x[x < low] = low
     x
     }
     if (class(ff) %in% c("forestFloor_regression", "forestFloor_multiClass")) {
     plotThese = checkPlotTest(plotTest, ff$isTrain)
     if (!(all(plotThese))) {
     if (class(ff) == "forestFloor_multiClass") {
     ff$FCarray = ff$FCarray[plotThese, , ]
     }
     else {
     if (class(ff) == "forestFloor_regression") {
     ff$FCmatrix = ff$FCmatrix[plotThese, ]
     }
     }
     ff$Y = ff$Y[plotThese]
     ff$X = ff$X[plotThese, ]
     }
     }
     if (class(ff) %in% c("forestFloor_regression", "forestFloor_multiClass")) {
     if (byResiduals) {
     if (is.null(ff$FCfit)) {
     print("no $FCfit found, computing tempoary LOO-kNN-gaussion fit to main affect")
     print("use ff = convolute_ff(ff) to compute a fixed fit")
     ff = convolute_ff(ff)
     }
     colM = ff$FCmatrix - ff$FCfit
     }
     else {
     if (X.matrix)
     colM = ff$X
     else colM = ff$FCmatrix
     }
     if (is.null(imp.weight))
     imp.weight = TRUE
     if (is.null(orderByImportance))
     orderByImportance = TRUE
     }
     else {
     colM = ff
     if (is.null(imp.weight))
     imp.weight = FALSE
     if (is.null(orderByImportance))
     orderByImportance = FALSE
     }
     if (orderByImportance)
     if (class(ff) %in% c("forestFloor_regression", "forestFloor_multiClass")) {
     colM = colM[, ff$imp_ind]
     }
     else {
     warning("orderByImportance=TRUE takes no effect for non 'forestFloor'-class. As if set to NULL or FALSE...")
     }
     if (!class(colM) %in% c("data.frame", "matrix")) {
     tryCatch({
     colM = matrix(colM, ncol = 1)
     }, error = function(e) stop(paste("input ff was neither data.frame or matrix and \ncould not be coerced to matrix:",
     e$message)))
     }
     colM = data.frame(colM)
     if (is.null(cols))
     cols = 1:dim(colM)[2]
     if (length(cols) < 1 || !is.numeric(cols) || any(!cols %in%
     1:dim(colM)[2])) {
     stop("no cols selected or is not integer/numeric or wrong coloumns")
     }
     sel.colM = data.frame(colM[, cols])
     sel.cols = 1:length(cols)
     if (is.null(RGB))
     if (length(cols) == 1)
     RGB = TRUE
     else RGB = FALSE
     if (!RGB) {
     if (is.null(saturation))
     saturation = 0.85
     if (is.null(brightness))
     brightness = 0.75
     if (is.null(hue))
     hue = 0.25
     }
     else {
     if (is.null(saturation))
     saturation = 1
     if (is.null(brightness))
     brightness = 0.75
     if (is.null(hue))
     hue = 0.66
     if (is.null(RGB.exp))
     RGB.exp = 1.2
     if (is.null(hue.range))
     hue.range = 2
     }
     as.numeric.factor <- function(x, rearrange = TRUE) {
     if (is.numeric(x))
     return(x)
     if (rearrange)
     x = match(x, levels(droplevels(x)))
     else x = match(x, levels(x))
     return(x)
     }
     for (i in 1:dim(sel.colM)[2]) {
     if (is.factor(sel.colM[, i])) {
     this.fac = as.numeric.factor(sel.colM[, i])
     sel.colM[, i] = this.fac
     }
     if (is.character(sel.colM[, i]))
     sel.colM[, i] = as.numeric(sel.colM[, i])
     }
     sel.colM = box.outliers(sel.colM, limit = outlier.lim)
     if (imp.weight && length(cols) > 1) {
     if (class(ff) %in% c("forestFloor_regression", "forestFloor_multiClass")) {
     sel.imp = ff$importance[cols]
     non.negative.imp = sel.imp + min(sel.imp)
     sumnorm.imp = non.negative.imp/sum(non.negative.imp)
     exp.imp = sumnorm.imp^imp.exp
     impM = t(replicate(dim(colM)[1], exp.imp))
     sel.colM = sel.colM * impM
     sel.colM = sel.colM/max(sel.colM)
     }
     else {
     warning("importance weighting only possible for class 'forestFloor'")
     }
     }
     if (any(!c(class(hue), class(saturation), class(brightness)) %in%
     c("numeric", "integer"))) {
     stop("hue, saturation and brightness must be of class numeric or integer")
     }
     hue = hue - floor(hue)
     saturation = max(min(saturation, 1), 0)
     brightness = max(min(brightness, 1), 0)
     if (RGB == TRUE) {
     if (is.null(bri.range))
     bri.range = 0.05
     if (is.null(alpha))
     alpha = 0.7
     len.colM = box.outliers(sel.colM, limit = Inf)
     if (dim(len.colM)[2] == 1)
     nX = as.numeric(len.colM[, 1])
     else nX = as.numeric(apply(len.colM, 1, mean))
     hsvcol = t(sapply(nX, function(x) rgb2hsv(x^RGB.exp,
     1 - x^RGB.exp - (1 - x)^RGB.exp, (1 - x)^RGB.exp)))
     hue.vec = hsvcol[, 1] * hue.range + hue
     hue.vec[hue.vec > 1] = hue.vec[hue.vec > 1] - floor(hue.vec[hue.vec >
     1])
     hsvcol[, 1] = hue.vec
     sat.range = auto.range(saturation)
     hsvcol[, 2] = span(hsvcol[, 2], saturation, sat.range)
     hsvcol[, 2] = contain(hsvcol[, 2])
     bri.range = auto.range(brightness)
     hsvcol[, 3] = span(hsvcol[, 3], brightness, bri.range)
     hsvcol[, 3] = contain(hsvcol[, 3])
     colours = apply(hsvcol, 1, function(x) hsv(x[1], x[2],
     x[3], alpha = alpha))
     return(colours)
     }
     col.df = length(cols)
     if (!max.df %in% c(1, 2, 3))
     stop("fcol input 'max.df' must be set to either 1, 2 or 3")
     if (col.df > max.df) {
     len.colM = box.outliers(prcomp(sel.colM)$x[, 1:max.df],
     limit = Inf)
     col.df = max.df
     }
     else {
     len.colM = box.outliers(sel.colM, limit = Inf)
     }
     if (is.null(hue.range)) {
     if (col.df == 1)
     hue.range = 0.85
     if (col.df == 2)
     hue.range = 1
     if (col.df == 3)
     hue.range = 1
     }
     if (is.null(sat.range)) {
     if (col.df == 1)
     sat.range = "not used"
     if (col.df == 2)
     sat.range = auto.range(saturation)
     if (col.df == 3)
     sat.range = auto.range(saturation)
     }
     if (is.null(bri.range)) {
     if (col.df == 1)
     bri.range = "not used"
     if (col.df == 2)
     bri.range = "not used"
     if (col.df == 3)
     bri.range = auto.range(brightness)
     }
     if (is.null(alpha))
     alpha = min(1, 400/dim(len.colM)[1])
     if (col.df == 1) {
     hue.vec = as.numeric(len.colM[, 1]) * hue.range + hue
     hue.vec[hue.vec > 1] = hue.vec[hue.vec > 1] - floor(hue.vec[hue.vec >
     1])
     colours = hsv(h = hue.vec, s = saturation, v = brightness,
     alpha = alpha)
     }
     if (col.df == 2) {
     hsvcol = t(rgb2hsv(len.colM[, 1], len.colM[, 2], 1 -
     apply(len.colM, 1, mean)))
     hue.vec = hsvcol[, 1] * hue.range + hue
     hue.vec[hue.vec > 1] = hue.vec[hue.vec > 1] - 1
     hsvcol[, 1] = hue.vec
     hsvcol[, 2] = ((len.colM[, 1] - mean(len.colM[, 1]))^2 +
     (len.colM[, 2] - mean(len.colM[, 2]))^2)^sat.range *
     saturation
     hsvcol[, 2] = hsvcol[, 2]/max(hsvcol[, 2])
     hsvcol[, 3] = brightness
     colours = hsv(hsvcol[, 1], hsvcol[, 2], hsvcol[, 3],
     alpha = alpha)
     }
     if (col.df == 3) {
     hsvcol = t(rgb2hsv(len.colM[, 1], len.colM[, 2], len.colM[,
     3]))
     hue.vec = hsvcol[, 1] * hue.range + hue
     hue.vec[hue.vec > 1] = hue.vec[hue.vec > 1] - 1
     hsvcol[, 1] = hue.vec
     span.sat = span(hsvcol[, 2], saturation, sat.range)
     hsvcol[, 2] = contain(span.sat)
     mean.bri = apply(len.colM, 1, mean)
     span.bri = span(mean.bri, brightness, bri.range)
     hsvcol[, 3] = contain(span.bri)
     colours = hsv(hsvcol[, 1], hsvcol[, 2], hsvcol[, 3],
     alpha = alpha)
     }
     return(colours)
     }
     <bytecode: 0x55ef54cf8ec0>
     <environment: namespace:forestFloor>
     --- function search by body ---
     Function fcol in namespace forestFloor has this body.
     ----------- END OF FAILURE REPORT --------------
     Error in if (!class(colM) %in% c("data.frame", "matrix")) { :
     the condition has length > 1
     Calls: fcol
     Execution halted
    Running the tests in ‘tests/testMultiClass.cpp.R’ failed.
    Complete output:
     > if(!interactive()) Sys.setenv(RGL_USE_NULL=TRUE) #disable RGL for headless machines
     > library(randomForest)
     randomForest 4.6-14
     Type rfNews() to see new features/changes/bug fixes.
     > library(forestFloor)
     > require(utils)
     >
     > data(iris)
     > iris
     Sepal.Length Sepal.Width Petal.Length Petal.Width Species
     1 5.1 3.5 1.4 0.2 setosa
     2 4.9 3.0 1.4 0.2 setosa
     3 4.7 3.2 1.3 0.2 setosa
     4 4.6 3.1 1.5 0.2 setosa
     5 5.0 3.6 1.4 0.2 setosa
     6 5.4 3.9 1.7 0.4 setosa
     7 4.6 3.4 1.4 0.3 setosa
     8 5.0 3.4 1.5 0.2 setosa
     9 4.4 2.9 1.4 0.2 setosa
     10 4.9 3.1 1.5 0.1 setosa
     11 5.4 3.7 1.5 0.2 setosa
     12 4.8 3.4 1.6 0.2 setosa
     13 4.8 3.0 1.4 0.1 setosa
     14 4.3 3.0 1.1 0.1 setosa
     15 5.8 4.0 1.2 0.2 setosa
     16 5.7 4.4 1.5 0.4 setosa
     17 5.4 3.9 1.3 0.4 setosa
     18 5.1 3.5 1.4 0.3 setosa
     19 5.7 3.8 1.7 0.3 setosa
     20 5.1 3.8 1.5 0.3 setosa
     21 5.4 3.4 1.7 0.2 setosa
     22 5.1 3.7 1.5 0.4 setosa
     23 4.6 3.6 1.0 0.2 setosa
     24 5.1 3.3 1.7 0.5 setosa
     25 4.8 3.4 1.9 0.2 setosa
     26 5.0 3.0 1.6 0.2 setosa
     27 5.0 3.4 1.6 0.4 setosa
     28 5.2 3.5 1.5 0.2 setosa
     29 5.2 3.4 1.4 0.2 setosa
     30 4.7 3.2 1.6 0.2 setosa
     31 4.8 3.1 1.6 0.2 setosa
     32 5.4 3.4 1.5 0.4 setosa
     33 5.2 4.1 1.5 0.1 setosa
     34 5.5 4.2 1.4 0.2 setosa
     35 4.9 3.1 1.5 0.2 setosa
     36 5.0 3.2 1.2 0.2 setosa
     37 5.5 3.5 1.3 0.2 setosa
     38 4.9 3.6 1.4 0.1 setosa
     39 4.4 3.0 1.3 0.2 setosa
     40 5.1 3.4 1.5 0.2 setosa
     41 5.0 3.5 1.3 0.3 setosa
     42 4.5 2.3 1.3 0.3 setosa
     43 4.4 3.2 1.3 0.2 setosa
     44 5.0 3.5 1.6 0.6 setosa
     45 5.1 3.8 1.9 0.4 setosa
     46 4.8 3.0 1.4 0.3 setosa
     47 5.1 3.8 1.6 0.2 setosa
     48 4.6 3.2 1.4 0.2 setosa
     49 5.3 3.7 1.5 0.2 setosa
     50 5.0 3.3 1.4 0.2 setosa
     51 7.0 3.2 4.7 1.4 versicolor
     52 6.4 3.2 4.5 1.5 versicolor
     53 6.9 3.1 4.9 1.5 versicolor
     54 5.5 2.3 4.0 1.3 versicolor
     55 6.5 2.8 4.6 1.5 versicolor
     56 5.7 2.8 4.5 1.3 versicolor
     57 6.3 3.3 4.7 1.6 versicolor
     58 4.9 2.4 3.3 1.0 versicolor
     59 6.6 2.9 4.6 1.3 versicolor
     60 5.2 2.7 3.9 1.4 versicolor
     61 5.0 2.0 3.5 1.0 versicolor
     62 5.9 3.0 4.2 1.5 versicolor
     63 6.0 2.2 4.0 1.0 versicolor
     64 6.1 2.9 4.7 1.4 versicolor
     65 5.6 2.9 3.6 1.3 versicolor
     66 6.7 3.1 4.4 1.4 versicolor
     67 5.6 3.0 4.5 1.5 versicolor
     68 5.8 2.7 4.1 1.0 versicolor
     69 6.2 2.2 4.5 1.5 versicolor
     70 5.6 2.5 3.9 1.1 versicolor
     71 5.9 3.2 4.8 1.8 versicolor
     72 6.1 2.8 4.0 1.3 versicolor
     73 6.3 2.5 4.9 1.5 versicolor
     74 6.1 2.8 4.7 1.2 versicolor
     75 6.4 2.9 4.3 1.3 versicolor
     76 6.6 3.0 4.4 1.4 versicolor
     77 6.8 2.8 4.8 1.4 versicolor
     78 6.7 3.0 5.0 1.7 versicolor
     79 6.0 2.9 4.5 1.5 versicolor
     80 5.7 2.6 3.5 1.0 versicolor
     81 5.5 2.4 3.8 1.1 versicolor
     82 5.5 2.4 3.7 1.0 versicolor
     83 5.8 2.7 3.9 1.2 versicolor
     84 6.0 2.7 5.1 1.6 versicolor
     85 5.4 3.0 4.5 1.5 versicolor
     86 6.0 3.4 4.5 1.6 versicolor
     87 6.7 3.1 4.7 1.5 versicolor
     88 6.3 2.3 4.4 1.3 versicolor
     89 5.6 3.0 4.1 1.3 versicolor
     90 5.5 2.5 4.0 1.3 versicolor
     91 5.5 2.6 4.4 1.2 versicolor
     92 6.1 3.0 4.6 1.4 versicolor
     93 5.8 2.6 4.0 1.2 versicolor
     94 5.0 2.3 3.3 1.0 versicolor
     95 5.6 2.7 4.2 1.3 versicolor
     96 5.7 3.0 4.2 1.2 versicolor
     97 5.7 2.9 4.2 1.3 versicolor
     98 6.2 2.9 4.3 1.3 versicolor
     99 5.1 2.5 3.0 1.1 versicolor
     100 5.7 2.8 4.1 1.3 versicolor
     101 6.3 3.3 6.0 2.5 virginica
     102 5.8 2.7 5.1 1.9 virginica
     103 7.1 3.0 5.9 2.1 virginica
     104 6.3 2.9 5.6 1.8 virginica
     105 6.5 3.0 5.8 2.2 virginica
     106 7.6 3.0 6.6 2.1 virginica
     107 4.9 2.5 4.5 1.7 virginica
     108 7.3 2.9 6.3 1.8 virginica
     109 6.7 2.5 5.8 1.8 virginica
     110 7.2 3.6 6.1 2.5 virginica
     111 6.5 3.2 5.1 2.0 virginica
     112 6.4 2.7 5.3 1.9 virginica
     113 6.8 3.0 5.5 2.1 virginica
     114 5.7 2.5 5.0 2.0 virginica
     115 5.8 2.8 5.1 2.4 virginica
     116 6.4 3.2 5.3 2.3 virginica
     117 6.5 3.0 5.5 1.8 virginica
     118 7.7 3.8 6.7 2.2 virginica
     119 7.7 2.6 6.9 2.3 virginica
     120 6.0 2.2 5.0 1.5 virginica
     121 6.9 3.2 5.7 2.3 virginica
     122 5.6 2.8 4.9 2.0 virginica
     123 7.7 2.8 6.7 2.0 virginica
     124 6.3 2.7 4.9 1.8 virginica
     125 6.7 3.3 5.7 2.1 virginica
     126 7.2 3.2 6.0 1.8 virginica
     127 6.2 2.8 4.8 1.8 virginica
     128 6.1 3.0 4.9 1.8 virginica
     129 6.4 2.8 5.6 2.1 virginica
     130 7.2 3.0 5.8 1.6 virginica
     131 7.4 2.8 6.1 1.9 virginica
     132 7.9 3.8 6.4 2.0 virginica
     133 6.4 2.8 5.6 2.2 virginica
     134 6.3 2.8 5.1 1.5 virginica
     135 6.1 2.6 5.6 1.4 virginica
     136 7.7 3.0 6.1 2.3 virginica
     137 6.3 3.4 5.6 2.4 virginica
     138 6.4 3.1 5.5 1.8 virginica
     139 6.0 3.0 4.8 1.8 virginica
     140 6.9 3.1 5.4 2.1 virginica
     141 6.7 3.1 5.6 2.4 virginica
     142 6.9 3.1 5.1 2.3 virginica
     143 5.8 2.7 5.1 1.9 virginica
     144 6.8 3.2 5.9 2.3 virginica
     145 6.7 3.3 5.7 2.5 virginica
     146 6.7 3.0 5.2 2.3 virginica
     147 6.3 2.5 5.0 1.9 virginica
     148 6.5 3.0 5.2 2.0 virginica
     149 6.2 3.4 5.4 2.3 virginica
     150 5.9 3.0 5.1 1.8 virginica
     > X = iris[,!names(iris) %in% "Species"]
     > Y = iris[,"Species"]
     > as.numeric(Y)
     [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
     [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
     [75] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3
     [112] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
     [149] 3 3
     > rf.42 = randomForest(X,Y,keep.forest=T,replace=F,keep.inbag=T,samp=15,ntree=100)
     > ff.42 = forestFloor(rf.42,X,calc_np = F,bootstrapFC = TRUE)
     >
     > #test accuracy of feature contributions
     > #y_hat_OOB = row sum FC + Y_grandMean
     > FCc = t(t(apply(ff.42$FCarray,c(1,3),sum))+as.vector(table(Y)/length(Y)))
     > FC.residuals = FCc-predict(rf.42,type="prob")
     > if(max(abs(FC.residuals))>1E-12) stop(
     + paste0("When testing if: y_hat_OOB = row sum FCmatrix + Y_grandMean
     + one/some FCs error exceeds allowed 1e-12, found.error=",max(abs(FC.residuals)))
     + )
     >
     > Xtest = iris[1:50,] #copy
     > Xtest = Xtest[,-5] #drop Species
     > Xtest[1:4] = lapply(iris[1:4],sample,50) #random resample 50 samples
     >
     >
     > #test same results are reached with Xtest
     > ff.43 = forestFloor(rf.42,X,Xtest,bootstrapFC = T)
     > if(max(abs(ff.43$FCarray[ff.43$isTrain,,]-ff.42$FCarray)) > 1E-12) stop(
     + "forestFloor with/without Xtest gives different feature contributions"
     + )
     > plot(ff.43,speedup_GOF = TRUE,plotTest = F)
     >
     >
     > pred = sapply(1:3,function(i) apply(ff.42$FCarray[,,i],1,sum))+1/3
     > rfPred = predict(rf.42,type="vote",norm.votes=T)
     > rfPred[is.nan(rfPred)] = 1/3
     > if(cor(as.vector(rfPred),as.vector(pred))^2<0.99) stop("fail testMultiClass")
     > attributes(ff.42)
     $names
     [1] "X" "Y" "importance" "imp_ind" "FCarray"
     [6] "sumOfInbags" "isTrain"
    
