penAFT.cva.default: Elastic net cross-validation of alpha and lambda for penAFT...

In agalecki/utilsag: Datasets and utility functions.

Elastic net cross-validation of alpha and lambda for penAFT model

Description

Do elastic net cross-validation of alpha and lambda simultaneously for penAFT models by calling penAFT::penAFT.cv() function

Usage

## Default S3 method:
penAFT.cva(
  x,
  logY,
  delta,
  alpha = seq(0, 1, len = 11)^3,
  nfolds = 10,
  seed = NULL,
  ...,
  outerParallel = NULL,
  checkInnerParallel = TRUE
)

Arguments

x	A matrix of predictor variables; or for the plotting methods, an object returned by penAFT.cva.
logY	A response vector ( with n-elements) of log-survival or log-censoring times.
delta	A binary vector indicating whether the jth element of logY is an observed log-survival time (d_j = 1) or a log-censoring time (d_j = 0) for j=1,..., n.
alpha	A vector of alpha values for which to do cross-validation. The default is a sequence of 11 values more closely spaced around alpha = 0. For the predict and coef methods, the specific value of alpha for which to return predictions/regression coefficients.
nfolds	The number of cross-validation folds to use. Defaults to 10.
seed	Seed value. It is recommended to provide seed value. As a result the same folds are used for every value of vector alpha. Defualts to NULL.
outerParallel	Method of parallelising the outer loop over alpha. See 'Details' below. If NULL, the loop is run sequentially.
checkInnerParallel	If the outer loop is run in parallel, check that the inner loop over lambda will not be in contention for cores.

Details

The penAFT.cva function does simultaneous cross-validation for both the alpha and lambda parameters in an elastic net model. The procedure is as outlined in the documentation for glmnet::cv.glmnet: it creates a vector foldid allocating the observations into folds, and then calls penAFT.cv in a loop over different values of alpha, but the same values of foldid each time.

Optionally this loop over alpha can be parallelised; currently, penAFT.cva knows about two methods of doing so:

• Via parLapply in the parallel package. To use this, set outerParallel to a valid cluster object created by makeCluster.

• Via rxExec as supplied by Microsoft R Server's RevoScaleR package. To use this, set outerParallel to a valid compute context created by RxComputeContext, or a character string specifying such a context.

If the outer loop is run in parallel, penAFT.cva can check if the inner loop (over lambda) is also set to run in parallel, and disable this if it would lead to contention for cores. This is done if it is likely that the parallelisation is local on a multicore machine, ie if outerParallel is a SOCKcluster object running on "localhost", or if the RevoScaleR compute context is local parallel.

Examples

## Not run: 
dtc <- proteins21
X  <- as.matrix(dtc[, 3:26])
logY  <- log(dtc[, "time"]) 
delta <- dtc[, "status"]   # status=0 indicates censored observation 
fit.en.cva <- penAFT.cva(x=X, logY = logY, delta = delta,
              alpha = seq(0, 1, len = 11)^3,
              nlambda = 50, lambda.ratio.min = 0.1, lambda = NULL,
              penalty = "EN", nfolds = 5, seed = 1234)

## End(Not run)

agalecki/utilsag documentation built on Jan. 25, 2023, 5:36 a.m.