targeted (version 0.6)

learner_sl: Construct a learner

Description

Constructs a learner class object for fitting a superlearner.

Usage

learner_sl(
  learners,
  info = NULL,
  nfolds = 5L,
  meta.learner = metalearner_nnls,
  model.score = mse,
  learner.args = NULL,
  ...
)

Value

learner object.

Arguments

learners

(list) List of learner objects (i.e. learner_glm)

info

(character) Optional information to describe the instantiated learner object.

nfolds

(integer) Number of folds to use in cross-validation to estimate the ensemble weights.

meta.learner

(function) Algorithm to learn the ensemble weights (default non-negative least squares). Must be a function of the response (nx1 vector), y, and the predictions (nxp matrix), pred, with p being the number of learners. Alternatively, this can be set to the character value "discrete", in which case the Discrete Super-Learner is applied where the model with the lowest risk (model-score) is given weight 1 and all other learners weight 0.

model.score

(function) Model scoring method (see learner)

learner.args

(list) Additional arguments to learner$new().

...

Additional arguments to superlearner

See Also

cv.learner_sl

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)
}
d <- sim1()

m <- list(
  "mean" = learner_glm(y ~ 1),
  "glm" = learner_glm(y ~ x1 + x2),
  "iso" = learner_isoreg(y ~ x1)
)

s <- learner_sl(m, nfolds = 10)
s$estimate(d)
pr <- s$predict(d)
if (interactive()) {
    plot(y ~ x1, data = d)
    points(d$x1, pr, col = 2, cex = 0.5)
    lines(cos(x1) + x1 ~ x1, data = d[order(d$x1), ],
          lwd = 4, col = lava::Col("darkblue", 0.3))
}
print(s)
# weights(s$fit)
# score(s$fit)

cvres <- cv(s, data = d, nfolds = 3, rep = 2)
cvres
# coef(cvres)
# score(cvres)