Predict class for the test set and calculate prediction error after finding the PPtree structure, .
Source:R/PPclassify.R
PPclassify.RdPredict class for the test set and calculate prediction error after finding the PPtree structure, .
References
Lee, YD, Cook, D., Park JW, and Lee, EK(2013) PPtree: Projection pursuit classification tree, Electronic Journal of Statistics, 7:1369-1386.
Examples
#crab data set
set.seed(143)
idx <-sample(1:200, 150)
Tree.crab <- PPtree_split('Type~.', data = crab[idx,], PPmethod = 'LDA', size.p = 1)
Tree.crab
#> $Tree.Struct
#> id L.node.ID R.F.node.ID Coef.ID Index
#> [1,] 1 2 3 1 0.8795096
#> [2,] 2 4 5 2 0.7606543
#> [3,] 3 6 7 3 0.8381403
#> [4,] 4 0 2 0 0.0000000
#> [5,] 5 0 1 0 0.0000000
#> [6,] 6 0 4 0 0.0000000
#> [7,] 7 0 3 0 0.0000000
#>
#> $projbest.node
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 0.6221931 0.04545035 0.1615019 -0.5949674 0.4803551
#> [2,] 0.4234606 0.80544215 -0.3837714 -0.1149470 0.1070084
#> [3,] 0.0780935 0.80140034 -0.4893062 0.2586300 -0.2129528
#>
#> $splitCutoff.node
#> Rule1 Rule2 Rule3 Rule4 Rule5 Rule6 Rule7 Rule8
#> 1 0.5431429 0.5838904 0.409899 0.3899181 0.536802 0.5756544 0.344993 0.3263904
#> 2 1.3486145 1.3091564 1.421485 1.4410025 1.373320 1.3358879 1.462184 1.4805776
#> 3 2.0242023 1.9959521 1.875268 1.8891210 2.075108 2.0469258 2.026585 2.0406553
#>
#> $origclass
#> 120 6 180 168 129 140
#> OrangeMale BlueMale OrangeFemale OrangeFemale OrangeMale OrangeMale
#> 150 65 136 105 146 171
#> OrangeMale BlueFemale OrangeMale OrangeMale OrangeMale OrangeFemale
#> 172 38 51 200 84 181
#> OrangeFemale BlueMale BlueFemale OrangeFemale BlueFemale OrangeFemale
#> 18 167 107 165 79 21
#> BlueMale OrangeFemale OrangeMale OrangeFemale BlueFemale BlueMale
#> 71 43 55 39 137 141
#> BlueFemale BlueMale BlueFemale BlueMale OrangeMale OrangeMale
#> 176 81 174 46 25 138
#> OrangeFemale BlueFemale OrangeFemale BlueMale BlueMale OrangeMale
#> 83 99 173 186 198 14
#> BlueFemale BlueFemale OrangeFemale OrangeFemale OrangeFemale BlueMale
#> 37 90 189 44 70 11
#> BlueMale BlueFemale OrangeFemale BlueMale BlueFemale BlueMale
#> 151 87 69 190 159 4
#> OrangeFemale BlueFemale BlueFemale OrangeFemale OrangeFemale BlueMale
#> 109 104 82 122 118 121
#> OrangeMale OrangeMale BlueFemale OrangeMale OrangeMale OrangeMale
#> 86 12 161 34 132 91
#> BlueFemale BlueMale OrangeFemale BlueMale OrangeMale BlueFemale
#> 114 49 26 170 8 42
