ASReml-S reference manual - VSN International

More documents

Recommendations

Info

Variety predicted.value standard.error estStatus1 1 1245.582 97.87621 Estimable2 2 1516.234 97.86434 Estimable3 3 1403.984 98.25699 Estimable4 4 1404.918 98.00456 Estimable5 5 1471.612 98.37778 Estimable...23 23 1316.874 98.05743 Estimable24 24 1557.522 98.14444 Estimable25 25 1573.888 97.99763 Estimable> barley2.pv$predictions$Variety$avsed[1] 60.510858.7 Unreplicated early generation variety trial 100> barley3.pv barley3.pv$predictions$Variety$pvalsVariety predicted.value standard.error estStatus1 1 1283.587 60.1994 Estimable2 2 1549.013 60.1994 Estimable3 3 1420.931 60.1994 Estimable4 4 1451.855 60.1994 Estimable5 5 1533.275 60.1994 Estimable...23 23 1329.109 60.1994 Estimable24 24 1546.470 60.1994 Estimable25 25 1630.629 60.1994 Estimable> barley3.pv$predictions$Variety$avsed[1] 62.01934Notice the differences in SEs and SEDs associated with the various models. Choosing amodel on the basis of smallest SE or SED is not recommended because the model is notnecessarily fitting the variability present in the data.8.7 Unreplicated early generation variety trialThis example is a further illustration of the approach to the analysis of field trials presentedin the previous section. The data are from an unreplicated field experiment conductedat Tullibigeal in south-western NSW. The trial was an S1 (early stage) wheatvariety evaluation trial and consisted of 525 test lines which were randomly assigned toplots in a 67 row × 10 column array. There was a check plot variety every 6 plots withineach column. That is, the check variety was sown on rows 1,7,13,. . . ,67 of each column.This variety was numbered 526. A further 6 replicated commercially available varieties(numbered 527 to 532) were also randomly assigned to plots with between 3 to 5 plots ofeach. The aim of these trials is to identify and retain the top, say 20%, lines for furthertesting. Cullis et al. [1989] considered the analysis of early generation variety trials andpresented a one-dimensional spatial analysis which was an extension of the approachdeveloped by Gleeson and Cullis [1987]. The test line effects are assumed random, whilethe check variety effects are considered fixed. This may not be sensible or justifiable formost trials and can lead to inconsistent comparisons between check varieties and test
8.7 Unreplicated early generation variety trial 101lines. Given the large amount of replication afforded to check varieties there will be verylittle shrinkage irrespective of the realised heritability.In the following we assume that the variety effect (including both check, replicated andunreplicated lines) is random. In addition to a one dimensional analysis we consider threefurther spatial models for comparison.> wheat names(wheat)[1] "yield" "weed" "Column" "Row" "Variety"where Variety, Row and Column are factors, yield is the response variate and weed isa covariate. Note that the data frame is sorted as Column nested within Row.We begin with a one-dimensional spatial model, which assumes the variance model forthe plot effects within columns is described by a first order autoregressive process.> wheat1.asr summary(wheat1.asr)$loglik[1] -4239.88> summary(wheat1.asr)$varcompgamma component std.error z.ratio constraintVariety 0.9594791 8.279572e+04 9.217376e+03 8.982569 PositiveR!variance 1.0000000 8.629236e+04 9.462026e+03 9.119861 PositiveR!Row.ar1 0.6723405 6.723405e-01 4.184392e-02 16.067817 UnconstrainedThe REML estimate of the autoregressive parameter indicates substantial within columnheterogeneity.A two dimensional spatial model is fitted with:> wheat2.asr summary(wheat2.asr)$loglik[1] -4233.647> summary(wheat2.asr)$varcompgamma component std.error z.ratio constraintVariety 1.0603771 8.811748e+04 8.884899e+03 9.917669 PositiveR!variance 1.0000000 8.310014e+04 9.340520e+03 8.896736 PositiveR!Row.ar1 0.6853871 6.853871e-01 4.115303e-02 16.654595 UnconstrainedR!Column.ar1 0.2859093 2.859093e-01 7.390416e-02 3.868650 UnconstrainedThe change in REML log-likelihood is significant (χ 2 1 = 12.46, P < 0.001) with the inclusionof the autoregressive parameter for Column. The sample variogram of the residualsfor the ar1×ar1 model, Figure 8.6, indicates a linear drift from column 1 to column 10.We include a linear regression coefficient pol(Column,-1) in the model to account forthis.> wheat3.asr
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 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
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

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?