ASReml-S reference manual - VSN International

More documents

Recommendations

Info

8.7 Unreplicated early generation variety trial 1020.80.60.40.28660Column430405021020RowFig. 8.6. Sample variogram of the AR1×AR1 model for the Tullibigeal data> summary(wheat3.asr)$loglik[1] -4225.631> summary(wheat3.asr)$varcompgamma component std.error z.ratio constraintVariety 1.1436952 8.898632e+04 8.976677e+03 9.913058 PositiveR!variance 1.0000000 7.780597e+04 8.854452e+03 8.787215 PositiveR!Row.ar1 0.6714360 6.714360e-01 4.287844e-02 15.659058 UnconstrainedR!Column.ar1 0.2660882 2.660882e-01 7.541536e-02 3.528303 Unconstrained> wald(wheat3.asr)Wald tests for fixed effectsResponse: yieldTerms added sequentially; adjusted for those aboveDf Sum of Sq Wald statistic Pr(Chisq)(Intercept) 1 551060049 7082 < 2.2e-16 ***weed 1 7155306 92 < 2.2e-16 ***pol(Column, -1) 1 679668 9 0.003121 **residual (MS) 77806> summary(wheat3.asr)$coef.fixedsolution std error z ratiopol(Column, -1) -139.5638 47.22053 -2.955575weed -182.7066 21.83804 -8.366439
(Intercept) 2872.7366 34.82716 82.4855248.7 Unreplicated early generation variety trial 1030.80.60.40.28660Column430405021020RowFig. 8.7. Sample variogram of the AR1×AR1 + pol(column,-1) model for the Tullibigeal dataThe linear regression of column number on yield is significant (Wald statistic = 8.74).The sample variogram (Figure 8.7) seems more satisfactory, though interpretation ofvariograms is often difficult, particularly for unreplicated trials. This is an issue for furtherresearch.The final model includes a nugget effect:> wheat4.asr summary(wheat4.asr)$loglik[1] -4220.261> summary(wheat4.asr)$varcompgamma component std.error z.ratio constraintVariety 1.3482286 7.377148e+04 1.041705e+04 7.081800 Positiveunits 0.5563897 3.044417e+04 8.074917e+03 3.770214 PositiveR!variance 1.0000000 5.471734e+04 1.062709e+04 5.148857 PositiveR!Row.ar1 0.8374999 8.374999e-01 4.486909e-02 18.665407 UnconstrainedR!Column.ar1 0.3753791 3.753791e-01 1.152641e-01 3.256687 UnconstrainedThe increase in REML log-likelihood from adding the units term is significant. Predictedvariety means can be obtained from this model using> wheat4.pv
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 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 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 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
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?