ASReml-S reference manual - VSN International

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maxiter maximum number of iterations (default is 10).stepsizeworkspacepworkspaceValueupdate shrinkage factor.7.2 The asreml object 70size of workspace for the REML routines measured in double precision words(groups of 8 bytes). The default is workspace=4e6 (or 32,000,000 bytes).size of workspace for forming predictions of linear functions of variables inthe model, measured in double precision words (groups of 8 bytes). Thedefault is workspace=4e6 (or 32,000,000 bytes).a list is returned to supply the control argument(s) to asreml. The values in asreml.control()can be supplied directly in a call to asreml; these values are filtered throughasreml.control() inside asreml.7.2 The asreml objectThe asreml class has methods for the following generic functions: coef(), fitted(), predict(),plot(), residuals() and summary(). A generic function wald() for significance testsusing Wald statistics has been defined with a method for asreml objects The followingcomponents are included in a valid asreml object.componentmonitorloglikgammasgammas.conscorecoefficientsvcoefffitted.valueslinear.predictorsresidualshatsigma2nedfaidescriptiona data frame of random components (rows) by iterations (columns)tracing the convergence sequence.the REML log-likelihood at termination.the vector of variance parameter estimates.the vector of boundary constraints applied the variance parameters(Positive, Fixed, Unconstrained)the score vector of length length(gammas).a list with three components, fixed, random and sparse, where the firstis a vector containing the estimated coefficients for the fixed effects,the second is a vector containing the estimated BLUPs for the randomeffects and the third is a vector containing the coefficients of absorbedfixed effects. The names of the coefficients are the same as those inthe formulae of the call to asreml.a list with three components, fixed, random and sparse, containingthe unscaled variances of the coefficients. The actual variances arecalculated as vcoeff*sigma2the vector of fitted values.the linear fit, given by the product of the model matrix and thecoefficients.the vector of residuals.the diagonal elements of the hat matrix.the residual variancethe residual degrees of freedoma vector of length ngamma(ngamma+1)/2 containing the lower trianglerow-wise of the inverse average information matrix on varianceparameters.
7.3 Methods and related functions 71CfixedCsparsefamilycallpredictionsG.paramR.parama vector containing the lower triangle row-wise of the inverse of theinformation matrix for the dense equations if Cfixed = T.inverse elements of the information matrix for the sparse terms nominatedin the Csparse argument to asreml.control(). The returned objectis a data frame giving the row, column and non-zero element ofC −1 .family object, the result of a call to asreml.family().an image of the call that produced the object.a list whose elements are named by the classifying factors of a predictoperation. Each element is in turn a list with components pvals,avsed, sed and vcov for a data frame of predicted values, the averagestandard error of difference and optional matrices of SEDs orcovariances of predicted values, respectively.a list object containing the constraints and final estimates of thevariance parameters from the random part of the call. This objectmay be used as the value of the G.param argument to provide initialparameter estimates to asreml.a list object containing the constraints and final estimates of thevariance parameters of the error structure of the model. This objectmay be used as the value of the R.param argument to provide initialparameter estimates to asreml for the error component of the model.7.3 Methods and related functions7.3.1 wald.asremlUsagewald(object, denDF = c(”none”,”default”,”numeric”,”algebraic”),ssType = c(”incremental”,”conditional”))Required argumentsobjectan asreml object.Optional argumentsdenDFssTypeValuecalculation of approximate denominator degrees of freedom. Can be ”none”,the default, to suppress the computations, ”numeric” for numerical methods,”algebraic” for algebraic methods or ”default” to autommatically choose numericor algebraic computations depending on problem size. The denominatordegrees of freedom are calculated according to Kenward and Roger [1997] forfixed terms in the dense part of the model.can be ”incremental” for incremental sum of squares, the default, or ”conditional”for F tests that respect both structural and intrinsic marginality.an object of class anova if denDF = ”none” and ssType = ”incremental” or adata frame otherwise.Detailswald.asreml() produces two styles of tables of Wald statistics depending on the settingsof denDF and ssType. If denDF = ”none” and ssType = ”incremental” (the defaults) a
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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
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 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 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..
Page 126 and 127: 8.9 Balanced longitudinal data - Ra
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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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