predict.cv.glmnetr {glmnetr}

Give predicteds based upon a cv.glmnetr() output object.

Description

Give predicteds based upon a cv.glmnetr() output object. By default lambda and gamma are chosen as the minimizing values for the relaxed lasso model. If gam=1 and lam=NULL then the best unrelaxed lasso model is chosen and if gam=0 and lam=NULL then the best fully relaxed lasso model is selected.

Usage

## S3 method for class 'cv.glmnetr'
predict(object, xs_new = NULL, lam = NULL, gam = NULL, comment = TRUE, ...)

Arguments

object

A cv.glmnetr (or nested.glmnetr) output object.

xs_new

The predictor matrix. If NULL, then betas are provided.

lam

The lambda value for choice of beta. If NULL, then lambda.min is used from the cross validated tuned relaxed model. We use the term lam instead of lambda as lambda usually denotes a vector in the package.

gam

The gamma value for choice of beta. If NULL, then gamma.min is used from the cross validated tuned relaxed model. We use the term gam instead of gamma as gamma usually denotes a vector in the package.

comment

Default of TRUE to write to console information on lam and gam selected for output. FALSE will suppress this write to console.

...

Additional arguments passed to the predict function.

Value

Either predicteds (xs_new*beta estimates based upon the predictor matrix xs_new) or model coefficients, based upon a cv.glmnetr() output object. When outputting coefficients (beta), creates a list with the first element, beta_, including 0 and non-0 terms and the second element, beta, including only non 0 terms.

See Also

summary.cv.glmnetr , cv.glmnetr , nested.glmnetr

Examples

# set seed for random numbers, optionally, to get reproducible results
set.seed(82545037)
sim.data=glmnetr.simdata(nrows=200, ncols=100, beta=NULL)
xs=sim.data$xs 
y_=sim.data$y_ 
event=sim.data$event
# for this example we use a small number for folds_n to shorten run time 
cv.glmnetr.fit = cv.glmnetr(xs, NULL, y_, NULL, family="gaussian", folds_n=3, limit=2) 
predict(cv.glmnetr.fit)