ASReml-S reference manual - VSN International

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pedigree7.3 Methods and related functions 76a data frame with three columns that correspond to the individual, male parentand female parent, respectively. The row giving the pedigree of an individualmust appear before any row where that individual appears as a parent.Founders use 0 (zero) or NA in the ancestor columns.Optional argumentsgroupsselfinginBreedmgsValueincludes genetic groups in the pedigree. The first g lines of the pedigree identifygenetic groups (with zero in both the male and female parent columns). Allother rows must specify one of the genetic groups as sire or dam if the actualparent is unknown.allows for partial selfing when the third field of pedigree is unknown. It indicatesthat progeny from a cross where the male parent is unknown is assumed to befrom selfing with probability s and from outcrossing with probability (1 − s).This is appropriate in some forestry tree breeding studies where seed collectedfrom a tree may have been pollinated by the mother tree or pollinated by someother tree [Dutkowski and Gilmour, 2001]. Do not use the selfing argument inconjunction with inBreed or mgs.the inbreeding coefficient for base individuals. This argument generates thenumerator relationship matrix for inbred lines. Each cross is assumed to beselfed several times to stabilize as an inbred line as is usual for cereal crops, forexample, before being evaluated or crossed with another line. Since inbreedingis usually associated with strong selection, it is not obvious that a pedigreeassumption of covariance of 0.5 between parent and offspring actually holds.The inBreed argument cannot be used in conjunction with selfing or mgs.if TRUE, the third identity in the pedigree is the male parent of the femaleparent (maternal grand-sire) rather than the female parent.a list with the following components:ginv a data frame with 3 columns holding the lower triangle of the inverse relationshipmatrix in sparse form. The first 2 columns are the row and column indices of thematrix element, respectively, and the third column holds the (inverse) matrix elementitself. Sort order is columns within rows, that is, the lower triangle row-wise. Thisdata frame has an attribute rowNames containing the vector of identifiers for the rowsmatrix.ainv the diagonal elements of the inverse relationship matrix.inbreeding the inbreeding coefficient for each individual, calculated as diag(A − I).det the determinant.Detailsasreml.Ainverse uses the method of Meuwissen and Luo [1992] to compute the inverserelationship matrix directly from the pedigree.7.3.10 asreml.constraintsUsageasreml.constraints(formula, gammas, drop.unused.levels=T, intercept=F, na.action=na.include)Required arguments
formulagammas7.3 Methods and related functions 77a model formula including at least one factor f of length n p , where n p is thenumber of variance parameters among which constraints are to be applied; thelevels of f correspond to the appropriate subset of κ, the n κ vector of varianceparameters. The number of distinct levels in f is less than n p and n p ≤ n κ .a data frame with n p rows in which to resolve the names in formula. That is,gammas must have at least one column, named f.Optional argumentsdrop.unused.levelsif TRUE, unused levels of factors are dropped.interceptif FALSE, the intercept is omitted from the model.na.actionValuehow missing values are handled. Possible options are na.include (thedefault), na.omit or na.fail.a n p ×n c matrix M specifying the variance parameter constraints, where n c is the lengthof the reduced vector of variance components. In the simplest case, for example, wheretwo parameters are constrained to be equal, n c = n p − 1 and the appropriate column ofM will contain ones in the rows corresponding to the parameters in question and zeroselsewhere.Detailsvariance parameter constraints can be specified by a matrix M in κ = Mθ + Es whereκ is the (full) n κ vector of unconstrained components and θ is the n c constrained vector.In asreml, a subset n p of the rows of M, typically those components to be constrained,may be specified. A default data frame (gammas.table) containing one factor, Gammas,whose levels are the n κ names of the variance parameters is returned by asreml whenstart.values=T. The n p row subset of this data frame may provide a convenient objectto hold the factors mentioned in formula. The matrix M is obtained from a call tomodel.frame.default() using formula and the factor(s) corresponding to Gammas.7.3.11 asreml.gammas.edUsageasreml.gammas.ed(param.list, window=T, display=getenv(”DISPLAY”))Required argumentsparam.listOptional argumentswindowa list containing components named G.param and R.param generated froma call to asreml(..., start.values=T). Each of these components is a list objectrepresenting the random and rcov model formulae and contains initialparameter values and constraints.UNIX only. If TRUE, the edit session is opened in a separate window.displayUNIX only. The X display to open the window in if window is TRUE.ValueThe edited version of the list object(s) from calls to asreml.gdflt() and/or asreml.rdflt()internally by asreml().Side effectsA temporary data file beginning with the string gammas is created and removed on exit.DetailsAllows editing of the initial value and constraint components of G and R list structures
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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
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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 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
Page 136 and 137: 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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