Download pdf guide - VSN International

6 Command file: Specifying the terms in the mixed model 1016.10 Missing valuesMissing values in the responseIt is sometimes computationally convenient toestimate missing values, for example, in spatialanalysis of regular arrays, see example 3ain Section 7.3. Missing values are estimated ifthe model term mv is included in the model.Formally, mv creates a factor with a covariatefor each missing value. The covariates arecoded 0 except in the record where the particularmissing value occurs, where it is coded-1.NIN Alliance Trial 1989variety.row 22column 11nin89.asd !skip 1yield ∼ mu variety !r repl,!f mv1 211 column AR1 .42422 row AR1 .904The action when mv is omitted from the model depends on whether a univariateor multivariate analysis is being performed. For a univariate analysis, ASRemldiscards records which have a missing response. In multivariate analyses, allrecords are retained and the R matrix is modified to reflect the missing valuepattern.Missing values in the explanatory variablesASReml will abort the analysis if it finds missing values in the design matrix unless!MVINCLUDE or !MVREMOVE is specified, see Section 5.8. !MVINCLUDE causes themissing value to be treated as a zero. !MVREMOVE causes ASReml to discard thewhole record. Records with missing values in particular fields can be explicitlydropped using the !D transformation, Table 5.1.Covariates: Treating missing values as zero in covariates is usually only sensibleif the covariate is centred (has mean of zero).Design factors: Where the factor level is zero (or missing and the !MVINCLUDEqualifier is specified), no level is assigned to the factor for that record.6.11 Some technical details about model fitting in ASRemlSparse versus denseASReml partitions the terms in the linear model into two parts: a dense setand a sparse set. The partition is at the !r point unless explicitly set with