cvr.glmnet {ipflasso}

Repeating cv.glmnet

Description

the same as cv.glmnet but with several ncv repetitions of CV: cross-validation repeated ncv times (i.e. for ncv different random partitions)

Usage

cvr.glmnet(X, Y, family, standardize=TRUE,alpha=1, nfolds, ncv, type.measure,...)

Arguments

X

a (nxp) matrix of predictors with observations in rows and predictors in columns

Y

n-vector giving the value of the response (either continuous, numeric-binary 0/1, or Surv object)

family

should be "gaussian" for continuous Y, "binomial" for binary Y, "cox" for Y of type Surv

standardize

whether the predictors should be standardized or not. Default is TRUE.

alpha

the elastic net mixing parameter: alpha=1 yields the L1 penalty (lasso), alpha=0 yields the L2 penalty. Default is alpha=1 (lasso).

nfolds

the number of folds of CV procedure.

ncv

the number of repetitions of CV. Not to be confused with nfolds. For example, if one repeats 50 times 5-fold-CV (i.e. considers 50 random partitions into 5 folds in turn and averages the results), nfolds equals 5 and ncv equals 50.

type.measure

The accuracy/error measure computed in cross-validation. If not specified, type.measure is "class" (classification error) if family="binomial", "mse" (mean squared error) if family="gaussian" and partial likelihood if family="cox". If family="binomial", one may specify type.measure="auc" (area under the ROC curve).

...

Other arguments to be passed to the function cv.glmnet.

Value

A list with the following arguments:

coeff

the matrix of coefficients with predictors corresponding to rows and lambda values corresponding to columns. The first rows contains the intercept of the model (for all families other than "cox").

lambda

the lambda sequence

cvm

the CV estimate of the measure specified by type.measure for each candidate lambda value, averaged over the ncv runs of cv.glmnet

Author(s)

Anne-Laure Boulesteix (https://www.en.ibe.med.uni-muenchen.de/mitarbeiter/professoren/boulesteix/index.html)

References

Boulesteix AL, De Bin R, Jiang X, Fuchs M, 2017. IPF-lasso: integrative L1-penalized regression with penalty factors for prediction based on multi-omics data. Comput Math Methods Med 2017:7691937.

Examples

# load ipflasso library
library(ipflasso)

# generate dummy data
X<-matrix(rnorm(50*200),50,200)
Y<-rbinom(50,1,0.5)

cvr.glmnet(X=X,Y=Y,family="binomial",standardize=FALSE,nfolds=5,ncv=10,type.measure="class")