Lrnr_screener_augment: Augmented Covariate Screener
In tlverse/sl3: Pipelines for Machine Learning and Super Learning

Lrnr_screener_augment

R Documentation

Augmented Covariate Screener

Description

This learner augments a set of screened covariates with covariates that should be included by default, even if the screener did not select them.

Format

R6Class object.

Value

Learner object with methods for training and prediction. See Lrnr_base for documentation on learners.

Parameters

screener: An instantiated screener.
default_covariates: Vector of covariate names to be automatically added to the vector selected by the screener, regardless of whether or not these covariates were selected by the screener.
...: Other parameters passed to screener.

Other Learners: Custom_chain, Lrnr_HarmonicReg, Lrnr_arima, Lrnr_bartMachine, Lrnr_base, Lrnr_bayesglm, Lrnr_caret, Lrnr_cv_selector, Lrnr_cv, Lrnr_dbarts, Lrnr_define_interactions, Lrnr_density_discretize, Lrnr_density_hse, Lrnr_density_semiparametric, Lrnr_earth, Lrnr_expSmooth, Lrnr_gam, Lrnr_ga, Lrnr_gbm, Lrnr_glm_fast, Lrnr_glm_semiparametric, Lrnr_glmnet, Lrnr_glmtree, Lrnr_glm, Lrnr_grfcate, Lrnr_grf, Lrnr_gru_keras, Lrnr_gts, Lrnr_h2o_grid, Lrnr_hal9001, Lrnr_haldensify, Lrnr_hts, Lrnr_independent_binomial, Lrnr_lightgbm, Lrnr_lstm_keras, Lrnr_mean, Lrnr_multiple_ts, Lrnr_multivariate, Lrnr_nnet, Lrnr_nnls, Lrnr_optim, Lrnr_pca, Lrnr_pkg_SuperLearner, Lrnr_polspline, Lrnr_pooled_hazards, Lrnr_randomForest, Lrnr_ranger, Lrnr_revere_task, Lrnr_rpart, Lrnr_rugarch, Lrnr_screener_coefs, Lrnr_screener_correlation, Lrnr_screener_importance, Lrnr_sl, Lrnr_solnp_density, Lrnr_solnp, Lrnr_stratified, Lrnr_subset_covariates, Lrnr_svm, Lrnr_tsDyn, Lrnr_ts_weights, Lrnr_xgboost, Pipeline, Stack, define_h2o_X(), undocumented_learner

Examples

library(data.table)

# load example data
data(cpp_imputed)
setDT(cpp_imputed)
cpp_imputed[, parity_cat := factor(ifelse(parity < 4, parity, 4))]
covars <- c(
  "apgar1", "apgar5", "parity_cat", "gagebrth", "mage", "meducyrs",
  "sexn"
)
outcome <- "haz"

# create sl3 task
task <- sl3_Task$new(data.table::copy(cpp_imputed),
  covariates = covars,
  outcome = outcome
)

screener_cor <- make_learner(
  Lrnr_screener_correlation,
  type = "rank",
  num_screen = 2
)
screener_augment <- Lrnr_screener_augment$new(screener_cor, covars)
screener_fit <- screener_augment$train(task)
selected <- screener_fit$fit_object$selected
screener_selected <- screener_fit$fit_object$screener_selected

tlverse/sl3 documentation built on Nov. 18, 2024, 12:46 a.m.