ipflasso: Integrative Lasso with Penalty Factors
Version 0.1

The core of the package is cvr2.ipflasso(), an extension of glmnet to be used when the (large) set of available predictors is partitioned into several modalities which potentially differ with respect to their information content in terms of prediction. For example, in biomedical applications patient outcome such as survival time or response to therapy may have to be predicted based on, say, mRNA data, miRNA data, methylation data, CNV data, clinical data, etc. The clinical predictors are on average often much more important for outcome prediction than the mRNA data. The ipflasso method takes this problem into account by using different penalty parameters for predictors from different modalities. The ratio between the different penalty parameters can be chosen by cross-validation.

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AuthorAnne-Laure Boulesteix, Mathias Fuchs
Date of publication2015-11-24 15:16:28
MaintainerAnne-Laure Boulesteix <boulesteix@ibe.med.uni-muenchen.de>
LicenseGPL
Version0.1
Package repositoryView on CRAN
InstallationInstall the latest version of this package by entering the following in R:
install.packages("ipflasso")

Man pages

cvr2.ipflasso: Cross-validated integrative lasso with cross-validated...
cvr.glmnet: Repeating cv.glmnet
cvr.ipflasso: Cross-validated integrative lasso with fixed penalty factors
ipflasso.predict: Using an IPF-lasso model for prediction of new observations
my.auc: Area under the curve (AUC)

Functions

cvr.glmnet Man page Source code
cvr.ipflasso Man page Source code
cvr2.ipflasso Man page Source code
ipflasso.predict Man page Source code
my.auc Man page Source code

Files

NAMESPACE
R
R/cvr.ipflasso.r
R/ipflasso.predict.r
R/my.auc.R
R/cvr.glmnet.R
R/cvr2.ipflasso.r
MD5
DESCRIPTION
man
man/cvr2.ipflasso.Rd
man/ipflasso.predict.Rd
man/cvr.ipflasso.Rd
man/my.auc.Rd
man/cvr.glmnet.Rd
ipflasso documentation built on May 20, 2017, 2:18 a.m.