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.
## S3 method for class 'merMod'
ranef(object, condVar = TRUE,
drop = FALSE, whichel = names(ans), postVar = FALSE, ...)
## S3 method for class 'ranef.mer'
dotplot(x, data, main = TRUE, transf = I, ...)
## S3 method for class 'ranef.mer'
qqmath(x, data, main = TRUE, ...)
## S3 method for class 'ranef.mer'
as.data.frame(x, ..., stringsAsFactors = default.stringsAsFactors())object |
an object of a class of fitted models with
random effects, typically a
|
condVar |
a 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
‘ |
whichel |
character vector of names of grouping factors for which the random effects should be returned. |
postVar |
a (deprecated) synonym for |
x |
a random-effects object (of class |
main |
include a main title, indicating the grouping factor, on each sub-plot? |
transf |
transformation for random effects: for example,
|
data |
This argument is required by the |
stringsAsFactors |
see |
... |
some methods for these generic functions require additional arguments. |
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 (or a list
of such arrays). 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; 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.
From ranef:
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".
If there is a single random-effects term for a given grouping factor, this attribute is a three-dimensional array with symmetric faces; each face contains the variance-covariance matrix for a particular level of the grouping factor.
If there is more than one random-effects term for
a given grouping factor (e.g. (1|f) + (0+x|f)),
this attribute is a list of arrays as described above,
one for each term.
(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.
From as.data.frame:
This function converts the random effects to a "long format" data frame with columns
grouping variable
random-effects term, e.g. “(Intercept)” or “Days”
level of the grouping variable (e.g., which Subject)
value of the conditional mean
conditional standard deviation
library(lattice) ## for dotplot, qqmath
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))
dotplot(rr1) ## default
qqmath(rr1)
## specify free scales in order to make Day effects more visible
dotplot(rr1,scales = list(x = list(relation = 'free')))[["Subject"]]
ranef(fm2)
op <- options(digits = 4)
ranef(fm3, drop = TRUE)
options(op)
## as.data.frame() provides RE's and conditional standard deviations:
str(dd <- as.data.frame(rr1))
if (require(ggplot2)) {
ggplot(dd, aes(y=grp,x=condval)) +
geom_point() + facet_wrap(~term,scales="free_x") +
geom_errorbarh(aes(xmin=condval -2*condsd,
xmax=condval +2*condsd), height=0)
}Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.