Lrnr_gam: GAM: Generalized Additive Models
| Lrnr_gam | R Documentation |
GAM: Generalized Additive Models
Description
This learner provides fitting procedures for generalized additive models,
using the routines from mgcv through a call to the function
gam. The mgcv package and the use of GAMs are
described thoroughly (with examples) in \insertCitemgcv;textualsl3,
while \insertCitehastie-gams;textualsl3 also provided an earlier quite
thorough look at GAMs.
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
-
formula: An optional argument specifying the formula of GAM. Input type can be formula or string, or a list of them. If not specified, continuous covariates will be smoothened with the smooth terms represented using "penalized thin plate regression splines". For a more detailed description, please consult the documentation forgam. -
family: An optional argument specifying the family of the GAM. Seefamilyandfamily.mgcvfor a list of available family functions. If left unspecified, it will be inferred depending on the detected type of the outcome. For now, GAM supports binomial and gaussian outcome types, ifformulais unspecified. For a more detailed description of this argument, please consult the documentation ofgam. -
method: An optional argument specifying the method for smoothing parameter selection. The default is global cross-validation (GCV). For more detaileds on this argument, consult the documentation ofgam. -
...: Other parameters passed togam. See its documentation for details.
References
\insertAllCitedSee 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_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_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)
# create task for prediction
cpp_task <- sl3_Task$new(
data = cpp_imputed,
covariates = c("bmi", "parity", "mage", "sexn"),
outcome = "haz"
)
# initialization, training, and prediction with the defaults
gam_lrnr <- Lrnr_gam$new()
gam_fit <- gam_lrnr$train(cpp_task)
gam_preds <- gam_fit$predict()
