Predict Directive

Underlying Principles

Our approach to prediction is a generalisation of that of Lane and Nelder (1982) who consider fixed effects models. They form fitted values for all combinations of the explanatory variables in the model, then take marginal means across the explanatory variables not relevent to the current prediction. Our case is more general in that random effects can be fitted in our (mixed) models. A full description can be found in Gilmour et al. (2004) and Welham et al. (2004).

Random factor terms may contribute to predictions in several ways. They may be evaluated at values specified by the user, they may be averaged over, or they may be omitted from the fitted values used to form the prediction. Averaging over the set of random effects gives a prediction specific to the random effects observed. We call this a `conditional' prediction. Omitting the term from the model produces a prediction at the population average (zero), that is, substituting the assumed population mean for an unknown random effect. We call this a `marginal' prediction. Note that in any prediction, some terms may be evaluated as conditional and others at marginal values, depending on the aim of prediction.

For fixed factors there is no pre-defined population average, so there is no natural interpretation for a prediction derived by omitting a fixed term from the fitted values. Averages must therefore be taken over all the levels present to give a sample specific average, or prediction must be at specified levels.

For covariate terms (fixed or random) the associated effect represents the coefficient of a linear trend in the data with respect to the covariate values. These terms should be evaluated at a given value of the covariate, or averaged over several given values. Omission of a covariate from the predictive model is equivalent to predicting at a zero covariate value, which is often inappropriate.

Interaction terms constructed from factors generate an effect for each combination of the factor levels, and behave like single factor terms in prediction. Interactions constructed from covariates fit a linear trend for the product of the covariate values and behave like a single covariate term. An interaction of a factor and a covariate fits a linear trend for the covariate for each level of the factor. For both fixed and random terms, a value for the covariate must be given, but the factor may be evaluated at a given level, averaged over or (for random terms) omitted.

Before considering some examples in detail, it is useful to consider the conceptual steps involved in the prediction process. Given the explanatory variables used to define the linear (mixed) model, the four main steps are
  • a: Choose the explanatory variable(s) and their respective value(s) for which predictions are required; the variables involved will be referred to as the classify set and together define the multiway table to be predicted.
  • b: Determine which variables should be averaged over to form predictions. The values to be averaged over must also be defined for each variable; the variables involved will be referred to as the averaging set. The combination of the classify set with these averaging variables defines a multiway hyper-table. Note that variables evaluated at only one value, for example, a covariate at its mean value, can be formally introduced as part of the classifying or averaging set.
  • c: Determine which terms from the linear mixed model are to be used in forming predictions for each cell in the multiway hyper-table in order to give appropriate conditional or marginal prediction.
  • d: Choose the weights to be used when averaging cells in the hyper-table to produce the multiway table to be reported.

    Note that after steps (a) and (b) there may be some explanatory variables in the fitted model that do not classify the hyper-table. These variables occur in terms that are ignored when forming the predicted values. It was concluded above that fixed terms could not sensibly be ignored in forming predictions, so that variables should only be omitted from the hyper-table when they only appear in random terms. Whether terms derived from these variables should be used when forming predictions depends on the application and aim of prediction.

    The main difference in this prediction process compared to that described by Lane and Nelder (1982) is the choice of whether to include or exclude model terms when forming predictions. In linear models, since all terms are fixed, terms not in the classify set must be in the averaging set.

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