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 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, ...)

Arguments

object: an object of a class of fitted models with random effects, typically a merMod object.
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 ‘(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?
transf: transformation for random effects: for example, exp for plotting parameters from a (generalized) logistic regression on the odds rather than log-odds scale
data: This argument is required by the dotplot and qqmath generic methods, but is not actually used.
level: confidence level for confidence intervals
...: some methods for these generic functions require additional arguments.

Value

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
grpvar

grouping variable

term

random-effects term, e.g. “(Intercept)” or “Days”

grp

level of the grouping variable (e.g., which Subject)

condval

value of the conditional mean

condsd

conditional standard deviation

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 (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.

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

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)
#> $Subject
#>     (Intercept)        Days
#> 308   2.2585509   9.1989758
#> 309 -40.3987381  -8.6196806
#> 310 -38.9604090  -5.4488565
#> 330  23.6906196  -4.8143503
#> 331  22.2603126  -3.0699116
#> 332   9.0395679  -0.2721770
#> 333  16.8405086  -0.2236361
#> 334  -7.2326151   1.0745816
#> 335  -0.3336684 -10.7521652
#> 337  34.8904868   8.6282652
#> 349 -25.2102286   1.1734322
#> 350 -13.0700342   6.6142178
#> 351   4.5778642  -3.0152621
#> 352  20.8636782   3.5360011
#> 369   3.2754656   0.8722149
#> 370 -25.6129993   4.8224850
#> 371   0.8070461  -0.9881562
#> 372  12.3145921   1.2840221
#> 
#> with conditional variances for “Subject” 
str(rr1 <- ranef(fm1))
#> List of 1
#>  $ Subject:'data.frame':	18 obs. of  2 variables:
#>   ..$ (Intercept): num [1:18] 2.26 -40.4 -38.96 23.69 22.26 ...
#>   ..$ Days       : num [1:18] 9.2 -8.62 -5.45 -4.81 -3.07 ...
#>   ..- attr(*, "postVar")= num [1:2, 1:2, 1:18] 145.71 -21.44 -21.44 5.31 145.71 ...
#>  - attr(*, "class")= chr "ranef.mer"
dotplot(rr1)  ## default
#> $Subject

#> 
qqmath(rr1)
#> $Subject

#> 
## 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")
#> $Subject

#> 
ranef(fm2)
#> $Subject
#>     (Intercept)        Days
#> 308   1.5126648   9.3234970
#> 309 -40.3738728  -8.5991757
#> 310 -39.1810279  -5.3877944
#> 330  24.5189244  -4.9686503
#> 331  22.9144470  -3.1939378
#> 332   9.2219759  -0.3084939
#> 333  17.1561243  -0.2872078
#> 334  -7.4517382   1.1159911
#> 335   0.5787623 -10.9059754
#> 337  34.7679030   8.6276228
#> 349 -25.7543312   1.2806892
#> 350 -13.8650598   6.7564064
#> 351   4.9159912  -3.0751356
#> 352  20.9290332   3.5122123
#> 369   3.2586448   0.8730514
#> 370 -26.4758468   4.9837910
#> 371   0.9056510  -1.0052938
#> 372  12.4217547   1.2584037
#> 
#> with conditional variances for “Subject” 
op <- options(digits = 4)
ranef(fm3, drop = TRUE)
#> $plate
#>        a        b        c        d        e        f        g        h 
#>  0.80455  0.80455  0.18167  0.33739  0.02595 -0.44120 -1.37552  0.80455 
#>        i        j        k        l        m        n        o        p 
#> -0.75264 -0.75264  0.96027  0.49311  1.42742  0.49311  0.96027  0.02595 
#>        q        r        s        t        u        v        w        x 
#> -0.28548 -0.28548 -1.37552  0.96027 -0.90836 -0.28548 -0.59692 -1.21980 
#> attr(,"postVar")
#>  [1] 0.07364 0.07364 0.07364 0.07364 0.07364 0.07364 0.07364 0.07364 0.07364
#> [10] 0.07364 0.07364 0.07364 0.07364 0.07364 0.07364 0.07364 0.07364 0.07364
#> [19] 0.07364 0.07364 0.07364 0.07364 0.07364 0.07364
#> 
#> $sample
#>        A        B        C        D        E        F 
#>  2.18706 -1.01048  1.93790 -0.09689 -0.01384 -3.00374 
#> attr(,"postVar")
#> [1] 0.04087 0.04087 0.04087 0.04087 0.04087 0.04087
#> 
#> with conditional variances for “plate” “sample” 
options(op)
## as.data.frame() provides RE's and conditional standard deviations:
str(dd <- as.data.frame(rr1))
#> 'data.frame':	36 obs. of  5 variables:
#>  $ grpvar : chr  "Subject" "Subject" "Subject" "Subject" ...
#>  $ term   : Factor w/ 2 levels "(Intercept)",..: 1 1 1 1 1 1 1 1 1 1 ...
#>  $ grp    : Factor w/ 18 levels "309","310","370",..: 9 1 2 17 16 12 14 6 7 18 ...
#>  $ condval: num  2.26 -40.4 -38.96 23.69 22.26 ...
#>  $ condsd : num  12.1 12.1 12.1 12.1 12.1 ...
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)
}
#> Loading required package: ggplot2