ASReml-S reference manual - VSN International

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1.3 Data sets used 21.2.2 Help and referencesGetting helpDocumentation for the asreml function, support functions and related methods are availablein Windows help format and in HTML form on Linux platforms. Typically, helpis available via the standard help mechanism; that is, help(asreml) or ?asreml displaysthe asreml documentation in text or HTML form depending on implementation and helpsystem state. The function asreml.man() displays a copy of this manual in PDF form.The statistical theory underlying the modelling illustrated in this manual is introducedin Chapter 2. An extended discussion, with special reference to the fitting of variancemodels to structures at the residual (R) and non-residual (random, G) levels, will appearin detail in a forthcoming publication.1.2.3 ConventionsThis manual uses the following typographic conventions:this font is used to denote operating system commands;this font is used to indicate user supplied arguments to operating system commands,including filenames.this font is used for ASReml-S function examples; this font for other S-PLUS functionsand their associated arguments,this font is used for emphasis and user supplied variables to S-PLUS functions,this font is used for verbatim output of S-PLUS function calls.The S-PLUS command prompt is denoted by ”>” and the operating system prompt by”%”.1.2.4 Using this guideChapter 3Chapter 4Chapter 8Users may find the introductory sections of Chapter 3 useful before reading further. Thisgives an introduction to analysis in ASReml-S using an example from the literature andcovers some common tasks from creating a data frame to setting initial values for variancecomponents.Variance modelling is a complex aspect of linear mixed modelling. Chapter 4 gives detailsof variance modelling in ASReml-S. You should refer to this chapter if you wish to fitmore complex variance models.Chapter 8 presents a wide range of additional worked examples.1.3 Data sets used1.3.1 Nebraska Intrastate Nursery (NIN) field experimentThe yield data from an advanced Nebraska Intrastate Nursery (NIN) breeding trial conductedat Alliance in 1988/89 are taken from Stroup et al. [1994]. Four replicates of 19released cultivars, 35 experimental wheat lines and 2 additional triticale lines were laid
1.3 Data sets used 3out in a 22 row by 11 column rectangular array of plots; the varieties were allocated to theplots using a randomised complete block (RCB) design. In field trials, complete replicatesare typically allocated to consecutive groups of whole columns or rows. In this trial thereplicates were not allocated to groups of whole columns, but rather, overlapped columns.Table 1.1 gives the allocation of varieties to plots in field plan order with replicates 1 and3 in ITALICS and replicates 2 and 4 in BOLD.Table 1.1: Trial layout and allocation of varieties to plots in the NIN field trialcolumnrow 1 2 3 4 5 6 7 8 9 10 111 - NE83407 BUCKSKIN NE87612 VONA NE87512 NE87408 CODY BUCKSKIN NE87612 KS8313742 - CENTURA NE86527 NE87613 NE87463 NE83407 NE83407 NE87612 NE83406 BUCKSKIN NE864823 - SCOUT66 NE86582 NE87615 NE86507 NE87403 NORKAN NE87457 NE87409 NE85556 NE856234 - COLT NE86606 NE87619 BUCKSKIN NE87457 REDLAND NE84557 NE87499 BRULE NE865275 - NE83498 NE86607 NE87627 ROUGHRIDER NE83406 KS831374 NE83T12 CENTURA NE86507 NE874516 - NE84557 ROUGHRIDER - NE86527 COLT COLT NE86507 NE83432 ROUGHRIDER NE874097 - NE83432 VONA CENTURA SCOUT66 NE87522 NE86527 TAM200 NE87512 VONA GAGE8 - NE85556 SIOUXLAND NE85623 NE86509 NORKAN VONA NE87613 ROUGHRIDER NE83404 NE834079 - NE85623 GAGE CODY NE86606 NE87615 TAM107 ARAPAHOE NE83498 CODY NE8761510 - CENTURAK78 NE83T12 NE86582 NE84557 NE85556 CENTURAK78 SCOUT66 - NE87463 ARAPAHOE11 - NORKAN NE86T666 NE87408 KS831374 TAM200 NE87627 NE87403 NE86T666 NE86582 CHEYENNE12 - KS831374 NE87403 NE87451 GAGE LANCOTA NE86T666 NE85623 NE87403 NE87499 REDLAND13 - TAM200 NE87408 NE83432 NE87619 NE86503 NE87615 NE86509 NE87512 NORKAN NE8343214 - NE86482 NE87409 CENTURAK78 NE87499 NE86482 NE86501 NE85556 NE87446 SCOUT66 NE8761915 - HOMESTEAD NE87446 NE83T12 CHEYENNE BRULE NE87522 HOMESTEAD CENTURA NE87513 NE8349816 LANCER LANCOTA NE87451 NE87409 NE86607 NE87612 CHEYENNE NE83404 NE86503 NE83T12 NE8761317 BRULE NE86501 NE87457 NE87513 NE83498 NE87613 SIOUXLAND NE86503 NE87408 CENTURAK78 NE8650118 REDLAND NE86503 NE87463 NE87627 NE83404 NE86T666 NE87451 NE86582 COLT NE87627 TAM20019 CODY NE86507 NE87499 ARAPAHOE NE87446 - GAGE NE87619 LANCER NE86606 NE8752220 ARAPAHOE NE86509 NE87512 LANCER SIOUXLAND NE86607 LANCER NE87463 NE83406 NE87457 NE8455721 NE83404 TAM107 NE87513 TAM107 HOMESTEAD LANCOTA NE87446 NE86606 NE86607 NE86509 TAM10722 NE83406 CHEYENNE NE87522 REDLAND NE86501 NE87513 NE86482 BRULE SIOUXLAND LANCOTA HOMESTEAD
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 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 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
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5Genetic analysis5.1 IntroductionIn
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5.2 Pedigree, G-inverse objects and
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5.4 Using Pedigree and G-inverse ob
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6Prediction from the linear model6.
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Optional argumentslevels6.2 The pre
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6.2 The predict method 64The predic
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6.4 Complicated weighting 666.4 Com
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7The asreml class and related metho
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maxiter maximum number of iteration
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7.3 Methods and related functions 7
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pedigree7.3 Methods and related fun
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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
Page 142 and 143:
References 130W. W. Stroup, P. S. B
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Index 132F statistics, 17fa(,k), 49
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