augPred: Augmented Predictions

Description

Predicted values are obtained at the specified values of primary. If object has a grouping structure (i.e. getGroups(object) is not NULL), predicted values are obtained for each group. If level has more than one element, predictions are obtained for each level of the max(level) grouping factor. If other covariates besides primary are used in the prediction model, their average (numeric covariates) or most frequent value (categorical covariates) are used to obtain the predicted values. The original observations are also included in the returned object.

Usage

augPred(object, primary, minimum, maximum, length.out, ...)

# S3 method for lme
augPred(object, primary = NULL,
        minimum = min(primary), maximum = max(primary),
        length.out = 51, level = Q, ...)

Value

a data frame with four columns representing, respectively, the values of the primary covariate, the groups (if object does not have a grouping structure, all elements will be 1), the predicted or observed values, and the type of value in the third column:

original for the observed values and predicted (single or no grouping factor) or predict.groupVar (multiple levels of grouping), with groupVar replaced by the actual grouping variable name (fixed is used for population predictions). The returned object inherits from class "augPred".

Arguments

object: a fitted model object from which predictions can be extracted, using a predict method.
primary: an optional one-sided formula specifying the primary covariate to be used to generate the augmented predictions. By default, if a covariate can be extracted from the data used to generate object (using getCovariate), it will be used as primary.
minimum: an optional lower limit for the primary covariate. Defaults to min(primary).
maximum: an optional upper limit for the primary covariate. Defaults to max(primary).
length.out: an optional integer with the number of primary covariate values at which to evaluate the predictions. Defaults to 51.
level: an optional integer vector specifying the desired prediction levels. Levels increase from outermost to innermost grouping, with level 0 representing the population (fixed effects) predictions. Defaults to the innermost level.
...: some methods for the generic may require additional arguments.

Author

José Pinheiro and Douglas Bates bates@stat.wisc.edu

References

Pinheiro, J. C. and Bates, D. M. (2000), Mixed-Effects Models in S and S-PLUS, Springer, New York.

Examples

Run this code

fm1 <- lme(Orthodont, random = ~1)
augPred(fm1, length.out = 2, level = c(0,1))

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