ASReml-S reference manual - VSN International

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1.3 Data sets used 41.3.2 Repeated measures on ratsGrowth curve data on the body weights of rats are taken from Box [1950]. A total of 27rats was divided randomly into 3 groups of 10, 7 and 10, respectively. Group 1 were keptas a control, group 2 had thyroxin and group 3 had thiouracil added to their drinkingwater. Five weekly measurements were taken on each individual and the raw results areshown in Figure 1.1.Weight60 80 100 120 140 160 180CCCXTTCTCCXTCTCXTTCXXCXTCTXCXTTXCXCXCCXTCXTTTXCCXCXCCXCXTTTT0 1 2 3 4WeekFig. 1.1. Weekly body weights of rats. C = Control, X = Thyroxin, T = Thiouracil1.3.3 Orange wether trialThree key traits for the Australian wool industry are the weight of wool grown per year,the cleanness and the diameter of that wool. Much of the wool is produced from wethersand most major producers have traditionally used a particular strain or bloodline. Toassess the importance of bloodline differences, many wether trials were conducted. Onetrial was conducted from 1984 to 1988 at Borenore near Orange. It involved 35 teamsof wethers representing 27 bloodlines. The file wether.dat shown below contains greasyfleece weight (kg), yield (percentage of clean fleece weight to greasy fleece weight) andfibre diameter (microns).An extract of orange.csvis given below:Tag, Site, Bloodline, Team, Year, gfw, yield, fdiam0101, 3, 21, 1, 1, 5.6, 74.3, 18.50101, 3, 21, 1, 2, 6.0, 71.2, 19.6
1.3 Data sets used 50101, 3, 21, 1, 3, 8.0, 75.7, 21.50102, 3, 21, 1, 1, 5.3, 70.9, 20.80102, 3, 21, 1, 2, 5.7, 66.1, 20.90102, 3, 21, 1, 3, 6.8, 70.3, 22.10103, 3, 21, 1, 1, 5.0, 80.7, 18.90103, 3, 21, 1, 2, 5.5, 75.5, 19.90103, 3, 21, 1, 3, 7.0, 76.6, 21.9..4013, 3, 43, 35, 1, 7.9, 75.9, 22.64013, 3, 43, 35, 2, 7.8, 70.3, 23.94013, 3, 43, 35, 3, 9.0, 76.2, 25.44014, 3, 43, 35, 1, 8.3, 66.5, 22.24014, 3, 43, 35, 2, 7.8, 63.9, 23.34014, 3, 43, 35, 3, 9.9, 69.8, 25.54015, 3, 43, 35, 1, 6.9, 75.1, 20.04015, 3, 43, 35, 2, 7.6, 71.2, 20.34015, 3, 43, 35, 3, 8.5, 78.1, 21.71.3.4 Beef cattle dataThese data appear among the examples in Harvey [1977] and are originally from Harvey[1960]. The data comprise 65 observations on individual calves indexed by factors Lineand Sire within line. The data as used here contain a covariate ageOfDam and 3 responsevariates average daily gain, age and initial weight labelled as y1, y2 and y3, respectively.An extract from harvey.dat is given below:Calf Sire Dam Line ageOfDam y1 y2 y3101 Sire 1 0 1 3 192 390 224102 Sire 1 0 1 3 154 403 265103 Sire 1 0 1 4 185 432 241104 Sire 1 0 1 4 183 457 225105 Sire 1 0 1 5 186 483 258106 Sire 1 0 1 5 177 469 267107 Sire 1 0 1 5 177 428 271108 Sire 1 0 1 5 163 439 247109 Sire 2 0 1 4 188 439 229110 Sire 2 0 1 4 178 407 226.161 Sire 9 0 3 4 184 483 244162 Sire 9 0 3 5 180 425 266163 Sire 9 0 3 5 177 420 246164 Sire 9 0 3 5 175 449 252165 Sire 9 0 3 5 164 405 242In a genetic analysis we can specify the relationship among individuals in a pedigreefile. This is a simple text file with columns for the individual’s identity and its male andfemale parents. The first 20 line of the pedigree file harvey.ped associated with thesedata are:Calf SireDam
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 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
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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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