targeted (version 0.6)

cv.learner_sl: Cross-validation for learner_sl

Description

Cross-validation estimation of the generalization error of the super learner and each of the separate models in the ensemble. Both the chosen model scoring metrics as well as the model weights of the stacked ensemble.

Usage

# S3 method for learner_sl
cv(object, data, nfolds = 5, rep = 1, model.score = scoring, ...)

Arguments

object

(learner_sl) Instantiated learner_sl object.

data

data.frame or matrix

nfolds

Number of folds (nfolds=0 simple test/train split into two folds 1:([n]/2), ([n]+1/2):n with last part used for testing)

rep

Number of repetitions (default 1)

model.score

Model scoring metric (default: MSE / Brier score). Must be a function with arguments response and prediction, and may optionally include weights, object and newdata arguments

...

Additional arguments parsed to elements in object

Examples

sim1 <- function(n = 5e2) {
   x1 <- rnorm(n, sd = 2)
   x2 <- rnorm(n)
   y <- x1 + cos(x1) + rnorm(n, sd = 0.5**.5)
   data.frame(y, x1, x2)
}
sl <- learner_sl(list(
                   "mean" = learner_glm(y ~ 1),
                   "glm" = learner_glm(y ~ x1),
                   "glm2" = learner_glm(y ~ x1 + x2)
                  ))
cv(sl, data = sim1(), rep = 2)