Statistical predictions with glmnet

被引：619

作者：

Engebretsen, Solveig ^{[1
,2
]}

Bohlin, Jon ^{[1
,3
,4
]}

机构：

[1] Norwegian Inst Publ Hlth, Dept Infect Dis Epidemiol & Modelling, Div Infect Control & Environm Hlth, Oslo, Norway

[2] Univ Oslo, Dept Biostat, Oslo Ctr Biostat & Epidemiol, Oslo, Norway

[3] Norwegian Inst Publ Hlth, Ctr Fertil & Hlth CEFH, Oslo, Norway

[4] Norwegian Univ Life Sci, Dept Prod Anim, Fac Vet Sci, As, Norway

来源：

CLINICAL EPIGENETICS | 2019年 / 11卷 / 01期

关键词：

Elastic net; glmnet package; Statistical prediction; Ultra-high dimensional regression; VARIABLE SELECTION; REGRESSION; REGULARIZATION; LASSO;

D O I：

10.1186/s13148-019-0730-1

中图分类号：

R73 [肿瘤学];

学科分类号：

100214 ;

摘要：

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.

引用

页数：3

共 23 条

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