cv.grid: Cross-validation runs for risk predition for a grid of...
In ePCR: Ensemble Penalized Cox Regression for Survival Prediction

Description Usage Arguments Value Examples

Expanded Cross-Validation function to run the whole CV in the lambda/alpha grid instead of just lambda-sequence with a pre-specified alpha

1 2	cv.grid(alphaseq = seq(from = 0, to = 1, by = 0.1), seed, x, y, folds = 10, nlamb = 100, verb = 0, scorefunc, plot = FALSE)

`alphaseq`	Sequence of alpha values to test, which should be within [0,1] (with alpha = 0 being ridge regression, 0 < alpha < 1 being elastic net, and alpha = 1 being LASSO)
`seed`	Random number generation seed for reproducibility
`x`	Data matrix x
`y`	The Surv-object response y
`folds`	Number of folds in the cross-validation
`nlamb`	Number of lambda values to test in each alpha; notice that these lambda values vary conditional to alpha
`verb`	Level of verbosity, with 0 as silent and 1 with additional output
`scorefunc`	Chosen scoring function, e.g. score.cindex or score.iAUC
`plot`	Whether a performance should be plotted at each varying alpha-value similar to cv.alpha-plots

List of matrices of cross-validation performance values over the alpha/lambda grid for mean/median/min/max/stdev of the chosen performance metric, with rows indicating various alpha-values and columns indicating lambda-values.

data(TYKSSIMU)
library(survival)
ydat <- Surv(event = yMEDISIMU[,"DEATH"], time = yMEDISIMU[,"LKADT_P"])
cvs <- cv.grid(x = xMEDISIMU, y = ydat, folds = 3, nlamb = 30, alphaseq = seq(0, 1, by=5), 
	scorefunc = score.iAUC, plot = TRUE, seed = 1)
cvs