lmtp (version 1.5.0)

lmtp_control: Set LMTP Estimation Parameters

Description

Set LMTP Estimation Parameters

Usage

lmtp_control(
  .bound = 1e+05,
  .trim = 0.999,
  .learners_outcome_folds = 10,
  .learners_trt_folds = 10,
  .return_full_fits = FALSE,
  .discrete = TRUE,
  .info = FALSE
)

Value

A list of parameters controlling the estimation procedure.

Arguments

.bound

[numeric(1)]
Determines that maximum and minimum values (scaled) predictions will be bounded by. The default is 1e-5, bounding predictions by 1e-5 and 0.9999.

.trim

[numeric(1)]
Determines the amount the density ratios should be trimmed. The default is 0.999, trimming the density ratios greater than the 0.999 percentile to the 0.999 percentile. A value of 1 indicates no trimming.

.learners_outcome_folds

[integer(1)]
The number of cross-validation folds for learners_outcome.

.learners_trt_folds

[integer(1)]
The number of cross-validation folds for learners_trt.

.return_full_fits

[logical(1)]
Return full 'mlr3superlearner' fits? Default is FALSE, return only 'mlr3superlearner' weights.

.discrete

[logical(1)]
Use discrete or ensemble super learner?

.info

[logical(1)]
Print super learner fitting info to the console?

Examples

lmtp_control(.trim = 0.975)