ASReml-S reference manual - VSN International

More documents

Recommendations

Info

List of Figures1.1 Weekly body weights of rats. C = Control, X = Thyroxin, T = Thiouracil 46.1 Predict tree structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 648.1 Residual plot for the rat data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 868.2 Residuals vs fitted values for the voltage data . . . . . . . . . . . . . . . . . . . . . . . . . 898.3 Trellis plot of plant height for each of 14 plants . . . . . . . . . . . . . . . . . . . . . . . 908.4 residual ∼ Plant | Time for the exp() variance model for the plantdata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 938.5 Sample variogram of the AR1×AR1 model for the Slate Hall data . . . . . . . 978.6 Sample variogram of the AR1×AR1 model for the Tullibigeal data . . . . . . 1028.7 Sample variogram of the AR1×AR1 + pol(column,-1) model for theTullibigeal data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1038.8 Rice bloodworm data: Plot of square root of root weight for treatedversus control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1068.9 BLUPs for treated plotted against BLUPs for control . . . . . . . . . . . . . . . . . . 1138.10 Estimated deviations from regression of treated on control for eachvariety plotted against estimate for control . . . . . . . . . . . . . . . . . . . . . . . . . . . 1148.11 Estimated difference between control and treated for each variety plottedagainst estimate for control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1148.12 Trellis plot of trunk circumference (mm) for each tree against age in dayssince 1 December 1968. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1158.13 Fitted cubic smoothing spline for tree 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1178.14 Plot of fitted cubic smoothing spline for model 1 . . . . . . . . . . . . . . . . . . . . . . 1198.15 Fitted values adjusted for Season and approximate confidence intervalsfor model 6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
1Introduction1.1 What ASReml-S can doASReml-S is designed to fit the general linear mixed model to moderately large data setswith complex variance models. ASReml-S has application in the analysis of• (un)balanced longitudinal data,• repeated measures data (multivariate analysis of variance and spline type models),• (un)balanced designed experiments,• multi-environment trials and meta analysis• regular or irregular spatial data.The computational engine of ASReml-S is the algorithm of Gilmour et al. [1995] adaptedfrom the standalone program ASReml [Gilmour et al., 2002]. The computational efficiencyof ASReml-S arises from using this Average Information REML algorithm (givingquadratic convergence) and sparse matrix operations. However, because of overheads inherentin S language implementations, some very large problems may need to use thestandalone ASReml program to overcome memory limitations.The asreml function returns an object of class asreml. Standard methods resid() andfitted() work with this object and methods for coef(), summary(), plot(), wald() andpredict() also exist.1.2 Getting started1.2.1 InstallationProduction versions of asreml for S language environments are available for several implementationson Microsoft Windows and Linux systems. Installation varies with eachsystem and instructions are contained in a separate document distributed with the archiveor available from the web site. If the instructions are inadequate then please contact VSNInternational at www.VSNi.co.uk for support.
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 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 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
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:
Page 88 and 89:
pedigree7.3 Methods and related fun
Page 90 and 91:
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 128 and 129:
Page 130 and 131:
Page 132 and 133:
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
Page 140 and 141:
ReferencesG. E. P. Box. Analysis of
Page 142 and 143:
References 130W. W. Stroup, P. S. B
Page 144 and 145:
Index 132F statistics, 17fa(,k), 49
show all

ASReml-S reference manual - VSN International

Create successful ePaper yourself

Delete template?

Save as template?