Use of the score test as a goodness-of-fit measure of the covariance structure in genetic analysis of longitudinal data

Genetics Selection Evolution

Robin Thompson¹  Ian MS White³  Florence Jaffrézic² 

[1] Rothamsted Experimental Station, IACR, Harpenden, Herts AL5 2JQ, UK and Roslin Institute (Edinburgh), Roslin, Midlothian EH25 9PS, UK;Station de génétique quantitative et appliquée, Institut national de la recherche agronomique, 78352 Jouy-en-Josas Cedex, France;Institute of Cell Animal and Population Biology, University of Edinburgh, West Mains Rd., Edinburgh EH9 3JT, UK

关键词: covariance structure;    goodness-of-fit measure;    score test;    genetic longitudinal data analysis;   

Others  :  1094465
DOI  :  10.1186/1297-9686-35-2-185

 received in 2002-05-13, accepted in 2002-08-07,  发布年份 2003

【 摘 要 】

Model selection is an essential issue in longitudinal data analysis since many different models have been proposed to fit the covariance structure. The likelihood criterion is commonly used and allows to compare the fit of alternative models. Its value does not reflect, however, the potential improvement that can still be reached in fitting the data unless a reference model with the actual covariance structure is available. The score test approach does not require the knowledge of a reference model, and the score statistic has a meaningful interpretation in itself as a goodness-of-fit measure. The aim of this paper was to show how the score statistic may be separated into the genetic and environmental parts, which is difficult with the likelihood criterion, and how it can be used to check parametric assumptions made on variance and correlation parameters. Selection of models for genetic analysis was applied to a dairy cattle example for milk production.

【 授权许可】

   
2003 INRA, EDP Sciences