.fit.enet {tidyfit}

ElasticNet regression or classification for tidyfit

Description

Fits an ElasticNet regression or classification on a 'tidyFit' R6 class. The function can be used with regress and classify.

Usage

## S3 method for class 'enet'
.fit(self, data = NULL)

Arguments

self

a 'tidyFit' R6 class.

data

a data frame, data frame extension (e.g. a tibble), or a lazy data frame (e.g. from dbplyr or dtplyr).

Details

Hyperparameters:

• lambda (penalty)

• alpha (L1-L2 mixing parameter)

Important method arguments (passed to m)

The ElasticNet regression is estimated using glmnet::glmnet. See ?glmnet for more details. For classification pass family = "binomial" to ... in m or use classify.

Implementation

If the response variable contains more than 2 classes, a multinomial response is used automatically.

An intercept is always included and features are standardized with coefficients transformed to the original scale.

If no hyperparameter grid is passed (is.null(control$lambda) and is.null(control$alpha)), dials::grid_regular() is used to determine a sensible default grid. The grid size is 100 for lambda and 5 for alpha. Note that the grid selection tools provided by glmnet::glmnet cannot be used (e.g. dfmax). This is to guarantee identical grids across groups in the tibble.

Value

A fitted 'tidyFit' class model.

Author(s)

Johann Pfitzinger

References

Jerome Friedman, Trevor Hastie, Robert Tibshirani (2010). Regularization Paths for Generalized Linear Models via Coordinate Descent. Journal of Statistical Software, 33(1), 1-22. URL https://www.jstatsoft.org/v33/i01/.

See Also

.fit.lasso, .fit.adalasso, .fit.ridge and m methods

Examples

# Load data
data <- tidyfit::Factor_Industry_Returns

# Stand-alone function
fit <- m("enet", Return ~ ., data, lambda = c(0, 0.1), alpha = 0.5)
fit

# Within 'regress' function
fit <- regress(data, Return ~ ., m("enet", alpha = c(0, 0.5), lambda = c(0.1)),
               .mask = c("Date", "Industry"), .cv = "vfold_cv")
coef(fit)