     $class
     [1] "forestFloor_multiClass"
    
     > args(forestFloor:::plot.forestFloor_multiClass)
     function (x, plot_seq = NULL, label.seq = NULL, plotTest = NULL,
     limitY = TRUE, col = NULL, colLists = NULL, orderByImportance = TRUE,
     fig.columns = NULL, plot_GOF = TRUE, GOF_args = list(), speedup_GOF = TRUE,
     jitter_these_cols = NULL, jitter.factor = NULL, ...)
     NULL
     > plot(ff.42,plot_GOF=T,cex=.7,
     + colLists=list("#FF0000A5",
     + "#00FF0050",
     + "#0000FF35")
     + )
     >
     > plot(ff.43,plot_GOF=T,cex=.7,
     + colLists=list("#FF0000A5",
     + "#00FF0050",
     + "#0000FF35")
     + )
     >
     > #use col interface
     > plot(ff.43,plot_GOF=T,cex=.7,
     + col=list("#FF0000A5","#00FF0050","#0000FF35") #one colour per class
     + )
     >
     > plot(ff.43,plot_GOF=T,cex=.7,
     + col=c("#FF0000A5","#00FF0050","#0000FF35") #one colour per class
     + )
     >
     >
     > #try to alter std par
     > plot(ff.42,plot_GOF=T,cex=.7,
     + colLists=list("#FF0000A5",
     + "#00FF0050",
     + "#0000FF35"),
     + mfrow=c(4,3)
     + )
     >
     >
     > show3d(ff.42,1:2,1:2,plot_GOF=T)
     > show3d(ff.42,1:2,1,plot_GOF=T)#test plotting only one feature contribution
     >
     >
     > #plot all effect 2D only
     > pars = plot_simplex3(ff.42,Xi=c(1:3),restore_par=F,zoom.fit=NULL,var.col=NULL,fig.cols=2,fig.rows=1,
     + fig3d=F,includeTotal=T,auto.alpha=.4,set_pars=T)
     > pars = plot_simplex3(ff.42,Xi=0,restore_par=F,zoom.fit=NULL,var.col=alist(alpha=.3,cols=1:4),
     + fig3d=F,includeTotal=T,auto.alpha=.8,set_pars=F)
     ----------- FAILURE REPORT --------------
     --- failure: the condition has length > 1 ---
     --- srcref ---
     :
     --- package (from environment) ---
     forestFloor
     --- call from context ---
     box.outliers(prcomp(sel.colM)$x[, 1:max.df], limit = Inf)
     --- call from argument ---
     if (class(x) == "data.frame") {
     sx = as.data.frame(sx, row.names = row.names(x))
     names(sx) = names(x)
     }
     --- R stacktrace ---
     where 1: box.outliers(prcomp(sel.colM)$x[, 1:max.df], limit = Inf)
     where 2: (function (ff, cols = NULL, orderByImportance = NULL, plotTest = NULL,
     X.matrix = TRUE, hue = NULL, saturation = NULL, brightness = NULL,
     hue.range = NULL, sat.range = NULL, bri.range = NULL, alpha = NULL,
     RGB = NULL, byResiduals = FALSE, max.df = 3, imp.weight = NULL,
     imp.exp = 1, outlier.lim = 3, RGB.exp = NULL)
     {
     if (!X.matrix)
     if (class(ff) == "forestFloor_multiClass")
     stop("cannot colour by feature contributions for object of class\n 'forestFloor_multiClass'. Set X.matrix=TRUE")
     ib <- function(x, low, high) (x - low) * (high - x) > 0
     span <- function(x, mid, width) if (min(x) != max(x)) {
     ((x - min(x))/(max(x) - min(x)) - 0.5) * width + mid
     }
     else {
     x[] = mid
     }
     auto.range = function(level, low = 0, high = 1) abs(min(level -
     low, high - level)) * 2
     contain = function(x, low = 0, high = 1) {
     x[x > high] = high
     x[x < low] = low
     x
     }
     if (class(ff) %in% c("forestFloor_regression", "forestFloor_multiClass")) {
     plotThese = checkPlotTest(plotTest, ff$isTrain)
     if (!(all(plotThese))) {
     if (class(ff) == "forestFloor_multiClass") {
     ff$FCarray = ff$FCarray[plotThese, , ]
     }
     else {
     if (class(ff) == "forestFloor_regression") {
     ff$FCmatrix = ff$FCmatrix[plotThese, ]
     }
     }
     ff$Y = ff$Y[plotThese]
     ff$X = ff$X[plotThese, ]
     }
     }
     if (class(ff) %in% c("forestFloor_regression", "forestFloor_multiClass")) {
     if (byResiduals) {
     if (is.null(ff$FCfit)) {
     print("no $FCfit found, computing tempoary LOO-kNN-gaussion fit to main affect")
     print("use ff = convolute_ff(ff) to compute a fixed fit")
     ff = convolute_ff(ff)
     }
     colM = ff$FCmatrix - ff$FCfit
     }
     else {
     if (X.matrix)
     colM = ff$X
     else colM = ff$FCmatrix
     }
     if (is.null(imp.weight))
     imp.weight = TRUE
     if (is.null(orderByImportance))
     orderByImportance = TRUE
     }
     else {
     colM = ff
     if (is.null(imp.weight))
     imp.weight = FALSE
     if (is.null(orderByImportance))
     orderByImportance = FALSE
     }
     if (orderByImportance)
     if (class(ff) %in% c("forestFloor_regression", "forestFloor_multiClass")) {
     colM = colM[, ff$imp_ind]
     }
     else {
     warning("orderByImportance=TRUE takes no effect for non 'forestFloor'-class. As if set to NULL or FALSE...")
     }
     if (!class(colM) %in% c("data.frame", "matrix")) {
     tryCatch({
     colM = matrix(colM, ncol = 1)
     }, error = function(e) stop(paste("input ff was neither data.frame or matrix and \ncould not be coerced to matrix:",
     e$message)))
     }
     colM = data.frame(colM)
     if (is.null(cols))
     cols = 1:dim(colM)[2]
     if (length(cols) < 1 || !is.numeric(cols) || any(!cols %in%
     1:dim(colM)[2])) {
     stop("no cols selected or is not integer/numeric or wrong coloumns")
     }
     sel.colM = data.frame(colM[, cols])
     sel.cols = 1:length(cols)
     if (is.null(RGB))
     if (length(cols) == 1)
     RGB = TRUE
     else RGB = FALSE
     if (!RGB) {
     if (is.null(saturation))
     saturation = 0.85
     if (is.null(brightness))
     brightness = 0.75
     if (is.null(hue))
     hue = 0.25
     }
     else {
     if (is.null(saturation))
     saturation = 1
     if (is.null(brightness))
     brightness = 0.75
     if (is.null(hue))
     hue = 0.66
     if (is.null(RGB.exp))
     RGB.exp = 1.2
     if (is.null(hue.range))
     hue.range = 2
     }
     as.numeric.factor <- function(x, rearrange = TRUE) {
     if (is.numeric(x))
     return(x)
     if (rearrange)
     x = match(x, levels(droplevels(x)))
     else x = match(x, levels(x))
     return(x)
     }
     for (i in 1:dim(sel.colM)[2]) {
     if (is.factor(sel.colM[, i])) {
     this.fac = as.numeric.factor(sel.colM[, i])
     sel.colM[, i] = this.fac
     }
     if (is.character(sel.colM[, i]))
     sel.colM[, i] = as.numeric(sel.colM[, i])
     }
     sel.colM = box.outliers(sel.colM, limit = outlier.lim)
     if (imp.weight && length(cols) > 1) {
     if (class(ff) %in% c("forestFloor_regression", "forestFloor_multiClass")) {
     sel.imp = ff$importance[cols]
     non.negative.imp = sel.imp + min(sel.imp)
     sumnorm.imp = non.negative.imp/sum(non.negative.imp)
     exp.imp = sumnorm.imp^imp.exp
     impM = t(replicate(dim(colM)[1], exp.imp))
     sel.colM = sel.colM * impM
     sel.colM = sel.colM/max(sel.colM)
     }
     else {
     warning("importance weighting only possible for class 'forestFloor'")
     }
     }
     if (any(!c(class(hue), class(saturation), class(brightness)) %in%
     c("numeric", "integer"))) {
     stop("hue, saturation and brightness must be of class numeric or integer")
     }
     hue = hue - floor(hue)
     saturation = max(min(saturation, 1), 0)
     brightness = max(min(brightness, 1), 0)
     if (RGB == TRUE) {
     if (is.null(bri.range))
     bri.range = 0.05
     if (is.null(alpha))
     alpha = 0.7
     len.colM = box.outliers(sel.colM, limit = Inf)
     if (dim(len.colM)[2] == 1)
     nX = as.numeric(len.colM[, 1])
     else nX = as.numeric(apply(len.colM, 1, mean))
     hsvcol = t(sapply(nX, function(x) rgb2hsv(x^RGB.exp,
     1 - x^RGB.exp - (1 - x)^RGB.exp, (1 - x)^RGB.exp)))
     hue.vec = hsvcol[, 1] * hue.range + hue
     hue.vec[hue.vec > 1] = hue.vec[hue.vec > 1] - floor(hue.vec[hue.vec >
     1])
     hsvcol[, 1] = hue.vec
     sat.range = auto.range(saturation)
     hsvcol[, 2] = span(hsvcol[, 2], saturation, sat.range)
     hsvcol[, 2] = contain(hsvcol[, 2])
     bri.range = auto.range(brightness)
     hsvcol[, 3] = span(hsvcol[, 3], brightness, bri.range)
     hsvcol[, 3] = contain(hsvcol[, 3])
     colours = apply(hsvcol, 1, function(x) hsv(x[1], x[2],
     x[3], alpha = alpha))
     return(colours)
     }
     col.df = length(cols)
     if (!max.df %in% c(1, 2, 3))
     stop("fcol input 'max.df' must be set to either 1, 2 or 3")
     if (col.df > max.df) {
     len.colM = box.outliers(prcomp(sel.colM)$x[, 1:max.df],
     limit = Inf)
     col.df = max.df
     }
     else {
     len.colM = box.outliers(sel.colM, limit = Inf)
     }
     if (is.null(hue.range)) {
     if (col.df == 1)
     hue.range = 0.85
     if (col.df == 2)
     hue.range = 1
     if (col.df == 3)
     hue.range = 1
     }
     if (is.null(sat.range)) {
     if (col.df == 1)
     sat.range = "not used"
     if (col.df == 2)
     sat.range = auto.range(saturation)
     if (col.df == 3)
     sat.range = auto.range(saturation)
     }
     if (is.null(bri.range)) {
     if (col.df == 1)
     bri.range = "not used"
     if (col.df == 2)
     bri.range = "not used"
     if (col.df == 3)
     bri.range = auto.range(brightness)
     }
     if (is.null(alpha))
     alpha = min(1, 400/dim(len.colM)[1])
     if (col.df == 1) {
     hue.vec = as.numeric(len.colM[, 1]) * hue.range + hue
     hue.vec[hue.vec > 1] = hue.vec[hue.vec > 1] - floor(hue.vec[hue.vec >
     1])
     colours = hsv(h = hue.vec, s = saturation, v = brightness,
     alpha = alpha)
     }
     if (col.df == 2) {
     hsvcol = t(rgb2hsv(len.colM[, 1], len.colM[, 2], 1 -
     apply(len.colM, 1, mean)))
     hue.vec = hsvcol[, 1] * hue.range + hue
     hue.vec[hue.vec > 1] = hue.vec[hue.vec > 1] - 1
     hsvcol[, 1] = hue.vec
     hsvcol[, 2] = ((len.colM[, 1] - mean(len.colM[, 1]))^2 +
     (len.colM[, 2] - mean(len.colM[, 2]))^2)^sat.range *
     saturation
     hsvcol[, 2] = hsvcol[, 2]/max(hsvcol[, 2])
     hsvcol[, 3] = brightness
     colours = hsv(hsvcol[, 1], hsvcol[, 2], hsvcol[, 3],
     alpha = alpha)
     }
     if (col.df == 3) {
     hsvcol = t(rgb2hsv(len.colM[, 1], len.colM[, 2], len.colM[,
     3]))
     hue.vec = hsvcol[, 1] * hue.range + hue
     hue.vec[hue.vec > 1] = hue.vec[hue.vec > 1] - 1
     hsvcol[, 1] = hue.vec
     span.sat = span(hsvcol[, 2], saturation, sat.range)
     hsvcol[, 2] = contain(span.sat)
     mean.bri = apply(len.colM, 1, mean)
     span.bri = span(mean.bri, brightness, bri.range)
     hsvcol[, 3] = contain(span.bri)
     colours = hsv(hsvcol[, 1], hsvcol[, 2], hsvcol[, 3],
     alpha = alpha)
     }
     return(colours)
     })(list(Sepal.Length = c(5.1, 4.9, 4.7, 4.6, 5, 5.4, 4.6, 5,
     4.4, 4.9, 5.4, 4.8, 4.8, 4.3, 5.8, 5.7, 5.4, 5.1, 5.7, 5.1, 5.4,
     5.1, 4.6, 5.1, 4.8, 5, 5, 5.2, 5.2, 4.7, 4.8, 5.4, 5.2, 5.5,
     4.9, 5, 5.5, 4.9, 4.4, 5.1, 5, 4.5, 4.4, 5, 5.1, 4.8, 5.1, 4.6,
     5.3, 5, 7, 6.4, 6.9, 5.5, 6.5, 5.7, 6.3, 4.9, 6.6, 5.2, 5, 5.9,
     6, 6.1, 5.6, 6.7, 5.6, 5.8, 6.2, 5.6, 5.9, 6.1, 6.3, 6.1, 6.4,
     6.6, 6.8, 6.7, 6, 5.7, 5.5, 5.5, 5.8, 6, 5.4, 6, 6.7, 6.3, 5.6,
     5.5, 5.5, 6.1, 5.8, 5, 5.6, 5.7, 5.7, 6.2, 5.1, 5.7, 6.3, 5.8,
     7.1, 6.3, 6.5, 7.6, 4.9, 7.3, 6.7, 7.2, 6.5, 6.4, 6.8, 5.7, 5.8,
     6.4, 6.5, 7.7, 7.7, 6, 6.9, 5.6, 7.7, 6.3, 6.7, 7.2, 6.2, 6.1,
     6.4, 7.2, 7.4, 7.9, 6.4, 6.3, 6.1, 7.7, 6.3, 6.4, 6, 6.9, 6.7,
     6.9, 5.8, 6.8, 6.7, 6.7, 6.3, 6.5, 6.2, 5.9), Sepal.Width = c(3.5,
     3, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1, 3.7, 3.4, 3, 3, 4,
     4.4, 3.9, 3.5, 3.8, 3.8, 3.4, 3.7, 3.6, 3.3, 3.4, 3, 3.4, 3.5,
     3.4, 3.2, 3.1, 3.4, 4.1, 4.2, 3.1, 3.2, 3.5, 3.6, 3, 3.4, 3.5,
     2.3, 3.2, 3.5, 3.8, 3, 3.8, 3.2, 3.7, 3.3, 3.2, 3.2, 3.1, 2.3,
     2.8, 2.8, 3.3, 2.4, 2.9, 2.7, 2, 3, 2.2, 2.9, 2.9, 3.1, 3, 2.7,
     2.2, 2.5, 3.2, 2.8, 2.5, 2.8, 2.9, 3, 2.8, 3, 2.9, 2.6, 2.4,
     2.4, 2.7, 2.7, 3, 3.4, 3.1, 2.3, 3, 2.5, 2.6, 3, 2.6, 2.3, 2.7,
     3, 2.9, 2.9, 2.5, 2.8, 3.3, 2.7, 3, 2.9, 3, 3, 2.5, 2.9, 2.5,
     3.6, 3.2, 2.7, 3, 2.5, 2.8, 3.2, 3, 3.8, 2.6, 2.2, 3.2, 2.8,
     2.8, 2.7, 3.3, 3.2, 2.8, 3, 2.8, 3, 2.8, 3.8, 2.8, 2.8, 2.6,
     3, 3.4, 3.1, 3, 3.1, 3.1, 3.1, 2.7, 3.2, 3.3, 3, 2.5, 3, 3.4,
     3), Petal.Length = c(1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5,
     1.4, 1.5, 1.5, 1.6, 1.4, 1.1, 1.2, 1.5, 1.3, 1.4, 1.7, 1.5, 1.7,
     1.5, 1, 1.7, 1.9, 1.6, 1.6, 1.5, 1.4, 1.6, 1.6, 1.5, 1.5, 1.4,
     1.5, 1.2, 1.3, 1.4, 1.3, 1.5, 1.3, 1.3, 1.3, 1.6, 1.9, 1.4, 1.6,
     1.4, 1.5, 1.4, 4.7, 4.5, 4.9, 4, 4.6, 4.5, 4.7, 3.3, 4.6, 3.9,
     3.5, 4.2, 4, 4.7, 3.6, 4.4, 4.5, 4.1, 4.5, 3.9, 4.8, 4, 4.9,
     4.7, 4.3, 4.4, 4.8, 5, 4.5, 3.5, 3.8, 3.7, 3.9, 5.1, 4.5, 4.5,
     4.7, 4.4, 4.1, 4, 4.4, 4.6, 4, 3.3, 4.2, 4.2, 4.2, 4.3, 3, 4.1,
     6, 5.1, 5.9, 5.6, 5.8, 6.6, 4.5, 6.3, 5.8, 6.1, 5.1, 5.3, 5.5,
     5, 5.1, 5.3, 5.5, 6.7, 6.9, 5, 5.7, 4.9, 6.7, 4.9, 5.7, 6, 4.8,
     4.9, 5.6, 5.8, 6.1, 6.4, 5.6, 5.1, 5.6, 6.1, 5.6, 5.5, 4.8, 5.4,
     5.6, 5.1, 5.1, 5.9, 5.7, 5.2, 5, 5.2, 5.4, 5.1), Petal.Width = c(0.2,
     0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, 0.2, 0.2, 0.1, 0.1,
     0.2, 0.4, 0.4, 0.3, 0.3, 0.3, 0.2, 0.4, 0.2, 0.5, 0.2, 0.2, 0.4,
     0.2, 0.2, 0.2, 0.2, 0.4, 0.1, 0.2, 0.2, 0.2, 0.2, 0.1, 0.2, 0.2,
     0.3, 0.3, 0.2, 0.6, 0.4, 0.3, 0.2, 0.2, 0.2, 0.2, 1.4, 1.5, 1.5,
     1.3, 1.5, 1.3, 1.6, 1, 1.3, 1.4, 1, 1.5, 1, 1.4, 1.3, 1.4, 1.5,
     1, 1.5, 1.1, 1.8, 1.3, 1.5, 1.2, 1.3, 1.4, 1.4, 1.7, 1.5, 1,
     1.1, 1, 1.2, 1.6, 1.5, 1.6, 1.5, 1.3, 1.3, 1.3, 1.2, 1.4, 1.2,
     1, 1.3, 1.2, 1.3, 1.3, 1.1, 1.3, 2.5, 1.9, 2.1, 1.8, 2.2, 2.1,
     1.7, 1.8, 1.8, 2.5, 2, 1.9, 2.1, 2, 2.4, 2.3, 1.8, 2.2, 2.3,
     1.5, 2.3, 2, 2, 1.8, 2.1, 1.8, 1.8, 1.8, 2.1, 1.6, 1.9, 2, 2.2,
     1.5, 1.4, 2.3, 2.4, 1.8, 1.8, 2.1, 2.4, 2.3, 1.9, 2.3, 2.5, 2.3,
     1.9, 2, 2.3, 1.8)), 0, alpha = 0.3, cols = 1:4)
     where 3: do.call(fcol, c(list(ff$X, i), var.col))
     where 4: plot.xy(xy.coords(x, y), type = type, ...)
     where 5: points.default(apply(xd1 * t(FC[, ]), 2, sum) + cent2[1], apply(xd2 *
     t(FC[, ]), 2, sum) + cent2[2], cex = 0.3, col = if (Col[1] ==
     "var.col") do.call(fcol, c(list(ff$X, i), var.col)) else Col)
     where 6: points(apply(xd1 * t(FC[, ]), 2, sum) + cent2[1], apply(xd2 *
     t(FC[, ]), 2, sum) + cent2[2], cex = 0.3, col = if (Col[1] ==
     "var.col") do.call(fcol, c(list(ff$X, i), var.col)) else Col)
     where 7: plot_simplex3(ff.42, Xi = 0, restore_par = F, zoom.fit = NULL,
     var.col = alist(alpha = 0.3, cols = 1:4), fig3d = F, includeTotal = T,
     auto.alpha = 0.8, set_pars = F)
    
