fit_glmnet_lm {ARTtransfer} | R Documentation |
fit_glmnet_lm: Sparse Linear Regression Wrapper for the ARTtransfer package
Description
This function fits a sparse linear regression model using 'glmnet()' from the R package glmnet for regression. It returns the coefficients, deviance on a validation set, and predictions on a test set. It is designed for use in the 'ART' adaptive and robust transfer learning framework.
Usage
fit_glmnet_lm(
X,
y,
X_val,
y_val,
X_test,
min_prod = 1e-05,
max_prod = 1 - 1e-05,
nfolds = 5,
...
)
Arguments
X |
A matrix of predictors for the training set. |
y |
A vector of responses for the training set. |
X_val |
A matrix of predictors for the validation set. If 'NULL', deviance is not calculated. |
y_val |
A vector of responses for the validation set. If 'NULL', deviance is not calculated. |
X_test |
A matrix of predictors for the test set. If 'NULL', predictions are not generated. |
min_prod |
A numeric value indicating the minimum probability bound for predictions (not used in this function but passed for compatibility). Default is '1e-5'. |
max_prod |
A numeric value indicating the maximum probability bound for predictions (not used in this function but passed for compatibility). Default is '1-1e-5'. |
nfolds |
An integer specifying the number of folds for cross-validation. Default is 5. |
... |
Additional arguments passed to the function. |
Value
A list containing:
dev |
The mean squared error (deviance) on the validation set if provided, otherwise 'NULL'. |
pred |
The predictions on the test set if 'X_test' is provided, otherwise 'NULL'. |
coef |
The fitted coefficients of the sparse linear model. |
Examples
# Fit a sparse linear model with validation and test data
X_train <- matrix(rnorm(100 * 5), 100, 5)
y_train <- X_train %*% rnorm(5) + rnorm(100)
X_val <- matrix(rnorm(50 * 5), 50, 5)
y_val <- X_val %*% rnorm(5) + rnorm(50)
X_test <- matrix(rnorm(20 * 5), 20, 5)
fit <- fit_glmnet_lm(X_train, y_train, X_val, y_val, X_test)