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Introduction to mixed modelling : beyond regression and analysis of variance

Author: Nick Galwey; Wiley InterScience (Online service)
Publisher: Chichester, England ; Hoboken, NJ : Wiley, ©2006.
Series: Wiley SCELC e-books.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
Mixed modelling is one of the most promising and exciting areas of statistical analysis, enabling more powerful interpretation of data through the recognition of random effects. However, many perceive mixed modelling as an intimidating and specialized technique. This book introduces mixed modelling analysis in a simple and straightforward way, allowing the reader to apply the technique confidently in a wide range of  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Galwey, Nick.
Introduction to mixed modelling.
Chichester, England ; Hoboken, NJ : Wiley, ©2006
(DLC) 2006023991
(OCoLC)70707837
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Nick Galwey; Wiley InterScience (Online service)
ISBN: 9780470035986 0470035986 9780470035962 047003596X
OCLC Number: 180771236
Description: 1 online resource (x, 366 pages) : illustrations
Contents: Introduction to Mixed Modelling; Contents; Preface; 1 The need for more than one random-effect term when fitting a regression line; 1.1 A data set with several observations of variable Y at each value of variable X; 1.2 Simple regression analysis. Use of the software GenStat to perform the analysis; 1.3 Regression analysis on the group means; 1.4 A regression model with a term for the groups; 1.5 Construction of the appropriate F test for the significance of the explanatory variable when groups are present; 1.6 The decision to regard a model term as random: a mixed model. 1.7 Comparison of the tests in a mixed model with a test of lack of fit1.8 The use of residual maximum likelihood to fit the mixed model; 1.9 Equivalence of the different analyses when the number of observations per group is constant; 1.10 Testing the assumptions of the analyses: inspection of the residual values; 1.11 Use of the software R to perform the analyses; 1.12 Fitting a mixed model using GenStat's GUI; 1.13 Summary; 1.14 Exercises; 2 The need for more than one random-effect term in a designed experiment; 2.1 The split plot design: a design with more than one random-effect term. 2.2 The analysis of variance of the split plot design: a random-effect term for the main plots2.3 Consequences of failure to recognise the main plots when analysing the split plot design; 2.4 The use of mixed modelling to analyse the split plot design; 2.5 A more conservative alternative to the Wald statistic; 2.6 Justification for regarding block effects as random; 2.7 Testing the assumptions of the analyses: inspection of the residual values; 2.8 Use of R to perform the analyses; 2.9 Summary; 2.10 Exercises; 3 Estimation of the variances of random-effect terms. 3.1 The need to estimate variance components3.2 A hierarchical random-effect model for a three-stage assay process; 3.3 The relationship between variance components and stratum mean squares; 3.4 Estimation of the variance components in the hierarchical random-effect model; 3.5 Design of an optimum strategy for future sampling; 3.6 Use of R to analyse the hierarchical three-stage assay process; 3.7 Genetic variation: a crop field trial with an unbalanced design; 3.8 Production of a balanced experimental design by 'padding' with missing values. 3.9 Regarding a treatment term as a random-effect term. The use of mixed-model analysis to analyse an unbalanced data set3.10 Comparison of a variance-component estimate with its standard error; 3.11 An alternative significance test for variance components; 3.12 Comparison among significance tests for variance components; 3.13 Inspection of the residual values; 3.14 Heritability. The prediction of genetic advance under selection; 3.15 Use of R to analyse the unbalanced field trial; 3.16 Estimation of variance components in the regression analysis on grouped data.
Series Title: Wiley SCELC e-books.
Responsibility: N.W. Galwey.

Abstract:

Mixed modelling is one of the most promising and exciting areas of statistical analysis, enabling more powerful interpretation of data through the recognition of random effects. However, many  Read more...

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"This book would be useful for anyone who sues GenStat and/or R desiring an introduction to applied mixed modeling, and they should certainly have a look." (Technometrics, August 2008) "This book Read more...

 
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