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

In sparsenet: Fit Sparse Linear Regression Models via Nonconvex Optimization

make predictions from a "cv.sparsenet" object.

Description

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

Usage

## S3 method for class 'cv.sparsenet'
predict(object, newx, which=c("parms.min","parms.1se"),...)
## S3 method for class 'cv.sparsenet'
coef(object, which=c("parms.min","parms.1se"),...)

Arguments

object	Fitted "cv.sparsenet" object.
newx	Matrix of new values for x at which predictions are to be made. Must be a matrix. See documentation for predict.sparsenet.
which	Either the paramaters of the minimum of the CV curves (default "parms.min" or the parameters corresponding to the one standard-error rule parms.1se)
...	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 the ... argument which is passed on to the predict method for sparsenet objects.

Author(s)

Rahul Mazumder, Jerome Friedman and Trevor Hastie

Maintainer: Trevor Hastie <[e-mail]>

References

Mazumder, Rahul, Friedman, Jerome and Hastie, Trevor (2011) SparseNet: Coordinate Descent with Nonconvex Penalties. JASA, Vol 106(495), 1125-38, https://hastie.su.domains/public/Papers/Sparsenet/Mazumder-SparseNetCoordinateDescent-2011.pdf

See Also

glmnet package, sparsenet, cv.sparsenet and print and plot methods for both.

Examples

x=matrix(rnorm(100*20),100,20)
y=rnorm(100)
fitcv=cv.sparsenet(x,y)
predict(fitcv,x)

sparsenet documentation built on Aug. 22, 2023, 9:11 a.m.