ranef: Extract the modes of the random effects

Description

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

ranef(object, ...)
## S3 method for class 'mer':
ranef(object, postVar = FALSE, drop = FALSE,
      whichel = names(wt), ...)

Arguments

object

an object of a class of fitted models with random effects, typically an "mer" object.

postVar

an optional logical argument indicating if the conditional variance-covariance matrices, also called the posterior variances, of the random effects should be added as an attribute. Default is FALSE.

drop

an optional logical argument indicating components of the return value that would be data frames with a single column, usually a column called (Intercept), should be returned as named vectors.

whichel

character vector of names of factors for which to return results.

...

some methods for this generic function require additional arguments.

Value

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 postVar is TRUE each of the data frames has an attribute called "postVar" which is a three-dimensional array with symmetric faces.
When drop is TRUE any components that would be data frames of a single column are converted to named numeric vectors.

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 postVar 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.

Examples

Run this code

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, postVar = TRUE))
dotplot(rr1,scales = list(x = list(relation = 'free')))[["Subject"]]
##str(ranef(fm2, postVar = TRUE)) ## code not yet written
op <- options(digits = 4)
ranef(fm3, drop = TRUE)
options(op)