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.

```
groupedData(formula, data, order.groups, FUN, outer, inner,
labels, units)
# S3 method for groupedData
update(object, formula, data, order.groups, FUN,
outer, inner, labels, units, …)
```

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 covariate when there is no other suitable
candidate. Multiple nested grouping factors can be listed separated
by the `/`

symbol as in `fact1/fact2`

. In an expression
like this the `fact2`

factor is nested within the `fact1`

factor.

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 present,
this argument can be either a single logical value (which will be
repeated for all grouping levels) or a list of logical values. If no
names are assigned to the list elements, they are assumed in the same
order as the group levels (outermost to innermost grouping). Ordering
within a level of grouping is done within the levels of the grouping
factors which are outer to it. Changing the grouping factor to an
ordered factor does not affect the ordering of the rows in the data
frame but it does affect the order of the panels in a trellis display
of the data or models fitted to the data. Defaults to `TRUE`

.

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-sided formulas. If no names are assigned to the list elements,
they are assumed in the same order as the group levels (outermost to
innermost grouping). An outer covariate is invariant within the sets
of rows defined by the grouping factor. Ordering of the groups is
done in such a way as to preserve adjacency of groups with the same
value of the outer variables. When plotting a groupedData object,
the argument `outer = TRUE`

causes the panels to be determined
by the `outer`

formula. The points within the panels are
associated by level of the grouping factor. Defaults to `NULL`

,
meaning that no outer covariates are present.

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-sided formulas. If no names are assigned to the list elements,
they are assumed in the same order as the group levels (outermost to
innermost grouping). An inner covariate can change
within the sets of rows defined by the grouping factor. An inner
formula can be used to associate points in a plot of a groupedData
object. Defaults to `NULL`

, meaning that no inner covariates
are present.

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 omitted.

…

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

an object of one of the classes `nfnGroupedData`

,
`nffGroupedData`

, or `nmGroupedData`

, and also inheriting
from classes `groupedData`

and `data.frame`

.

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.

`formula`

, `gapply`

,
`gsummary`

,
`lme`

,
`plot.nffGroupedData`

,
`plot.nfnGroupedData`

,
`plot.nmGroupedData`

,
`reStruct`

```
# NOT RUN {
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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