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

In YaohuiZeng/grpregOverlap: Penalized Regression Models with Overlapping Grouped Covariates

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, ...)

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 either grpregOverlap or 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.

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).

cve	The error for each value of lambda, averaged across the cross-validation folds.
lambda	The sequence of regularization parameter values along which the cross-validation error was calculated.
fit	The fitted grpreg object for the whole data.
min	The index of lambda corresponding to lambda.min.
lambda.min	The value of lambda with the minimum cross-validation error.
null.dev	The cross-validated deviance for Cox model with max(lambda). See details in cv.grpsurv.

Author(s)

Yaohui Zeng and Patrick Breheny

Maintainer: Yaohui Zeng <[e-mail]>

References

• Breheny P (2014). R package 'grpreg'. https://CRAN.R-project.org/package=grpreg/grpreg.pdf

See Also

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

YaohuiZeng/grpregOverlap documentation built on Aug. 10, 2020, 3:13 p.m.