ASReml-S reference manual - VSN International

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4.7 Constraining variance parameters 522. extract the variance component names as the dimnames attribute of M.an exampleA default data frame gammas.table to work with is generated by setting the start.valuesargument to asreml. This data frame contains a factor, Gammas, the levels of which arethe names of the variance parameters. Other factors or variates created in this data frameand named in the formula argument to asreml.constraints() are used to generate M.By way of example, consider a model containing a first order interaction term (A:B, say)where the outer factor (A) is of order 7 and we wish to model it with an unstructuredvariance matrix with some parameters constrained. Suppose the required pattern of constraintsamong the variance parameters is:v 11v 21 v 22v 31 v 32 v 33v 31 v 32 0 v 33v 31 v 32 0 0 v 33v 31 v 32 0 0 0 v 33v 31 v 32 0 0 0 0 v 33That is, there are only 7 distinct parameters from the original 28 and one of these is tobe fixed at zero. Furthermore, suppose that none of the remaining variance parametersfrom other terms in the model are to be subject to any constraints.The following call> model.gam
4.7 Constraining variance parameters 531 2 3 4 5 6 7v 1,1 1 0 0 0 0 0 0v 2,1 0 1 0 0 0 0 0v 2,2 0 0 1 0 0 0 0v 3,1 0 0 0 1 0 0 0v 3,2 0 0 0 0 1 0 0v 3,3 0 0 0 0 0 1 0v 4,1 0 0 0 1 0 0 0v 4,2 0 0 0 0 1 0 0v 4,3 0 0 0 0 0 0 1v 4,4 0 0 0 0 0 1 0v 5,1 0 0 0 1 0 0 0v 5,2 0 0 0 0 1 0 0v 5,3 0 0 0 0 0 0 1v 5,4 0 0 0 0 0 0 1v 5,5 0 0 0 0 0 1 0v 6,1 0 0 0 1 0 0 0v 6,2 0 0 0 0 1 0 0v 6,3 0 0 0 0 0 0 1v 6,4 0 0 0 0 0 0 1v 6,5 0 0 0 0 0 0 1v 6,6 0 0 0 0 0 1 0v 7,1 0 0 0 1 0 0 0v 7,2 0 0 0 0 1 0 0v 7,3 0 0 0 0 0 0 1v 7,4 0 0 0 0 0 0 1v 7,5 0 0 0 0 0 0 1v 7,6 0 0 0 0 0 0 1v 7,7 0 0 0 0 0 1 0The final step before fitting the model is to fix the parameters corresponding to level 7of fac to zero. This is achieved by editing gammas.txt and setting the appropriate valuesto zero and boundary constraint codes to F. The modified values and the matrix M areused through the G.param and constraints arguments to asreml, respectively. That ismodel.asr
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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
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 34 and 35: 3.3 Introducing the asreml function
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 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
Page 88 and 89: pedigree7.3 Methods and related fun
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
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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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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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