ASReml-S reference manual - VSN International

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7.3 Methods and related functions 78normally obtained from a call to asreml setting the start.values argument to TRUE. Adata frame view of the object is created and written to a temporary file for editing.The edited file is read back in and merged with the original list object. The defaulteditor (options()$editor) is invoked to edit the file. On UNIX systems, the editor is openedoptionally in a separate window - useful for vi sessions - set window to FALSE for windowbased editors.7.3.12 asreml.read.tableUsageasreml.read.table(...)Required arguments... arguments are as for read.table(). One of header or col.names must be specifiedto name the variables.Valuea data frame with as many rows as the file has lines (or one less if header=T) and asmany variables as the file has fields (or one less if one variable was used for row names).Fields are initially read in as character data. If all the items in a field are numeric, thecorresponding variable is numeric. Otherwise, it is a factor except as controlled by theas.is argument. All lines must have the same number of fields (except the header, whichcan have one less if the first field is to be used for row names). asreml.read.table invokesa simple convention: fields whose name begins with a capital letter are converted tofactors.7.3.13 asreml.variogramUsageasreml.variogram(x, y, z, composite = T, metric = c(”euclidean”, ”manhattan”), angle = 0,angle.tol = 180, nlag = 20, maxdist = 0.5, xlag = NA, lag.tol = NA, grid = T)Required argumentsxzOptional argumentsycompositemetricangleangle.tolnumeric vector of x coordinates. May also be a matrix or data frame with3 columns which are taken to be the (x,y) coordinates and the response z,respectively.response vector.numeric vector of y coordinatesfor data on a regular grid. If TRUE, the average of the variograms in quadrants(x,y) and(x,-y) is returned. Otherwise, both variograms are returnedand identified as quadrants 1 and 4.distance between (x, y) points. Valid measures are “euclidean” or “manhattan”.a vector of directions. Angles are measured in degrees anticlockwise fromthe x axis. Default is 0.the angle subtended by each direction. That is, an arc angle ± angle.tol/2.Default is 180 which gives an omnidirectional variogram.nlag the maximum number of lags. Default is 20.maxdistthe fraction of the maximum distance to include in the calculation. Thedefault is half the maximum distance in the data.
7.3 Methods and related functions 79xlagthe width of the lags. If missing, xlag is set to maxdist / nlag.lag.tol the distance tolerance. If missing, lag.tol is set to xlag / 2.gridif TRUE, forces polar variograms if (x,y) specifies a regular grid.Valuea dataframe including the following components:xthe original x coordinates.ygammadistancenpanglethe original y coordinates.the variogram estimate.the average distance for pairs in the lag.the number of pairs in the lag.direction if not a regular grid.DetailsFor one dimensional data the y coordinates need not be supplied and a vector of ones isgenerated. If grid = T the data is assumed sorted with the x coordinates changing thefastest; an error is returned if this is not the case.7.3.14 addAsremlMenuaddAsremlMenu()ValueTRUE if successful.DetailsA drop-down asreml specific menu is added to the main menu bar on MS Windows platforms.A call to addAsremlMenu() is included in the installation procedure.7.3.15 removeAsremlMenuremoveAsremlMenu()ValueTRUE if successful.DetailsRemoves the drop-down asreml specific menu from the main S-PLUS menu bar.
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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
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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
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
Page 134 and 135: A.3 Aliasing and singularities 122W
Page 136 and 137: Table B.1: Details of the available
Page 138 and 139: 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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