cv.PCLasso: Cross-validation for 'PCLasso'
In weiliu123/PCLassoCox: A protein complex-based group lasso-Cox model for accurate prognosis and risk protein complex discovery

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

Perform k-fold cross validations for the PCLasso model with grouped covariates over a grid of values for the regularization parameter lambda.

cv.PCLasso(
  x,
  y,
  group,
  penalty = c("grLasso", "grMCP", "grSCAD", "gel", "cMCP"),
  nfolds = 5,
  standardize = TRUE,
  ...
)

`x`	A n x p design matrix of gene expression measurements with n samples and p genes, as in `PCLasso`.
`y`	The time-to-event outcome, as a two-column matrix or `Surv` object, as in `PCLasso`. The first column should be time on study (follow up time); the second column should be a binary variable with 1 indicating that the event has occurred and 0 indicating (right) censoring.
`group`	A list of groups as in `PCLasso`. The feature (gene) names in `group` should be consistent with the feature (gene) names in `x`.
`penalty`	The penalty to be applied to the model. For group selection, one of grLasso, grMCP, or grSCAD. For bi-level selection, one of gel or cMCP. See `grpsurv` in the R package `grpreg` for details.
`nfolds`	The number of cross-validation folds. Default is 5.
`standardize`	Logical flag for `x` standardization, prior to fitting the model. Default is `TRUE`.
`...`	Arguments to be passed to `cv.grpsurv` in the R package `grpreg`.

The function calls PCLasso nfolds times, each time leaving out 1/nfolds of the data. The cross-validation error is based on the deviance. The numbers for each outcome class are balanced across the folds; i.e., the number of outcomes in which y is equal to 1 is the same for each fold, or possibly off by 1 if the numbers do not divide evenly. cv.PCLasso uses the approach of calculating the full Cox partial likelihood using the cross-validated set of linear predictors. See cv.grpsurv in the R package grpreg for details.

An object with S3 class "cv.PCLasso" containing:

`cv.fit`	An object of class "cv.grpsurv".
`group.dt`	Groups with features (genes) not included in `x` being filtered out.

Wei Liu

PCLasso: a protein complex-based group lasso-Cox model for accurate prognosis and risk protein complex discovery. To be published.

Park, H., Niida, A., Miyano, S. and Imoto, S. (2015) Sparse overlapping group lasso for integrative multi-omics analysis. Journal of computational biology: a journal of computational molecular cell biology, 22, 73-84.

predict.cv.PCLasso

# load data
data(GBM)
data(PCGroup)

x = GBM$GBM.train$Exp
y = GBM$GBM.train$survData

# fit model
cv.fit1 <- cv.PCLasso(x, y, group = PCGroup, penalty = "grLasso",
nfolds = 10)