ASReml-S reference manual - VSN International

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References 130W. W. Stroup, P. S. Baenziger, and D. K. Mulitze. Removing spatial variation from wheat yieldtrials: a comparison of methods. Crop Science, 86:62–66, 1994.R. Thompson. Maximum likelihood estimation of variance components. Math. OperationsforschStatistics, Series, Statistics, 11:545–561, 1980.R. Thompson, B.R. Cullis, A. Smith, and A.R. Gilmour. A sparse implementation of the averageinformation algorithm for factor analytic and reduced rank variance models. Australian andNew Zealand Journal of Statistics, 45:445–459, 2003.A. P. Verbyla. A conditional derivation of residual maximum likelihood. Australian Journal ofStatistics, 32:227–230, 1990.A. P. Verbyla, B. R. Cullis, M. G. Kenward, and S. J. Welham. The analysis of designedexperiments and longitudinal data by using smoothing splines (with discussion). AppliedStatistics, 48:269–311, 1999.D. Waddington, S. J. Welham, A. R. Gilmour, and R. Thompson. Comparisons of some glmmestimators for a simple binomial model. Genstat Newsletter, 30:13–24, 1994.R. Webster and M. A. Oliver. Geostatistics for Environmental Scientists. John Wiley and Sons,Chichester, 2001.S. J. Welham. Glmm fits a generalized linear mixed model. In R.W. Payne and P.W. Lane,editors, GenStat Reference Manual 3: Procedure Library PL17,, pages 260–265. VSN International,Hemel Hempstead, UK, 2005.S. J. Welham and R. Thompson. Adjusted likelihood ratio tests for fixed effects. Journal of theRoyal Statistical Society, Series B, 59:701–714, 1997.S. J. Welham, B. R. Cullis, B. J. Gogel, A. R. Gilmour, and R. Thompson. Prediction in linearmixed models. Australian and New Zealand Journal of Statistics, 46:325–347, 2004.R. D. Wolfinger. Heterogeneous variance-covariance structures for repeated measures. Journalof Agricultural, Biological, and Environmental Statistics, 1:362–389, 1996.Russ Wolfinger and Michael O’Connell. Generalized linear mixed models: A pseudo-likelihoodapproach. Journal of Statistical Computation and Simulation, 48:233–243, 1993.
Index>, 2%, 2AI algorithm, 12AI matrix, 14AIC, 15ainverse, 57Akaike Information Criteria, 15aliasing, 121examples, 122anovawald(), 23, 35asremlclass, 22methods, 71object, 22, 70asreml functionsaddAsremlMenu(), 79asreml.Ainverse(), 75asreml.constraints(), 76asreml.control(), 68asreml.gammas.ed(), 77asreml.read.table(), 21, 78asreml.variogram(), 78removeAsremlMenu, 79asreml methodscoef.asreml(), 72fitted.asreml(), 72plot.asreml(), 73predict.asreml(), 74resid.asreml(), 74summary.asreml(), 74update.asreml(), 75wald.asreml(), 71asreml()call, 24, 68optional arguments, 24required arguments, 24asreml() argumentdata, 22fixed, 22na.method.Y, 31random, 22rcov, 22, 23, 30ASReml.exe, 1at(), 30Average Information, 1balanced repeated measures, 89Bayesian Information Criteria, 15BIC, 15BLUE, 13BLUP, 13, 23C-inverseCfixed, 69Csparse, 69coef(), 23coefficientscoef.fixed, 23coef.random, 23coef.sparse, 23combining variance models, 14conditional distribution, 11conditional factor, 30conditional Wald statistics, 17conventions, 2correlated random effects, 13correlationmodel, 10correlation models, 41covariance model, 10isotropic, 9csv files, 21dense equations, 121diagnostics, 15direct product, 7, 9, 37distributionconditional, 11marginal, 11documentation, 2error varianceheterogeneity, 9estimation, 10expected information matrix, 12
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Analysis of mixed modelsfor S langu
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D.G. ButlerQueensland Department of
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ContentsPreface . . . . . . . . . .
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ContentsviiB Available variance mod
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List of Figures1.1 Weekly body weig
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1.3 Data sets used 21.2.2 Help and
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1.3 Data sets used 41.3.2 Repeated
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1.3 Data sets used 6101 Sire 1 0102
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2.1 The linear mixed model 8Direct
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2.2 Estimation 10There is a corresp
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2.2 Estimation 12where H ij = ∂ 2
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2.5 Inference for random effects 14
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2.6 Inference for fixed effects 16T
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2.6 Inference for fixed effects 18t
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3Fitting the mixed model3.1 Introdu
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3.3 Introducing the asreml function
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3.6 Getting help3.7 The asreml func
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3.8 Fixed terms 26na.method.Xkeep.o
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3.8 Fixed terms 28Summary of reserv
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3.10 Conditional factors: the at()
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Table 3.2. Families and link functi
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3.15 Multivariate analysis 34R!trai
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Source df F_inc F_con MSource df dd
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4.1.1 Specifying variance models in
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4.2 A sequence of structures for th
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Σ = [σ ij ] :{σii = σ 2 , ∀i
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4.4 Variance model functions 44init
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The Matérn class4.4 Variance model
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4.4 Variance model functions 48Chol
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Required argumentsobj4.5 Rules for
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4.7 Constraining variance parameter
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5Genetic analysis5.1 IntroductionIn
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5.2 Pedigree, G-inverse objects and
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5.4 Using Pedigree and G-inverse ob
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6Prediction from the linear model6.
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Optional argumentslevels6.2 The pre
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6.2 The predict method 64The predic
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6.4 Complicated weighting 666.4 Com
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7The asreml class and related metho
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maxiter maximum number of iteration
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7.3 Methods and related functions 7
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pedigree7.3 Methods and related fun
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Page 92 and 93: 8Examples8.1 IntroductionThis secti
Page 94 and 95: 8.2 Split Plot Design 82In this exa
Page 96 and 97: 8.3 Unbalanced nested design 84[4,]
Page 98 and 99: 8.3 Unbalanced nested design 86adju
Page 100 and 101: 8.4 Sources of variability in unbal
Page 102 and 103: 8.5 Balanced repeated measures 902
Page 104 and 105: 8.5 Balanced repeated measures 92Ta
Page 106 and 107: 8.5 Balanced repeated measures 94wh
Page 108 and 109: 8.6 Spatial analysis of a field exp
Page 110 and 111: 8.6 Spatial analysis of a field exp
Page 112 and 113: Variety predicted.value standard.er
Page 114 and 115: 8.7 Unreplicated early generation v
Page 116 and 117: 8.7 Unreplicated early generation v
Page 118 and 119: 8.8 Paired Case-Control Study 106Th
Page 120 and 121: Terms added sequentially; adjusted
Page 122 and 123: 8.8 Paired Case-Control Study 110[
Page 124 and 125: 8.8 Paired Case-Control Study 112..
Page 126 and 127: 8.9 Balanced longitudinal data - Ra
Page 134 and 135: A.3 Aliasing and singularities 122W
Page 136 and 137: Table B.1: Details of the available
Page 138 and 139: B Available variance models 126Deta
Page 140 and 141: ReferencesG. E. P. Box. Analysis of
Page 144 and 145: Index 132F statistics, 17fa(,k), 49
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