     --- value of length: 2 type: logical ---
     [1] FALSE FALSE
     --- function from context ---
     function (x, limit = 1.5, normalize = TRUE)
     {
     sx = scale(x)
     if (limit != FALSE) {
     sx[sx > limit] = limit
     sx[-sx > limit] = -limit
     }
     if (normalize) {
     sx.span = max(sx) - min(sx)
     sx = sx - min(sx)
     sx = sx/sx.span
     }
     else {
     obs = attributes(sx)$dim[1]
     if (dim(sx)[2] > 1) {
     sx = sx * t(replicate(obs, attributes(sx)$"scaled:scale")) +
     t(replicate(obs, attributes(sx)$"scaled:center"))
     }
     else {
     sx = sx * attributes(sx)$"scaled:scale" + attributes(sx)$"scaled:center"
     }
     }
     if (class(x) == "data.frame") {
     sx = as.data.frame(sx, row.names = row.names(x))
     names(sx) = names(x)
     }
     return(sx)
     }
     <bytecode: 0x5593e3a15788>
     <environment: namespace:forestFloor>
     --- function search by body ---
     Function box.outliers in namespace forestFloor has this body.
     ----------- END OF FAILURE REPORT --------------
     Error in if (class(x) == "data.frame") { : the condition has length > 1
     Calls: plot_simplex3 ... points.default -> plot.xy -> do.call -> <Anonymous> -> box.outliers
     Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc

Version: 1.11.1
Check: tests
Result: ERROR
     Running ‘allExamples.R’
     Running ‘rfPermute_supported.R’
     Running ‘stdUsage.R’ [14s/16s]
     Running ‘testBinaryClass.R’
     Running ‘testCaret.R’ [21s/25s]
     Running ‘testMultiClass.cpp.R’
     Running ‘test_Xtestmerger.R’
    Running the tests in ‘tests/stdUsage.R’ failed.
    Complete output:
     > if(!interactive()) Sys.setenv(RGL_USE_NULL=TRUE) #disable RGL for headless machines
     > library(forestFloor)
     > library(randomForest)
     randomForest 4.6-14
     Type rfNews() to see new features/changes/bug fixes.
     > #simulate data
     > obs=2000
     > vars = 6
     >
     > X = data.frame(replicate(vars,rnorm(obs)))
     > Xtest = data.frame(replicate(vars,rnorm(obs)*3))
     > Y = with(X, X1^2 + sin(X2*pi) + 2 * X3 * X4 + .5 * rnorm(obs))
     >
     >
     > #grow a forest, remeber to include inbag
     > rf41=randomForest(X,Y,keep.inbag = TRUE,sampsize=499,ntree=100,importance = F)
     > #compute feature contributions
     > out = tryCatch({ff41 = forestFloor(rf41,X,bootstrapFC = TRUE,impType=1)},warning = function(w) w)
     > if(out$message != "found nothing of importance, revert to fallback") stop("wrong warning")
     >
     > #if(colnames(ff41$importance) != "%IncMSE") stop("wrong imp")
     > ff41 = forestFloor(rf41,X,bootstrapFC = TRUE,impType=2)
     > if(colnames(ff41$importance) != "IncNodePurity") stop("wrong imp")
     > ff41 = forestFloor(rf41,X,bootstrapFC = TRUE,impType=NULL)
     > if(colnames(ff41$importance) != "IncNodePurity") stop("wrong imp")
     >
     > #grow a forest, remeber to include inbag
     > rf42=randomForest(X,Y,keep.inbag = TRUE,sampsize=499,ntree=100,importance = T)
     > #compute feature contributions
     > ff42 = forestFloor(rf42,X,bootstrapFC = TRUE,impType=1)
     > if(colnames(ff42$importance) != "%IncMSE") stop("wrong imp")
     > ff42 = forestFloor(rf42,X,bootstrapFC = TRUE,impType=2)
     > if(colnames(ff42$importance) != "IncNodePurity") stop("wrong imp")
     > ff42 = forestFloor(rf42,X,bootstrapFC = TRUE,impType=NULL)
     > if(colnames(ff42$importance) != "%IncMSE") stop("wrong imp")
     >
     >
     >
     >
     > #test accuracy of feature contributions
     > #y_hat_OOB = row sum FC + Y_grandMean
     > FC.residuals = rf42$predicted - apply(ff42$FCmatrix[ff42$isTrain,],1,sum) - mean(Y)
     > if(max(abs(FC.residuals))>1E-12) stop(
     + paste0("When testing if: y_hat_OOB = row sum FCmatrix + Y_grandMean
     + one/some FCs error exceed allowed 1e-12, found.error=",max(abs(FC.residuals)))
     + )
     >
     > #test same results are reached with Xtest
     > ff43 = forestFloor(rf42,X,Xtest,bootstrapFC = TRUE)
     > if(max(abs(ff43$FCmatrix[ff43$isTrain,]-ff42$FCmatrix)) > 1E-12) stop(
     + "forestFloor with/without Xtest gives different feature contributions"
     + )
     >
     >
     > #print forestFloor
     > print(ff42)
     this is a forestFloor_regression object
    
     this object can be plotted in 2D with plot(x), see help(plot.forestFloor)
    
     this object can be plotted in 3D with show3d(x), see help(show3d)
    
    
    
     x contains following internal elements:
     FCmatrix X Y imp_ind importance isTrain>
     > #plot partial functions of most important variables first
     > plot(ff42,orderByImportance=TRUE)
     [1] "compute goodness-of-fit with leave-one-out k-nearest neighbor(guassian weighting), kknn package"
     > plot(ff43,orderByImportance=TRUE)
     [1] "compute goodness-of-fit with leave-one-out k-nearest neighbor(guassian weighting), kknn package"
     >
     > #Non interacting functions are well displayed, whereas X3 and X4 are not
     > #by applying different colourgradient, interactions reveal themself
     > #also a k-nearest neighbor fit is applied to evaluate goodness of fit
     > Col=fcol(ff43,3,orderByImportance=FALSE)
     > plot(ff43,col=Col,plot_GOF=TRUE,speed=T)
     [1] "compute goodness-of-fit with leave-one-out k-nearest neighbor(guassian weighting), kknn package"
     >
     >
     > ##make test set grey tone to show if point of test is extrapolated
     > Col=fcol(ff43,3,orderByImportance=FALSE,plotTest="andTrain",alpha=.2)
     > Col[ff43$isTrain] = "#000000FF"
     > plot(ff43,col=Col,speed=T,plotTest="andTrain",plot_GOF=F)
     >
     >
     > #if ever needed, k-nearest neighbor parameters for goodness-of-fit can be access through convolute_ff
     > #a new fit will be calculated and added to forstFloor object as ff42$FCfit
     > ff43 = convolute_ff(ff43,userArgs.kknn=alist(kernel="epanechnikov",kmax=5))
     > plot(ff43,col=Col,plot_GOF=TRUE)
     >
     > #in 3D the interaction between X3 and X reveals itself completely
     > show3d(ff43,3:4,col=Col,plot.rgl=list(size=5),orderByImportance=FALSE)
     > Col=fcol(ff43,1:2,orderByImportance=FALSE)
     > show3d(ff43,1:2,col=Col,plot.rgl=list(size=5),orderByImportance=FALSE)
     >
     >
     > Col=fcol(ff43,1:2,plotTest="andTrain",orderByImportance=FALSE)
     > show3d(ff43,1:2,col=Col,plot.rgl=list(size=5),orderByImportance=FALSE)
     >
     >
     >
     > #although no interaction, a joined additive effect of X1 and X2
     > #colour by FC-component FC1 and FC2 summed
     > Col = fcol(ff43,1:2,orderByImportance=FALSE,X.m=FALSE,RGB=TRUE,plotTest = "a")
     ----------- FAILURE REPORT --------------
     --- failure: the condition has length > 1 ---
     --- srcref ---
     :
     --- package (from environment) ---
     forestFloor
     --- call from context ---
     fcol(ff43, 1:2, orderByImportance = FALSE, X.m = FALSE, RGB = TRUE,
     plotTest = "a")
     --- call from argument ---
     if (!class(colM) %in% c("data.frame", "matrix")) {
     tryCatch({
     colM = matrix(colM, ncol = 1)
     }, error = function(e) stop(paste("input ff was neither data.frame or matrix and \ncould not be coerced to matrix:",
     e$message)))
     }
     --- R stacktrace ---
     where 1: fcol(ff43, 1:2, orderByImportance = FALSE, X.m = FALSE, RGB = TRUE,
     plotTest = "a")
    
