ASReml-S reference manual - VSN International

PrefaceASReml-S fits the linear mixed model using Residual Maximum Likelihood (REML) andis a joint venture between the Queensland Department of Primary Industries & Fisheries(QDPI&F) and the Biometrics Program of the NSW Department of Primary Industries.The ASReml-S engine uses the numerical routines from the program ASReml TM [Gilmouret al., 2002], under joint development through the NSW Department of Primary Indistriesand IACR-Rothamsted. This guide describes Version 2.00 of ASReml-S, released inFebruary 2007.Linear mixed effects models provide a rich and flexible tool for the analysis of manydatasets commonly arising in the agricultural, biological, medical and environmentalsciences. Typical applications include the analysis of balanced and unbalanced longitudinaldata, repeated measures, balanced and unbalanced designed experiments, multienvironmenttrials, multivariate datasets and regular or irregular spatial data.This reference manual documents the features of the methods for objects of class asreml.It does not consider the statistical issues involved in fitting models. The authors arecontributing to the preparation of other documents that are focused on the statisticalissues rather than the computing issues. ASReml-S requires that a dynamic link library(Microsoft Windows TM ) or shared object file (Linux) containing the numerical methodsbe loaded at runtime.One of the strengths of ASReml-S is the flexible syntax for specifying the variance modelsfor the random effects in the linear mixed model and the scope this offers the user.There is a potential cost for this complexity. Users should be aware of the dangers ofeither overfitting or attempting to fit inappropriate variance models to small or highlyunbalanced data sets. We stress the importance of the use of data driven diagnostics andencourage the user to read the examples chapter, in which we have attempted to notonly present the syntax of ASReml-S in the context of real analyses but also to indicatesome of the modelling approaches we have found useful.Another strength is that the REML routines use the Average Information (AI) algorithmand sparse matrix methods for fitting the linear mixed model. This enables ASReml-S toefficiently analyse large and complex datasets.This manual consists of nine chapters. Chapter 1 introduces ASReml-S and describes theconventions used throughout the manual and describes the various data sets used forillustration; Chapter 2 presents an general overview of basic theory; Chapter 3 presentsan introduction to fitting models in ASReml-S followed by a more detailed descriptionof fitting the linear mixed model; Chapter 4 is a key chapter that presents the syntaxfor specifying variance models for random effects in the model; Chapter 3.15 describesthe model specification for a multivariate analyses; Chapter 5 describes special functionsand methods for genetic analyses; Chapter 6 outlines the prediction of linear functions