Ecpc: an R-package for generic co-data models for high-dimensional prediction.

ABSTRACT:

Background

High-dimensional prediction considers data with more variables than samples. Generic research goals are to find the best predictor or to select variables. Results may be improved by exploiting prior information in the form of co-data, providing complementary data not on the samples, but on the variables. We consider adaptive ridge penalised generalised linear and Cox models, in which the variable-specific ridge penalties are adapted to the co-data to give a priori more weight to more important variables. The R-package ecpc originally accommodated various and possibly multiple co-data sources, including categorical co-data, i.e. groups of variables, and continuous co-data. Continuous co-data, however, were handled by adaptive discretisation, potentially inefficiently mode

SUBMITTER: van Nee MM 

PROVIDER: S-EPMC10134536 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature