ASReml-S reference manual - VSN International

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3.3 Introducing the asreml function call3.4 Components of the fitted model: the asreml object 22The complete asreml function call for a simple randomised complete block (RCB) analysisof the NIN yield data is> nin89.asr nin89.asr nin89.asr
3.5 A note on data order 23• the REML log-likelihood,• best linear unbiased predictors (BLUPs) of the random effects,• generalised least squares estimates of the fixed effects,• REML estimates of variance components,• (optionally) part of the inverse coefficient matrix,• the inverse of the average information matrix,• residuals and fitted values from the linear model.A complete description of the components of an asreml object are given in Section 7.2.summary()Variance componentsCoefficients3.4.1 Methods and related functionsSpecific instances of the standard extractor functions coef(), resid() and fitted() exist, asdo summary(), plot() and predict() (see Chapter 6) methods. An anova type method isimplemented by wald() (see Section 3.16),The summary.asreml() function returns a list with a range of components:> names(summary(nin89.asr))[1] "call" "distribution" "link" "loglik"[5] "nedf" "sigma" "deviance" "heterogeneity"[9] "varcomp" "coef.fixed" "coef.random" "coef.sparse"[13] "residuals"The variance components are returned in> summary(nin89.asr)\$varcompgamma component std.error z.ratio constraintRep 0.1993231 9.882913 8.792685 1.123993 PositiveR!variance 1.0000000 49.582378 5.458841 9.082950 Positiveand the coefficients from the fixed, random and sparse parts of the model are summarisedin the coef.fixed, coef.random and coef.sparse components. For example, the fixed effectsfor Variety are given by> summary(nin89.asr)\$coef.fixedsolution std error z ratioVariety_ARAPAHOE 0.0000 NA NAVariety_BRULE -3.3625 4.979087 -0.675324649Variety_BUCKSKIN -3.8750 4.979087 -0.778255171...Variety_TAM200 -8.2000 4.979087 -1.646888363Variety_VONA -5.8375 4.979087 -1.172403758(Intercept) 29.4375 3.855601 7.6349964523.5 A note on data orderThe observations must be presented in the order specified by the error model, that is, thevalue of the rcov argument. The assumption of separability is implicit in the use of thecolon operator (:). Furthermore, the sort order outer:inner of the observations is impliedby the order of appearance of the factors in the rcov formula. In the case, for example,wherercov = ∼ ar1(Column):ar1(Row)the data is assumed to be sorted as rows within columns.Note that if the sort order of observations is incorrect an error is generated.
Page 1 and 2: Analysis of mixed modelsfor S langu
Page 3 and 4: D.G. ButlerQueensland Department of
Page 6: ContentsPreface . . . . . . . . . .
Page 10 and 11: ContentsviiB Available variance mod
Page 12 and 13: List of Figures1.1 Weekly body weig
Page 14 and 15: 1.3 Data sets used 21.2.2 Help and
Page 16 and 17: 1.3 Data sets used 41.3.2 Repeated
Page 18 and 19: 1.3 Data sets used 6101 Sire 1 0102
Page 20 and 21: 2.1 The linear mixed model 8Direct
Page 22 and 23: 2.2 Estimation 10There is a corresp
Page 24 and 25: 2.2 Estimation 12where H ij = ∂ 2
Page 26 and 27: 2.5 Inference for random effects 14
Page 28 and 29: 2.6 Inference for fixed effects 16T
Page 30 and 31: 2.6 Inference for fixed effects 18t
Page 32 and 33: 3Fitting the mixed model3.1 Introdu
Page 36 and 37: 3.6 Getting help3.7 The asreml func
Page 38 and 39: 3.8 Fixed terms 26na.method.Xkeep.o
Page 40 and 41: 3.8 Fixed terms 28Summary of reserv
Page 42 and 43: 3.10 Conditional factors: the at()
Page 44 and 45: Table 3.2. Families and link functi
Page 46 and 47: 3.15 Multivariate analysis 34R!trai
Page 48 and 49: Source df F_inc F_con MSource df dd
Page 50 and 51: 4.1.1 Specifying variance models in
Page 52 and 53: 4.2 A sequence of structures for th
Page 54 and 55: Σ = [σ ij ] :{σii = σ 2 , ∀i
Page 56 and 57: 4.4 Variance model functions 44init
Page 58 and 59: The Matérn class4.4 Variance model
Page 60 and 61: 4.4 Variance model functions 48Chol
Page 62 and 63: Required argumentsobj4.5 Rules for
Page 64 and 65: 4.7 Constraining variance parameter
Page 66 and 67: 5Genetic analysis5.1 IntroductionIn
Page 68 and 69: 5.2 Pedigree, G-inverse objects and
Page 70 and 71: 5.4 Using Pedigree and G-inverse ob
Page 72 and 73: 6Prediction from the linear model6.
Page 74 and 75: Optional argumentslevels6.2 The pre
Page 76 and 77: 6.2 The predict method 64The predic
Page 78 and 79: 6.4 Complicated weighting 666.4 Com
Page 80 and 81: 7The asreml class and related metho
Page 82 and 83: maxiter maximum number of iteration
Page 84 and 85:
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
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8.2 Split Plot Design 82In this exa
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8.3 Unbalanced nested design 84[4,]
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8.3 Unbalanced nested design 86adju
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8.4 Sources of variability in unbal
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8.5 Balanced repeated measures 902
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8.5 Balanced repeated measures 92Ta
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8.5 Balanced repeated measures 94wh
Page 108 and 109:
8.6 Spatial analysis of a field exp
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8.6 Spatial analysis of a field exp
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Variety predicted.value standard.er
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8.7 Unreplicated early generation v
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8.7 Unreplicated early generation v
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8.8 Paired Case-Control Study 106Th
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Terms added sequentially; adjusted
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8.8 Paired Case-Control Study 110[
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8.8 Paired Case-Control Study 112..
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8.9 Balanced longitudinal data - Ra
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Page 130 and 131:
Page 132 and 133:
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A.3 Aliasing and singularities 122W
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Table B.1: Details of the available
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B Available variance models 126Deta
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ReferencesG. E. P. Box. Analysis of
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References 130W. W. Stroup, P. S. B
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Index 132F statistics, 17fa(,k), 49
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ASReml-S reference manual - VSN International

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