ASReml-S reference manual - VSN International

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7.3 Methods and related functions 747.3.5 predict.asremlUsagepredict(object, classify = list(), levels=list(), average=list(), present=list(), parallel=list(),except=list(), ignore=list(), use=list(), onlyuse=list(), aliased=list(), sed=list(), vcov=list())ValueAdds a list object, predictions, of length the number of predict tables to the asremlobject (see Chapter 8). Each element is a list with components:pvalssedvcovavseda dataframe of predicted values.optional matrix of standard errors of difference.optional matrix of variances of predicted values.summary standard error of difference.7.3.6 resid.asremlUsageresid(object, type = c(”deviance”, ”pearson”, ”working”, ”response”), spatial = c( ”trend”, ”plot”))Required argumentsobjectan asreml object.Optional argumentstypespatialValuetype of residuals, with choices deviance, pearson, working or response; devianceis the default.if a second independent error term has been fitted by including units in therandom formula, the residuals will have the unit BLUPs added if spatial =”plot”.the vector of residuals.7.3.7 summary.asremlUsagesummary(object, all = T)Required argumentsobjectan asreml object.Optional argumentsallValueif TRUE the residuals and coefficients from the fixed, random and absorbedfactors are included in the returned object.a list with the appropriate summary information. The components include call, distribution,link, loglik, nedf, sigma, deviance, variance heterogeneity, varcomp, coef.fixed ,coef.random, coef.sparse and residuals.Detailscalldistributionthe call to asreml().error distribution.
7.3 Methods and related functions 75linklogliknedfsigmadevianceheterogeneityvarcompCfixedcoef.fixedcoef.randomcoef.sparseresidualslink function.REML log-likelihood at termination.error degrees of freedom.residual standard deviation.residual deviance.variance heterogeneity = deviance/nedfa numeric matrix of variance component ratios, values and standarderrors for the random model terms.inverse of the coefficients matrix for terms in the fixed formula.a numeric matrix of generalised least squares estimates and standarderrors for the fixed component of the model.a numeric matrix of BLUPs and standard errors for the random componentof the call.a numeric matrix of generalised least squares estimates and standarderrors for the fixed terms included in the sparse formula.the residuals from the fit.7.3.8 update.asremlUsageupdate(object, ..., evaluate=T)Required argumentsobjectan asreml object.Optional arguments... any other arguments (except currently fixed, random or sparse) that are appropriatefor the particular call. These must all be named in the same manner asif arguments to asreml itself.evaluateValueif TRUE (the default), the new call is evaluated; otherwise, the call is returnedas an unevaluated expression.either a new updated object, or else an unevaluated expression for creating such anobject.Detailsupdate.asreml currently replaces the arguments R.param and G.param with object$R.paramand object$G.param, respectively, creating a new fitted object using the values from a previousmodel as starting values.7.3.9 asreml.AinverseUsageasreml.Ainverse(pedigree, groups = 0, method = 0, selfing = NA,inBreed = NA, mgs = FALSE)Required arguments
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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
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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 84 and 85: 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
Page 134 and 135: 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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