Lrnr_screener_correlation: Correlation Screening Procedures

In jeremyrcoyle/sl3: Pipelines for Machine Learning and Super Learning

Description

This learner provides covariate screening procedures by running a test of correlation (Pearson default) with the cor.test function, and then selecting the (1) top ranked variables (default), or (2) the variables with a pvalue lower than some pre-specified threshold.

Format

R6Class object.

Value

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

Parameters

method = 'pearson'

Correlation coefficient used for test.

type = c('rank', 'threshold')

Screen covariates by (1) rank (default), which chooses the top num_screen correlated covariates; or (2) threshold, which chooses covariates with a correlation- test- based pvalue lower the threshold and a minimum of min_screen covariates.

num_screen = 5

Number of covariates to select.

pvalue_threshold = 0.1

Maximum p-value threshold. Covariates with a pvalue lower than this threshold will be retained, and at least min_screen most significant covariates will be selected.

min_screen = 2

Minimum number of covariates to select. Used in pvalue_threshold screening procedure.

See Also

Other Learners: Custom_chain, Lrnr_HarmonicReg, Lrnr_arima, Lrnr_bartMachine, Lrnr_base, Lrnr_bayesglm, Lrnr_bilstm, 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_glmnet, Lrnr_glm, 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_augment, Lrnr_screener_coefs, 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

jeremyrcoyle/sl3 documentation built on Feb. 3, 2022, 9:12 a.m.