Convert numeric entries which large/infinite (absolute) values in a data.frame or task. Only numeric/integer columns are affected.

capLargeValues(obj, target = character(0L), cols = NULL,
  threshold = Inf, impute = threshold, what = "abs")

Arguments

obj

(data.frame | Task)
Input data.

target

(character)
Name of the column(s) specifying the response. Target columns will not be capped. Default is character(0).

cols

(character)
Which columns to convert. Default is all numeric columns.

threshold

(numeric(1))
Threshold for capping. Every entry whose absolute value is equal or larger is converted. Default is Inf.

impute

(numeric(1))
Replacement value for large entries. Large negative entries are converted to -impute. Default is threshold.

what

(character(1))
What kind of entries are affected? “abs” means abs(x) > threshold, “pos” means abs(x) > threshold && x > 0, “neg” means abs(x) > threshold && x < 0. Default is “abs”.

Value

(data.frame)

See also

Examples

capLargeValues(iris, threshold = 5, impute = 5)
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1 5.0 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 5.0 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 5.0 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 5.0 4.0 1.2 0.2 setosa #> 16 5.0 4.4 1.5 0.4 setosa #> 17 5.0 3.9 1.3 0.4 setosa #> 18 5.0 3.5 1.4 0.3 setosa #> 19 5.0 3.8 1.7 0.3 setosa #> 20 5.0 3.8 1.5 0.3 setosa #> 21 5.0 3.4 1.7 0.2 setosa #> 22 5.0 3.7 1.5 0.4 setosa #> 23 4.6 3.6 1.0 0.2 setosa #> 24 5.0 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 5.0 3.5 1.5 0.2 setosa #> 29 5.0 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 5.0 3.4 1.5 0.4 setosa #> 33 5.0 4.1 1.5 0.1 setosa #> 34 5.0 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 5.0 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 5.0 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 5.0 3.8 1.9 0.4 setosa #> 46 4.8 3.0 1.4 0.3 setosa #> 47 5.0 3.8 1.6 0.2 setosa #> 48 4.6 3.2 1.4 0.2 setosa #> 49 5.0 3.7 1.5 0.2 setosa #> 50 5.0 3.3 1.4 0.2 setosa #> 51 5.0 3.2 4.7 1.4 versicolor #> 52 5.0 3.2 4.5 1.5 versicolor #> 53 5.0 3.1 4.9 1.5 versicolor #> 54 5.0 2.3 4.0 1.3 versicolor #> 55 5.0 2.8 4.6 1.5 versicolor #> 56 5.0 2.8 4.5 1.3 versicolor #> 57 5.0 3.3 4.7 1.6 versicolor #> 58 4.9 2.4 3.3 1.0 versicolor #> 59 5.0 2.9 4.6 1.3 versicolor #> 60 5.0 2.7 3.9 1.4 versicolor #> 61 5.0 2.0 3.5 1.0 versicolor #> 62 5.0 3.0 4.2 1.5 versicolor #> 63 5.0 2.2 4.0 1.0 versicolor #> 64 5.0 2.9 4.7 1.4 versicolor #> 65 5.0 2.9 3.6 1.3 versicolor #> 66 5.0 3.1 4.4 1.4 versicolor #> 67 5.0 3.0 4.5 1.5 versicolor #> 68 5.0 2.7 4.1 1.0 versicolor #> 69 5.0 2.2 4.5 1.5 