Construct a learner

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,
  ...
)

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 base learner predictions (nxp matrix), pred, with p being the number of learners. The function can optionally accept a model.score argument for scoring the base learners. See metalearner_nnls, metalearner_convexcomb and metalearner_discrete for the available meta learners.
model.score: (function) Method for scoring the predictions of each base learner. Expects two arguments; vector of response variable and prediction from a base learner (see targeted:::mse for additional details).
learner.args: (list) Additional arguments to learner$new().
...: Additional arguments to superlearner

Value

learner 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)
}
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)
#> ────────── learner object ──────────
#> superlearner
#> 	mean
#> 	glm
#> 	iso 
#> 
#> Estimate arguments: learners=<list>, nfolds=10, meta.learner=<function>, model.score=<function> 
#> Predict arguments:   
#> Formula: y ~ 1 <environment: 0x55ba6b44ef00> 
#> ─────────────────────────────────────
#>         score     weight
#> mean 5.011196 0.06440568
#> glm  1.058765 0.08033375
#> iso  0.584849 0.85526057
# weights(s$fit)
# score(s$fit)

cvres <- cv(s, data = d, nfolds = 3, rep = 2)
cvres
#> 
#> 3-fold cross-validation with 2 repetitions
#> 
#> ── mse 
#>         mean      sd     min     max
#> sl   0.62401 0.04650 0.56521 0.67953
#> mean 5.01482 0.47872 4.43024 5.72789
#> glm  1.05843 0.08263 0.93481 1.18194
#> iso  0.59872 0.04513 0.53342 0.66822
#> 
#> ── mae 
#>         mean      sd     min     max
#> sl   0.62822 0.01889 0.60529 0.65086
#> mean 1.79246 0.04575 1.75501 1.87045
#> glm  0.82495 0.02906 0.78183 0.85334
#> iso  0.61530 0.01998 0.58478 0.64198
#> 
#> ── weight 
#>         mean      sd     min     max
#> sl         -       -       -       -
#> mean 0.07765 0.06591 0.00000 0.18818
#> glm  0.08344 0.03393 0.04148 0.14000
#> iso  0.83891 0.06840 0.72423 0.91004
# coef(cvres)
# score(cvres)

Usage

Arguments

Value

See also

Examples