The REML algorithm provides several important types of analysis, that are useful in a wide range of application areas including biology, medicine, industry and finance. In biology they are usually known as linear mixed models, but in some application areas (e.g. education) they may be called multi-level models. Genstat’s REML facilities are powerful and comprehensive, but nevertheless very straightforward and easy to use.
We have a number of tutorial videos that demonstrate how to perform REML in Genstat
This book is designed to introduce you to these techniques, and give you the knowledge and confidence to use them correctly and effectively. One of the key features of REML is that it can analyse data that involve more than one source of error variation. In this respect it is similar to the Genstat ANOVA algorithm, and the similarities and differences between the two methods are explored in detail in Chapter 1. An important advantage of REML over ANOVA is that it can analyse unbalanced designs. It also has a powerful prediction algorithm that extends the ideas in Genstat’s regression prediction algorithm to cover random as well as fixed effects.
Chapter 2 covers the use of REML for meta analysis, showing how you can do a simultaneous analysis of several disparate data sets to obtain combined estimates for the treatments of interest. A further advantage of REML is explored in Chapter 3, where we show how it can model spatial correlations between observations in two-dimensions. These methods have proved very successful, for example in the analysis of field experiments to assess new plant varieties. The designs often contain too many varieties for the conventional blocking techniques (e.g. the use of randomized-block designs) to be effective. So instead, for example, auto-regressive models are fitted to the spatial correlations across the field.
Chapter 4 examines the use of correlation modelling in the analysis of repeated measurements. Here the correlation is in a single dimension, namely time, and REML provides a powerful alternative to conventional methods such as repeated-measures ANOVA or the analysis of contrasts over time. The book works through a series of straightforward examples, with frequent practicals to allow you to try the methods for yourself. The examples work mainly through the menus of Genstat for Windows, so there is no need for prior knowledge of the Genstat command language.