ASReml-S reference manual - VSN International

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7.3 Methods and related functions 72pseudo analysis of variance table is returned based on incremental sums of squares withrows corresponding to each of the fixed terms in the object, plus an additional row forthe residuals. The model sum of squares is partitioned into its fixed term components,and the sum of squares for each term listed in the table is adjusted for the terms listedin the rows above. The denominator degrees of freedom are not computed.If either denDF or ssType are not set at their default values, a data frame is returned thatwill include columns for the approximate denominator degrees of freedom and incrementaland conditional F statistics depending on the combination of options chosen.The principle used in determining the conditional tests is that a term cannot be adjustedfor another term which encompasses it explicitly (for example, A:C cannot be adjustedfor A:B:C or implicitly (for example, REGION¡ cannot be adjusted for LOCATION whenlocations are nested in regions although coded independently).The numerator degrees of freedom for each term is determinaed as the number of nonsingularequations involved in the term. However, the calculation of the denominator dfis in general not trivial and is computationally expensive. Numerical derivatives requirean extra evaluation of the mixed model equations for every variance parameter whilealgebraic derivatives require a large dense matrix, potentially of order the number ofequations plus the number of observations. The calculations are supressed by default.7.3.2 coef.asremlUsagecoef(object)Required argumentsobject an asreml object.Valuea list of length 3 with the following components:fixed Generalised least squares estimates for fixed terms in the model.randomsparseBLUPs from the random part of the modelGeneralised least squares estimates for fixed terms in the model thatwere included in the sparse section.7.3.3 fitted.asremlUsagefitted(object, type = c(”response”, ”link”))Required argumentsobject an asreml object.Optional argumentstype fitted values on the scale of the response (default) or link functionValuevector of fitted values.
7.3 Methods and related functions 737.3.4 plot.asremlUsageplot(object, option=c(”v”, ”f”, ”r”, ”t”), forceMET=”separate”variogram = plot.asreml.default(object)$variogram,resid.form = plot.asreml.default(object)$resid.form,fitted.form = plot.asreml.default(object)$fitted.form,trend = plot.asreml.default(object)$trend,display=T, spatial=”plot”, npanels=list(v=NA,f=NA,r=NA,t=20), ...)Required argumentsobjectOptional argumentsoptionforceMETvariogramresid.formfitted.formtrenddisplayspatialan asreml object.if one or more of v, f, r or t is specified then diagnostic plots including thesample variogram, fitted values, residuals and trends in residuals are produced.if not ”none”, the option argument is ignored and an alternative set ofplots of the sample variogram and trends in residuals is produced for Multi-Environment Trials. If ”separate” (the default) each sample variogram and eachresidual plot for each trial appears in a separate frame. If ”together”, the samplevariogram and residual plot appear in a single frame for each trial. If ”none”then the default ploting method is used.a trellis graphics formula, with no response specified, suitable for wireframe() orxyplot() depending on dimension. The x, y and any conditioning variables mustappear in the model frame; the response is created internally from resid(object)after any subset and missing value methods have been applied. For example, ∼Row*Column | Location creates a wireframe() panel for each level of Location.a trellis graphics formula ,suitable for histogram(). Currently the x-variablemust be the keyword Residuals.a trellis graphics formula suitable for xyplot() Currently no response is allowedand the x-variable must be the keyword Residuals.a list of trellis graphics formulae suitable for xyplot() Currently no response isallowed, the keyword Residuals is used internally. The x-variable and conditioningvariables must be objects in the model frame. A trellis object is created foreach formula in the list. For example, trend = list(∼ Row | Column) results ina multipanel plot of residuals against Row conditioned on Column.The plots are produced as a list of trellis objects. If display=TRUE then theseobjects are printed.If “plot” and an independent error has been fitted with units in the randomformula, these are added to the residuals, otherwise if “trend” then units arenot added even if present in the model.npanels A list specifying the maximum number of panels per page. If NA then a suitabledefault is determined.... graphical parameters can be supplied to plot.asreml().ValueAn invisible list of trellis graph objects is produced. Up to 4 diagnostic plots are produced:(v) the sample variogram, (f) fitted values versus residuals, (r) distribution of residualsand (t) trends in residuals using any factors specified in the R component of the model.If more than one trellis plot is requested then they appear as separate pages (MicrosoftWindows) or a dialogue is started for interactive viewing (SUN Solaris).
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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
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 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 86 and 87: 7.3 Methods and related functions 7
Page 88 and 89: pedigree7.3 Methods and related fun
Page 90 and 91: 7.3 Methods and related functions 7
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
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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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