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.
Usage
learner_stratify(
formula,
learner,
stratify = NULL,
info = NULL,
learner.args = list(),
...
)Arguments
- formula
formula specifying outcome and design matrix
- learner
(learner) learner object
- stratify
(character,formula) variables to stratify by
- info
optional description of the model
- learner.args
(list) optional arguments to the learner constructor
- ...
additional arguments passed to the learner constructor
Examples
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] 0.27517660 0.30335786 0.03560212 0.27836499 0.15517654 0.28005110