This function creates a stratified learner from an existing
learner wrapper function such as learner_glm or learner_xgboost. The
stratification variable can be specified either using the stratify
argument (which can be given as a string "a" or a formula , for example ~
I(a==0)), or it can be defined as a special term directly in the formula, y
~ ... + stratify(a). The formula will subsequently be passed to the
learner_ wrapper without the stratify special term.
learner_stratify(
formula,
learner,
stratify = NULL,
info = NULL,
learner.args = list(),
...
)formula specifying outcome and design matrix
(learner) learner object
(character,formula) variables to stratify by
optional description of the model
(list) optional arguments to the learner constructor
additional arguments passed to the learner constructor
learner object
simdata <- function(n=1000) {
a <- rbinom(n, 1, 0.5)
x <- rnorm(n)
y <- rbinom(n, 1, plogis(-1 + a + a * x))
data.frame(y, a, x)
}
d <- simdata()
lr <- learner_stratify(
y ~ x + stratify(a),
learner_glm,
family=binomial()
)
lr$estimate(d)
lr$predict(head(d))
#> [1] 0.2631709 0.2538526 0.7677843 0.2773313 0.2745183 0.5803691