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10 Tabulation of the data and prediction from the model 161polynomials are predicted at their design points. Covariates grouped into a singleterm (using !G qualifier page 49) are treated as covariates.Model terms mv and units are always ignored.Prediction at particular values of a covariate or particular levels of a factor isachieved by listing the values after the variate/factor name. Where there is asequence of values, use the notation a b ... n to represent the sequence of valuesfrom a to n with step size b − a. The default stepsize is 1 (in which case b maybe omitted). A colon (:) may replace the ellipsis (...). An increasing sequenceis assumed. When giving particular values for factors, the default is to use thecoded level (1:n) rather than the label (alphabetical or integer). To use the label,precede it with a quote (").The second step is to specify the averaging set. The default averaging set isthose explanatory variables involved in fixed effect model terms that are not inthe classifying set. By default variables that only define random model terms areignored. The qualifier !AVERAGE allows these variables to be added to the defaultaveraging set.The third step is to select the linear model terms to use in prediction. The defaultis that all model terms based entirely on variables in the classifying and averagingsets are used. Two qualifiers allow this default to be modified by adding (!USE) orremoving (!IGNORE) model terms. The qualifier !ONLYUSE explicitly specifies themodel terms to use, ignoring all others. The qualifier !EXCEPT explicitly specifiesthe model terms not to use, including all others. These qualifiers may implicitlymodify the averaging set by including variables defining terms in the predictedmodel not in the classify set. It is sometimes easier to specify the classify set andthe model terms to use and allow ASReml to construct the averaging set.The fourth step is to choose the weights to use when averaging over dimensionsin the hyper-table. The default is to simply average over the specified levels butthe qualifier !AVERAGE factor weights allows other weights to be specified.For example,yield ∼ site variety !r site.variety at(site).blockpredict varietyputs variety in the classify set, site in the averaging set and block in the ignoreset. Consequently, ASReml forms the site×variety hyper-table from modelterms site, variety and site.variety but ignoring all terms in at(site).block,