Introduction to Mixed Modelling : Beyond Regression and Analysis of Variance.

• Κύριος συγγραφέας: Galwey, Nick
• Μορφή: Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Hoboken : Wiley, 2014.
• Έκδοση: 2nd ed.
• Θέματα: Multilevel models (Statistics); Experimental design.; Regression analysis.; Analysis of variance.; Gemischtes Modell.; Varianzanalyse.; Regressionsanalyse.; Electronic books.

This book first introduces the criterion of REstricted Maximum Likelihood (REML) for the fitting of a mixed model to data before illustrating how to apply mixed model analysis to a wide range of situations, how to estimate the variance due to each random-effect term in the model, and how to obtain a...

Πίνακας περιεχομένων:

• Cover; Title Page; Copyright; Contents; Preface; Chapter 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 specify a model term as random: A mixed model.
• 1.7 Comparison of the tests in a mixed model with a test of lack of fit; 1.8 The use of REsidual Maximum Likelihood (REML) 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 Use of the software SAS to perform the analyses; 1.13 Fitting a mixed model using GenStat's Graphical User Interface (GUI); 1.14 Summary; 1.15 Exercises; References.
• Chapter 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 plots; 2.3 Consequences of failure to recognize 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 F and Wald statistics; 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 Use of SAS to perform the analyses; 2.10 Summary; 2.11 Exercises; References; Chapter 3 Estimation of the variances of random-effect terms; 3.1 The need to estimate variance components; 3.2 A hierarchical random-effects 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-effects 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 Use of SAS to analyse the hierarchical three-stage assay process; 3.8 Genetic variation: A crop field trial with an unbalanced design; 3.9 Production of a balanced experimental design by `padding'' with missing values; 3.10 Specification of a treatment term as a random-effect term: The use of mixed-model analysis to analyse an unbalanced data set; 3.11 Comparison of a variance component estimate with its standard error; 3.12 An alternative significance test for variance components.
• 3.13 Comparison among significance tests for variance components.