ranef {lme4}R Documentation

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, level = 0.95, ...)
## S3 method for class 'ranef.mer'
 qqmath(x, data, main = TRUE, level = 0.95, ...)
## S3 method for class 'ranef.mer'
 as.data.frame(x, ...)



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


a logical argument indicating if the conditional variance-covariance matrices of the random effects should be added as an attribute.


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?


character vector of names of grouping factors for which the random effects should be returned.


a (deprecated) synonym for condVar


a random-effects object (of class ranef.mer) produced by ranef


include a main title, indicating the grouping factor, on each sub-plot?


transformation for random effects: for example, exp for plotting parameters from a (generalized) logistic regression on the odds rather than log-odds scale


This argument is required by the dotplot and qqmath generic methods, but is not actually used.


confidence level for confidence intervals


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.



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.


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)
str(rr1 <- ranef(fm1))
dotplot(rr1)  ## default
## specify free scales in order to make Day effects more visible
dotplot(rr1,scales = list(x = list(relation = 'free')))[["Subject"]]
## plot options: ... can specify appearance of vertical lines with
## lty.v, col.line.v, lwd.v, etc..
dotplot(rr1, lty = 3, lty.v = 2, col.line.v = "purple",
        col = "red", col.line.h = "gray")
op <- options(digits = 4)
ranef(fm3, drop = TRUE)
## 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)

[Package lme4 version 1.1-35.3 Index]