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

In adelie: Group Lasso and Elastic Net Solver for Generalized Linear Models

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 class 'cv.grpnet'
predict(object, newx, lambda = c("lambda.1se", "lambda.min"), ...)

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.
...	Not used. Other arguments to predict.

Details

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

Value

The object returned depends on the arguments.

Author(s)

James Yang, Trevor Hastie, and Balasubramanian Narasimhan
Maintainer: Trevor Hastie [e-mail]

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 \Sexpr[results=rd]{tools:::Rd_expr_doi("10.48550/arXiv.2405.08631")}.

See Also

grpnet, and print, and coef methods, and cv.grpnet.

Examples

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))

adelie documentation built on Sept. 11, 2024, 6:36 p.m.