     --- value of length: 2 type: logical ---
     [1] FALSE TRUE
     --- function from context ---
     function (ff, cols = NULL, orderByImportance = NULL, plotTest = NULL,
     X.matrix = TRUE, hue = NULL, saturation = NULL, brightness = NULL,
     hue.range = NULL, sat.range = NULL, bri.range = NULL, alpha = NULL,
     RGB = NULL, byResiduals = FALSE, max.df = 3, imp.weight = NULL,
     imp.exp = 1, outlier.lim = 3, RGB.exp = NULL)
     {
     if (!X.matrix)
     if (class(ff) == "forestFloor_multiClass")
     stop("cannot colour by feature contributions for object of class\n 'forestFloor_multiClass'. Set X.matrix=TRUE")
     ib <- function(x, low, high) (x - low) * (high - x) > 0
     span <- function(x, mid, width) if (min(x) != max(x)) {
     ((x - min(x))/(max(x) - min(x)) - 0.5) * width + mid
     }
     else {
     x[] = mid
     }
     auto.range = function(level, low = 0, high = 1) abs(min(level -
     low, high - level)) * 2
     contain = function(x, low = 0, high = 1) {
     x[x > high] = high
     x[x < low] = low
     x
     }
     if (class(ff) %in% c("forestFloor_regression", "forestFloor_multiClass")) {
     plotThese = checkPlotTest(plotTest, ff$isTrain)
     if (!(all(plotThese))) {
     if (class(ff) == "forestFloor_multiClass") {
     ff$FCarray = ff$FCarray[plotThese, , ]
     }
     else {
     if (class(ff) == "forestFloor_regression") {
     ff$FCmatrix = ff$FCmatrix[plotThese, ]
     }
     }
     ff$Y = ff$Y[plotThese]
     ff$X = ff$X[plotThese, ]
     }
     }
     if (class(ff) %in% c("forestFloor_regression", "forestFloor_multiClass")) {
     if (byResiduals) {
     if (is.null(ff$FCfit)) {
     print("no $FCfit found, computing tempoary LOO-kNN-gaussion fit to main affect")
     print("use ff = convolute_ff(ff) to compute a fixed fit")
     ff = convolute_ff(ff)
     }
     colM = ff$FCmatrix - ff$FCfit
     }
     else {
     if (X.matrix)
     colM = ff$X
     else colM = ff$FCmatrix
     }
     if (is.null(imp.weight))
     imp.weight = TRUE
     if (is.null(orderByImportance))
     orderByImportance = TRUE
     }
     else {
     colM = ff
     if (is.null(imp.weight))
     imp.weight = FALSE
     if (is.null(orderByImportance))
     orderByImportance = FALSE
     }
     if (orderByImportance)
     if (class(ff) %in% c("forestFloor_regression", "forestFloor_multiClass")) {
     colM = colM[, ff$imp_ind]
     }
     else {
     warning("orderByImportance=TRUE takes no effect for non 'forestFloor'-class. As if set to NULL or FALSE...")
     }
     if (!class(colM) %in% c("data.frame", "matrix")) {
     tryCatch({
     colM = matrix(colM, ncol = 1)
     }, error = function(e) stop(paste("input ff was neither data.frame or matrix and \ncould not be coerced to matrix:",
     e$message)))
     }
     colM = data.frame(colM)
     if (is.null(cols))
     cols = 1:dim(colM)[2]
     if (length(cols) < 1 || !is.numeric(cols) || any(!cols %in%
     1:dim(colM)[2])) {
     stop("no cols selected or is not integer/numeric or wrong coloumns")
     }
     sel.colM = data.frame(colM[, cols])
     sel.cols = 1:length(cols)
     if (is.null(RGB))
     if (length(cols) == 1)
     RGB = TRUE
     else RGB = FALSE
     if (!RGB) {
     if (is.null(saturation))
     saturation = 0.85
     if (is.null(brightness))
     brightness = 0.75
     if (is.null(hue))
     hue = 0.25
     }
     else {
     if (is.null(saturation))
     saturation = 1
     if (is.null(brightness))
     brightness = 0.75
     if (is.null(hue))
     hue = 0.66
     if (is.null(RGB.exp))
     RGB.exp = 1.2
     if (is.null(hue.range))
     hue.range = 2
     }
     as.numeric.factor <- function(x, rearrange = TRUE) {
     if (is.numeric(x))
     return(x)
     if (rearrange)
     x = match(x, levels(droplevels(x)))
     else x = match(x, levels(x))
     return(x)
     }
     for (i in 1:dim(sel.colM)[2]) {
     if (is.factor(sel.colM[, i])) {
     this.fac = as.numeric.factor(sel.colM[, i])
     sel.colM[, i] = this.fac
     }
     if (is.character(sel.colM[, i]))
     sel.colM[, i] = as.numeric(sel.colM[, i])
     }
     sel.colM = box.outliers(sel.colM, limit = outlier.lim)
     if (imp.weight && length(cols) > 1) {
     if (class(ff) %in% c("forestFloor_regression", "forestFloor_multiClass")) {
     sel.imp = ff$importance[cols]
     non.negative.imp = sel.imp + min(sel.imp)
     sumnorm.imp = non.negative.imp/sum(non.negative.imp)
     exp.imp = sumnorm.imp^imp.exp
     impM = t(replicate(dim(colM)[1], exp.imp))
     sel.colM = sel.colM * impM
     sel.colM = sel.colM/max(sel.colM)
     }
     else {
     warning("importance weighting only possible for class 'forestFloor'")
     }
     }
     if (any(!c(class(hue), class(saturation), class(brightness)) %in%
     c("numeric", "integer"))) {
     stop("hue, saturation and brightness must be of class numeric or integer")
     }
     hue = hue - floor(hue)
     saturation = max(min(saturation, 1), 0)
     brightness = max(min(brightness, 1), 0)
     if (RGB == TRUE) {
     if (is.null(bri.range))
     bri.range = 0.05
     if (is.null(alpha))
     alpha = 0.7
     len.colM = box.outliers(sel.colM, limit = Inf)
     if (dim(len.colM)[2] == 1)
     nX = as.numeric(len.colM[, 1])
     else nX = as.numeric(apply(len.colM, 1, mean))
     hsvcol = t(sapply(nX, function(x) rgb2hsv(x^RGB.exp,
     1 - x^RGB.exp - (1 - x)^RGB.exp, (1 - x)^RGB.exp)))
     hue.vec = hsvcol[, 1] * hue.range + hue
     hue.vec[hue.vec > 1] = hue.vec[hue.vec > 1] - floor(hue.vec[hue.vec >
     1])
     hsvcol[, 1] = hue.vec
     sat.range = auto.range(saturation)
     hsvcol[, 2] = span(hsvcol[, 2], saturation, sat.range)
     hsvcol[, 2] = contain(hsvcol[, 2])
     bri.range = auto.range(brightness)
     hsvcol[, 3] = span(hsvcol[, 3], brightness, bri.range)
     hsvcol[, 3] = contain(hsvcol[, 3])
     colours = apply(hsvcol, 1, function(x) hsv(x[1], x[2],
     x[3], alpha = alpha))
     return(colours)
     }
     col.df = length(cols)
     if (!max.df %in% c(1, 2, 3))
     stop("fcol input 'max.df' must be set to either 1, 2 or 3")
     if (col.df > max.df) {
     len.colM = box.outliers(prcomp(sel.colM)$x[, 1:max.df],
     limit = Inf)
     col.df = max.df
     }
     else {
     len.colM = box.outliers(sel.colM, limit = Inf)
     }
     if (is.null(hue.range)) {
     if (col.df == 1)
     hue.range = 0.85
     if (col.df == 2)
     hue.range = 1
     if (col.df == 3)
     hue.range = 1
     }
     if (is.null(sat.range)) {
     if (col.df == 1)
     sat.range = "not used"
     if (col.df == 2)
     sat.range = auto.range(saturation)
     if (col.df == 3)
     sat.range = auto.range(saturation)
     }
     if (is.null(bri.range)) {
     if (col.df == 1)
     bri.range = "not used"
     if (col.df == 2)
     bri.range = "not used"
     if (col.df == 3)
     bri.range = auto.range(brightness)
     }
     if (is.null(alpha))
     alpha = min(1, 400/dim(len.colM)[1])
     if (col.df == 1) {
     hue.vec = as.numeric(len.colM[, 1]) * hue.range + hue
     hue.vec[hue.vec > 1] = hue.vec[hue.vec > 1] - floor(hue.vec[hue.vec >
     1])
     colours = hsv(h = hue.vec, s = saturation, v = brightness,
     alpha = alpha)
     }
     if (col.df == 2) {
     hsvcol = t(rgb2hsv(len.colM[, 1], len.colM[, 2], 1 -
     apply(len.colM, 1, mean)))
     hue.vec = hsvcol[, 1] * hue.range + hue
     hue.vec[hue.vec > 1] = hue.vec[hue.vec > 1] - 1
     hsvcol[, 1] = hue.vec
     hsvcol[, 2] = ((len.colM[, 1] - mean(len.colM[, 1]))^2 +
     (len.colM[, 2] - mean(len.colM[, 2]))^2)^sat.range *
     saturation
     hsvcol[, 2] = hsvcol[, 2]/max(hsvcol[, 2])
     hsvcol[, 3] = brightness
     colours = hsv(hsvcol[, 1], hsvcol[, 2], hsvcol[, 3],
     alpha = alpha)
     }
     if (col.df == 3) {
     hsvcol = t(rgb2hsv(len.colM[, 1], len.colM[, 2], len.colM[,
     3]))
     hue.vec = hsvcol[, 1] * hue.range + hue
     hue.vec[hue.vec > 1] = hue.vec[hue.vec > 1] - 1
     hsvcol[, 1] = hue.vec
     span.sat = span(hsvcol[, 2], saturation, sat.range)
     hsvcol[, 2] = contain(span.sat)
     mean.bri = apply(len.colM, 1, mean)
     span.bri = span(mean.bri, brightness, bri.range)
     hsvcol[, 3] = contain(span.bri)
     colours = hsv(hsvcol[, 1], hsvcol[, 2], hsvcol[, 3],
     alpha = alpha)
     }
     return(colours)
     }
     <bytecode: 0x7ac65b0>
     <environment: namespace:forestFloor>
     --- function search by body ---
     Function fcol in namespace forestFloor has this body.
     ----------- END OF FAILURE REPORT --------------
     Error in if (!class(colM) %in% c("data.frame", "matrix")) { :
     the condition has length > 1
     Calls: fcol
     Execution halted
    Running the tests in ‘tests/testMultiClass.cpp.R’ failed.
    Complete output:
     > if(!interactive()) Sys.setenv(RGL_USE_NULL=TRUE) #disable RGL for headless machines
     > library(randomForest)
     randomForest 4.6-14
     Type rfNews() to see new features/changes/bug fixes.
     > library(forestFloor)
     > require(utils)
     >
     > data(iris)
     > iris
     Sepal.Length Sepal.Width Petal.Length Petal.Width Species
     1 5.1 3.5 1.4 0.2 setosa
     2 4.9 3.0 1.4 0.2 setosa
     3 4.7 3.2 1.3 0.2 setosa
     4 4.6 3.1 1.5 0.2 setosa
     5 5.0 3.6 1.4 0.2 setosa
     6 5.4 3.9 1.7 0.4 setosa
     7 4.6 3.4 1.4 0.3 setosa
     8 5.0 3.4 1.5 0.2 setosa
     9 4.4 2.9 1.4 0.2 setosa
     10 4.9 3.1 1.5 0.1 setosa
     11 5.4 3.7 1.5 0.2 setosa
     12 4.8 3.4 1.6 0.2 setosa
     13 4.8 3.0 1.4 0.1 setosa
     14 4.3 3.0 1.1 0.1 setosa
     15 5.8 4.0 1.2 0.2 setosa
     16 5.7 4.4 1.5 0.4 setosa
     17 5.4 3.9 1.3 0.4 setosa
     18 5.1 3.5 1.4 0.3 setosa
     19 5.7 3.8 1.7 0.3 setosa
     20 5.1 3.8 1.5 0.3 setosa
     21 5.4 3.4 1.7 0.2 setosa
     22 5.1 3.7 1.5 0.4 setosa
     23 4.6 3.6 1.0 0.2 setosa
     24 5.1 3.3 1.7 0.5 setosa
     25 4.8 3.4 1.9 0.2 setosa
     26 5.0 3.0 1.6 0.2 setosa
     27 5.0 3.4 1.6 0.4 setosa
     28 5.2 3.5 1.5 0.2 setosa
     29 5.2 3.4 1.4 0.2 setosa
     30 4.7 3.2 1.6 0.2 setosa
     31 4.8 3.1 1.6 0.2 setosa
     32 5.4 3.4 1.5 0.4 setosa
     33 5.2 4.1 1.5 0.1 setosa
     34 5.5 4.2 1.4 0.2 setosa
     35 4.9 3.1 1.5 0.2 setosa
     36 5.0 3.2 1.2 0.2 setosa
     37 5.5 3.5 1.3 0.2 setosa
     38 4.9 3.6 1.4 0.1 setosa
     39 4.4 3.0 1.3 0.2 setosa
     40 5.1 3.4 1.5 0.2 setosa
     41 5.0 3.5 1.3 0.3 setosa
     42 4.5 2.3 1.3 0.3 setosa
     43 4.4 3.2 1.3 0.2 setosa
     44 5.0 3.5 1.6 0.6 setosa
     45 5.1 3.8 1.9 0.4 setosa
     46 4.8 3.0 1.4 0.3 setosa
     47 5.1 3.8 1.6 0.2 setosa
     48 4.6 3.2 1.4 0.2 setosa
     49 5.3 3.7 1.5 0.2 setosa
     50 5.0 3.3 1.4 0.2 setosa
     51 7.0 3.2 4.7 1.4 versicolor
     52 6.4 3.2 4.5 1.5 versicolor
     53 6.9 3.1 4.9 1.5 versicolor
     54 5.5 2.3 4.0 1.3 versicolor
     55 6.5 2.8 4.6 1.5 versicolor
     56 5.7 2.8 4.5 1.3 versicolor
     57 6.3 3.3 4.7 1.6 versicolor
     58 4.9 2.4 3.3 1.0 versicolor
     59 6.6 2.9 4.6 1.3 versicolor
     60 5.2 2.7 3.9 1.4 versicolor
     61 5.0 2.0 3.5 1.0 versicolor
     62 5.9 3.0 4.2 1.5 versicolor
     63 6.0 2.2 4.0 1.0 versicolor
     64 6.1 2.9 4.7 1.4 versicolor
     65 5.6 2.9 3.6 1.3 versicolor
     66 6.7 3.1 4.4 1.4 versicolor
     67 5.6 3.0 4.5 1.5 versicolor
     68 5.8 2.7 4.1 1.0 versicolor
     69 6.2 2.2 4.5 1.5 versicolor
     70 5.6 2.5 3.9 1.1 versicolor
     71 5.9 3.2 4.8 1.8 versicolor
     72 6.1 2.8 4.0 1.3 versicolor
     73 6.3 2.5 4.9 1.5 versicolor
     74 6.1 2.8 4.7 1.2 versicolor
     75 6.4 2.9 4.3 1.3 versicolor
     76 6.6 3.0 4.4 1.4 versicolor
     77 6.8 2.8 4.8 1.4 versicolor
     78 6.7 3.0 5.0 1.7 versicolor
     79 6.0 2.9 4.5 1.5 versicolor
     80 5.7 2.6 3.5 1.0 versicolor
     81 5.5 2.4 3.8 1.1 versicolor
     82 5.5 2.4 3.7 1.0 versicolor
     83 5.8 2.7 3.9 1.2 versicolor
     84 6.0 2.7 5.1 1.6 versicolor
     85 5.4 3.0 4.5 1.5 versicolor
     86 6.0 3.4 4.5 1.6 versicolor
     87 6.7 3.1 4.7 1.5 versicolor
     88 6.3 2.3 4.4 1.3 versicolor
     89 5.6 3.0 4.1 1.3 versicolor
     90 5.5 2.5 4.0 1.3 versicolor
     91 5.5 2.6 4.4 1.2 versicolor
     92 6.1 3.0 4.6 1.4 versicolor
     93 5.8 2.6 4.0 1.2 versicolor
     94 5.0 2.3 3.3 1.0 versicolor
     95 5.6 2.7 4.2 1.3 versicolor
     96 5.7 3.0 4.2 1.2 versicolor
     97 5.7 2.9 4.2 1.3 versicolor
     98 6.2 2.9 4.3 1.3 versicolor
     99 5.1 2.5 3.0 1.1 versicolor
     100 5.7 2.8 4.1 1.3 versicolor
     101 6.3 3.3 6.0 2.5 virginica
     102 5.8 2.7 5.1 1.9 virginica
     103 7.1 3.0 5.9 2.1 virginica
     104 6.3 2.9 5.6 1.8 virginica
     105 6.5 3.0 5.8 2.2 virginica
     106 7.6 3.0 6.6 2.1 virginica
     107 4.9 2.5 4.5 1.7 virginica
     108 7.3 2.9 6.3 1.8 virginica
     109 6.7 2.5 5.8 1.8 virginica
     110 7.2 3.6 6.1 2.5 virginica
     111 6.5 3.2 5.1 2.0 virginica
     112 6.4 2.7 5.3 1.9 virginica
     113 6.8 3.0 5.5 2.1 virginica
     114 5.7 2.5 5.0 2.0 virginica
     115 5.8 2.8 5.1 2.4 virginica
     116 6.4 3.2 5.3 2.3 virginica
     117 6.5 3.0 5.5 1.8 virginica
     118 7.7 3.8 6.7 2.2 virginica
     119 7.7 2.6 6.9 2.3 virginica
     120 6.0 2.2 5.0 1.5 virginica
     121 6.9 3.2 5.7 2.3 virginica
     122 5.6 2.8 4.9 2.0 virginica
     123 7.7 2.8 6.7 2.0 virginica
     124 6.3 2.7 4.9 1.8 virginica
     125 6.7 3.3 5.7 2.1 virginica
     126 7.2 3.2 6.0 1.8 virginica
     127 6.2 2.8 4.8 1.8 virginica
     128 6.1 3.0 4.9 1.8 virginica
     129 6.4 2.8 5.6 2.1 virginica
     130 7.2 3.0 5.8 1.6 virginica
     131 7.4 2.8 6.1 1.9 virginica
     132 7.9 3.8 6.4 2.0 virginica
     133 6.4 2.8 5.6 2.2 virginica
     134 6.3 2.8 5.1 1.5 virginica
     135 6.1 2.6 5.6 1.4 virginica
     136 7.7 3.0 6.1 2.3 virginica
     137 6.3 3.4 5.6 2.4 virginica
     138 6.4 3.1 5.5 1.8 virginica
     139 6.0 3.0 4.8 1.8 virginica
     140 6.9 3.1 5.4 2.1 virginica
     141 6.7 3.1 5.6 2.4 virginica
     142 6.9 3.1 5.1 2.3 virginica
     143 5.8 2.7 5.1 1.9 virginica
     144 6.8 3.2 5.9 2.3 virginica
     145 6.7 3.3 5.7 2.5 virginica
     146 6.7 3.0 5.2 2.3 virginica
     147 6.3 2.5 5.0 1.9 virginica
     148 6.5 3.0 5.2 2.0 virginica
     149 6.2 3.4 5.4 2.3 virginica
     150 5.9 3.0 5.1 1.8 virginica
     > X = iris[,!names(iris) %in% "Species"]
     > Y = iris[,"Species"]
     > as.numeric(Y)
     [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
     [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
     [75] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3
     [112] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
     [149] 3 3
     > rf.42 = randomForest(X,Y,keep.forest=T,replace=F,keep.inbag=T,samp=15,ntree=100)
     > ff.42 = forestFloor(rf.42,X,calc_np = F,bootstrapFC = TRUE)
     >
     > #test accuracy of feature contributions
     > #y_hat_OOB = row sum FC + Y_grandMean
     > FCc = t(t(apply(ff.42$FCarray,c(1,3),sum))+as.vector(table(Y)/length(Y)))
     > FC.residuals = FCc-predict(rf.42,type="prob")
     > if(max(abs(FC.residuals))>1E-12) stop(
     + paste0("When testing if: y_hat_OOB = row sum FCmatrix + Y_grandMean
     + one/some FCs error exceeds allowed 1e-12, found.error=",max(abs(FC.residuals)))
     + )
     >
     > Xtest = iris[1:50,] #copy
     > Xtest = Xtest[,-5] #drop Species
     > Xtest[1:4] = lapply(iris[1:4],sample,50) #random resample 50 samples
     >
     >
     > #test same results are reached with Xtest
     > ff.43 = forestFloor(rf.42,X,Xtest,bootstrapFC = T)
     > if(max(abs(ff.43$FCarray[ff.43$isTrain,,]-ff.42$FCarray)) > 1E-12) stop(
     + "forestFloor with/without Xtest gives different feature contributions"
     + )
     > plot(ff.43,speedup_GOF = TRUE,plotTest = F)
     >
     >
     > pred = sapply(1:3,function(i) apply(ff.42$FCarray[,,i],1,sum))+1/3
     > rfPred = predict(rf.42,type="vote",norm.votes=T)
     > rfPred[is.nan(rfPred)] = 1/3
     > if(cor(as.vector(rfPred),as.vector(pred))^2<0.99) stop("fail testMultiClass")
     > attributes(ff.42)
     $names
     [1] "X" "Y" "importance" "imp_ind" "FCarray"
     [6] "sumOfInbags" "isTrain"
    
     $class
     [1] "forestFloor_multiClass"
    
