# ranef

##### Extract the modes of the random effects

A generic function to extract the conditional modes of the random effects from a fitted model object. For linear mixed models the conditional modes of the random effects are also the conditional means.

##### Usage

```
# S3 method for merMod
ranef (object, condVar = FALSE,
drop = FALSE, whichel = names(ans), postVar=FALSE, ...)
# S3 method for ranef.mer
dotplot (x, data, main=TRUE, ...)
# S3 method for ranef.mer
qqmath (x, data, main=TRUE, ...)
```

##### Arguments

- object
- an object of a class of fitted models with
random effects, typically a
`object.`

- condVar
- an optional logical argument indicating if the conditional variance-covariance matrices of the random effects should be added as an attribute.
- drop
- should components of the return value that would be data frames
with a single column, usually a column called
‘
`(Intercept)`

’, be returned as named vectors instead? - whichel
- character vector of names of grouping factors for which the random effects should be returned.
- postVar
- a (deprecated) synonym for
`condVar`

- x
- a random-effects object (of class
`ranef.mer`

) produced by`ranef`

- main
- include a main title, indicating the grouping factor, on each sub-plot?
- data
- This argument is required by the
`dotplot`

and`qqmath`

generic methods, but is not actually used. - …
- some methods for these generic functions require additional arguments.

##### Details

If grouping factor i has k levels and j random effects
per level the ith component of the list returned by
`ranef`

is a data frame with k rows and j columns.
If `condVar`

is `TRUE`

the `"postVar"`

attribute is an array of dimension j by j by k. The kth
face of this array is a positive definite symmetric j by
j matrix. If there is only one grouping factor in the
model the variance-covariance matrix for the entire
random effects vector, conditional on the estimates of
the model parameters and on the data will be block
diagonal and this j by j matrix is the kth diagonal
block. With multiple grouping factors the faces of the
`"postVar"`

attributes are still the diagonal blocks
of this conditional variance-covariance matrix but the
matrix itself is no longer block diagonal.

##### Value

An object of class `ranef.mer`

composed of
a list of data frames, one for each grouping factor for
the random effects. The number of rows in the data frame
is the number of levels of the grouping factor. The
number of columns is the dimension of the random effect
associated with each level of the factor. If `condVar`

is `TRUE`

each of the data frames
has an attribute called `"postVar"`

which is a
three-dimensional array with symmetric faces; each face
contains the variance-covariance matrix for a particular
level of the grouping factor. (The name
of this attribute is a historical artifact,
and may be changed to `condVar`

at some point in the future.) When `drop`

is `TRUE`

any components that would
be data frames of a single column are converted to named
numeric vectors.

##### Note

To produce a (list of) “caterpillar plots” of the random
effects apply `dotplot`

to
the result of a call to `ranef`

with ```
condVar =
TRUE
```

; `qqmath`

will generate
a list of Q-Q plots.

##### Examples

```
require(lattice)
fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy)
fm2 <- lmer(Reaction ~ Days + (1|Subject) + (0+Days|Subject), sleepstudy)
fm3 <- lmer(diameter ~ (1|plate) + (1|sample), Penicillin)
ranef(fm1)
str(rr1 <- ranef(fm1, condVar = TRUE))
dotplot(rr1) ## default
## specify free scales in order to make Day effects more visible
dotplot(rr1,scales = list(x = list(relation = 'free')))[["Subject"]]
if(FALSE) { ##-- condVar=TRUE is not yet implemented for multiple terms -- FIXME
str(ranef(fm2, condVar = TRUE))
}
op <- options(digits = 4)
ranef(fm3, drop = TRUE)
options(op)
```

*Documentation reproduced from package lme4, version 1.1-13, License: GPL (>= 2)*