- Page 1 and 2: Analysis of mixed modelsfor S langu
- Page 3 and 4: D.G. ButlerQueensland Department of
- Page 9 and 10: Contentsvi7 The asreml class and re
- Page 11 and 12: List of Tables1.1 Trial layout and
- Page 13 and 14: 1Introduction1.1 What ASReml-S can
- Page 15 and 16: 1.3 Data sets used 3out in a 22 row
- Page 17 and 18: 1.3 Data sets used 50101, 3, 21, 1,
- Page 19 and 20: 2Some theory2.1 The linear mixed mo
- Page 21 and 22: 2.1 The linear mixed model 9differe
- Page 23 and 24: 2.2 Estimation 112.2.1 Variance par
- Page 25 and 26: 2.3 What are BLUPs? 132.2.2 Fixed a
- Page 27 and 28: 2.5 Inference for random effects 15
- Page 29 and 30: 2.6.2 Incremental and Conditional W
- Page 31 and 32: 2.6 Inference for fixed effects 192
- Page 33 and 34: NE84557,13,1113,509,1,4,25.45,8.6,7
- Page 35 and 36: 3.5 A note on data order 23• the
- Page 37 and 38: covG.paramR.parampredictconstraints
- Page 39 and 40: 3.8 Fixed terms 27> asreml(fixed =
- Page 41 and 42: 3.9 Random terms 29could be used to
- Page 43 and 44: 3.13 Generalized linear models 313.
- Page 45 and 46: 3.15 Multivariate analysis 333.15 M
- Page 47 and 48: 3.16 Testing of terms: the wald() m
- Page 49 and 50: 4Specifying the variance structures
- Page 51 and 52: Model 1a: RCB analysis - blocks ran
- Page 53 and 54: 4.3 Types of variance models 41Tabl
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4.4 Variance model functions 434.4.
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4.4 Variance model functions 45init
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4.4 Variance model functions 47•
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4.4 Variance model functions 49the
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4.7 Constraining variance parameter
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4.7 Constraining variance parameter
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5.2 Pedigree, G-inverse objects and
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5.3 Generating an A-inverse matrix
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5.4 Using Pedigree and G-inverse ob
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6.2 The predict method 61Interactio
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6.2 The predict method 63sedvcova l
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6.3 Aliasing 65or removing terms, r
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6.5 Further examples 67predict(obj.
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7.1 Introduction 69predictpoints a
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7.3 Methods and related functions 7
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7.3 Methods and related functions 7
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7.3 Methods and related functions 7
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formulagammas7.3 Methods and relate
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7.3 Methods and related functions 7
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8.2 Split Plot Design 811. the stra
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8.2 Split Plot Design 83- Variety i
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Table 8.2. Rat data: ANOVA decompos
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8.4 Sources of variability in unbal
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8.5 Balanced repeated measures 891.
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8.5 Balanced repeated measures 91HC
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8.5 Balanced repeated measures 93A
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8.6 Spatial analysis of a field exp
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8.6 Spatial analysis of a field exp
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8.6 Spatial analysis of a field exp
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8.7 Unreplicated early generation v
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(Intercept) 2872.7366 34.82716 82.4
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2 5.5 527 2697.068 133.44066 Estima
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Run/Tmt/Variety = Run + Run:Tmt + R
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8.8 Paired Case-Control Study 109Pa
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8.8 Paired Case-Control Study 111Th
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8.9 Balanced longitudinal data - Ra
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8.9 Balanced longitudinal data - Ra
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8.9 Balanced longitudinal data - Ra
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8.9 Balanced longitudinal data - Ra
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ASome technical details about model
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BAvailable variance modelsTable B.1
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B Available variance models 125Deta
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B Available variance models 127Deta
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References 129H. Goldstein and J. R
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Index>, 2%, 2AI algorithm, 12AI mat
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Index 133random regression, 10RCB,