predict.cv.grpnet: make predictions from a "cv.grpnet" object.

Description

This function makes predictions from a cross-validated grpnet model, using the stored "grpnet.fit" object, and the optimal value chosen for lambda.

Usage

# S3 method for cv.grpnet
predict(object, newx, lambda = c("lambda.1se", "lambda.min"), ...)

Value

The object returned depends on the arguments.

Arguments

object: Fitted "cv.grpnet".
newx: Matrix of new values for x at which predictions are to be made. Can be a matrix, a sparse matrix as in Matrix package, or else any of the matrix forms allowable in the adelie package. This argument is not used for type="coefficients".
lambda: Value(s) of the penalty parameter lambda at which predictions are required. Default is the value lambda="lambda.1se" stored on the CV object. Alternatively lambda="lambda.min" can be used. If lambda is numeric, it is taken as the value(s) of lambda to be used.
...: Other arguments to predict.grpnet, such at type.

Author

James Yang, Trevor Hastie, and Balasubramanian Narasimhan
Maintainer: Trevor Hastie hastie@stanford.edu

Details

This function makes it easier to use the results of cross-validation to make a prediction.

References

Yang, James and Hastie, Trevor. (2024) A Fast and Scalable Pathwise-Solver for Group Lasso and Elastic Net Penalized Regression via Block-Coordinate Descent. arXiv tools:::Rd_expr_doi("10.48550/arXiv.2405.08631").

Examples

Run this code


x = matrix(rnorm(100 * 20), 100, 20)
y = rnorm(100)
cv.fit = cv.grpnet(x, glm.gaussian(y))
predict(cv.fit, newx = x[1:5, ])
coef(cv.fit)
coef(cv.fit, lambda = "lambda.min")
predict(cv.fit, newx = x[1:5, ], lambda = c(0.001, 0.002))

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