     > args(forestFloor:::plot.forestFloor_multiClass)
     function (x, plot_seq = NULL, label.seq = NULL, plotTest = NULL,
     limitY = TRUE, col = NULL, colLists = NULL, orderByImportance = TRUE,
     fig.columns = NULL, plot_GOF = TRUE, GOF_args = list(), speedup_GOF = TRUE,
     jitter_these_cols = NULL, jitter.factor = NULL, ...)
     NULL
     > plot(ff.42,plot_GOF=T,cex=.7,
     + colLists=list("#FF0000A5",
     + "#00FF0050",
     + "#0000FF35")
     + )
     >
     > plot(ff.43,plot_GOF=T,cex=.7,
     + colLists=list("#FF0000A5",
     + "#00FF0050",
     + "#0000FF35")
     + )
     >
     > #use col interface
     > plot(ff.43,plot_GOF=T,cex=.7,
     + col=list("#FF0000A5","#00FF0050","#0000FF35") #one colour per class
     + )
     >
     > plot(ff.43,plot_GOF=T,cex=.7,
     + col=c("#FF0000A5","#00FF0050","#0000FF35") #one colour per class
     + )
     >
     >
     > #try to alter std par
     > plot(ff.42,plot_GOF=T,cex=.7,
     + colLists=list("#FF0000A5",
     + "#00FF0050",
     + "#0000FF35"),
     + mfrow=c(4,3)
     + )
     >
     >
     > show3d(ff.42,1:2,1:2,plot_GOF=T)
     > show3d(ff.42,1:2,1,plot_GOF=T)#test plotting only one feature contribution
     >
     >
     > #plot all effect 2D only
     > pars = plot_simplex3(ff.42,Xi=c(1:3),restore_par=F,zoom.fit=NULL,var.col=NULL,fig.cols=2,fig.rows=1,
     + fig3d=F,includeTotal=T,auto.alpha=.4,set_pars=T)
     > pars = plot_simplex3(ff.42,Xi=0,restore_par=F,zoom.fit=NULL,var.col=alist(alpha=.3,cols=1:4),
     + fig3d=F,includeTotal=T,auto.alpha=.8,set_pars=F)
     ----------- FAILURE REPORT --------------
     --- failure: the condition has length > 1 ---
     --- srcref ---
     :
     --- package (from environment) ---
     forestFloor
     --- call from context ---
     box.outliers(prcomp(sel.colM)$x[, 1:max.df], limit = Inf)
     --- call from argument ---
     if (class(x) == "data.frame") {
     sx = as.data.frame(sx, row.names = row.names(x))
     names(sx) = names(x)
     }
     --- R stacktrace ---
     where 1: box.outliers(prcomp(sel.colM)$x[, 1:max.df], limit = Inf)
     where 2: (function (ff, cols = NULL, orderByImportance = NULL, plotTest = NULL,
     X.matrix = TRUE, hue = NULL, saturation = NULL, brightness = NULL,
     hue.range = NULL, sat.range = NULL, bri.range = NULL, alpha = NULL,
     RGB = NULL, byResiduals = FALSE, max.df = 3, imp.weight = NULL,
     imp.exp = 1, outlier.lim = 3, RGB.exp = NULL)
     {
     if (!X.matrix)
     if (class(ff) == "forestFloor_multiClass")
     stop("cannot colour by feature contributions for object of class\n 'forestFloor_multiClass'. Set X.matrix=TRUE")
     ib <- function(x, low, high) (x - low) * (high - x) > 0
     span <- function(x, mid, width) if (min(x) != max(x)) {
     ((x - min(x))/(max(x) - min(x)) - 0.5) * width + mid
     }
     else {
     x[] = mid
     }
     auto.range = function(level, low = 0, high = 1) abs(min(level -
     low, high - level)) * 2
     contain = function(x, low = 0, high = 1) {
     x[x > high] = high
     x[x < low] = low
     x
     }
     if (class(ff) %in% c("forestFloor_regression", "forestFloor_multiClass")) {
     plotThese = checkPlotTest(plotTest, ff$isTrain)
     if (!(all(plotThese))) {
     if (class(ff) == "forestFloor_multiClass") {
     ff$FCarray = ff$FCarray[plotThese, , ]
     }
     else {
     if (class(ff) == "forestFloor_regression") {
     ff$FCmatrix = ff$FCmatrix[plotThese, ]
     }
     }
     ff$Y = ff$Y[plotThese]
     ff$X = ff$X[plotThese, ]
     }
     }
     if (class(ff) %in% c("forestFloor_regression", "forestFloor_multiClass")) {
     if (byResiduals) {
     if (is.null(ff$FCfit)) {
     print("no $FCfit found, computing tempoary LOO-kNN-gaussion fit to main affect")
     print("use ff = convolute_ff(ff) to compute a fixed fit")
     ff = convolute_ff(ff)
     }
     colM = ff$FCmatrix - ff$FCfit
     }
     else {
     if (X.matrix)
     colM = ff$X
     else colM = ff$FCmatrix
     }
     if (is.null(imp.weight))
     imp.weight = TRUE
     if (is.null(orderByImportance))
     orderByImportance = TRUE
     }
     else {
     colM = ff
     if (is.null(imp.weight))
     imp.weight = FALSE
     if (is.null(orderByImportance))
     orderByImportance = FALSE
     }
     if (orderByImportance)
     if (class(ff) %in% c("forestFloor_regression", "forestFloor_multiClass")) {
     colM = colM[, ff$imp_ind]
     }
     else {
     warning("orderByImportance=TRUE takes no effect for non 'forestFloor'-class. As if set to NULL or FALSE...")
     }
     if (!class(colM) %in% c("data.frame", "matrix")) {
     tryCatch({
     colM = matrix(colM, ncol = 1)
     }, error = function(e) stop(paste("input ff was neither data.frame or matrix and \ncould not be coerced to matrix:",
     e$message)))
     }
     colM = data.frame(colM)
     if (is.null(cols))
     cols = 1:dim(colM)[2]
     if (length(cols) < 1 || !is.numeric(cols) || any(!cols %in%
     1:dim(colM)[2])) {
     stop("no cols selected or is not integer/numeric or wrong coloumns")
     }
     sel.colM = data.frame(colM[, cols])
     sel.cols = 1:length(cols)
     if (is.null(RGB))
     if (length(cols) == 1)
     RGB = TRUE
     else RGB = FALSE
     if (!RGB) {
     if (is.null(saturation))
     saturation = 0.85
     if (is.null(brightness))
     brightness = 0.75
     if (is.null(hue))
     hue = 0.25
     }
     else {
     if (is.null(saturation))
     saturation = 1
     if (is.null(brightness))
     brightness = 0.75
     if (is.null(hue))
     hue = 0.66
     if (is.null(RGB.exp))
     RGB.exp = 1.2
     if (is.null(hue.range))
     hue.range = 2
     }
     as.numeric.factor <- function(x, rearrange = TRUE) {
     if (is.numeric(x))
     return(x)
     if (rearrange)
     x = match(x, levels(droplevels(x)))
     else x = match(x, levels(x))
     return(x)
     }
     for (i in 1:dim(sel.colM)[2]) {
     if (is.factor(sel.colM[, i])) {
     this.fac = as.numeric.factor(sel.colM[, i])
     sel.colM[, i] = this.fac
     }
     if (is.character(sel.colM[, i]))
     sel.colM[, i] = as.numeric(sel.colM[, i])
     }
     sel.colM = box.outliers(sel.colM, limit = outlier.lim)
     if (imp.weight && length(cols) > 1) {
     if (class(ff) %in% c("forestFloor_regression", "forestFloor_multiClass")) {
     sel.imp = ff$importance[cols]
     non.negative.imp = sel.imp + min(sel.imp)
     sumnorm.imp = non.negative.imp/sum(non.negative.imp)
     exp.imp = sumnorm.imp^imp.exp
     impM = t(replicate(dim(colM)[1], exp.imp))
     sel.colM = sel.colM * impM
     sel.colM = sel.colM/max(sel.colM)
     }
     else {
     warning("importance weighting only possible for class 'forestFloor'")
     }
     }
     if (any(!c(class(hue), class(saturation), class(brightness)) %in%
     c("numeric", "integer"))) {
     stop("hue, saturation and brightness must be of class numeric or integer")
     }
     hue = hue - floor(hue)
     saturation = max(min(saturation, 1), 0)
     brightness = max(min(brightness, 1), 0)
     if (RGB == TRUE) {
     if (is.null(bri.range))
     bri.range = 0.05
     if (is.null(alpha))
     alpha = 0.7
     len.colM = box.outliers(sel.colM, limit = Inf)
     if (dim(len.colM)[2] == 1)
     nX = as.numeric(len.colM[, 1])
     else nX = as.numeric(apply(len.colM, 1, mean))
     hsvcol = t(sapply(nX, function(x) rgb2hsv(x^RGB.exp,
     1 - x^RGB.exp - (1 - x)^RGB.exp, (1 - x)^RGB.exp)))
     hue.vec = hsvcol[, 1] * hue.range + hue
     hue.vec[hue.vec > 1] = hue.vec[hue.vec > 1] - floor(hue.vec[hue.vec >
     1])
     hsvcol[, 1] = hue.vec
     sat.range = auto.range(saturation)
     hsvcol[, 2] = span(hsvcol[, 2], saturation, sat.range)
     hsvcol[, 2] = contain(hsvcol[, 2])
     bri.range = auto.range(brightness)
     hsvcol[, 3] = span(hsvcol[, 3], brightness, bri.range)
     hsvcol[, 3] = contain(hsvcol[, 3])
     colours = apply(hsvcol, 1, function(x) hsv(x[1], x[2],
     x[3], alpha = alpha))
     return(colours)
     }
     col.df = length(cols)
     if (!max.df %in% c(1, 2, 3))
     stop("fcol input 'max.df' must be set to either 1, 2 or 3")
     if (col.df > max.df) {
     len.colM = box.outliers(prcomp(sel.colM)$x[, 1:max.df],
     limit = Inf)
     col.df = max.df
     }
     else {
     len.colM = box.outliers(sel.colM, limit = Inf)
     }
     if (is.null(hue.range)) {
     if (col.df == 1)
     hue.range = 0.85
     if (col.df == 2)
     hue.range = 1
     if (col.df == 3)
     hue.range = 1
     }
     if (is.null(sat.range)) {
     if (col.df == 1)
     sat.range = "not used"
     if (col.df == 2)
     sat.range = auto.range(saturation)
     if (col.df == 3)
     sat.range = auto.range(saturation)
     }
     if (is.null(bri.range)) {
     if (col.df == 1)
     bri.range = "not used"
     if (col.df == 2)
     bri.range = "not used"
     if (col.df == 3)
     bri.range = auto.range(brightness)
     }
     if (is.null(alpha))
     alpha = min(1, 400/dim(len.colM)[1])
     if (col.df == 1) {
     hue.vec = as.numeric(len.colM[, 1]) * hue.range + hue
     hue.vec[hue.vec > 1] = hue.vec[hue.vec > 1] - floor(hue.vec[hue.vec >
     1])
     colours = hsv(h = hue.vec, s = saturation, v = brightness,
     alpha = alpha)
     }
     if (col.df == 2) {
     hsvcol = t(rgb2hsv(len.colM[, 1], len.colM[, 2], 1 -
     apply(len.colM, 1, mean)))
     hue.vec = hsvcol[, 1] * hue.range + hue
     hue.vec[hue.vec > 1] = hue.vec[hue.vec > 1] - 1
     hsvcol[, 1] = hue.vec
     hsvcol[, 2] = ((len.colM[, 1] - mean(len.colM[, 1]))^2 +
     (len.colM[, 2] - mean(len.colM[, 2]))^2)^sat.range *
     saturation
     hsvcol[, 2] = hsvcol[, 2]/max(hsvcol[, 2])
     hsvcol[, 3] = brightness
     colours = hsv(hsvcol[, 1], hsvcol[, 2], hsvcol[, 3],
     alpha = alpha)
     }
     if (col.df == 3) {
     hsvcol = t(rgb2hsv(len.colM[, 1], len.colM[, 2], len.colM[,
     3]))
     hue.vec = hsvcol[, 1] * hue.range + hue
     hue.vec[hue.vec > 1] = hue.vec[hue.vec > 1] - 1
     hsvcol[, 1] = hue.vec
     span.sat = span(hsvcol[, 2], saturation, sat.range)
     hsvcol[, 2] = contain(span.sat)
     mean.bri = apply(len.colM, 1, mean)
     span.bri = span(mean.bri, brightness, bri.range)
     hsvcol[, 3] = contain(span.bri)
     colours = hsv(hsvcol[, 1], hsvcol[, 2], hsvcol[, 3],
     alpha = alpha)
     }
     return(colours)
     })(list(Sepal.Length = c(5.1, 4.9, 4.7, 4.6, 5, 5.4, 4.6, 5,
     4.4, 4.9, 5.4, 4.8, 4.8, 4.3, 5.8, 5.7, 5.4, 5.1, 5.7, 5.1, 5.4,
     5.1, 4.6, 5.1, 4.8, 5, 5, 5.2, 5.2, 4.7, 4.8, 5.4, 5.2, 5.5,
     4.9, 5, 5.5, 4.9, 4.4, 5.1, 5, 4.5, 4.4, 5, 5.1, 4.8, 5.1, 4.6,
     5.3, 5, 7, 6.4, 6.9, 5.5, 6.5, 5.7, 6.3, 4.9, 6.6, 5.2, 5, 5.9,
     6, 6.1, 5.6, 6.7, 5.6, 5.8, 6.2, 5.6, 5.9, 6.1, 6.3, 6.1, 6.4,
     6.6, 6.8, 6.7, 6, 5.7, 5.5, 5.5, 5.8, 6, 5.4, 6, 6.7, 6.3, 5.6,
     5.5, 5.5, 6.1, 5.8, 5, 5.6, 5.7, 5.7, 6.2, 5.1, 5.7, 6.3, 5.8,
     7.1, 6.3, 6.5, 7.6, 4.9, 7.3, 6.7, 7.2, 6.5, 6.4, 6.8, 5.7, 5.8,
     6.4, 6.5, 7.7, 7.7, 6, 6.9, 5.6, 7.7, 6.3, 6.7, 7.2, 6.2, 6.1,
     6.4, 7.2, 7.4, 7.9, 6.4, 6.3, 6.1, 7.7, 6.3, 6.4, 6, 6.9, 6.7,
     6.9, 5.8, 6.8, 6.7, 6.7, 6.3, 6.5, 6.2, 5.9), Sepal.Width = c(3.5,
     3, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1, 3.7, 3.4, 3, 3, 4,
     4.4, 3.9, 3.5, 3.8, 3.8, 3.4, 3.7, 3.6, 3.3, 3.4, 3, 3.4, 3.5,
     3.4, 3.2, 3.1, 3.4, 4.1, 4.2, 3.1, 3.2, 3.5, 3.6, 3, 3.4, 3.5,
     2.3, 3.2, 3.5, 3.8, 3, 3.8, 3.2, 3.7, 3.3, 3.2, 3.2, 3.1, 2.3,
     2.8, 2.8, 3.3, 2.4, 2.9, 2.7, 2, 3, 2.2, 2.9, 2.9, 3.1, 3, 2.7,
     2.2, 2.5, 3.2, 2.8, 2.5, 2.8, 2.9, 3, 2.8, 3, 2.9, 2.6, 2.4,
     2.4, 2.7, 2.7, 3, 3.4, 3.1, 2.3, 3, 2.5, 2.6, 3, 2.6, 2.3, 2.7,
     3, 2.9, 2.9, 2.5, 2.8, 3.3, 2.7, 3, 2.9, 3, 3, 2.5, 2.9, 2.5,
     3.6, 3.2, 2.7, 3, 2.5, 2.8, 3.2, 3, 3.8, 2.6, 2.2, 3.2, 2.8,
     2.8, 2.7, 3.3, 3.2, 2.8, 3, 2.8, 3, 2.8, 3.8, 2.8, 2.8, 2.6,
     3, 3.4, 3.1, 3, 3.1, 3.1, 3.1, 2.7, 3.2, 3.3, 3, 2.5, 3, 3.4,
     3), Petal.Length = c(1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5,
     1.4, 1.5, 1.5, 1.6, 1.4, 1.1, 1.2, 1.5, 1.3, 1.4, 1.7, 1.5, 1.7,
     1.5, 1, 1.7, 1.9, 1.6, 1.6, 1.5, 1.4, 1.6, 1.6, 1.5, 1.5, 1.4,
     1.5, 1.2, 1.3, 1.4, 1.3, 1.5, 1.3, 1.3, 1.3, 1.6, 1.9, 1.4, 1.6,
     1.4, 1.5, 1.4, 4.7, 4.5, 4.9, 4, 4.6, 4.5, 4.7, 3.3, 4.6, 3.9,
     3.5, 4.2, 4, 4.7, 3.6, 4.4, 4.5, 4.1, 4.5, 3.9, 4.8, 4, 4.9,
     4.7, 4.3, 4.4, 4.8, 5, 4.5, 3.5, 3.8, 3.7, 3.9, 5.1, 4.5, 4.5,
     4.7, 4.4, 4.1, 4, 4.4, 4.6, 4, 3.3, 4.2, 4.2, 4.2, 4.3, 3, 4.1,
     6, 5.1, 5.9, 5.6, 5.8, 6.6, 4.5, 6.3, 5.8, 6.1, 5.1, 5.3, 5.5,
     5, 5.1, 5.3, 5.5, 6.7, 6.9, 5, 5.7, 4.9, 6.7, 4.9, 5.7, 6, 4.8,
     4.9, 5.6, 5.8, 6.1, 6.4, 5.6, 5.1, 5.6, 6.1, 5.6, 5.5, 4.8, 5.4,
     5.6, 5.1, 5.1, 5.9, 5.7, 5.2, 5, 5.2, 5.4, 5.1), Petal.Width = c(0.2,
     0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, 0.2, 0.2, 0.1, 0.1,
     0.2, 0.4, 0.4, 0.3, 0.3, 0.3, 0.2, 0.4, 0.2, 0.5, 0.2, 0.2, 0.4,
     0.2, 0.2, 0.2, 0.2, 0.4, 0.1, 0.2, 0.2, 0.2, 0.2, 0.1, 0.2, 0.2,
     0.3, 0.3, 0.2, 0.6, 0.4, 0.3, 0.2, 0.2, 0.2, 0.2, 1.4, 1.5, 1.5,
     1.3, 1.5, 1.3, 1.6, 1, 1.3, 1.4, 1, 1.5, 1, 1.4, 1.3, 1.4, 1.5,
     1, 1.5, 1.1, 1.8, 1.3, 1.5, 1.2, 1.3, 1.4, 1.4, 1.7, 1.5, 1,
     1.1, 1, 1.2, 1.6, 1.5, 1.6, 1.5, 1.3, 1.3, 1.3, 1.2, 1.4, 1.2,
     1, 1.3, 1.2, 1.3, 1.3, 1.1, 1.3, 2.5, 1.9, 2.1, 1.8, 2.2, 2.1,
     1.7, 1.8, 1.8, 2.5, 2, 1.9, 2.1, 2, 2.4, 2.3, 1.8, 2.2, 2.3,
     1.5, 2.3, 2, 2, 1.8, 2.1, 1.8, 1.8, 1.8, 2.1, 1.6, 1.9, 2, 2.2,
     1.5, 1.4, 2.3, 2.4, 1.8, 1.8, 2.1, 2.4, 2.3, 1.9, 2.3, 2.5, 2.3,
     1.9, 2, 2.3, 1.8)), 0, alpha = 0.3, cols = 1:4)
     where 3: do.call(fcol, c(list(ff$X, i), var.col))
     where 4: plot.xy(xy.coords(x, y), type = type, ...)
     where 5: points.default(apply(xd1 * t(FC[, ]), 2, sum) + cent2[1], apply(xd2 *
     t(FC[, ]), 2, sum) + cent2[2], cex = 0.3, col = if (Col[1] ==
     "var.col") do.call(fcol, c(list(ff$X, i), var.col)) else Col)
     where 6: points(apply(xd1 * t(FC[, ]), 2, sum) + cent2[1], apply(xd2 *
     t(FC[, ]), 2, sum) + cent2[2], cex = 0.3, col = if (Col[1] ==
     "var.col") do.call(fcol, c(list(ff$X, i), var.col)) else Col)
     where 7: plot_simplex3(ff.42, Xi = 0, restore_par = F, zoom.fit = NULL,
     var.col = alist(alpha = 0.3, cols = 1:4), fig3d = F, includeTotal = T,
     auto.alpha = 0.8, set_pars = F)
    
