ASReml-S reference manual - VSN International

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6.2 The predict method 64The predict function call for this example is:predict(obj, classify = c(’A’,’B’), present = list(’A’ = c(f 1 , f 2 , . . .),’B’ = list(c(f 11 , f 12 , . . .),c(f 21 , f 22 , . . .), prwts = c(f 11 , f 12 , . . .))),sed = list(’A’ = TRUE, ’B’ = FALSE))and is illustrated by the tree diagram in Figure 6.2.1. The problem could of course besimplified somewhat by two separate predict() calls for each of ’A’ and ’B’.Fig. 6.1. Predict tree structurepredict✭✭✭✭✭✭✭✭✭✭✭✪✪❤❤❤❤❤❤❤❤❤❤classifypresentsed ❧ ✏ ✏✏✏✏ ✧❡ ✧✧ ◗ ◗’A’ ’B’ A✟✟✟B❍ A B❍❍f 1f 2list1 list2 prwts TRUE FALSE.f 11f 12.f 21f 22.w 1w 2.6.2.2 The prediction processmv, unitsPredictions are formed as an extra process in the final iteration. predict.asreml() parsesthe argument list and calls update.asreml() using the final parameter estimates in therequired asreml object. Additional arguments to asreml may be included in the call topredict.asreml(), such as requesting extra memory, adding spline predict points or controllingthe number of additional iterations, bound by the rules of update.asreml().By default, factors are predicted at each level, simple covariates are predicted at theiroverall mean and covariates used as a basis for splines or orthogonal polynomials arepredicted at their design points. Covariates grouped into a single term using the grp()model function) are treated as covariates.Special model terms mv and units are always ignored.Prediction at particular values of a covariate or particular levels of a factor is achievedby:1. Including the variables in the classify set and specifying any non-default values atwhich predictions are to be made by using the levels argument.2. Specifying the averaging set. The default averaging set is those explanatory variablesinvolved in fixed effect model terms that are not in the classifying set. By defaultvariables that only define random model terms are ignored. The average argumentallows these variables to be added to the default averaging set.3. Determining the linear model terms to use in prediction. The default rule is that allmodel terms based entirely on the classifying and averaging set are used. The useand ignore arguments allow this default set of model terms to be modified by adding
6.3 Aliasing 65or removing terms, respectively. The onlyuse argument explicitly specifies the modelterms to use, ignoring all others. The argument except explicitly specifies the modelterms not to use, including all others. These arguments may implicitly modify theaveraging set by including variables defining terms in the predicted model not in theclassify set. It is sometimes easier to specify the classify set and the prediction linearmodel and allow asreml to construct the averaging set.4. Choosing the weights for forming means over dimensions in the hyper-table. Thedefault is to average over the specified levels but the average argument can be usedto specify weights to be used in averaging over a factor.For example,obj.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
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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
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 74 and 75: Optional argumentslevels6.2 The pre
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[
Page 124 and 125: 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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