#> OrangeMale BlueMale BlueMale OrangeFemale BlueMale BlueMale
#> 64 179 160 100 48 187
#> BlueFemale OrangeFemale OrangeFemale BlueFemale BlueMale OrangeFemale
#> 94 156 197 89 93 3
#> BlueFemale OrangeFemale OrangeFemale BlueFemale BlueFemale BlueMale
#> 155 63 178 177 185 47
#> OrangeFemale BlueFemale OrangeFemale OrangeFemale OrangeFemale BlueMale
#> 1 182 97 112 123 119
#> BlueMale OrangeFemale BlueFemale OrangeMale OrangeMale OrangeMale
#> 72 154 30 133 128 192
#> BlueFemale OrangeFemale BlueMale OrangeMale OrangeMale OrangeFemale
#> 13 23 164 96 194 17
#> BlueMale BlueMale OrangeFemale BlueFemale OrangeFemale BlueMale
#> 27 62 35 117 59 53
#> BlueMale BlueFemale BlueMale OrangeMale BlueFemale BlueFemale
#> 103 124 135 19 113 52
#> OrangeMale OrangeMale OrangeMale BlueMale OrangeMale BlueFemale
#> 85 9 31 41 78 88
#> BlueFemale BlueMale BlueMale BlueMale BlueFemale BlueFemale
#> 75 74 144 110 57 33
#> BlueFemale BlueFemale OrangeMale OrangeMale BlueFemale BlueMale
#> 50 60 152 193 158 73
#> BlueMale BlueFemale OrangeFemale OrangeFemale OrangeFemale BlueFemale
#> 5 169 139 56 143 148
#> BlueMale OrangeFemale OrangeMale BlueFemale OrangeMale OrangeMale
#> 131 149 67 80 66 32
#> OrangeMale OrangeMale BlueFemale BlueFemale BlueFemale BlueMale
#> Levels: BlueFemale BlueMale OrangeFemale OrangeMale
#>
#> $origclass_num
#> [1] 4 2 3 3 4 4 4 1 4 4 4 3 3 2 1 3 1 3 2 3 4 3 1 2 1 2 1 2 4 4 3 1 3 2 2 4 1
#> [38] 1 3 3 3 2 2 1 3 2 1 2 3 1 1 3 3 2 4 4 1 4 4 4 1 2 3 2 4 1 4 2 2 3 2 2 1 3
#> [75] 3 1 2 3 1 3 3 1 1 2 3 1 3 3 3 2 2 3 1 4 4 4 1 3 2 4 4 3 2 2 3 1 3 2 2 1 2
#> [112] 4 1 1 4 4 4 2 4 1 1 2 2 2 1 1 1 1 4 4 1 2 2 1 3 3 3 1 2 3 4 1 4 4 4 4 1 1
#> [149] 1 2
#>
#> $origdata
#> FL RW CL CW BD
#> 120 15.1 11.4 30.2 33.3 14.0
#> 6 10.8 9.0 23.0 26.5 9.8
#> 180 18.5 14.6 37.0 42.0 16.6
#> 168 16.2 14.0 31.6 35.6 13.7
#> 129 17.5 12.7 34.6 38.4 16.1
#> 140 19.4 14.4 39.8 44.3 17.9
#> 150 23.1 15.7 47.6 52.8 21.6
#> 65 11.6 11.4 23.7 27.7 10.0
#> 136 18.6 13.5 36.9 40.2 17.0
#> 105 12.5 9.4 23.2 26.0 10.8
#> 146 21.6 14.8 43.4 48.2 20.1
#> 171 17.5 14.3 34.5 39.6 15.6
#> 172 17.5 14.4 34.5 39.0 16.0
#> 38 17.1 12.6 36.4 42.0 15.1
#> 51 7.2 6.5 14.7 17.1 6.1
#> 200 23.1 20.2 46.2 52.5 21.1
#> 84 15.1 13.3 31.8 36.3 13.5
#> 181 18.6 14.5 34.7 39.4 15.0
#> 18 13.1 10.9 28.3 32.4 11.2
#> 167 16.1 13.7 31.4 36.1 13.9
#> 107 12.7 10.4 26.0 28.8 12.1
#> 165 15.7 13.6 31.0 34.8 13.8
