WOMBAT is a program to facilitate analyses fitting a linear, mixed model via restricted maximum likelihood (REML). It is assumed that traits analysed are continuous and have a multivariate normal distribution.
WOMBAT is set up with quantitative genetic analyses in mind, but is readily applicable in other areas. Its main purpose is the estimation of (co)variance components and the resulting genetic parameters. It is particularly suited to analyses of moderately large to large data sets from livestock improvement programmes, fitting relatively simple models. It can, however, also be used as simple generalised least-squares program, to obtain estimates of fixed and predictions (BLUP) of random effects. In addition, it provides the facilities to simulate data for a given data and pedigree structure, invert a matrix or combine estimates from different analyses.
WOMBAT consists of a single program. All information on the model of analysis, input files and their layout, and (starting) values for (co)variance components is specified in a parameter file. A large number of run options are available to choose between (partial) analyses steps, REML algorithms to locate the maximum of the likelihood function, strategies to re-order the mixed model equations, and parameterisations of the model.
WOMBAT offers few features related to data editing, such as selection of subsets of records, transformation of variables or tabulation of data features. There are a number of general statistical packages to choose from which perform these tasks admirably, including free software such as the R package .
This document is a manual with instructions how to use WOMBAT. It does not endeavour to explain restricted maximum likelihood estimation and related issues, such as approximation of sampling errors, likelihood ratio test, use of information criteria, or how to assess significance of fixed effects.
Throughout the manual, it is assumed that users have a thorough knowledge of mixed linear models and a sound understanding of maximum likelihood inference in general. Numerous textbooks covering these topics are available. To get the best from WOMBAT, users should also be familiar with some of the technical aspects of REML estimation, in particular properties of various algorithms to maximise the likelihood, ordering strategies and parameterisations – otherwise many of the (advanced) options provided will not make a great deal of sense.