sparsenet (version 1.6)

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

Value

The object returned depends the ... argument which is passed on to the predict method for sparsenet objects.

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.

Author

Rahul Mazumder, Jerome Friedman and Trevor Hastie

Maintainer: Trevor Hastie <[e-mail]>

Details

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

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)