#> 79 13.9 13.0 30.0 34.9 13.1
#> 21 14.3 11.6 31.3 35.5 12.7
#> 71 12.8 11.7 27.1 31.2 11.9
#> 43 18.0 13.7 39.2 44.4 16.2
#> 55 9.5 8.2 19.6 22.4 7.8
#> 39 17.1 12.7 36.7 41.9 15.6
#> 137 18.8 13.4 37.2 41.1 17.5
#> 141 20.1 13.7 40.6 44.5 18.0
#> 176 18.0 16.3 37.9 43.0 17.2
#> 81 14.9 13.2 30.1 35.6 12.0
#> 174 17.6 14.0 34.0 38.6 15.5
#> 46 19.3 13.8 40.9 46.5 16.8
#> 25 15.0 11.9 32.5 37.2 13.6
#> 138 18.8 13.8 39.2 43.3 17.9
#> 83 15.0 14.2 32.8 37.4 14.0
#> 99 17.5 16.7 38.6 44.5 17.0
#> 173 17.5 14.7 33.3 37.6 14.6
#> 186 19.7 16.7 39.9 43.6 18.2
#> 198 21.9 17.2 42.6 47.4 19.5
#> 14 12.8 10.2 27.2 31.8 10.9
#> 37 16.9 13.2 37.3 42.7 15.6
#> 90 15.5 13.8 33.4 38.7 14.7
#> 189 20.0 16.7 40.4 45.1 17.7
#> 44 18.8 15.8 42.1 49.0 17.8
#> 70 12.6 12.2 26.1 31.6 11.2
#> 11 12.2 10.8 27.3 31.6 10.9
#> 151 10.7 9.7 21.4 24.0 9.8
#> 87 15.2 14.3 33.9 38.5 14.7
#> 69 12.0 11.1 25.4 29.2 11.0
#> 190 20.1 17.2 39.8 44.1 18.6
#> 159 14.7 13.2 29.6 33.4 12.9
#> 4 9.6 7.9 20.1 23.1 8.2
#> 109 13.4 10.1 26.6 29.6 12.0
#> 104 11.4 9.0 22.7 24.8 10.1
#> 82 15.0 13.8 31.7 36.9 14.0
#> 122 15.4 11.1 30.2 33.6 13.5
#> 118 14.6 11.3 29.9 33.5 12.8
#> 121 15.1 11.5 30.9 34.0 13.9
#> 86 15.1 13.8 31.7 36.6 13.0
#> 12 12.3 11.0 26.8 31.5 11.4
#> 161 15.0 12.3 30.1 33.3 14.0
#> 34 16.4 13.0 35.7 41.8 15.2
#> 132 18.0 13.4 36.7 41.3 17.1
#> 91 15.6 13.9 32.8 37.9 13.4
#> 114 14.1 10.7 28.7 31.9 13.3
#> 49 19.8 14.3 42.4 48.9 18.3
#> 26 15.2 12.1 32.3 36.7 13.6
#> 170 17.1 14.5 33.1 37.2 14.6
#> 8 11.6 9.1 24.5 28.4 10.4
#> 42 17.9 14.1 39.7 44.6 16.8
#> 64 11.6 11.0 24.6 28.5 10.4
#> 179 18.4 15.7 36.5 41.6 16.4
#> 160 14.9 13.0 30.0 33.7 13.3
#> 100 19.2 16.5 40.9 47.9 18.1
#> 48 19.8 14.2 43.2 49.7 18.6
#> 187 19.9 16.6 39.4 43.9 17.9
#> 94 15.8 15.0 34.5 40.3 15.3
#> 156 14.0 11.9 27.0 31.4 12.6
#> 197 21.7 17.1 41.7 47.2 19.6
#> 89 15.4 13.3 32.4 37.6 13.8
#> 93 15.7 13.9 33.6 38.5 14.1
#> 3 9.2 7.8 19.0 22.4 7.7
#> 155 12.9 11.2 25.8 29.1 11.9
#> 63 11.5 11.0 24.7 29.2 10.1
#> 178 18.4 15.5 35.6 40.0 15.9
#> 177 18.3 15.7 35.1 40.5 16.1
#> 185 19.1 16.3 37.9 42.6 17.2
#> 47 19.7 15.3 41.9 48.5 17.8
#> 1 8.1 6.7 16.1 19.0 7.0
#> 182 18.8 15.2 35.8 40.5 16.6
#> 97 16.7 16.1 36.6 41.9 15.4
#> 112 14.1 10.4 28.9 31.8 13.5
#> 123 15.7 12.2 31.7 34.2 14.2
#> 119 14.7 11.1 29.0 32.1 13.1
#> 72 12.8 12.2 26.7 31.1 11.1
#> 154 12.6 11.5 25.0 28.1 11.5
#> 30 16.1 11.6 33.8 39.0 14.4
#> 133 18.2 13.7 38.8 42.7 17.2
