ASReml-S reference manual - VSN International

More documents

Recommendations

Info

6.4 Complicated weighting 666.4 Complicated weightingGenerally, when forming a prediction table, it is necessary to average over (or ignore)some potential dimensions of the prediction table. By default, asreml uses equal weights(1/f for a factor with f levels). More complicated weighting is achieved by using theaverage argument to set specific (unequal) weights for each level of a factor. However,sometimes the weights to be used need to be defined with respect to two or more factors.The simplest case is when there are missing cells and weighting is equal for those cellsin a multiway table that are present; achieved by using the present argument. This isfurther generalized by allowing weights for use by the present averaging process via anamed component prwts of the present list.The factors in the table of weights are specified with the present argument and the tableof weights with the prwts component of the present list. There may be a maximum of twoindependent lists of factors in the present list, and, if specified prwts applies to the firstlist only. The order of factors in the tables of weights must correspond to the order inthe present list with later factors nested within preceding factors. Check the output toensure that the values in the tables of weights are applied in the correct order.Consider a rather complicated example from a rotation experiment conducted over severalyears. This particular analysis followed the analysis of the daily live weight gain perhectare of the sheep grazing the plots. There were periods when no sheep grazed. Differentflocks grazed in the different years. Daily liveweight gain was assessed between 5 and 8times in the various years. To obtain a measure of total productivity in terms of sheepliveweight, we need to weight the daily per sheep figures by the number of sheep grazingdays per month. Treatment effects for each year can be obtained from:predict(obj.asr, classify = "year:crop:pasture:lime",levels = list("year:crop:pasture:lime" = list(year=1,crop=1)),average = list("year:crop:pasture:lime" = list(month=c(56,55,56,53,57,63 rep(0,6)))))predict(obj.asr, classify = "year:crop:pasture:lime",levels = list("year:crop:pasture:lime" = list(year=2,crop=1)),average = list("year:crop:pasture:lime" = list(month=c(36,0,0,53,23,24,54,54,43,35,0,0))))predict(obj.asr, classify = "year:crop:pasture:lime",levels = list("year:crop:pasture:lime" = list(year=3,crop=1)),average = list("year:crop:pasture:lime" = list(month=c(70,0,21,17,0,0,0,70,0,0,53,0))))predict(obj.asr, classify = "year:crop:pasture:lime",levels = list("year:crop:pasture:lime" = list(year=4,crop=1)),average = list("year:crop:pasture:lime" = list(month=c(53,56,22,92,19,44,0,0,36,0,0,49))))predict(obj.asr, classify = "year:crop:pasture:lime",levels = list("year:crop:pasture:lime" = list(year=5,crop=1)),average = list("year:crop:pasture:lime" = list(month=c(0,22,0,53,70,22,0,51,16,51,0,0))))and averages over years from:
6.5 Further examples 67predict(obj.asr, classify="crop:pasture:lime",levels = list("crop:pasture:lime" = list(crop=1)),present = list("crop:pasture:lime" = list(c("year","month"),prwts=c(56,55,56,53,57,63,0,0,0,0,0,0,36,0,0,53,23,24,54,54,43,35,0,0,70,0,,21,17,0,0,0,70,0,0,53,0,53,56,22,92,19,44,0,0,36,0,0,49,0,22,0,53,70,22,0,51,16,51,0,0}/5)))Both sets of predict() calls are given to show how the weights were derived and used.Notice that the order in c("year", "month") implies that the weight coefficients arepresented in standard order with the levels for months cycling within levels for years.6.5 Further examples• Predict variety means from an RCB analysis of the NIN field trial data.> nin89.asr nin89.pv nin89.asr nin89.pv nin89.asr nin89.pv obj.asr obj.pv obj.pv orange.asr orange.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 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 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:
8.9 Balanced longitudinal data - Ra
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?