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grpregOverlap (version 2.2-0)

cv.grpsurvOverlap: Cross-validation for choosing regularization parameter lambda for Cox models.

Description

Performs k-fold cross validation for penalized regression models with overlapping grouped covariates over a grid of values for the regularization parameter lambda.

Usage

cv.grpsurvOverlap(X, y, group, ..., nfolds = 10, seed, cv.ind,  returnY = FALSE, trace = FALSE)

Arguments

X
The design matrix, without an intercept, as in grpregOverlap.
y
The time-to-event outcome matrix for survival analysis, as explained in grpregOverlap.
group
A list of vectors containing group information, as in grpregOverlap.
...
Additional arguments to grpregOverlap.
nfolds
The number of cross-validation folds. Default is 10.
seed
Set the seed of the random number generator to obtain reproducible results.
cv.ind
User specified indices of which fold each observation belongs to. By default the observations are randomly assigned.
returnY
Should the linear predictors from the cross-validation folds be returned? Default is FALSE; if TRUE, this will return a matrix in which the element for row i, column j is the fitted value for observation i from the fold in which observation i was excluded from the fit, at the jth value of lambda. See details in cv.grpsurv
trace
If set to TRUE, print out the progress of the cross-validation. Default is FALSE.

Value

An object with S3 class "cv.grpsurvOverlap", which inherits from class "cv.grpregOverlap" and "cv.grpsurv". The following variables are contained in the class (adopted from cv.grpsurv).

Details

This function is built upon cv.grpsurv. The plot, summary, and predict functions are also supported. See details about the cross-validation approach for fitting survival models in cv.grpsurv.

References

See Also

grpregOverlap, predict.grpregOverlap, summary, and cv.grpreg.