versicolor #> 70 5.0 2.5 3.9 1.1 versicolor #> 71 5.0 3.2 4.8 1.8 versicolor #> 72 5.0 2.8 4.0 1.3 versicolor #> 73 5.0 2.5 4.9 1.5 versicolor #> 74 5.0 2.8 4.7 1.2 versicolor #> 75 5.0 2.9 4.3 1.3 versicolor #> 76 5.0 3.0 4.4 1.4 versicolor #> 77 5.0 2.8 4.8 1.4 versicolor #> 78 5.0 3.0 5.0 1.7 versicolor #> 79 5.0 2.9 4.5 1.5 versicolor #> 80 5.0 2.6 3.5 1.0 versicolor #> 81 5.0 2.4 3.8 1.1 versicolor #> 82 5.0 2.4 3.7 1.0 versicolor #> 83 5.0 2.7 3.9 1.2 versicolor #> 84 5.0 2.7 5.0 1.6 versicolor #> 85 5.0 3.0 4.5 1.5 versicolor #> 86 5.0 3.4 4.5 1.6 versicolor #> 87 5.0 3.1 4.7 1.5 versicolor #> 88 5.0 2.3 4.4 1.3 versicolor #> 89 5.0 3.0 4.1 1.3 versicolor #> 90 5.0 2.5 4.0 1.3 versicolor #> 91 5.0 2.6 4.4 1.2 versicolor #> 92 5.0 3.0 4.6 1.4 versicolor #> 93 5.0 2.6 4.0 1.2 versicolor #> 94 5.0 2.3 3.3 1.0 versicolor #> 95 5.0 2.7 4.2 1.3 versicolor #> 96 5.0 3.0 4.2 1.2 versicolor #> 97 5.0 2.9 4.2 1.3 versicolor #> 98 5.0 2.9 4.3 1.3 versicolor #> 99 5.0 2.5 3.0 1.1 versicolor #> 100 5.0 2.8 4.1 1.3 versicolor #> 101 5.0 3.3 5.0 2.5 virginica #> 102 5.0 2.7 5.0 1.9 virginica #> 103 5.0 3.0 5.0 2.1 virginica #> 104 5.0 2.9 5.0 1.8 virginica #> 105 5.0 3.0 5.0 2.2 virginica #> 106 5.0 3.0 5.0 2.1 virginica #> 107 4.9 2.5 4.5 1.7 virginica #> 108 5.0 2.9 5.0 1.8 virginica #> 109 5.0 2.5 5.0 1.8 virginica #> 110 5.0 3.6 5.0 2.5 virginica #> 111 5.0 3.2 5.0 2.0 virginica #> 112 5.0 2.7 5.0 1.9 virginica #> 113 5.0 3.0 5.0 2.1 virginica #> 114 5.0 2.5 5.0 2.0 virginica #> 115 5.0 2.8 5.0 2.4 virginica #> 116 5.0 3.2 5.0 2.3 virginica #> 117 5.0 3.0 5.0 1.8 virginica #> 118 5.0 3.8 5.0 2.2 virginica #> 119 5.0 2.6 5.0 2.3 virginica #> 120 5.0 2.2 5.0 1.5 virginica #> 121 5.0 3.2 5.0 2.3 virginica #> 122 5.0 2.8 4.9 2.0 virginica #> 123 5.0 2.8 5.0 2.0 virginica #> 124 5.0 2.7 4.9 1.8 virginica #> 125 5.0 3.3 5.0 2.1 virginica #> 126 5.0 3.2 5.0 1.8 virginica #> 127 5.0 2.8 4.8 1.8 virginica #> 128 5.0 3.0 4.9 1.8 virginica #> 129 5.0 2.8 5.0 2.1 virginica #> 130 5.0 3.0 5.0 1.6 virginica #> 131 5.0 2.8 5.0 1.9 virginica #> 132 5.0 3.8 5.0 2.0 virginica #> 133 5.0 2.8 5.0 2.2 virginica #> 134 5.0 2.8 5.0 1.5 virginica #> 135 5.0 2.6 5.0 1.4 virginica #> 136 5.0 3.0 5.0 2.3 virginica #> 137 5.0 3.4 5.0 2.4 virginica #> 138 5.0 3.1 5.0 1.8 virginica #> 139 5.0 3.0 4.8 1.8 virginica #> 140 5.0 3.1 5.0 2.1 virginica #> 141 5.0 3.1 5.0 2.4 virginica #> 142 5.0 3.1 5.0 2.3 virginica #> 143 5.0 2.7 5.0 1.9 virginica #> 144 5.0 3.2 5.0 2.3 virginica #> 145 5.0 3.3 5.0 2.5 virginica #> 146 5.0 3.0 5.0 2.3 virginica #> 147 5.0 2.5 5.0 1.9 virginica #> 148 5.0 3.0 5.0 2.0 virginica #> 149 5.0 3.4 5.0 2.3 virginica #> 150 5.0 3.0 5.0 1.8 virginica