wrap_glmnet.RdFits a `glmnet` penalized regression model with a consistent interface. Supports regression and binary classification.
A matrix or data.frame of features.
A factor or character vector for classification, numeric for regression.
Additional arguments passed to [glmnet::glmnet()]. Pass `family = "binomial"` for binary classification.
A fitted `wrap_glmnet` object.
A matrix or data.frame of new observations.
`"class"` (default) for class labels, `"prob"` for a probability matrix. Ignored for regression.
Lambda value for prediction. Defaults to the midpoint of the lambda path. Pass `s = cv_fit$lambda.min` if using [glmnet::cv.glmnet()].
An object of class `wrap_glmnet` with fields:
The fitted glmnet model.
Class levels (NULL for regression).
"classification" or "regression".
Multiclass (`family = "multinomial"`) is not yet supported. For lambda selection, a specific `s` value can be passed to `predict()`. By default the midpoint of the lambda path is used. For optimal lambda, use [glmnet::cv.glmnet()] externally and pass `s = fit$lambda.min`.
# \donttest{
X <- as.matrix(iris[iris$Species != "virginica", 1:4])
y <- droplevels(iris[iris$Species != "virginica", "Species"])
mod <- wrap_glmnet(X, y, family = "binomial")
predict(mod, newx = X, type = "class")
#> [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 versicolor versicolor
#> [73] versicolor versicolor versicolor versicolor versicolor versicolor
#> [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
#> Levels: setosa versicolor
predict(mod, newx = X, type = "prob")
#> setosa versicolor
#> 1 0.987162476 0.012837524
#> 2 0.978222799 0.021777201
#> 3 0.985149785 0.014850215
#> 4 0.976740818 0.023259182
#> 5 0.988456017 0.011543983
#> 6 0.976128195 0.023871805
#> 7 0.981411182 0.018588818
#> 8 0.983044703 0.016955297
#> 9 0.975810653 0.024189347
#> 10 0.982120067 0.017879933
#> 11 0.987661643 0.012338357
#> 12 0.979869911 0.020130089
#> 13 0.983265177 0.016734823
#> 14 0.990028450 0.009971550
#> 15 0.994673874 0.005326126
#> 16 0.990030689 0.009969311
#> 17 0.987995263 0.012004737
#> 18 0.983274420 0.016725580
#> 19 0.979605634 0.020394366
#> 20 0.985537552 0.014462448
#> 21 0.976115098 0.023884902
#> 22 0.979073715 0.020926285
#> 23 0.994230941 0.005769059
#> 24 0.942528486 0.057471514
#> 25 0.966450029 0.033549971
#> 26 0.969383865 0.030616135
#> 27 0.966045113 0.033954887
#> 28 0.984746751 0.015253249
#> 29 0.985726081 0.014273919
#> 30 0.975160545 0.024839455
#> 31 0.972418772 0.027581228
#> 32 0.971336487 0.028663513
#> 33 0.993825975 0.006174025
#> 34 0.993910528 0.006089472
#> 35 0.976740818 0.023259182
#> 36 0.987502490 0.012497510
#> 37 0.989199807 0.010800193
#> 38 0.991150391 0.008849609
#> 39 0.981652500 0.018347500
#> 40 0.983044703 0.016955297
#> 41 0.985919990 0.014080010
#> 42 0.950668719 0.049331281
#> 43 0.985149785 0.014850215
#> 44 0.948761636 0.051238364
#> 45 0.962803613 0.037196387
#> 46 0.971704822 0.028295178
#> 47 0.986813334 0.013186666
#> 48 0.982362086 0.017637914
#> 49 0.987661643 0.012338357
#> 50 0.984131552 0.015868448
#> 51 0.006874998 0.993125002
#> 52 0.007452406 0.992547594
#> 53 0.003339010 0.996660990
#> 54 0.011566372 0.988433628
#> 55 0.004083623 0.995916377
#> 56 0.008286700 0.991713300
#> 57 0.004485214 0.995514786
#> 58 0.090226617 0.909773383
#> 59 0.007751134 0.992248866
#> 60 0.016117641 0.983882359
#> 61 0.043493251 0.956506749
#> 62 0.010129093 0.989870907
#> 63 0.022977422 0.977022578
#> 64 0.004989001 0.995010999
#> 65 0.042965873 0.957034127
#> 66 0.010396118 0.989603882
#> 67 0.006019142 0.993980858
#> 68 0.032695383 0.967304617
#> 69 0.002555587 0.997444413
#> 70 0.028718141 0.971281859
#> 71 0.001981559 0.998018441
#> 72 0.019638465 0.980361535
#> 73 0.001754453 0.998245547
#> 74 0.007645149 0.992354851
#> 75 0.013028213 0.986971787
#> 76 0.009346250 0.990653750
#> 77 0.003766236 0.996233764
#> 78 0.001474221 0.998525779
#> 79 0.005408873 0.994591127
#> 80 0.079762661 0.920237339
#> 81 0.030657663 0.969342337
#> 82 0.046960398 0.953039602
#> 83 0.027263469 0.972736531
#> 84 0.001172949 0.998827051
#> 85 0.006019142 0.993980858
#> 86 0.007067079 0.992932921
#> 87 0.004730437 0.995269563
#> 88 0.005780313 0.994219687
#> 89 0.020426861 0.979573139
#> 90 0.014301526 0.985698474
#> 91 0.010390339 0.989609661
#> 92 0.006606123 0.993393877
#> 93 0.020694951 0.979305049
#> 94 0.081781170 0.918218830
#> 95 0.012521725 0.987478275
#> 96 0.022387225 0.977612775
#> 97 0.015479258 0.984520742
#> 98 0.013028213 0.986971787
#> 99 0.124852867 0.875147133
#> 100 0.016539899 0.983460101
# }