Run a Super Learner for the provided subset of features

run_sl(
  Y = NULL,
  X = NULL,
  V = 5,
  SL.library = "SL.glm",
  univariate_SL.library = NULL,
  s = 1,
  cv_folds = NULL,
  sample_splitting = TRUE,
  ss_folds = NULL,
  split = 1,
  verbose = FALSE,
  progress_bar = NULL,
  indx = 1,
  weights = rep(1, nrow(X)),
  cross_fitted_se = TRUE,
  full = NULL,
  vector = TRUE,
  ...
)

run_sl(
  Y = NULL,
  X = NULL,
  V = 5,
  SL.library = "SL.glm",
  univariate_SL.library = NULL,
  s = 1,
  cv_folds = NULL,
  sample_splitting = TRUE,
  ss_folds = NULL,
  split = 1,
  verbose = FALSE,
  progress_bar = NULL,
  indx = 1,
  weights = rep(1, nrow(X)),
  cross_fitted_se = TRUE,
  full = NULL,
  vector = TRUE,
  ...
)

Arguments

Y: the outcome
X: the covariates
V: the number of folds
SL.library: the library of candidate learners
univariate_SL.library: the library of candidate learners for single-covariate regressions
s: the subset of interest
cv_folds: the CV folds
sample_splitting: logical; should we use sample-splitting for predictiveness estimation?
ss_folds: the sample-splitting folds; only used if sample_splitting = TRUE
split: the split to use for sample-splitting; only used if sample_splitting = TRUE
verbose: should we print progress? defaults to FALSE
progress_bar: the progress bar to print to (only if verbose = TRUE)
indx: the index to pass to progress bar (only if verbose = TRUE)
weights: weights to pass to estimation procedure
cross_fitted_se: if TRUE, uses a cross-fitted estimator of the standard error; otherwise, uses the entire dataset
full: should this be considered a "full" or "reduced" regression? If NULL (the default), this is determined automatically; a full regression corresponds to s being equal to the full covariate vector. For SPVIMs, can be entered manually.
vector: should we return a vector (TRUE) or a list (FALSE)?
...: other arguments to Super Learner

Value

a list of length V, with the results of predicting on the hold-out data for each v in 1 through V