summary.cv.glmnetr {glmnetr}

Output summary of a cv.glmnetr() output object.

Description

Summarize the cross-validation informed model fit. The fully penalized (gamma=1) beta estimate will not be given by default but can too be output using printg1=TRUE.

Usage

## S3 method for class 'cv.glmnetr'
summary(object, printg1 = "FALSE", orderall = FALSE, ...)

Arguments

object

a cv.glmnetr() output object.

printg1

TRUE to also print out the fully penalized lasso beta, else FALSE to suppress.

orderall

By default (orderall=FALSE) the order terms enter into the lasso model is given for the number of terms that enter in lasso minimizing loss model. If orderall=TRUE then all terms that are included in any lasso fit are described.

...

Additional arguments passed to the summary function.

Value

Coefficient estimates (beta)

See Also

predict.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=100, 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) 
summary(cv.glmnetr.fit)