ASReml-S reference manual - VSN International

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Optional argumentslevels6.2 The predict method 62a list of length the number of classified tables, and named by the classify set.Each list component is also a list, named by the margins of the classified table,of vectors specifying the levels at which predictions are required. If omitted,factors are predicted at each level, simple covariates are predicted at theiroverall mean and covariates used as a basis for splines or orthogonal polynomialsare predicted at their design points. The factors mv and units are alwaysignored.averagepresentparallelexceptignoreuseonlyusealiaseda list of length the number of classified tables, and named by the classify set,specifying which variables to include in the averaging set and optional vectorsof weights. Optionally, each component of the list is also a list, named by themargins of the classified table, of vectors specifying the weights to use in theaveraging process. If omitted, equal weights are used.a list of length the number of classified tables, and named by the classify set,specifying which variables to include in the present set for each table. Thepresent set is used when averaging is to be based only on cells with data. Eachcomponent of the list may be a character vector specifying the variables to beused in present averaging, or, may in turn also be a list. In this case, therecan be a maximum of two components, each a character vector of variablenames, representing non-overlapping present categorisations and one optionalcomponent named prwts containing a vector of weights to be used for averagingthe first present table only. The vector(s) of names may include variables inthe classify set but not those in the average set. At present, the model mustcontain the factor as a simple main effect.a list of length the number of classified tables, and named by the classify set,specifying which variables to expand in parallel. Each component of the list isa character vector specifying up to four of the classifying variables to expandin parallel.a list of length the number of classified tables, and named by the classify set,specifying which variables to exclude in the prediction process. That is, theprediction model includes all fitted model terms not in the exclude list. Eachcomponent of the list is a character vector specifying the variables to be excluded.a list of length the number of classified tables, and named by the classify set,specifying which variables to ignore in the prediction process. Each componentof the list is a character vector specifying the variables to be ignored.a list of length the number of classified tables, and named by the classify set,specifying which variables to add to the prediction model after the default ruleshave been invoked. Each component of the list is a character vector specifyingthe variables to be used.a list of length the number of classified tables, and named by the classify set,specifying which variables form the prediction model, that is, the default rulesare not invoked. Each component of the list is a character vector specifying thevariables to be used.a list of length the number of classified tables, and named by the classify set.Each component is a logical scalar (default F) that determines whether or notthe predicted value is returned for non-estimable functions.
6.2 The predict method 63sedvcova list of length the number of classified tables, and named by the classify set.Each component is a logical scalar (default F) that determines whether or notthe full standard error of difference matrix is returned.a list of length the number of classified tables, and named by the classify set.Each component is a logical scalar (default F) that determines whether or notthe full variance matrix of the predicted values is returned.6.2.1 An illustrationA simple example is the prediction of variety means from fitting model 2a (Section 4.2)to the NIN field trial data. Recall that a randomised block model with random replicateeffects is fitted to this data by:> rcb.asr rcb.pv rcb.pv$predictions$Variety$Variety$pvalsNotes:- Rep terms are ignored unless specifically included- mv is averaged over fixed levels- Variety is included in the prediction- (Intercept) is included in the prediction- mv is ignored in this predictionVariety predicted.value standard.error est.status1 ARAPAHOE 29.4375 3.855687 Estimable2 BRULE 26.0750 3.855687 Estimable3 BUCKSKIN 25.5625 3.855687 Estimable...54 TAM107 28.4000 3.855687 Estimable55 TAM200 21.2375 3.855687 Estimable56 VONA 23.6000 3.855687 Estimable$Variety$avsed[1] 4.979075The list orientated nature of predict.asreml() is potentially complex, particularly whenmore than one classified table is specified in a single function call. The principal reason forthis potential complexity lies in the fact that each predict.asreml() call requires at least onefurther iteration of the fitting routines (to construct the prediction design matrix). Forsituations where execution time is not limiting, it may be simpler to call predict.asreml()separately for each required classification table.To illustrate the potentially complex syntax of a predict function call, consider a hypotheticalexample where two predict tables are required, classified by factors (or compoundterms) ’A’ and ’B’. For the predictions classified by ’A’, present averaging is necessaryand the table of standard errors of difference is required. For those classified by ’B’, twomutually exclusive lists defining present averaging are required and we wish to weightthe cells of the first present hyper-table. The table of SED’s is not desired in this case.
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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
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 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 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
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[
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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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