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5 Command file: Reading the data 67Table 5.4: List of occasionally used job control qualifiersqualifieraction!ASMV n!ASUV!COLFAC vindicates a multivariate analysis is required although the datais presented in a univariate form. ’Multivariate Analysis’ isused in the narrow sense where an unstructured error variancematrix is fitted across traits, records are independent, andobservations may be missing for particular traits, see Chapter8 for a complete discussion.The data is presumed arranged in lots of n records where n isthe number of traits. It may be necessary to expand the datafile to achieve this structure, inserting a missing value NA onthe additional records. This option is sometimes relevant forsome forms of repeated measures analysis. There will need tobe a factor in the data to code for trait as the intrinsic Traitfactor is undefined when the data is presented in a univariatemanner.indicates that a univariate analysis is required although thedata is presented in a multivariate form. Specifically, it allowsyou to have an error variance other than I ⊗ Σ where Σ isthe unstructured (US, see Table 7.3) variance structure. Ifthere are missing values in the data, include !f mv on theend of the linear model. It is often also necessary to specifythe !S2==1 qualifier on the R-structure lines. The intrinsicfactor Trait is defined and may be used in the model. SeeChapter 8 for more information.This option is used for repeated measures analysis when thevariance structure required is not the standard multivariateunstructured matrix.is used with !SECTION v and !ROWFAC v to instruct ASReml toset up R structures for analysing a multi-environment trialwith a separable first order autoregressive model for each site(environment). v is the name of a factor or variate containingcolumn numbers (1 . . . n c where n c is the number of columns)on which the data is to be sorted. See !SECTION for moredetail.