Statistical predictions with glmnet

Elastic net type regression methods have become very popular for prediction of certain outcomes in epigenome-wide association studies (EWAS). The methods considered accept biased coefficient estimates in return for lower variance thus obtaining improved prediction accuracy. We provide guidelines on...

Full description

Saved in:

Bibliographic Details
Published in:	Clinical epigenetics Vol. 11; no. 1; p. 123
Main Authors:	Engebretsen, Solveig, Bohlin, Jon
Format:	Journal Article
Language:	English
Published:	Germany BioMed Central Ltd 23-08-2019 BioMed Central
Subjects:	Analysis Bias Economic models Epidemiology Epigenetic inheritance Letter to the Editor Methods Public health Resveratrol Statistical prediction Norway Statistical prediction Elastic net glmnet package Ultra-high dimensional regression
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	Elastic net type regression methods have become very popular for prediction of certain outcomes in epigenome-wide association studies (EWAS). The methods considered accept biased coefficient estimates in return for lower variance thus obtaining improved prediction accuracy. We provide guidelines on how to obtain parsimonious models with low mean squared error and include easy to follow walk-through examples for each step in R.
Bibliography:	NFR/262700
ISSN:	1868-7075 1868-7083 1868-7083 1868-7075
DOI:	10.1186/s13148-019-0730-1