Predicts labels or vote proportions from a pprf model (classification mode).
predict.pprf_classification.RdPredicts labels or vote proportions from a pprf model (classification mode).
Usage
# S3 method for class 'pprf_classification'
predict(object, new_data = NULL, type = NULL, ...)Arguments
- object
A
pprf_classificationmodel.- new_data
A data frame or matrix of new observations. If
NULL, the first positional argument in...is used for backward compatibility.- type
The type of prediction:
"class"(default) returns a factor of predicted labels,"prob"returns a data frame of vote proportions.- ...
For backward compatibility, the first positional argument is treated as
new_datawhennew_dataisNULL.
Value
If type = "class", a factor of predicted labels. If type = "prob", a data frame with one column per group, each row summing to 1.
Examples
model <- pprf(Species ~ ., data = iris)
predict(model, iris)
#> [1] setosa setosa setosa setosa setosa setosa
#> [7] setosa setosa setosa setosa setosa setosa
#> [13] setosa setosa setosa setosa setosa setosa
#> [19] setosa setosa setosa setosa setosa setosa
#> [25] setosa setosa setosa setosa setosa setosa
#> [31] setosa setosa setosa setosa setosa setosa
#> [37] setosa setosa setosa setosa setosa setosa
#> [43] setosa setosa setosa setosa setosa setosa
#> [49] setosa setosa versicolor versicolor versicolor versicolor
#> [55] versicolor versicolor versicolor versicolor versicolor versicolor
#> [61] versicolor versicolor versicolor versicolor versicolor versicolor
#> [67] versicolor versicolor versicolor versicolor virginica versicolor
#> [73] versicolor versicolor versicolor versicolor versicolor virginica
#> [79] versicolor versicolor versicolor versicolor versicolor versicolor
#> [85] versicolor versicolor versicolor versicolor versicolor versicolor
#> [91] versicolor versicolor versicolor versicolor versicolor versicolor
#> [97] versicolor versicolor versicolor versicolor virginica virginica
#> [103] virginica virginica virginica virginica versicolor virginica
#> [109] virginica virginica virginica virginica virginica virginica
#> [115] virginica virginica virginica virginica virginica versicolor
#> [121] virginica virginica virginica virginica virginica virginica
#> [127] virginica virginica virginica virginica virginica virginica
#> [133] virginica versicolor versicolor virginica virginica virginica
#> [139] virginica virginica virginica virginica virginica virginica
#> [145] virginica virginica virginica virginica virginica virginica
#> Levels: setosa versicolor virginica
predict(model, iris, type = "prob")
#> setosa versicolor virginica
#> 1 1.00 0.00 0.00
#> 2 1.00 0.00 0.00
#> 3 1.00 0.00 0.00
#> 4 1.00 0.00 0.00
#> 5 1.00 0.00 0.00
#> 6 1.00 0.00 0.00
#> 7 1.00 0.00 0.00
#> 8 1.00 0.00 0.00
#> 9 1.00 0.00 0.00
#> 10 1.00 0.00 0.00
#> 11 1.00 0.00 0.00
#> 12 1.00 0.00 0.00
#> 13 1.00 0.00 0.00
#> 14 1.00 0.00 0.00
