Predicted values are obtained at the specified values of
primary for each object. If either object1 or
object2 have a grouping structure
(i.e. getGroups(object) is not NULL), predicted values
are obtained for each group. When both objects determine groups, the
group levels must be the same. If other covariates besides
primary are used in the prediction model, their group-wise averages
(numeric covariates) or most frequent values (categorical covariates)
are used to obtain the predicted values. The original observations are
also included in the returned object.
comparePred(object1, object2, primary, minimum, maximum,
length.out, level, …)fitted model objects, from which predictions can
be extracted using the predict method.
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
the objects (using getCovariate), it will be used as
primary.
an optional lower limit for the primary
covariate. Defaults to min(primary), after primary is
evaluated in the data used in fitting object1.
an optional upper limit for the primary
covariate. Defaults to max(primary), after primary is
evaluated in the data used in fitting object1.
an optional integer with the number of primary covariate values at which to evaluate the predictions. Defaults to 51.
an optional integer specifying the desired prediction level. Levels increase from outermost to innermost grouping, with level 0 representing the population (fixed effects) predictions. Only one level can be specified. Defaults to the innermost level.
some methods for the generic may require additional arguments.
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: the
objects' names are used to classify the predicted values and
original is used for the observed values. The returned object
inherits from classes comparePred and augPred.
# NOT RUN {
fm1 <- lme(distance ~ age * Sex, data = Orthodont, random = ~ age)
fm2 <- update(fm1, distance ~ age)
comparePred(fm1, fm2, length.out = 2)
# }
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