cv.grpsurvOverlap: Cross-validation for choosing regularization parameter lambda...
In grpregOverlap: Penalized Regression Models with Overlapping Grouped Covariates

Description Usage Arguments Details Value Author(s) References See Also

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

1 2	cv.grpsurvOverlap(X, y, group, ..., nfolds = 10, seed, cv.ind, returnY = FALSE, trace = FALSE)

`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.

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.

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`.