cv.PCLasso: Cross-validation for 'PCLasso'
In PCLassoReg: Group Regression Models for Risk Protein Complex Identification

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"),
  nfolds = 5,
  standardize = TRUE,
  ...
)

`x`	A n x p design matrix of gene/protein expression measurements with n samples and p genes/proteins, 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/protein) names in `group` should be consistent with the feature (gene/protein) 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 censored samples are balanced across the folds. 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".
`complexes.dt`	Complexes with features (genes/proteins) 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. Brief Bioinform, 2021.

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(survivalData)
data(PCGroups)

x = survivalData$Exp
y = survivalData$survData

PC.Human <- getPCGroups(Groups = PCGroups, Organism = "Human",
Type = "EntrezID")

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