globalboosttest: Testing the additional predictive value of high-dimensional data

'globalboosttest' implements a permutation-based testing procedure to globally test the (additional) predictive value of a large set of predictors given that a small set of predictors is already available. Currently, 'globalboosttest' supports binary outcomes (via logistic regression) and survival outcomes (via Cox regression). It is based on boosting regression as implemented in the package 'mboost'.

Author
Anne-Laure Boulesteix <boulesteix@ibe.med.uni-muenchen.de>, Torsten Hothorn <torsten.hothorn@stat.uni-muenchen.de>.
Date of publication
2012-10-29 08:58:54
Maintainer
Anne-Laure Boulesteix <boulesteix@ibe.med.uni-muenchen.de>
License
GPL (>= 2)
Version
1.1-0
URLs

View on CRAN

Man pages

globalboosttest
Testing the additional predictive value of high-dimensional...
globalboosttest-internal
Internal globalboosttest Functions
simdatabin
Simulated data with binary outcome
simdatasurv
Simulated data with survival outcome

Files in this package

globalboosttest
globalboosttest/data
globalboosttest/data/simdatabin.RData
globalboosttest/data/simdatasurv.RData
globalboosttest/NAMESPACE
globalboosttest/man
globalboosttest/man/simdatabin.Rd
globalboosttest/man/globalboosttest.Rd
globalboosttest/man/globalboosttest-internal.Rd
globalboosttest/man/simdatasurv.Rd
globalboosttest/DESCRIPTION
globalboosttest/MD5
globalboosttest/R
globalboosttest/R/globalboosttest.r