Extract random effects

Extracts the random effects and their standard errors.

extract_random_effects(
  model,
  re = NULL,
  ci_level = 0.95,
  digits = 3,
  add_group_N = FALSE,
  ...
)

# S3 method for merMod
extract_random_effects(
  model,
  re = NULL,
  ci_level = 0.95,
  digits = 3,
  add_group_N = FALSE,
  condvar = TRUE,
  ...
)

# S3 method for glmmTMB
extract_random_effects(
  model,
  re = NULL,
  ci_level = 0.95,
  digits = 3,
  add_group_N = FALSE,
  component = "cond",
  condvar = TRUE,
  ...
)

# S3 method for lme
extract_random_effects(
  model,
  re = NULL,
  ci_level = NULL,
  digits = 3,
  add_group_N = FALSE,
  ...
)

# S3 method for brmsfit
extract_random_effects(
  model,
  re = NULL,
  ci_level = 0.95,
  digits = 3,
  add_group_N = FALSE,
  component = NULL,
  ...
)

# S3 method for stanreg
extract_random_effects(
  model,
  re = NULL,
  ci_level = 0.95,
  digits = 3,
  add_group_N = FALSE,
  component = NULL,
  ...
)

# S3 method for gam
extract_random_effects(
  model,
  re = NULL,
  ci_level = 0.95,
  digits = 3,
  add_group_N = FALSE,
  ...
)

extract_ranef(
  model,
  re = NULL,
  ci_level = 0.95,
  digits = 3,
  add_group_N = FALSE,
  ...
)

Arguments

model

An appropriate model. See details.

re

The name of the grouping variable for the random effects. Default is NULL to return all.

ci_level

Where possible, confidence level < 1, typically above 0.90. A value of 0 will not report it. Default is .95. Not applicable to nlme objects.

digits

Rounding. Default is 3.

add_group_N

Add group sample sizes to output? Default is FALSE.

...

Other arguments specific to the method. Unused at present.

condvar

Include conditional variance. Used in lme4 and glmmTMB objects.

component

For glmmTMB objects, which of the two components 'cond' or 'zi' to select. Default is 'cond'. For brmsfit objects, this can filter results to a certain part of the output, e.g. 'sigma' or 'zi' of distributional models, or a specific outcome name of a multivariate model. In this case component is a regular expression that ends the name of the parameters of the output (e.g. '__component'). For stanreg objects, this could be the

Value

data frame of the random effects

Details

Relative to ranef for the various packages, this just adds the standard errors and cluster ids as columns, and uncertainty intervals.

Current models supported:

merMod

glmmTMB

brms

nlme

brms

rstanarm

mgcv

Note

The nlme package only provides the estimated random effect parameters, not their uncertainty, so it isn't provided.

merMod and glmmTMB objects results are based on the estimated conditional variances, i.e. condvar = TRUE. This is likely an underestimate relative to brms results.

For mgcv, the Vp (Bayesian) estimated variance covariance matrix is used.

See also

lme4::ranef(), glmmTMB::ranef(), nlme::ranef(), brms::ranef(), rstanarm::ranef(), mgcv::gamObject(), mgcv::smooth.construct.re.smooth.spec()

Other extract: extract_cor_structure(), extract_fixed_effects(), extract_het_var(), extract_model_data(), extract_random_coefs(), extract_vc()

Examples

library(lme4)
library(mixedup)

lmer_model <- lmer(Reaction ~ Days + (1 + Days | Subject), data = sleepstudy)
extract_random_effects(lmer_model)
#> # A tibble: 36 × 7
#>    group_var effect    group   value    se lower_2.5 upper_97.5
#>    <chr>     <chr>     <fct>   <dbl> <dbl>     <dbl>      <dbl>
#>  1 Subject   Intercept 308     2.26   12.1   -21.4         25.9
#>  2 Subject   Intercept 309   -40.4    12.1   -64.1        -16.7
#>  3 Subject   Intercept 310   -39.0    12.1   -62.6        -15.3
#>  4 Subject   Intercept 330    23.7    12.1     0.032       47.3
#>  5 Subject   Intercept 331    22.3    12.1    -1.40        45.9
#>  6 Subject   Intercept 332     9.04   12.1   -14.6         32.7
#>  7 Subject   Intercept 333    16.8    12.1    -6.82        40.5
#>  8 Subject   Intercept 334    -7.23   12.1   -30.9         16.4
#>  9 Subject   Intercept 335    -0.334  12.1   -24.0         23.3
#> 10 Subject   Intercept 337    34.9    12.1    11.2         58.5
#> # … with 26 more rows