#> 128 17.5 12.0 34.4 37.3 15.3
#> 192 20.5 17.5 40.0 45.5 19.2
#> 13 12.6 10.0 27.7 31.7 11.4
#> 23 15.0 10.9 31.4 36.4 13.2
#> 164 15.6 14.1 31.0 34.5 13.8
#> 96 16.4 14.0 34.2 39.8 15.2
#> 194 20.9 16.5 39.9 44.7 17.5
#> 17 13.1 10.6 28.2 32.3 11.0
#> 27 15.4 11.8 33.0 37.5 13.6
#> 62 11.2 10.0 22.8 26.9 9.4
#> 35 16.6 13.5 38.1 43.4 14.9
#> 117 14.2 11.3 29.2 32.2 13.5
#> 59 10.4 9.7 21.7 25.4 8.3
#> 53 9.1 8.1 18.5 21.6 7.7
#> 103 10.7 8.6 20.7 22.7 9.2
#> 124 16.2 11.8 32.3 35.3 14.7
#> 135 18.6 13.4 37.8 41.9 17.3
#> 19 13.3 11.1 27.8 32.3 11.3
#> 113 14.1 10.5 29.1 31.6 13.1
#> 52 9.0 8.5 19.3 22.7 7.7
#> 85 15.1 13.5 31.9 37.0 13.8
#> 9 11.8 9.6 24.2 27.8 9.7
#> 31 16.1 12.8 34.9 40.7 15.7
#> 41 17.7 13.6 38.7 44.5 16.0
#> 78 13.7 12.5 28.6 33.8 11.9
#> 88 15.3 14.2 32.6 38.3 13.8
#> 75 13.1 11.5 27.6 32.6 11.1
#> 74 13.0 11.4 27.3 31.8 11.3
#> 144 21.5 15.5 45.5 49.7 20.9
#> 110 13.7 11.0 27.5 30.5 12.2
#> 57 10.1 9.3 20.9 24.4 8.4
#> 33 16.3 12.7 35.6 40.9 14.9
#> 50 21.3 15.7 47.1 54.6 20.0
#> 60 10.8 9.5 22.5 26.3 9.1
#> 152 11.4 9.2 21.7 24.1 9.7
#> 193 20.6 17.5 41.5 46.2 19.2
#> 158 14.3 12.2 28.1 31.8 12.5
#> 73 12.8 12.2 27.9 31.9 11.5
#> 5 9.8 8.0 20.3 23.0 8.2
#> 169 16.7 14.3 32.3 37.0 14.7
#> 139 19.4 14.1 39.1 43.2 17.8
#> 56 9.8 8.9 20.4 23.9 8.8
#> 143 21.0 15.0 42.9 47.2 19.4
#> 148 22.1 15.8 44.6 49.6 20.5
#> 131 17.9 12.9 36.9 40.9 16.5
#> 149 23.0 16.8 47.2 52.1 21.5
#> 67 11.9 11.4 26.0 30.1 10.9
#> 80 14.7 12.5 30.1 34.7 12.5
#> 66 11.7 10.6 24.9 28.5 10.4
#> 32 16.2 13.3 36.0 41.7 15.4
#>
#> attr(,"class")
#> [1] "list" "PPtree_split"
PPclassify(Tree.crab, test.data = crab[-idx, 2:6], Rule = 1,true.class = crab[-idx, 1])
#> $predict.error
#> [1] 0.1
#>
#> $predict.class
#> [1] BlueFemale BlueFemale BlueFemale BlueMale BlueFemale
#> [6] BlueMale BlueMale BlueMale BlueMale BlueMale
#> [11] BlueMale BlueMale BlueMale BlueFemale BlueFemale
#> [16] BlueFemale BlueFemale BlueFemale BlueFemale BlueFemale
#> [21] BlueFemale BlueFemale OrangeMale OrangeMale OrangeMale
#> [26] OrangeMale OrangeMale OrangeMale OrangeMale OrangeMale
#> [31] OrangeMale OrangeMale OrangeMale OrangeMale OrangeMale
#> [36] OrangeMale OrangeMale OrangeMale OrangeFemale OrangeFemale
#> [41] OrangeFemale OrangeFemale OrangeFemale OrangeFemale OrangeFemale
#> [46] OrangeFemale OrangeFemale OrangeFemale OrangeFemale OrangeFemale
#> Levels: BlueFemale BlueMale OrangeFemale OrangeMale
#>