| augPred {nlme} | R Documentation |
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.
augPred(object, primary, minimum, maximum, length.out, ...)
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. |
... |
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:
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.
This function is generic; method functions can be written to handle
specific classes of objects. Classes which already have methods for
this function include: gls, lme, and lmList.
Jose Pinheiro Jose.Pinheiro@pharma.novartis.com and Douglas Bates bates@stat.wisc.edu
Pinheiro, J. C. and Bates, D. M. (2000), Mixed-Effects Models in S and S-PLUS, Springer, New York.
plot.augPred, getGroups,
predict
fm1 <- lme(Orthodont, random = ~1) augPred(fm1, length.out = 2, level = c(0,1))