     --- value of length: 2 type: logical ---
     [1] FALSE FALSE
     --- function from context ---
     function (x, limit = 1.5, normalize = TRUE)
     {
     sx = scale(x)
     if (limit != FALSE) {
     sx[sx > limit] = limit
     sx[-sx > limit] = -limit
     }
     if (normalize) {
     sx.span = max(sx) - min(sx)
     sx = sx - min(sx)
     sx = sx/sx.span
     }
     else {
     obs = attributes(sx)$dim[1]
     if (dim(sx)[2] > 1) {
     sx = sx * t(replicate(obs, attributes(sx)$"scaled:scale")) +
     t(replicate(obs, attributes(sx)$"scaled:center"))
     }
     else {
     sx = sx * attributes(sx)$"scaled:scale" + attributes(sx)$"scaled:center"
     }
     }
     if (class(x) == "data.frame") {
     sx = as.data.frame(sx, row.names = row.names(x))
     names(sx) = names(x)
     }
     return(sx)
     }
     <bytecode: 0x8cddbd0>
     <environment: namespace:forestFloor>
     --- function search by body ---
     Function box.outliers in namespace forestFloor has this body.
     ----------- END OF FAILURE REPORT --------------
     Error in if (class(x) == "data.frame") { : the condition has length > 1
     Calls: plot_simplex3 ... points.default -> plot.xy -> do.call -> <Anonymous> -> box.outliers
     Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang

Version: 1.11.1
Check: tests
Result: ERROR
     Running ‘allExamples.R’
     Running ‘rfPermute_supported.R’
     Running ‘stdUsage.R’ [13s/15s]
     Running ‘testBinaryClass.R’
     Running ‘testCaret.R’ [20s/23s]
     Running ‘testMultiClass.cpp.R’
     Running ‘test_Xtestmerger.R’
    Running the tests in ‘tests/stdUsage.R’ failed.
    Complete output:
     > if(!interactive()) Sys.setenv(RGL_USE_NULL=TRUE) #disable RGL for headless machines
     > library(forestFloor)
     > library(randomForest)
     randomForest 4.6-14
     Type rfNews() to see new features/changes/bug fixes.
     > #simulate data
     > obs=2000
     > vars = 6
     >
     > X = data.frame(replicate(vars,rnorm(obs)))
     > Xtest = data.frame(replicate(vars,rnorm(obs)*3))
     > Y = with(X, X1^2 + sin(X2*pi) + 2 * X3 * X4 + .5 * rnorm(obs))
     >
     >
     > #grow a forest, remeber to include inbag
     > rf41=randomForest(X,Y,keep.inbag = TRUE,sampsize=499,ntree=100,importance = F)
     > #compute feature contributions
     > out = tryCatch({ff41 = forestFloor(rf41,X,bootstrapFC = TRUE,impType=1)},warning = function(w) w)
     > if(out$message != "found nothing of importance, revert to fallback") stop("wrong warning")
     >
     > #if(colnames(ff41$importance) != "%IncMSE") stop("wrong imp")
     > ff41 = forestFloor(rf41,X,bootstrapFC = TRUE,impType=2)
     > if(colnames(ff41$importance) != "IncNodePurity") stop("wrong imp")
     > ff41 = forestFloor(rf41,X,bootstrapFC = TRUE,impType=NULL)
     > if(colnames(ff41$importance) != "IncNodePurity") stop("wrong imp")
     >
     > #grow a forest, remeber to include inbag
     > rf42=randomForest(X,Y,keep.inbag = TRUE,sampsize=499,ntree=100,importance = T)
     > #compute feature contributions
     > ff42 = forestFloor(rf42,X,bootstrapFC = TRUE,impType=1)
     > if(colnames(ff42$importance) != "%IncMSE") stop("wrong imp")
     > ff42 = forestFloor(rf42,X,bootstrapFC = TRUE,impType=2)
     > if(colnames(ff42$importance) != "IncNodePurity") stop("wrong imp")
     > ff42 = forestFloor(rf42,X,bootstrapFC = TRUE,impType=NULL)
     > if(colnames(ff42$importance) != "%IncMSE") stop("wrong imp")
     >
     >
     >
     >
     > #test accuracy of feature contributions
     > #y_hat_OOB = row sum FC + Y_grandMean
     > FC.residuals = rf42$predicted - apply(ff42$FCmatrix[ff42$isTrain,],1,sum) - mean(Y)
     > if(max(abs(FC.residuals))>1E-12) stop(
     + paste0("When testing if: y_hat_OOB = row sum FCmatrix + Y_grandMean
     + one/some FCs error exceed allowed 1e-12, found.error=",max(abs(FC.residuals)))
     + )
     >
     > #test same results are reached with Xtest
     > ff43 = forestFloor(rf42,X,Xtest,bootstrapFC = TRUE)
     > if(max(abs(ff43$FCmatrix[ff43$isTrain,]-ff42$FCmatrix)) > 1E-12) stop(
     + "forestFloor with/without Xtest gives different feature contributions"
     + )
     >
     >
     > #print forestFloor
     > print(ff42)
     this is a forestFloor_regression object
    
     this object can be plotted in 2D with plot(x), see help(plot.forestFloor)
    
     this object can be plotted in 3D with show3d(x), see help(show3d)
    
    
    
     x contains following internal elements:
     FCmatrix X Y imp_ind importance isTrain>
     > #plot partial functions of most important variables first
     > plot(ff42,orderByImportance=TRUE)
     [1] "compute goodness-of-fit with leave-one-out k-nearest neighbor(guassian weighting), kknn package"
     > plot(ff43,orderByImportance=TRUE)
     [1] "compute goodness-of-fit with leave-one-out k-nearest neighbor(guassian weighting), kknn package"
     >
     > #Non interacting functions are well displayed, whereas X3 and X4 are not
     > #by applying different colourgradient, interactions reveal themself
     > #also a k-nearest neighbor fit is applied to evaluate goodness of fit
     > Col=fcol(ff43,3,orderByImportance=FALSE)
     > plot(ff43,col=Col,plot_GOF=TRUE,speed=T)
     [1] "compute goodness-of-fit with leave-one-out k-nearest neighbor(guassian weighting), kknn package"
     >
     >
     > ##make test set grey tone to show if point of test is extrapolated
     > Col=fcol(ff43,3,orderByImportance=FALSE,plotTest="andTrain",alpha=.2)
     > Col[ff43$isTrain] = "#000000FF"
     > plot(ff43,col=Col,speed=T,plotTest="andTrain",plot_GOF=F)
     >
     >
     > #if ever needed, k-nearest neighbor parameters for goodness-of-fit can be access through convolute_ff
     > #a new fit will be calculated and added to forstFloor object as ff42$FCfit
     > ff43 = convolute_ff(ff43,userArgs.kknn=alist(kernel="epanechnikov",kmax=5))
     > plot(ff43,col=Col,plot_GOF=TRUE)
     >
     > #in 3D the interaction between X3 and X reveals itself completely
     > show3d(ff43,3:4,col=Col,plot.rgl=list(size=5),orderByImportance=FALSE)
     > Col=fcol(ff43,1:2,orderByImportance=FALSE)
     > show3d(ff43,1:2,col=Col,plot.rgl=list(size=5),orderByImportance=FALSE)
     >
     >
     > Col=fcol(ff43,1:2,plotTest="andTrain",orderByImportance=FALSE)
     > show3d(ff43,1:2,col=Col,plot.rgl=list(size=5),orderByImportance=FALSE)
     >
     >
     >
     > #although no interaction, a joined additive effect of X1 and X2
     > #colour by FC-component FC1 and FC2 summed
     > Col = fcol(ff43,1:2,orderByImportance=FALSE,X.m=FALSE,RGB=TRUE,plotTest = "a")
     ----------- FAILURE REPORT --------------
     --- failure: the condition has length > 1 ---
     --- srcref ---
     :
     --- package (from environment) ---
     forestFloor
     --- call from context ---
     fcol(ff43, 1:2, orderByImportance = FALSE, X.m = FALSE, RGB = TRUE,
     plotTest = "a")
     --- call from argument ---
     if (!class(colM) %in% c("data.frame", "matrix")) {
     tryCatch({
     colM = matrix(colM, ncol = 1)
     }, error = function(e) stop(paste("input ff was neither data.frame or matrix and \ncould not be coerced to matrix:",
     e$message)))
     }
     --- R stacktrace ---
     where 1: fcol(ff43, 1:2, orderByImportance = FALSE, X.m = FALSE, RGB = TRUE,
     plotTest = "a")
    
