ASReml estimates variance components under a general linear mixed model by residual maximum likelihood (REML)

ASReml estimates variance components under a general linear mixed model by residual maximum likelihood (REML) The authors gratefully acknowledge the Grains Research and Development Corporation of Australia for their financial support. We thank the Qld Department of Primary Industries & Fisheries and the NSW Department of Primary Industries for permitting this research to be undertaken and for providing a stimulating environment for applied biometrical consulting and research. We also sincerely thank The Department of Primary Industries and Fisheries (DPI&F) seeks to maximise the economic potential of Queensland's primary industries on a sustainable basis. Except as permitted by the Copyright Act 1968, no part of the work may in any form or by any electronic, mechanical, photocopying, recording, or any other means be reproduced, stored in a retrieval system or be broadcast or transmitted without the prior written permission of DPI&F. The information contained herein is subject to change without notice. The copyright owner shall not be liable for technical or other errors or omissions contained herein. The reader/user accepts all risks and responsibility for losses, damages, costs and other consequences resulting directly or indirectly from using this information. Enquiries about reproduction, including downloading or printing the web version, should be directed to [email protected] or telephone +61 7 3225 1398. Preface ASReml-R fits the linear mixed model using Residual Maximum Likelihood (REML) and is a joint venture between the Queensland Department of Primary Industries & Fisheries (QDPI&F) and the Biometrics Program of the NSW Department of Primary Industries. ASReml-R uses the numerical routines from the standalone program ASReml TM [Gilmour et al., 2002], under joint development through the NSW Linear mixed effects models provide a rich and flexible tool for the analysis of many datasets commonly arising in the agricultural, biological, medical and environmental sciences. Typical applications include the analysis of balanced and unbalanced longitudinal data, repeated measures, balanced and unbalanced designed experiments, multi-environment trials, multivariate datasets and regular or irregular spatial data. This reference manual documents the features of the methods for objects of class asreml. Outside of the worked examples, it does not consider the statistical issues involved in fitting models. The authors are contributing to the preparation of other documents that are focused on the statistical issues rather than the computing issues. ASReml-R requires that a dynamic link library (Microsoft Windows TM) or shared object file (Linux) containing the numerical methods be loaded at runtime. The features of …