Regression Tree
data("Boston",package ="MASS")# set the p-value of the permutation test to 0.01htt_boston<-HTT(medv~ . ,data = Boston,controls =htt_control(pt =0.01))htt_boston
# Hypothesis Testing Tree # # node, split, n, pvalue# * denotes terminal node# # [1] root (n = 506, pvalue = 0)# | [2] rm<=7.437 (n = 476, pvalue = 0)# | | [4] lstat<=15 (n = 314, pvalue = 0)# | | | [6] rm<=6.797 (n = 256, pvalue = 0)# | | | | [8] lstat<=4.615 (n = 10) *# | | | | [9] lstat>4.615 (n = 246, pvalue = 0)# | | | | | [12] rm<=6.543 (n = 212, pvalue = 0)# | | | | | | [14] lstat<=7.57 (n = 42) *# | | | | | | [15] lstat>7.57 (n = 170) *# | | | | | [13] rm>6.543 (n = 34) *# | | | [7] rm>6.797 (n = 58) *# | | [5] lstat>15 (n = 162, pvalue = 0)# | | | [10] crim<=0.65402 (n = 46) *# | | | [11] crim>0.65402 (n = 116, pvalue = 0)# | | | | [16] crim<=11.36915 (n = 77) *# | | | | [17] crim>11.36915 (n = 39) *# | [3] rm>7.437 (n = 30) *
# print the split informationhtt_boston$frame
# node parent leftChild rightChild statistic pval split var isleaf n# 1 1 0 2 3 2258.92680 0.00 7.437 rm 0 506# 2 2 1 4 5 1126.14057 0.00 15 lstat 0 476# 3 3 1 NA NA 54.73540 NA <leaf> ptratio 1 30# 4 4 2 6 7 750.08329 0.00 6.797 rm 0 314# 5 5 2 10 11 201.23810 0.00 0.65402 crim 0 162# 6 6 4 8 9 284.52923 0.00 4.615 lstat 0 256# 7 7 4 NA NA 54.33706 NA <leaf> lstat 1 58# 8 8 6 NA NA 0.00000 NA <leaf> <NA> 1 10# 9 9 6 12 13 188.93990 0.00 6.543 rm 0 246# 10 10 5 NA NA 73.70296 NA <leaf> dis 1 46# 11 11 5 16 17 115.47482 0.00 11.36915 crim 0 116# 12 12 9 14 15 126.15810 0.00 7.57 lstat 0 212# 13 13 9 NA NA 20.83679 NA <leaf> nox 1 34# 14 14 12 NA NA 12.63760 NA <leaf> dis 1 42# 15 15 12 NA NA 66.02809 NA <leaf> crim 1 170# 16 16 11 NA NA 32.28858 NA <leaf> lstat 1 77# 17 17 11 NA NA 76.00906 0.02 <leaf> nox 1 39# yval# 1 22.53281# 2 21.11071# 3 45.09667# 4 24.45924# 5 14.62037# 6 22.73242# 7 32.08103# 8 33.13000# 9 22.30976# 10 18.32826# 11 13.15000# 12 21.68821# 13 26.18529# 14 23.95000# 15 21.12941# 16 14.35195# 17 10.77692
# Visualize HTTplot(htt_boston)
Classification Tree
htt_iris<-HTT(Species~.,data = iris,controls =htt_control(pt =0.01))plot(htt_iris,layout ="tree")
# predictiontable(predict(htt_iris), iris[,5])
# # setosa versicolor virginica# setosa 50 0 0# versicolor 0 49 5# virginica 0 1 45
Multivariate regression Tree
data("ENB")set.seed(1)idx=sample(1:nrow(ENB),floor(nrow(ENB)*0.8))train= ENB[idx, ]test= ENB[-idx, ]htt_enb=HTT(cbind(Y1, Y2)~ . ,data = train,controls =htt_control(pt =0.05,R =99))# predictionpred=predict(htt_enb,newdata = test)test_y= test[,9:10]# MAEcolMeans(abs(pred- test_y))
# Y1 Y2 # 0.4808483 1.2228675
# MSEcolMeans(abs(pred- test_y)^2)
# Y1 Y2 # 1.039948 3.594125