groupedData: Construct a groupedData Object

Description

An object of the groupedData class is constructed from the formula and data by attaching the formula as an attribute of the data, along with any of outer, inner, labels, and units that are given. If order.groups is TRUE the grouping factor is converted to an ordered factor with the ordering determined by FUN. Depending on the number of grouping levels and the type of primary covariate, the returned object will be of one of three classes: nfnGroupedData - numeric covariate, single level of nesting; nffGroupedData - factor covariate, single level of nesting; and nmGroupedData - multiple levels of nesting. Several modeling and plotting functions can use the formula stored with a groupedData object to construct default plots and models.

Usage

groupedData(formula, data, order.groups, FUN, outer, inner,
 labels, units)
## S3 method for class 'groupedData':
update(object, formula, data, order.groups, FUN,
outer, inner, labels, units, \dots)

Arguments

object

an object inheriting from class groupedData.

formula

a formula of the form resp ~ cov | group where resp is the response, cov is the primary covariate, and group is the grouping factor. The expression 1 can be used for the primary co

data

a data frame in which the expressions in formula can be evaluated. The resulting groupedData object will consist of the same data values in the same order but with additional attributes.

order.groups

an optional logical value, or list of logical values, indicating if the grouping factors should be converted to ordered factors according to the function FUN applied to the response from each group. If multiple levels of grouping are

FUN

an optional summary function that will be applied to the values of the response for each level of the grouping factor, when order.groups = TRUE, to determine the ordering. Defaults to the max function.

outer

an optional one-sided formula, or list of one-sided formulas, indicating covariates that are outer to the grouping factor(s). If multiple levels of grouping are present, this argument can be either a single one-sided formula, or a list of one

inner

an optional one-sided formula, or list of one-sided formulas, indicating covariates that are inner to the grouping factor(s). If multiple levels of grouping are present, this argument can be either a single one-sided formula, or a list of one-

labels

an optional list of character strings giving labels for the response and the primary covariate. The label for the primary covariate is named x and that for the response is named y. Either label can be omitted.

units

an optional list of character strings giving the units for the response and the primary covariate. The units string for the primary covariate is named x and that for the response is named y. Either units string can be

...

some methods for this generic require additional arguments. None are used in this method.

Value

an object of one of the classes nfnGroupedData, nffGroupedData, or nmGroupedData, and also inheriting from classes groupedData and data.frame.

References

Bates, D.M. and Pinheiro, J.C. (1997), "Software Design for Longitudinal Data", in "Modelling Longitudinal and Spatially Correlated Data: Methods, Applications and Future Directions", T.G. Gregoire (ed.), Springer-Verlag, New York.

Pinheiro, J.C. and Bates, D.M. (1997) "Future Directions in Mixed-Effects Software: Design of NLME 3.0" available at http://nlme.stat.wisc.edu/

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

Examples

Run this code

Orth.new <-  # create a new copy of the groupedData object
  groupedData( distance ~ age | Subject,
              data = as.data.frame( Orthodont ),
              FUN = mean,
              outer = ~ Sex,
              labels = list( x = "Age",
                y = "Distance from pituitary to pterygomaxillary fissure" ),
              units = list( x = "(yr)", y = "(mm)") )
plot( Orth.new )         # trellis plot by Subject
formula( Orth.new )      # extractor for the formula
gsummary( Orth.new )     # apply summary by Subject
fm1 <- lme( Orth.new )   # fixed and groups formulae extracted from object
Orthodont2 <- update(Orthodont, FUN = mean)

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