ASReml-S reference manual - VSN International

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3.6 Getting help3.7 The asreml function call 24A complete description of the asreml object is given in Chapter 7 and can be obtainedfrom the help system within S-PLUS:> ?aremlor> help(asreml)generates text based help or html help depending on platform and help system state.On Windows systems, the samm.chm help file stored in the ASReml-S installation directoryand on all systems, this manual (samm.pdf ) is available in the ASReml-S installation tree.3.7 The asreml function callThe full syntax for a call to asreml isasreml(fixed = y ∼ 1, random, sparse, rcov = ∼ units, G.param, R.param,predict = predict.asreml(), constraints = asreml.constraints(), data = sys.parent(),subset,family = asreml.gaussian(), weights = NULL, offset = NULL,na.method.Y = ”include”, na.method.X = ”fail”,keep.order = F, fixgammas = F, as.multivariate = NULL, model.frame = F, start.values = F,dump.model = F, model = F, control = asreml.control(...), ...)3.7.1 Required argumentsfixed3.7.2 Optional argumentsdatarandomsparsea formula object specifying the fixed effects part of the model, withthe response on the left of a ∼ operator, and the terms, separated by+ operators, on the right. All names used in the formula should bedefined as variables in the data frame defined in data. A model withthe intercept as the only fixed effect can be specified as ∼ 1. Theremust be at least one fixed effect specified. If the response evaluatesto a matrix then a factor trait with levels dimnames(y)[[2]] is addedto the model frame.a data frame in which to interpret the variables named in fixed, random,sparse, and rcov. If the data argument to asreml is missing, thefunction sets data to sys.parent() and the context for interpretingnames will be the next function up the calling stack.a formula object, specifying the random effects part of the model,with the terms, separated by + operators, on the right of a ∼ operator.This argument has the same general characteristics as fixed,but there will be no left side to the ∼ expression. Variance structuresimposed on random terms are specified using special functionsdescribed below.a formula object, specifying the fixed effects to be absorbed, withthe terms, separated by + operators, on the right of a ∼ operator.This argument has the same general characteristics as fixed, but therewill be no left side to the expression. This argument would be used,for example, for those fixed covariates, factors and interactions wherestandard errors and tests of significance are not required. These effectsare estimated using sparse matrix methods that typically require lessmemory and execution time.
covG.paramR.parampredictconstraintssubsetfamilyweights3.7 The asreml function call 25a formula object, specifying the error structure of the model, withthe terms, separated by + operators, on the right of a ∼ operator.This argument has the same general characteristics as fixed, but therewill be no left side to the ∼ expression. The default is the keywordunits which is defined as factor(seq(1,nrow(data))) and included inthe model frame. A variance model for the residual component of themodel can be specified using special functions described below.a list object, generated by a call to asreml.gdflt using the random formula,representing variance structures of random terms of the modelto hold initial parameter estimates and constraints.a list object, generated by a call to asreml.rdflt using the rcov formula,representing the error structure of the model to hold initial parameterestimates and constraints.a list object of length the number of predict tables (the length ofthe classify vector) and named by the classifying terms where eachelement is in turn a list with components pvals, sed, vcov and avsed.a matrix specifying constraints among the variance components withas many rows as there are variance parameters and as many columnsas there are constraints (see below).a logical vector identifying which subset of the rows of the data shouldbe used in the fit. All observations are included by default.family object - a list of functions and expressions for defining thelink and variance functions. This option is under development andcurrently only gaussian with an identity link function is supportedvia the asreml family function asreml.gaussian(). In addition to thelink argument, this function takes an additional dispersion argumentas in asreml.gaussian(link=”identity”,dispersion=NA). The default forasreml.gaussian() is NA which implies that asreml will estimate theparameter, otherwise asreml will fix the parameter at the nominatedvalue.character string or name identifying the column of data to use asweights in the fit.offset character string or name identifying the column of data toinclude as an offset in the model. This is ignored if family=gaussian(link=”identity”).na.method.Ycharacter string to control filtering of missing data in the response.Possible values are "include" (the default), "omit" and "fail". Thisis applied to the model.frame after any subset argument has beenused. The default ("include") is to estimate missing values; thismay be necessary in spatial models to preserve the spatial structure.The value "omit" deletes observations that contain missing values inthe response variate, but missing values in covariates may be retaineddepending on na.method.X.
Page 1 and 2: Analysis of mixed modelsfor S langu
Page 3 and 4: D.G. ButlerQueensland Department of
Page 6: ContentsPreface . . . . . . . . . .
Page 10 and 11: ContentsviiB Available variance mod
Page 12 and 13: List of Figures1.1 Weekly body weig
Page 14 and 15: 1.3 Data sets used 21.2.2 Help and
Page 16 and 17: 1.3 Data sets used 41.3.2 Repeated
Page 18 and 19: 1.3 Data sets used 6101 Sire 1 0102
Page 20 and 21: 2.1 The linear mixed model 8Direct
Page 22 and 23: 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 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 86 and 87:
7.3 Methods and related functions 7
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pedigree7.3 Methods and related fun
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7.3 Methods and related functions 7
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8Examples8.1 IntroductionThis secti
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8.2 Split Plot Design 82In this exa
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8.3 Unbalanced nested design 84[4,]
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8.3 Unbalanced nested design 86adju
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8.4 Sources of variability in unbal
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8.5 Balanced repeated measures 902
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8.5 Balanced repeated measures 92Ta
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8.5 Balanced repeated measures 94wh
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8.6 Spatial analysis of a field exp
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8.6 Spatial analysis of a field exp
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Variety predicted.value standard.er
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8.7 Unreplicated early generation v
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8.7 Unreplicated early generation v
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8.8 Paired Case-Control Study 106Th
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Terms added sequentially; adjusted
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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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