ipflasso: Integrative Lasso with Penalty Factors

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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.

Author
Anne-Laure Boulesteix, Mathias Fuchs
Date of publication
2015-11-24 15:16:28
Maintainer
Anne-Laure Boulesteix <boulesteix@ibe.med.uni-muenchen.de>
License
GPL
Version
0.1

View on CRAN

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)

Files in this package

ipflasso
ipflasso/NAMESPACE
ipflasso/R
ipflasso/R/cvr.ipflasso.r
ipflasso/R/ipflasso.predict.r
ipflasso/R/my.auc.R
ipflasso/R/cvr.glmnet.R
ipflasso/R/cvr2.ipflasso.r
ipflasso/MD5
ipflasso/DESCRIPTION
ipflasso/man
ipflasso/man/cvr2.ipflasso.Rd
ipflasso/man/ipflasso.predict.Rd
ipflasso/man/cvr.ipflasso.Rd
ipflasso/man/my.auc.Rd
ipflasso/man/cvr.glmnet.Rd