ASReml-S reference manual - VSN International

Optional argumentslevels6.2 The predict method 62a list of length the number of classified tables, and named by the classify set.Each list component is also a list, named by the margins of the classified table,of vectors specifying the levels at which predictions are required. If omitted,factors are predicted at each level, simple covariates are predicted at theiroverall mean and covariates used as a basis for splines or orthogonal polynomialsare predicted at their design points. The factors mv and units are alwaysignored.averagepresentparallelexceptignoreuseonlyusealiaseda list of length the number of classified tables, and named by the classify set,specifying which variables to include in the averaging set and optional vectorsof weights. Optionally, each component of the list is also a list, named by themargins of the classified table, of vectors specifying the weights to use in theaveraging process. If omitted, equal weights are used.a list of length the number of classified tables, and named by the classify set,specifying which variables to include in the present set for each table. Thepresent set is used when averaging is to be based only on cells with data. Eachcomponent of the list may be a character vector specifying the variables to beused in present averaging, or, may in turn also be a list. In this case, therecan be a maximum of two components, each a character vector of variablenames, representing non-overlapping present categorisations and one optionalcomponent named prwts containing a vector of weights to be used for averagingthe first present table only. The vector(s) of names may include variables inthe classify set but not those in the average set. At present, the model mustcontain the factor as a simple main effect.a list of length the number of classified tables, and named by the classify set,specifying which variables to expand in parallel. Each component of the list isa character vector specifying up to four of the classifying variables to expandin parallel.a list of length the number of classified tables, and named by the classify set,specifying which variables to exclude in the prediction process. That is, theprediction model includes all fitted model terms not in the exclude list. Eachcomponent of the list is a character vector specifying the variables to be excluded.a list of length the number of classified tables, and named by the classify set,specifying which variables to ignore in the prediction process. Each componentof the list is a character vector specifying the variables to be ignored.a list of length the number of classified tables, and named by the classify set,specifying which variables to add to the prediction model after the default ruleshave been invoked. Each component of the list is a character vector specifyingthe variables to be used.a list of length the number of classified tables, and named by the classify set,specifying which variables form the prediction model, that is, the default rulesare not invoked. Each component of the list is a character vector specifying thevariables to be used.a list of length the number of classified tables, and named by the classify set.Each component is a logical scalar (default F) that determines whether or notthe predicted value is returned for non-estimable functions.