     --- value of length: 2 type: logical ---
     [1] FALSE TRUE
     --- function from context ---
     function (ff, cols = NULL, orderByImportance = NULL, plotTest = NULL,
     X.matrix = TRUE, hue = NULL, saturation = NULL, brightness = NULL,
     hue.range = NULL, sat.range = NULL, bri.range = NULL, alpha = NULL,
     RGB = NULL, byResiduals = FALSE, max.df = 3, imp.weight = NULL,
     imp.exp = 1, outlier.lim = 3, RGB.exp = NULL)
     {
     if (!X.matrix)
     if (class(ff) == "forestFloor_multiClass")
     stop("cannot colour by feature contributions for object of class\n 'forestFloor_multiClass'. Set X.matrix=TRUE")
     ib <- function(x, low, high) (x - low) * (high - x) > 0
     span <- function(x, mid, width) if (min(x) != max(x)) {
     ((x - min(x))/(max(x) - min(x)) - 0.5) * width + mid
     }
     else {
     x[] = mid
     }
     auto.range = function(level, low = 0, high = 1) abs(min(level -
     low, high - level)) * 2
     contain = function(x, low = 0, high = 1) {
     x[x > high] = high
     x[x < low] = low
     x
     }
     if (class(ff) %in% c("forestFloor_regression", "forestFloor_multiClass")) {
     plotThese = checkPlotTest(plotTest, ff$isTrain)
     if (!(all(plotThese))) {
     if (class(ff) == "forestFloor_multiClass") {
     ff$FCarray = ff$FCarray[plotThese, , ]
     }
     else {
     if (class(ff) == "forestFloor_regression") {
     ff$FCmatrix = ff$FCmatrix[plotThese, ]
     }
     }
     ff$Y = ff$Y[plotThese]
     ff$X = ff$X[plotThese, ]
     }
     }
     if (class(ff) %in% c("forestFloor_regression", "forestFloor_multiClass")) {
     if (byResiduals) {
     if (is.null(ff$FCfit)) {
     print("no $FCfit found, computing tempoary LOO-kNN-gaussion fit to main affect")
     print("use ff = convolute_ff(ff) to compute a fixed fit")
     ff = convolute_ff(ff)
     }
     colM = ff$FCmatrix - ff$FCfit
     }
     else {
     if (X.matrix)
     colM = ff$X
     else colM = ff$FCmatrix
     }
     if (is.null(imp.weight))
     imp.weight = TRUE
     if (is.null(orderByImportance))
     orderByImportance = TRUE
     }
     else {
     colM = ff
     if (is.null(imp.weight))
     imp.weight = FALSE
     if (is.null(orderByImportance))
     orderByImportance = FALSE
     }
     if (orderByImportance)
     if (class(ff) %in% c("forestFloor_regression", "forestFloor_multiClass")) {
     colM = colM[, ff$imp_ind]
     }
     else {
     warning("orderByImportance=TRUE takes no effect for non 'forestFloor'-class. As if set to NULL or FALSE...")
     }
     if (!class(colM) %in% c("data.frame", "matrix")) {
     tryCatch({
     colM = matrix(colM, ncol = 1)
     }, error = function(e) stop(paste("input ff was neither data.frame or matrix and \ncould not be coerced to matrix:",
     e$message)))
     }
     colM = data.frame(colM)
     if (is.null(cols))
     cols = 1:dim(colM)[2]
     if (length(cols) < 1 || !is.numeric(cols) || any(!cols %in%
     1:dim(colM)[2])) {
     stop("no cols selected or is not integer/numeric or wrong coloumns")
     }
     sel.colM = data.frame(colM[, cols])
     sel.cols = 1:length(cols)
     if (is.null(RGB))
     if (length(cols) == 1)
     RGB = TRUE
     else RGB = FALSE
     if (!RGB) {
     if (is.null(saturation))
     saturation = 0.85
     if (is.null(brightness))
     brightness = 0.75
     if (is.null(hue))
     hue = 0.25
     }
     else {
     if (is.null(saturation))
     saturation = 1
     if (is.null(brightness))
     brightness = 0.75
     if (is.null(hue))
     hue = 0.66
     if (is.null(RGB.exp))
     RGB.exp = 1.2
     if (is.null(hue.range))
     hue.range = 2
     }
     as.numeric.factor <- function(x, rearrange = TRUE) {
     if (is.numeric(x))
     return(x)
     if (rearrange)
     x = match(x, levels(droplevels(x)))
     else x = match(x, levels(x))
     return(x)
     }
     for (i in 1:dim(sel.colM)[2]) {
     if (is.factor(sel.colM[, i])) {
     this.fac = as.numeric.factor(sel.colM[, i])
     sel.colM[, i] = this.fac
     }
     if (is.character(sel.colM[, i]))
     sel.colM[, i] = as.numeric(sel.colM[, i])
     }
     sel.colM = box.outliers(sel.colM, limit = outlier.lim)
     if (imp.weight && length(cols) > 1) {
     if (class(ff) %in% c("forestFloor_regression", "forestFloor_multiClass")) {
     sel.imp = ff$importance[cols]
     non.negative.imp = sel.imp + min(sel.imp)
     sumnorm.imp = non.negative.imp/sum(non.negative.imp)
     exp.imp = sumnorm.imp^imp.exp
     impM = t(replicate(dim(colM)[1], exp.imp))
     sel.colM = sel.colM * impM
     sel.colM = sel.colM/max(sel.colM)
     }
     else {
     warning("importance weighting only possible for class 'forestFloor'")
     }
     }
     if (any(!c(class(hue), class(saturation), class(brightness)) %in%
     c("numeric", "integer"))) {
     stop("hue, saturation and brightness must be of class numeric or integer")
     }
     hue = hue - floor(hue)
     saturation = max(min(saturation, 1), 0)
     brightness = max(min(brightness, 1), 0)
     if (RGB == TRUE) {
     if (is.null(bri.range))
     bri.range = 0.05
     if (is.null(alpha))
     alpha = 0.7
     len.colM = box.outliers(sel.colM, limit = Inf)
     if (dim(len.colM)[2] == 1)
     nX = as.numeric(len.colM[, 1])
     else nX = as.numeric(apply(len.colM, 1, mean))
     hsvcol = t(sapply(nX, function(x) rgb2hsv(x^RGB.exp,
     1 - x^RGB.exp - (1 - x)^RGB.exp, (1 - x)^RGB.exp)))
     hue.vec = hsvcol[, 1] * hue.range + hue
     hue.vec[hue.vec > 1] = hue.vec[hue.vec > 1] - floor(hue.vec[hue.vec >
     1])
     hsvcol[, 1] = hue.vec
     sat.range = auto.range(saturation)
     hsvcol[, 2] = span(hsvcol[, 2], saturation, sat.range)
     hsvcol[, 2] = contain(hsvcol[, 2])
     bri.range = auto.range(brightness)
     hsvcol[, 3] = span(hsvcol[, 3], brightness, bri.range)
     hsvcol[, 3] = contain(hsvcol[, 3])
     colours = apply(hsvcol, 1, function(x) hsv(x[1], x[2],
     x[3], alpha = alpha))
     return(colours)
     }
     col.df = length(cols)
     if (!max.df %in% c(1, 2, 3))
     stop("fcol input 'max.df' must be set to either 1, 2 or 3")
     if (col.df > max.df) {
     len.colM = box.outliers(prcomp(sel.colM)$x[, 1:max.df],
     limit = Inf)
     col.df = max.df
     }
     else {
     len.colM = box.outliers(sel.colM, limit = Inf)
     }
     if (is.null(hue.range)) {
     if (col.df == 1)
     hue.range = 0.85
     if (col.df == 2)
     hue.range = 1
     if (col.df == 3)
     hue.range = 1
     }
     if (is.null(sat.range)) {
     if (col.df == 1)
     sat.range = "not used"
     if (col.df == 2)
     sat.range = auto.range(saturation)
     if (col.df == 3)
     sat.range = auto.range(saturation)
     }
     if (is.null(bri.range)) {
     if (col.df == 1)
     bri.range = "not used"
     if (col.df == 2)
     bri.range = "not used"
     if (col.df == 3)
     bri.range = auto.range(brightness)
     }
     if (is.null(alpha))
     alpha = min(1, 400/dim(len.colM)[1])
     if (col.df == 1) {
     hue.vec = as.numeric(len.colM[, 1]) * hue.range + hue
     hue.vec[hue.vec > 1] = hue.vec[hue.vec > 1] - floor(hue.vec[hue.vec >
     1])
     colours = hsv(h = hue.vec, s = saturation, v = brightness,
     alpha = alpha)
     }
     if (col.df == 2) {
     hsvcol = t(rgb2hsv(len.colM[, 1], len.colM[, 2], 1 -
     apply(len.colM, 1, mean)))
     hue.vec = hsvcol[, 1] * hue.range + hue
     hue.vec[hue.vec > 1] = hue.vec[hue.vec > 1] - 1
     hsvcol[, 1] = hue.vec
     hsvcol[, 2] = ((len.colM[, 1] - mean(len.colM[, 1]))^2 +
     (len.colM[, 2] - mean(len.colM[, 2]))^2)^sat.range *
     saturation
     hsvcol[, 2] = hsvcol[, 2]/max(hsvcol[, 2])
     hsvcol[, 3] = brightness
     colours = hsv(hsvcol[, 1], hsvcol[, 2], hsvcol[, 3],
     alpha = alpha)
     }
     if (col.df == 3) {
     hsvcol = t(rgb2hsv(len.colM[, 1], len.colM[, 2], len.colM[,
     3]))
     hue.vec = hsvcol[, 1] * hue.range + hue
     hue.vec[hue.vec > 1] = hue.vec[hue.vec > 1] - 1
     hsvcol[, 1] = hue.vec
     span.sat = span(hsvcol[, 2], saturation, sat.range)
     hsvcol[, 2] = contain(span.sat)
     mean.bri = apply(len.colM, 1, mean)
     span.bri = span(mean.bri, brightness, bri.range)
     hsvcol[, 3] = contain(span.bri)
     colours = hsv(hsvcol[, 1], hsvcol[, 2], hsvcol[, 3],
     alpha = alpha)
     }
     return(colours)
     }
     <bytecode: 0x8134180>
     <environment: namespace:forestFloor>
     --- function search by body ---
     Function fcol in namespace forestFloor has this body.
     ----------- END OF FAILURE REPORT --------------
     Error in if (!class(colM) %in% c("data.frame", "matrix")) { :
     the condition has length > 1
     Calls: fcol
     Execution halted
    Running the tests in ‘tests/testMultiClass.cpp.R’ failed.
    Complete output:
     > if(!interactive()) Sys.setenv(RGL_USE_NULL=TRUE) #disable RGL for headless machines
     > library(randomForest)
     randomForest 4.6-14
     Type rfNews() to see new features/changes/bug fixes.
     > library(forestFloor)
     > require(utils)
     >
     > data(iris)
     > iris
     Sepal.Length Sepal.Width Petal.Length Petal.Width Species
     1 5.1 3.5 1.4 0.2 setosa
     2 4.9 3.0 1.4 0.2 setosa
     3 4.7 3.2 1.3 0.2 setosa
     4 4.6 3.1 1.5 0.2 setosa
     5 5.0 3.6 1.4 0.2 setosa
     6 5.4 3.9 1.7 0.4 setosa
     7 4.6 3.4 1.4 0.3 setosa
     8 5.0 3.4 1.5 0.2 setosa
     9 4.4 2.9 1.4 0.2 setosa
     10 4.9 3.1 1.5 0.1 setosa
     11 5.4 3.7 1.5 0.2 setosa
     12 4.8 3.4 1.6 0.2 setosa
     13 4.8 3.0 1.4 0.1 setosa
     14 4.3 3.0 1.1 0.1 setosa
     15 5.8 4.0 1.2 0.2 setosa
     16 5.7 4.4 1.5 0.4 setosa
     17 5.4 3.9 1.3 0.4 setosa
     18 5.1 3.5 1.4 0.3 setosa
     19 5.7 3.8 1.7 0.3 setosa
     20 5.1 3.8 1.5 0.3 setosa
     21 5.4 3.4 1.7 0.2 setosa
     22 5.1 3.7 1.5 0.4 setosa
     23 4.6 3.6 1.0 0.2 setosa
     24 5.1 3.3 1.7 0.5 setosa
     25 4.8 3.4 1.9 0.2 setosa
     26 5.0 3.0 1.6 0.2 setosa
     27 5.0 3.4 1.6 0.4 setosa
     28 5.2 3.5 1.5 0.2 setosa
     29 5.2 3.4 1.4 0.2 setosa
     30 4.7 3.2 1.6 0.2 setosa
     31 4.8 3.1 1.6 0.2 setosa
     32 5.4 3.4 1.5 0.4 setosa
     33 5.2 4.1 1.5 0.1 setosa
     34 5.5 4.2 1.4 0.2 setosa
     35 4.9 3.1 1.5 0.2 setosa
     36 5.0 3.2 1.2 0.2 setosa
     37 5.5 3.5 1.3 0.2 setosa
     38 4.9 3.6 1.4 0.1 setosa
     39 4.4 3.0 1.3 0.2 setosa
     40 5.1 3.4 1.5 0.2 setosa
     41 5.0 3.5 1.3 0.3 setosa
     42 4.5 2.3 1.3 0.3 setosa
     43 4.4 3.2 1.3 0.2 setosa
     44 5.0 3.5 1.6 0.6 setosa
     45 5.1 3.8 1.9 0.4 setosa
     46 4.8 3.0 1.4 0.3 setosa
     47 5.1 3.8 1.6 0.2 setosa
     48 4.6 3.2 1.4 0.2 setosa
     49 5.3 3.7 1.5 0.2 setosa
     50 5.0 3.3 1.4 0.2 setosa
     51 7.0 3.2 4.7 1.4 versicolor
     52 6.4 3.2 4.5 1.5 versicolor
     53 6.9 3.1 4.9 1.5 versicolor
     54 5.5 2.3 4.0 1.3 versicolor
     55 6.5 2.8 4.6 1.5 versicolor
     56 5.7 2.8 4.5 1.3 versicolor
     57 6.3 3.3 4.7 1.6 versicolor
     58 4.9 2.4 3.3 1.0 versicolor
     59 6.6 2.9 4.6 1.3 versicolor
     60 5.2 2.7 3.9 1.4 versicolor
     61 5.0 2.0 3.5 1.0 versicolor
     62 5.9 3.0 4.2 1.5 versicolor
     63 6.0 2.2 4.0 1.0 versicolor
     64 6.1 2.9 4.7 1.4 versicolor
     65 5.6 2.9 3.6 1.3 versicolor
     66 6.7 3.1 4.4 1.4 versicolor
     67 5.6 3.0 4.5 1.5 versicolor
     68 5.8 2.7 4.1 1.0 versicolor
     69 6.2 2.2 4.5 1.5 versicolor
     70 5.6 2.5 3.9 1.1 versicolor
     71 5.9 3.2 4.8 1.8 versicolor
     72 6.1 2.8 4.0 1.3 versicolor
     73 6.3 2.5 4.9 1.5 versicolor
     74 6.1 2.8 4.7 1.2 versicolor
     75 6.4 2.9 4.3 1.3 versicolor
     76 6.6 3.0 4.4 1.4 versicolor
     77 6.8 2.8 4.8 1.4 versicolor
     78 6.7 3.0 5.0 1.7 versicolor
     79 6.0 2.9 4.5 1.5 versicolor
     80 5.7 2.6 3.5 1.0 versicolor
     81 5.5 2.4 3.8 1.1 versicolor
     82 5.5 2.4 3.7 1.0 versicolor
     83 5.8 2.7 3.9 1.2 versicolor
     84 6.0 2.7 5.1 1.6 versicolor
     85 5.4 3.0 4.5 1.5 versicolor
     86 6.0 3.4 4.5 1.6 versicolor
     87 6.7 3.1 4.7 1.5 versicolor
     88 6.3 2.3 4.4 1.3 versicolor
     89 5.6 3.0 4.1 1.3 versicolor
     90 5.5 2.5 4.0 1.3 versicolor
     91 5.5 2.6 4.4 1.2 versicolor
     92 6.1 3.0 4.6 1.4 versicolor
     93 5.8 2.6 4.0 1.2 versicolor
     94 5.0 2.3 3.3 1.0 versicolor
     95 5.6 2.7 4.2 1.3 versicolor
     96 5.7 3.0 4.2 1.2 versicolor
     97 5.7 2.9 4.2 1.3 versicolor
     98 6.2 2.9 4.3 1.3 versicolor
     99 5.1 2.5 3.0 1.1 versicolor
     100 5.7 2.8 4.1 1.3 versicolor
     101 6.3 3.3 6.0 2.5 virginica
     102 5.8 2.7 5.1 1.9 virginica
     103 7.1 3.0 5.9 2.1 virginica
     104 6.3 2.9 5.6 1.8 virginica
     105 6.5 3.0 5.8 2.2 virginica
     106 7.6 3.0 6.6 2.1 virginica
     107 4.9 2.5 4.5 1.7 virginica
     108 7.3 2.9 6.3 1.8 virginica
     109 6.7 2.5 5.8 1.8 virginica
     110 7.2 3.6 6.1 2.5 virginica
     111 6.5 3.2 5.1 2.0 virginica
     112 6.4 2.7 5.3 1.9 virginica
     113 6.8 3.0 5.5 2.1 virginica
     114 5.7 2.5 5.0 2.0 virginica
     115 5.8 2.8 5.1 2.4 virginica
     116 6.4 3.2 5.3 2.3 virginica
     117 6.5 3.0 5.5 1.8 virginica
     118 7.7 3.8 6.7 2.2 virginica
     119 7.7 2.6 6.9 2.3 virginica
     120 6.0 2.2 5.0 1.5 virginica
     121 6.9 3.2 5.7 2.3 virginica
     122 5.6 2.8 4.9 2.0 virginica
     123 7.7 2.8 6.7 2.0 virginica
     124 6.3 2.7 4.9 1.8 virginica
     125 6.7 3.3 5.7 2.1 virginica
     126 7.2 3.2 6.0 1.8 virginica
     127 6.2 2.8 4.8 1.8 virginica
     128 6.1 3.0 4.9 1.8 virginica
     129 6.4 2.8 5.6 2.1 virginica
     130 7.2 3.0 5.8 1.6 virginica
     131 7.4 2.8 6.1 1.9 virginica
     132 7.9 3.8 6.4 2.0 virginica
     133 6.4 2.8 5.6 2.2 virginica
     134 6.3 2.8 5.1 1.5 virginica
     135 6.1 2.6 5.6 1.4 virginica
     136 7.7 3.0 6.1 2.3 virginica
     137 6.3 3.4 5.6 2.4 virginica
     138 6.4 3.1 5.5 1.8 virginica
     139 6.0 3.0 4.8 1.8 virginica
     140 6.9 3.1 5.4 2.1 virginica
     141 6.7 3.1 5.6 2.4 virginica
     142 6.9 3.1 5.1 2.3 virginica
     143 5.8 2.7 5.1 1.9 virginica
     144 6.8 3.2 5.9 2.3 virginica
     145 6.7 3.3 5.7 2.5 virginica
     146 6.7 3.0 5.2 2.3 virginica
     147 6.3 2.5 5.0 1.9 virginica
     148 6.5 3.0 5.2 2.0 virginica
     149 6.2 3.4 5.4 2.3 virginica
     150 5.9 3.0 5.1 1.8 virginica
     > X = iris[,!names(iris) %in% "Species"]
     > Y = iris[,"Species"]
     > as.numeric(Y)
     [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
     [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
     [75] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3
     [112] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
     [149] 3 3
     > rf.42 = randomForest(X,Y,keep.forest=T,replace=F,keep.inbag=T,samp=15,ntree=100)
     > ff.42 = forestFloor(rf.42,X,calc_np = F,bootstrapFC = TRUE)
     >
     > #test accuracy of feature contributions
     > #y_hat_OOB = row sum FC + Y_grandMean
     > FCc = t(t(apply(ff.42$FCarray,c(1,3),sum))+as.vector(table(Y)/length(Y)))
     > FC.residuals = FCc-predict(rf.42,type="prob")
     > if(max(abs(FC.residuals))>1E-12) stop(
     + paste0("When testing if: y_hat_OOB = row sum FCmatrix + Y_grandMean
     + one/some FCs error exceeds allowed 1e-12, found.error=",max(abs(FC.residuals)))
     + )
     >
     > Xtest = iris[1:50,] #copy
     > Xtest = Xtest[,-5] #drop Species
     > Xtest[1:4] = lapply(iris[1:4],sample,50) #random resample 50 samples
     >
     >
     > #test same results are reached with Xtest
     > ff.43 = forestFloor(rf.42,X,Xtest,bootstrapFC = T)
     > if(max(abs(ff.43$FCarray[ff.43$isTrain,,]-ff.42$FCarray)) > 1E-12) stop(
     + "forestFloor with/without Xtest gives different feature contributions"
     + )
     > plot(ff.43,speedup_GOF = TRUE,plotTest = F)
     >
     >
     > pred = sapply(1:3,function(i) apply(ff.42$FCarray[,,i],1,sum))+1/3
     > rfPred = predict(rf.42,type="vote",norm.votes=T)
     > rfPred[is.nan(rfPred)] = 1/3
     > if(cor(as.vector(rfPred),as.vector(pred))^2<0.99) stop("fail testMultiClass")
     > attributes(ff.42)
     $names
     [1] "X" "Y" "importance" "imp_ind" "FCarray"
     [6] "sumOfInbags" "isTrain"
    
     $class
     [1] "forestFloor_multiClass"
    
