Flexible Co-Data Learning for High-Dimensional Prediction

Fit linear, logistic and Cox survival regression models penalised with adaptive multi-group ridge penalties. The multi-group penalties correspond to groups of covariates defined by (multiple) co-data sources. Group hyperparameters are estimated with an empirical Bayes method of moments, penalised with an extra level of hyper shrinkage. Various types of hyper shrinkage may be used for various co-data. The method accommodates inclusion of unpenalised covariates, posterior selection of covariates and multiple data types. The model fit is used to predict for new samples. The name 'ecpc' stands for Empirical Bayes, Co-data learnt, Prediction and Covariate selection. See Van Nee et al. (2020) .


Reference manual

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2.0 by Mirrelijn M. van Nee, 15 days ago


Browse source code at https://github.com/cran/ecpc

Authors: Mirrelijn M. van Nee [aut, cre] , Lodewyk F.A. Wessels [aut] , Mark A. van de Wiel [aut]

Documentation:   PDF Manual  

GPL (>= 3) license

Imports glmnet, stats, Matrix, gglasso, mvtnorm, CVXR, multiridge, survival, pROC

Suggests Rsolnp, expm, mgcv, foreach, doParallel, parallel, ggplot2, ggraph, igraph, scales, dplyr, magrittr

See at CRAN