EN_predict: Predictive Modeling using Elastic Net
In DMTL: Tools for Applying Distribution Mapping Based Transfer Learning

Description Usage Arguments Value Note Examples

This function trains a Elastic Net regressor using the training data provided and predict response for the test features. This implementation depends on the glmnet package.

EN_predict(
  x_train,
  y_train,
  x_test,
  lims,
  optimize = FALSE,
  alpha = 0.8,
  seed = NULL,
  verbose = FALSE,
  parallel = FALSE
)

`x_train`	Training features for designing the EN regressor.
`y_train`	Training response for designing the EN regressor.
`x_test`	Test features for which response values are to be predicted. If `x_test` is not given, the function will return the trained model.
`lims`	Vector providing the range of the response values for modeling. If missing, these values are estimated from the training response.
`optimize`	Flag for model tuning. If `TRUE`, performs a grid search for parameters. If `FALSE`, uses the parameters provided. Defaults to `FALSE`.
`alpha`	EN mixing parameter with 0 ≤ α ≤ 1. `alpha = 1` is the lasso penalty, and `alpha = 0` the ridge penalty. Defaults to `0.8`. Valid only when `optimize = FALSE`.
`seed`	Seed for random number generator (for reproducible outcomes). Defaults to `NULL`.
`verbose`	Flag for printing the tuning progress when `optimize = TRUE`. Defaults to `FALSE`.
`parallel`	Flag for allowing parallel processing when performing grid search i.e., `optimimze = TRUE`. Defaults to `FALSE`.

If x_test is missing, the trained EN regressor.

If x_test is provided, the predicted values using the model.

The response values are filtered to be bound by range in lims.

set.seed(86420)
x <- matrix(rnorm(3000, 0.2, 1.2), ncol = 3);    colnames(x) <- paste0("x", 1:3)
y <- 0.3*x[, 1] + 0.1*x[, 2] - x[, 3] + rnorm(1000, 0, 0.05)

## Get the model only...
model <- EN_predict(x_train = x[1:800, ], y_train = y[1:800], alpha = 0.6)

## Get predictive performance...
y_pred <- EN_predict(x_train = x[1:800, ], y_train = y[1:800], x_test = x[801:1000, ])
y_test <- y[801:1000]
print(performance(y_test, y_pred, measures = "RSQ"))