Predicts the labels or group indicators of a set of observations using a pptr model.
predict.pptr.RdPredicts the labels or group indicators of a set of observations using a pptr model.
Usage
# S3 method for class 'pptr'
predict(object, new_data = NULL, type = "class", ...)Arguments
- object
A pptr model.
- new_data
A data frame or matrix of new observations to predict. 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 with 1.0 for the predicted group and 0.0 elsewhere.- ...
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.
See also
pptr for training, formula.pptr, summary.pptr
Examples
# Example 1: with the `iris` dataset
model <- pptr(Type ~ ., 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 versicolor
#> [79] versicolor versicolor versicolor versicolor versicolor virginica
#> [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 virginica virginica
#> [109] virginica virginica virginica virginica virginica virginica
#> [115] virginica virginica virginica virginica virginica virginica
#> [121] virginica virginica virginica virginica virginica virginica
#> [127] virginica virginica virginica virginica virginica virginica
#> [133] virginica versicolor virginica virginica virginica virginica
#> [139] virginica virginica virginica virginica virginica virginica
#> [145] virginica virginica virginica virginica virginica virginica
#> Levels: setosa versicolor virginica
# Example 2: with the `crabs` dataset
model <- pptr(Type ~ ., data = crabs)
predict(model, crabs)
#> [1] B B B B B B B B B B B B B B B B B B B B B B B B B B B B B O B B B B B O O
#> [38] O O O O O O O O O O O O O B B B B B B B B B B B B B B B B B B B B B B B B
#> [75] B B B B B B B B B B B B B B B B B B B B B O O O O O B B B B B B B B B B B
#> [112] B B B B B B B B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O
#> [149] O O B B B B B B B B B B O O O O O O O O O O O O O O O O O O O O O O O O O
#> [186] O O O O O O O O O O O O O O O
#> Levels: B O
# Example 3: vote proportions
model <- pptr(Type ~ ., data = iris)
predict(model, iris, type = "prob")
#> setosa versicolor virginica
#> 1 1 0 0
#> 2 1 0 0
#> 3 1 0 0
#> 4 1 0 0
#> 5 1 0 0
#> 6 1 0 0
#> 7 1 0 0
#> 8 1 0 0
#> 9 1 0 0
#> 10 1 0 0
#> 11 1 0 0
#> 12 1 0 0
#> 13 1 0 0
#> 14 1 0 0
#> 15 1 0 0
#> 16 1 0 0
#> 17 1 0 0
#> 18 1 0 0
#> 19 1 0 0
#> 20 1 0 0
#> 21 1 0 0
#> 22 1 0 0
#> 23 1 0 0
#> 24 1 0 0
#> 25 1 0 0
#> 26 1 0 0
#> 27 1 0 0
#> 28 1 0 0
#> 29 1 0 0
#> 30 1 0 0
#> 31 1 0 0
#> 32 1 0 0
#> 33 1 0 0
#> 34 1 0 0
#> 35 1 0 0
#> 36 1 0 0
#> 37 1 0 0
#> 38 1 0 0
#> 39 1 0 0
#> 40 1 0 0
#> 41 1 0 0
#> 42 1 0 0
#> 43 1 0 0
#> 44 1 0 0
#> 45 1 0 0
#> 46 1 0 0
#> 47 1 0 0
#> 48 1 0 0
#> 49 1 0 0
#> 50 1 0 0
#> 51 0 1 0
#> 52 0 1 0
#> 53 0 1 0
#> 54 0 1 0
#> 55 0 1 0
#> 56 0 1 0
#> 57 0 1 0
#> 58 0 1 0
#> 59 0 1 0
#> 60 0 1 0
#> 61 0 1 0
#> 62 0 1 0
#> 63 0 1 0
#> 64 0 1 0
#> 65 0 1 0
#> 66 0 1 0
#> 67 0 1 0
#> 68 0 1 0
#> 69 0 1 0
#> 70 0 1 0
#> 71 0 0 1
#> 72 0 1 0
#> 73 0 1 0
#> 74 0 1 0
#> 75 0 1 0
#> 76 0 1 0
#> 77 0 1 0
#> 78 0 1 0
#> 79 0 1 0
#> 80 0 1 0
#> 81 0 1 0
#> 82 0 1 0
#> 83 0 1 0
#> 84 0 0 1
#> 85 0 1 0
#> 86 0 1 0
#> 87 0 1 0
#> 88 0 1 0
#> 89 0 1 0
#> 90 0 1 0
#> 91 0 1 0
#> 92 0 1 0
#> 93 0 1 0
#> 94 0 1 0
#> 95 0 1 0
#> 96 0 1 0
#> 97 0 1 0
#> 98 0 1 0
#> 99 0 1 0
#> 100 0 1 0
#> 101 0 0 1
#> 102 0 0 1
#> 103 0 0 1
#> 104 0 0 1
#> 105 0 0 1
#> 106 0 0 1
#> 107 0 0 1
#> 108 0 0 1
#> 109 0 0 1
#> 110 0 0 1
#> 111 0 0 1
#> 112 0 0 1
#> 113 0 0 1
#> 114 0 0 1
#> 115 0 0 1
#> 116 0 0 1
#> 117 0 0 1
#> 118 0 0 1
#> 119 0 0 1
#> 120 0 0 1
#> 121 0 0 1
#> 122 0 0 1
#> 123 0 0 1
#> 124 0 0 1
#> 125 0 0 1
#> 126 0 0 1
#> 127 0 0 1
#> 128 0 0 1
#> 129 0 0 1
#> 130 0 0 1
#> 131 0 0 1
#> 132 0 0 1
#> 133 0 0 1
#> 134 0 1 0
#> 135 0 0 1
#> 136 0 0 1
#> 137 0 0 1
#> 138 0 0 1
#> 139 0 0 1
#> 140 0 0 1
#> 141 0 0 1
#> 142 0 0 1
#> 143 0 0 1
#> 144 0 0 1
#> 145 0 0 1
#> 146 0 0 1
#> 147 0 0 1
#> 148 0 0 1
#> 149 0 0 1
#> 150 0 0 1