Create Logistic Features
create_logist_features.RdThis function takes a matrix or dataframe of features, removes columns that are entirely NA, and then applies three logistic transformations to each column. Each transformed set of features is appended with suffixes "_early", "_linear", or "_late" to differentiate between them. The resulting matrix combines all transformed features.
Value
A matrix containing the transformed features with columns named according to the transformation applied (i.e., "_early", "_linear", or "_late").
See also
logist for the logistic transformation function.
Examples
# Create a sample matrix
sample_features <- matrix(rnorm(100), ncol = 5)
transformed_features <- create_logist_features(sample_features)
head(transformed_features)
#> V1_high-energy V1_higher-energy V1_low-energy V1_sigmoid V2_high-energy
#> [1,] 0.006669470 0.000022389745 -0.336385193 0.0016587290 0.016886267
#> [2,] 0.015194518 0.000117204021 0.063742721 0.0086228195 0.016027914
#> [3,] 0.003976017 0.000007935848 -0.543645366 0.0005885478 0.006994522
#> [4,] 0.013348714 0.000090291318 -0.001392821 0.0066559143 0.019299783
#> [5,] 0.018220130 0.000169038405 0.154149501 0.0123893993 0.034055131
#> [6,] 0.023646487 0.000286267360 0.279466150 0.0208039625 0.012753208
#> V2_higher-energy V2_low-energy V2_sigmoid V3_high-energy V3_higher-energy
#> [1,] 0.00014500086 0.11650712 0.010646236 0.013859432 0.00009738224
#> [2,] 0.00013052225 0.09048961 0.009593319 0.009742111 0.00004791892
#> [3,] 0.00002463337 -0.31504444 0.001824645 0.013057593 0.00008637080
#> [4,] 0.00018986990 0.18238058 0.013895138 0.011813424 0.00007060768
#> [5,] 0.00059995979 0.43991567 0.042635577 0.016691842 0.00014165351
#> [6,] 0.00008236589 -0.02435646 0.006075209 0.052051866 0.00142704555
#> V3_low-energy V3_sigmoid V4_high-energy V4_higher-energy V4_low-energy
#> [1,] 0.01750692 0.007174904 0.022796605 0.00026583364 0.26230905
#> [2,] -0.15843484 0.003543384 0.009617056 0.00004669075 -0.16475654
#> [3,] -0.01249058 0.006368738 0.023247253 0.00027657219 0.27150521
#> [4,] -0.06278815 0.005212428 0.011846275 0.00007100326 -0.06139678
#> [5,] 0.11074320 0.010403007 0.007451298 0.00002796854 -0.28617110
#> [6,] 0.59724554 0.095817799 0.008238755 0.00003421931 -0.23923513
#> V4_sigmoid V5_high-energy V5_higher-energy V5_low-energy V5_sigmoid
#> [1,] 0.019347516 0.03466020 0.00062184453 0.4471128 0.044122637
#> [2,] 0.003452878 0.02546449 0.00033258125 0.3136587 0.024089208
#> [3,] 0.020113459 0.01940534 0.00019197267 0.1850414 0.014046877
#> [4,] 0.005241478 0.01763976 0.00015834923 0.1381667 0.011614992
#> [5,] 0.002071180 0.01019263 0.00005247696 -0.1362124 0.003879132
#> [6,] 0.002532909 0.02320802 0.00027562874 0.2707136 0.020046214