ElasticNet regression or classification for tidyfit

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).

Value

A fitted 'tidyFit' class model.

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.

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/.

Author

Johann Pfitzinger

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
#> # A tibble: 2 × 5
#>   estimator_fct  `size (MB)` grid_id  model_object settings        
#>   <chr>                <dbl> <chr>    <list>       <list>          
#> 1 glmnet::glmnet        3.06 #001|001 <tidyFit>    <tibble [1 × 3]>
#> 2 glmnet::glmnet        3.06 #001|002 <tidyFit>    <tibble [1 × 3]>

# 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)
#> # A tibble: 7 × 4
#> # Groups:   model [1]
#>   model term        estimate model_info      
#>   <chr> <chr>          <dbl> <list>          
#> 1 enet  (Intercept)  0.00557 <tibble [1 × 2]>
#> 2 enet  Mkt-RF       0.953   <tibble [1 × 2]>
#> 3 enet  SMB          0.0232  <tibble [1 × 2]>
#> 4 enet  HML          0.0641  <tibble [1 × 2]>
#> 5 enet  RMW          0.153   <tibble [1 × 2]>
#> 6 enet  CMA          0.0926  <tibble [1 × 2]>
#> 7 enet  RF           0.959   <tibble [1 × 2]>

ElasticNet regression or classification for `tidyfit`