     > args(forestFloor:::plot.forestFloor_multiClass)
     function (x, plot_seq = NULL, label.seq = NULL, plotTest = NULL,
     limitY = TRUE, col = NULL, colLists = NULL, orderByImportance = TRUE,
     fig.columns = NULL, plot_GOF = TRUE, GOF_args = list(), speedup_GOF = TRUE,
     jitter_these_cols = NULL, jitter.factor = NULL, ...)
     NULL
     > plot(ff.42,plot_GOF=T,cex=.7,
     + colLists=list("#FF0000A5",
     + "#00FF0050",
     + "#0000FF35")
     + )
     >
     > plot(ff.43,plot_GOF=T,cex=.7,
     + colLists=list("#FF0000A5",
     + "#00FF0050",
     + "#0000FF35")
     + )
     >
     > #use col interface
     > plot(ff.43,plot_GOF=T,cex=.7,
     + col=list("#FF0000A5","#00FF0050","#0000FF35") #one colour per class
     + )
     >
     > plot(ff.43,plot_GOF=T,cex=.7,
     + col=c("#FF0000A5","#00FF0050","#0000FF35") #one colour per class
     + )
     >
     >
     > #try to alter std par
     > plot(ff.42,plot_GOF=T,cex=.7,
     + colLists=list("#FF0000A5",
     + "#00FF0050",
     + "#0000FF35"),
     + mfrow=c(4,3)
     + )
     >
     >
     > show3d(ff.42,1:2,1:2,plot_GOF=T)
     > show3d(ff.42,1:2,1,plot_GOF=T)#test plotting only one feature contribution
     >
     >
     > #plot all effect 2D only
     > pars = plot_simplex3(ff.42,Xi=c(1:3),restore_par=F,zoom.fit=NULL,var.col=NULL,fig.cols=2,fig.rows=1,
     + fig3d=F,includeTotal=T,auto.alpha=.4,set_pars=T)
     > pars = plot_simplex3(ff.42,Xi=0,restore_par=F,zoom.fit=NULL,var.col=alist(alpha=.3,cols=1:4),
     + fig3d=F,includeTotal=T,auto.alpha=.8,set_pars=F)
     ----------- FAILURE REPORT --------------
     --- failure: the condition has length > 1 ---
     --- srcref ---
     :
     --- package (from environment) ---
     forestFloor
     --- call from context ---
     box.outliers(prcomp(sel.colM)$x[, 1:max.df], limit = Inf)
     --- call from argument ---
     if (class(x) == "data.frame") {
     sx = as.data.frame(sx, row.names = row.names(x))
     names(sx) = names(x)
     }
     --- R stacktrace ---
     where 1: box.outliers(prcomp(sel.colM)$x[, 1:max.df], limit = Inf)
     where 2: (function (ff, cols = NULL, orderByImportance = NULL, plotTest = NULL,
     X.matrix = TRUE, hue = NULL, saturation = NULL, brightness = NULL,
     hue.range = NULL, sat.range = NULL, bri.range = NULL, alpha = NULL,
     RGB = NULL, byResiduals = FALSE, max.df = 3, imp.weight = NULL,
     imp.exp = 1, outlier.lim = 3, RGB.exp = NULL)
     {
     if (!X.matrix)
     if (class(ff) == "forestFloor_multiClass")
     stop("cannot colour by feature contributions for object of class\n 'forestFloor_multiClass'. Set X.matrix=TRUE")
     ib <- function(x, low, high) (x - low) * (high - x) > 0
     span <- function(x, mid, width) if (min(x) != max(x)) {
     ((x - min(x))/(max(x) - min(x)) - 0.5) * width + mid
     }
     else {
     x[] = mid
     }
     auto.range = function(level, low = 0, high = 1) abs(min(level -
     low, high - level)) * 2
     contain = function(x, low = 0, high = 1) {
     x[x > high] = high
     x[x < low] = low
     x
     }
     if (class(ff) %in% c("forestFloor_regression", "forestFloor_multiClass")) {
     plotThese = checkPlotTest(plotTest, ff$isTrain)
     if (!(all(plotThese))) {
     if (class(ff) == "forestFloor_multiClass") {
     ff$FCarray = ff$FCarray[plotThese, , ]
     }
     else {
     if (class(ff) == "forestFloor_regression") {
     ff$FCmatrix = ff$FCmatrix[plotThese, ]
     }
     }
     ff$Y = ff$Y[plotThese]
     ff$X = ff$X[plotThese, ]
     }
     }
     if (class(ff) %in% c("forestFloor_regression", "forestFloor_multiClass")) {
     if (byResiduals) {
     if (is.null(ff$FCfit)) {
     print("no $FCfit found, computing tempoary LOO-kNN-gaussion fit to main affect")
     print("use ff = convolute_ff(ff) to compute a fixed fit")
     ff = convolute_ff(ff)
     }
     colM = ff$FCmatrix - ff$FCfit
     }
     else {
     if (X.matrix)
     colM = ff$X
     else colM = ff$FCmatrix
     }
     if (is.null(imp.weight))
     imp.weight = TRUE
     if (is.null(orderByImportance))
     orderByImportance = TRUE
     }
     else {
     colM = ff
     if (is.null(imp.weight))
     imp.weight = FALSE
     if (is.null(orderByImportance))
     orderByImportance = FALSE
     }
     if (orderByImportance)
     if (class(ff) %in% c("forestFloor_regression", "forestFloor_multiClass")) {
     colM = colM[, ff$imp_ind]
     }
     else {
     warning("orderByImportance=TRUE takes no effect for non 'forestFloor'-class. As if set to NULL or FALSE...")
     }
     if (!class(colM) %in% c("data.frame", "matrix")) {
     tryCatch({
     colM = matrix(colM, ncol = 1)
     }, error = function(e) stop(paste("input ff was neither data.frame or matrix and \ncould not be coerced to matrix:",
     e$message)))
     }
     colM = data.frame(colM)
     if (is.null(cols))
     cols = 1:dim(colM)[2]
     if (length(cols) < 1 || !is.numeric(cols) || any(!cols %in%
     1:dim(colM)[2])) {
     stop("no cols selected or is not integer/numeric or wrong coloumns")
     }
     sel.colM = data.frame(colM[, cols])
     sel.cols = 1:length(cols)
     if (is.null(RGB))
     if (length(cols) == 1)
     RGB = TRUE
     else RGB = FALSE
     if (!RGB) {
     if (is.null(saturation))
     saturation = 0.85
     if (is.null(brightness))
     brightness = 0.75
     if (is.null(hue))
     hue = 0.25
     }
     else {
     if (is.null(saturation))
     saturation = 1
     if (is.null(brightness))
     brightness = 0.75
     if (is.null(hue))
     hue = 0.66
     if (is.null(RGB.exp))
     RGB.exp = 1.2
     if (is.null(hue.range))
     hue.range = 2
     }
     as.numeric.factor <- function(x, rearrange = TRUE) {
     if (is.numeric(x))
     return(x)
     if (rearrange)
     x = match(x, levels(droplevels(x)))
     else x = match(x, levels(x))
     return(x)
     }
     for (i in 1:dim(sel.colM)[2]) {
     if (is.factor(sel.colM[, i])) {
     this.fac = as.numeric.factor(sel.colM[, i])
     sel.colM[, i] = this.fac
     }
     if (is.character(sel.colM[, i]))
     sel.colM[, i] = as.numeric(sel.colM[, i])
     }
     sel.colM = box.outliers(sel.colM, limit = outlier.lim)
     if (imp.weight && length(cols) > 1) {
     if (class(ff) %in% c("forestFloor_regression", "forestFloor_multiClass")) {
     sel.imp = ff$importance[cols]
     non.negative.imp = sel.imp + min(sel.imp)
     sumnorm.imp = non.negative.imp/sum(non.negative.imp)
     exp.imp = sumnorm.imp^imp.exp
     impM = t(replicate(dim(colM)[1], exp.imp))
     sel.colM = sel.colM * impM
     sel.colM = sel.colM/max(sel.colM)
     }
     else {
     warning("importance weighting only possible for class 'forestFloor'")
     }
     }
     if (any(!c(class(hue), class(saturation), class(brightness)) %in%
     c("numeric", "integer"))) {
     stop("hue, saturation and brightness must be of class numeric or integer")
     }
     hue = hue - floor(hue)
     saturation = max(min(saturation, 1), 0)
     brightness = max(min(brightness, 1), 0)
     if (RGB == TRUE) {
     if (is.null(bri.range))
     bri.range = 0.05
     if (is.null(alpha))
     alpha = 0.7
     len.colM = box.outliers(sel.colM, limit = Inf)
     if (dim(len.colM)[2] == 1)
     nX = as.numeric(len.colM[, 1])
     else nX = as.numeric(apply(len.colM, 1, mean))
     hsvcol = t(sapply(nX, function(x) rgb2hsv(x^RGB.exp,
     1 - x^RGB.exp - (1 - x)^RGB.exp, (1 - x)^RGB.exp)))
     hue.vec = hsvcol[, 1] * hue.range + hue
     hue.vec[hue.vec > 1] = hue.vec[hue.vec > 1] - floor(hue.vec[hue.vec >
     1])
     hsvcol[, 1] = hue.vec
     sat.range = auto.range(saturation)
     hsvcol[, 2] = span(hsvcol[, 2], saturation, sat.range)
     hsvcol[, 2] = contain(hsvcol[, 2])
     bri.range = auto.range(brightness)
     hsvcol[, 3] = span(hsvcol[, 3], brightness, bri.range)
     hsvcol[, 3] = contain(hsvcol[, 3])
     colours = apply(hsvcol, 1, function(x) hsv(x[1], x[2],
     x[3], alpha = alpha))
     return(colours)
     }
     col.df = length(cols)
     if (!max.df %in% c(1, 2, 3))
     stop("fcol input 'max.df' must be set to either 1, 2 or 3")
     if (col.df > max.df) {
     len.colM = box.outliers(prcomp(sel.colM)$x[, 1:max.df],
     limit = Inf)
     col.df = max.df
     }
     else {
     len.colM = box.outliers(sel.colM, limit = Inf)
     }
     if (is.null(hue.range)) {
     if (col.df == 1)
     hue.range = 0.85
     if (col.df == 2)
     hue.range = 1
     if (col.df == 3)
     hue.range = 1
     }
     if (is.null(sat.range)) {
     if (col.df == 1)
     sat.range = "not used"
     if (col.df == 2)
     sat.range = auto.range(saturation)
     if (col.df == 3)
     sat.range = auto.range(saturation)
     }
     if (is.null(bri.range)) {
     if (col.df == 1)
     bri.range = "not used"
     if (col.df == 2)
     bri.range = "not used"
     if (col.df == 3)
     bri.range = auto.range(brightness)
     }
     if (is.null(alpha))
     alpha = min(1, 400/dim(len.colM)[1])
     if (col.df == 1) {
     hue.vec = as.numeric(len.colM[, 1]) * hue.range + hue
     hue.vec[hue.vec > 1] = hue.vec[hue.vec > 1] - floor(hue.vec[hue.vec >
     1])
     colours = hsv(h = hue.vec, s = saturation, v = brightness,
     alpha = alpha)
     }
     if (col.df == 2) {
     hsvcol = t(rgb2hsv(len.colM[, 1], len.colM[, 2], 1 -
     apply(len.colM, 1, mean)))
     hue.vec = hsvcol[, 1] * hue.range + hue
     hue.vec[hue.vec > 1] = hue.vec[hue.vec > 1] - 1
     hsvcol[, 1] = hue.vec
     hsvcol[, 2] = ((len.colM[, 1] - mean(len.colM[, 1]))^2 +
     (len.colM[, 2] - mean(len.colM[, 2]))^2)^sat.range *
     saturation
     hsvcol[, 2] = hsvcol[, 2]/max(hsvcol[, 2])
     hsvcol[, 3] = brightness
     colours = hsv(hsvcol[, 1], hsvcol[, 2], hsvcol[, 3],
     alpha = alpha)
     }
     if (col.df == 3) {
     hsvcol = t(rgb2hsv(len.colM[, 1], len.colM[, 2], len.colM[,
     3]))
     hue.vec = hsvcol[, 1] * hue.range + hue
     hue.vec[hue.vec > 1] = hue.vec[hue.vec > 1] - 1
     hsvcol[, 1] = hue.vec
     span.sat = span(hsvcol[, 2], saturation, sat.range)
     hsvcol[, 2] = contain(span.sat)
     mean.bri = apply(len.colM, 1, mean)
     span.bri = span(mean.bri, brightness, bri.range)
     hsvcol[, 3] = contain(span.bri)
     colours = hsv(hsvcol[, 1], hsvcol[, 2], hsvcol[, 3],
     alpha = alpha)
     }
     return(colours)
     })(list(Sepal.Length = c(5.1, 4.9, 4.7, 4.6, 5, 5.4, 4.6, 5,
     4.4, 4.9, 5.4, 4.8, 4.8, 4.3, 5.8, 5.7, 5.4, 5.1, 5.7, 5.1, 5.4,
     5.1, 4.6, 5.1, 4.8, 5, 5, 5.2, 5.2, 4.7, 4.8, 5.4, 5.2, 5.5,
     4.9, 5, 5.5, 4.9, 4.4, 5.1, 5, 4.5, 4.4, 5, 5.1, 4.8, 5.1, 4.6,
     5.3, 5, 7, 6.4, 6.9, 5.5, 6.5, 5.7, 6.3, 4.9, 6.6, 5.2, 5, 5.9,
     6, 6.1, 5.6, 6.7, 5.6, 5.8, 6.2, 5.6, 5.9, 6.1, 6.3, 6.1, 6.4,
     6.6, 6.8, 6.7, 6, 5.7, 5.5, 5.5, 5.8, 6, 5.4, 6, 6.7, 6.3, 5.6,
     5.5, 5.5, 6.1, 5.8, 5, 5.6, 5.7, 5.7, 6.2, 5.1, 5.7, 6.3, 5.8,
     7.1, 6.3, 6.5, 7.6, 4.9, 7.3, 6.7, 7.2, 6.5, 6.4, 6.8, 5.7, 5.8,
     6.4, 6.5, 7.7, 7.7, 6, 6.9, 5.6, 7.7, 6.3, 6.7, 7.2, 6.2, 6.1,
     6.4, 7.2, 7.4, 7.9, 6.4, 6.3, 6.1, 7.7, 6.3, 6.4, 6, 6.9, 6.7,
     6.9, 5.8, 6.8, 6.7, 6.7, 6.3, 6.5, 6.2, 5.9), Sepal.Width = c(3.5,
     3, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1, 3.7, 3.4, 3, 3, 4,
     4.4, 3.9, 3.5, 3.8, 3.8, 3.4, 3.7, 3.6, 3.3, 3.4, 3, 3.4, 3.5,
     3.4, 3.2, 3.1, 3.4, 4.1, 4.2, 3.1, 3.2, 3.5, 3.6, 3, 3.4, 3.5,
     2.3, 3.2, 3.5, 3.8, 3, 3.8, 3.2, 3.7, 3.3, 3.2, 3.2, 3.1, 2.3,
     2.8, 2.8, 3.3, 2.4, 2.9, 2.7, 2, 3, 2.2, 2.9, 2.9, 3.1, 3, 2.7,
     2.2, 2.5, 3.2, 2.8, 2.5, 2.8, 2.9, 3, 2.8, 3, 2.9, 2.6, 2.4,
     2.4, 2.7, 2.7, 3, 3.4, 3.1, 2.3, 3, 2.5, 2.6, 3, 2.6, 2.3, 2.7,
     3, 2.9, 2.9, 2.5, 2.8, 3.3, 2.7, 3, 2.9, 3, 3, 2.5, 2.9, 2.5,
     3.6, 3.2, 2.7, 3, 2.5, 2.8, 3.2, 3, 3.8, 2.6, 2.2, 3.2, 2.8,
     2.8, 2.7, 3.3, 3.2, 2.8, 3, 2.8, 3, 2.8, 3.8, 2.8, 2.8, 2.6,
     3, 3.4, 3.1, 3, 3.1, 3.1, 3.1, 2.7, 3.2, 3.3, 3, 2.5, 3, 3.4,
     3), Petal.Length = c(1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5,
     1.4, 1.5, 1.5, 1.6, 1.4, 1.1, 1.2, 1.5, 1.3, 1.4, 1.7, 1.5, 1.7,
     1.5, 1, 1.7, 1.9, 1.6, 1.6, 1.5, 1.4, 1.6, 1.6, 1.5, 1.5, 1.4,
     1.5, 1.2, 1.3, 1.4, 1.3, 1.5, 1.3, 1.3, 1.3, 1.6, 1.9, 1.4, 1.6,
     1.4, 1.5, 1.4, 4.7, 4.5, 4.9, 4, 4.6, 4.5, 4.7, 3.3, 4.6, 3.9,
     3.5, 4.2, 4, 4.7, 3.6, 4.4, 4.5, 4.1, 4.5, 3.9, 4.8, 4, 4.9,
     4.7, 4.3, 4.4, 4.8, 5, 4.5, 3.5, 3.8, 3.7, 3.9, 5.1, 4.5, 4.5,
     4.7, 4.4, 4.1, 4, 4.4, 4.6, 4, 3.3, 4.2, 4.2, 4.2, 4.3, 3, 4.1,
     6, 5.1, 5.9, 5.6, 5.8, 6.6, 4.5, 6.3, 5.8, 6.1, 5.1, 5.3, 5.5,
     5, 5.1, 5.3, 5.5, 6.7, 6.9, 5, 5.7, 4.9, 6.7, 4.9, 5.7, 6, 4.8,
     4.9, 5.6, 5.8, 6.1, 6.4, 5.6, 5.1, 5.6, 6.1, 5.6, 5.5, 4.8, 5.4,
     5.6, 5.1, 5.1, 5.9, 5.7, 5.2, 5, 5.2, 5.4, 5.1), Petal.Width = c(0.2,
     0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, 0.2, 0.2, 0.1, 0.1,
     0.2, 0.4, 0.4, 0.3, 0.3, 0.3, 0.2, 0.4, 0.2, 0.5, 0.2, 0.2, 0.4,
     0.2, 0.2, 0.2, 0.2, 0.4, 0.1, 0.2, 0.2, 0.2, 0.2, 0.1, 0.2, 0.2,
     0.3, 0.3, 0.2, 0.6, 0.4, 0.3, 0.2, 0.2, 0.2, 0.2, 1.4, 1.5, 1.5,
     1.3, 1.5, 1.3, 1.6, 1, 1.3, 1.4, 1, 1.5, 1, 1.4, 1.3, 1.4, 1.5,
     1, 1.5, 1.1, 1.8, 1.3, 1.5, 1.2, 1.3, 1.4, 1.4, 1.7, 1.5, 1,
     1.1, 1, 1.2, 1.6, 1.5, 1.6, 1.5, 1.3, 1.3, 1.3, 1.2, 1.4, 1.2,
     1, 1.3, 1.2, 1.3, 1.3, 1.1, 1.3, 2.5, 1.9, 2.1, 1.8, 2.2, 2.1,
     1.7, 1.8, 1.8, 2.5, 2, 1.9, 2.1, 2, 2.4, 2.3, 1.8, 2.2, 2.3,
     1.5, 2.3, 2, 2, 1.8, 2.1, 1.8, 1.8, 1.8, 2.1, 1.6, 1.9, 2, 2.2,
     1.5, 1.4, 2.3, 2.4, 1.8, 1.8, 2.1, 2.4, 2.3, 1.9, 2.3, 2.5, 2.3,
     1.9, 2, 2.3, 1.8)), 0, alpha = 0.3, cols = 1:4)
     where 3: do.call(fcol, c(list(ff$X, i), var.col))
     where 4: plot.xy(xy.coords(x, y), type = type, ...)
     where 5: points.default(apply(xd1 * t(FC[, ]), 2, sum) + cent2[1], apply(xd2 *
     t(FC[, ]), 2, sum) + cent2[2], cex = 0.3, col = if (Col[1] ==
     "var.col") do.call(fcol, c(list(ff$X, i), var.col)) else Col)
     where 6: points(apply(xd1 * t(FC[, ]), 2, sum) + cent2[1], apply(xd2 *
     t(FC[, ]), 2, sum) + cent2[2], cex = 0.3, col = if (Col[1] ==
     "var.col") do.call(fcol, c(list(ff$X, i), var.col)) else Col)
     where 7: plot_simplex3(ff.42, Xi = 0, restore_par = F, zoom.fit = NULL,
     var.col = alist(alpha = 0.3, cols = 1:4), fig3d = F, includeTotal = T,
     auto.alpha = 0.8, set_pars = F)
    
     --- value of length: 2 type: logical ---
     [1] FALSE FALSE
     --- function from context ---
     function (x, limit = 1.5, normalize = TRUE)
     {
     sx = scale(x)
     if (limit != FALSE) {
     sx[sx > limit] = limit
     sx[-sx > limit] = -limit
     }
     if (normalize) {
     sx.span = max(sx) - min(sx)
     sx = sx - min(sx)
     sx = sx/sx.span
     }
     else {
     obs = attributes(sx)$dim[1]
     if (dim(sx)[2] > 1) {
     sx = sx * t(replicate(obs, attributes(sx)$"scaled:scale")) +
     t(replicate(obs, attributes(sx)$"scaled:center"))
     }
     else {
     sx = sx * attributes(sx)$"scaled:scale" + attributes(sx)$"scaled:center"
     }
     }
     if (class(x) == "data.frame") {
     sx = as.data.frame(sx, row.names = row.names(x))
     names(sx) = names(x)
     }
     return(sx)
     }
     <bytecode: 0x907d810>
     <environment: namespace:forestFloor>
     --- function search by body ---
     Function box.outliers in namespace forestFloor has this body.
     ----------- END OF FAILURE REPORT --------------
     Error in if (class(x) == "data.frame") { : the condition has length > 1
     Calls: plot_simplex3 ... points.default -> plot.xy -> do.call -> <Anonymous> -> box.outliers
     Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc

Version: 1.11.1
Check: whether package can be installed
Result: WARN
    Found the following significant warnings:
     Warning: 'rgl_init' failed, running with rgl.useNULL = TRUE
Flavor: r-release-osx-x86_64

Version: 1.11.1
Check: whether package can be installed
Result: ERROR
    Installation failed.
Flavor: r-oldrel-windows-ix86+x86_64