#> 15 1.00 0.00 0.00
#> 16 1.00 0.00 0.00
#> 17 1.00 0.00 0.00
#> 18 1.00 0.00 0.00
#> 19 1.00 0.00 0.00
#> 20 1.00 0.00 0.00
#> 21 1.00 0.00 0.00
#> 22 1.00 0.00 0.00
#> 23 1.00 0.00 0.00
#> 24 1.00 0.00 0.00
#> 25 1.00 0.00 0.00
#> 26 1.00 0.00 0.00
#> 27 1.00 0.00 0.00
#> 28 1.00 0.00 0.00
#> 29 1.00 0.00 0.00
#> 30 1.00 0.00 0.00
#> 31 1.00 0.00 0.00
#> 32 1.00 0.00 0.00
#> 33 1.00 0.00 0.00
#> 34 1.00 0.00 0.00
#> 35 1.00 0.00 0.00
#> 36 1.00 0.00 0.00
#> 37 1.00 0.00 0.00
#> 38 1.00 0.00 0.00
#> 39 1.00 0.00 0.00
#> 40 1.00 0.00 0.00
#> 41 1.00 0.00 0.00
#> 42 0.69 0.31 0.00
#> 43 1.00 0.00 0.00
#> 44 1.00 0.00 0.00
#> 45 1.00 0.00 0.00
#> 46 1.00 0.00 0.00
#> 47 1.00 0.00 0.00
#> 48 1.00 0.00 0.00
#> 49 1.00 0.00 0.00
#> 50 1.00 0.00 0.00
#> 51 0.00 0.81 0.19
#> 52 0.00 0.82 0.18
#> 53 0.00 0.81 0.19
#> 54 0.00 1.00 0.00
#> 55 0.00 0.81 0.19
#> 56 0.00 1.00 0.00
#> 57 0.00 0.84 0.16
#> 58 0.02 0.98 0.00
#> 59 0.00 0.81 0.19
#> 60 0.00 1.00 0.00
#> 61 0.02 0.98 0.00
#> 62 0.00 1.00 0.00
#> 63 0.00 0.99 0.01
#> 64 0.00 1.00 0.00
#> 65 0.00 1.00 0.00
#> 66 0.00 0.81 0.19
#> 67 0.00 0.98 0.02
#> 68 0.00 1.00 0.00
#> 69 0.00 0.77 0.23
#> 70 0.00 1.00 0.00
#> 71 0.00 0.37 0.63
#> 72 0.00 1.00 0.00
#> 73 0.00 0.80 0.20
#> 74 0.00 1.00 0.00
#> 75 0.00 0.81 0.19
#> 76 0.00 0.81 0.19
#> 77 0.00 0.80 0.20
#> 78 0.00 0.35 0.65
#> 79 0.00 1.00 0.00
#> 80 0.00 1.00 0.00
#> 81 0.00 1.00 0.00
#> 82 0.00 1.00 0.00
#> 83 0.00 1.00 0.00
#> 84 0.00 0.64 0.36
#> 85 0.10 0.81 0.09
#> 86 0.12 0.83 0.05
#> 87 0.00 0.81 0.19
#> 88 0.00 0.96 0.04
#> 89 0.00 1.00 0.00
#> 90 0.00 1.00 0.00
#> 91 0.00 1.00 0.00
#> 92 0.00 0.98 0.02
#> 93 0.00 1.00 0.00
#> 94 0.02 0.98 0.00
#> 95 0.00 1.00 0.00
#> 96 0.00 1.00 0.00
#> 97 0.00 1.00 0.00
#> 98 0.00 0.95 0.05
#> 99 0.13 0.87 0.00
#> 100 0.00 1.00 0.00
#> 101 0.00 0.03 0.97
#> 102 0.00 0.19 0.81
#> 103 0.00 0.00 1.00
#> 104 0.00 0.04 0.96
#> 105 0.00 0.00 1.00
#> 106 0.00 0.00 1.00
#> 107 0.00 0.54 0.46
#> 108 0.00 0.00 1.00
#> 109 0.00 0.01 0.99
#> 110 0.00 0.00 1.00
#> 111 0.00 0.01 0.99
#> 112 0.00 0.02 0.98
#> 113 0.00 0.00 1.00
#> 114 0.00 0.19 0.81
#> 115 0.00 0.19 0.81
#> 116 0.00 0.01 0.99
#> 117 0.00 0.00 1.00
#> 118 0.00 0.00 1.00
#> 119 0.00 0.00 1.00
#> 120 0.00 0.55 0.45
#> 121 0.00 0.00 1.00
#> 122 0.00 0.23 0.77
#> 123 0.00 0.00 1.00
#> 124 0.00 0.22 0.78
#> 125 0.00 0.00 1.00
#> 126 0.00 0.03 0.97
#> 127 0.00 0.41 0.59
#> 128 0.00 0.30 0.70
#> 129 0.00 0.01 0.99
#> 130 0.00 0.33 0.67
#> 131 0.00 0.00 1.00
#> 132 0.00 0.08 0.92
#> 133 0.00 0.01 0.99
#> 134 0.00 0.65 0.35
#> 135 0.00 0.73 0.27
#> 136 0.00 0.00 1.00
#> 137 0.00 0.03 0.97
#> 138 0.00 0.00 1.00
#> 139 0.00 0.35 0.65
#> 140 0.00 0.00 1.00
#> 141 0.00 0.00 1.00
#> 142 0.00 0.12 0.88
#> 143 0.00 0.19 0.81
#> 144 0.00 0.00 1.00
#> 145 0.00 0.00 1.00
#> 146 0.00 0.00 1.00
#> 147 0.00 0.15 0.85
#> 148 0.00 0.00 1.00
#> 149 0.00 0.05 0.95
#> 150 0.00 0.19 0.81