Lrnr_glm: Generalized Linear Models

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

Generalized Linear Models

Description

This learner provides fitting procedures for generalized linear models using the stats package glm.fit function.

Format

An R6Class object inheriting from Lrnr_base.

Value

A learner object inheriting from Lrnr_base with methods for training and prediction. For a full list of learner functionality, see the complete documentation of Lrnr_base.

Parameters

• intercept = TRUE: Should an intercept be included in the model?

• ...: Other parameters passed to glm or glm.fit.

See Also

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_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_augment, 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

data(cpp_imputed)
covs <- c("apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs")
task <- sl3_Task$new(cpp_imputed, covariates = covs, outcome = "haz")

# simple, main-terms GLM
lrnr_glm <- make_learner(Lrnr_glm)
glm_fit <- lrnr_glm$train(task)
glm_preds <- glm_fit$predict()

# We can include interaction terms by 'piping' them into this learner.
# Note that both main terms and the specified interactions will be included
# in the regression model.
interaction <- list(c("apgar1", "parity"))
lrnr_interaction <- Lrnr_define_interactions$new(interactions = interaction)
lrnr_glm_w_interaction <- make_learner(Pipeline, lrnr_interaction, lrnr_glm)
fit <- lrnr_glm_w_interaction$train(task)
coefs <- coef(fit$learner_fits$Lrnr_glm_TRUE)

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