R/extract_random_coefs.R
extract_random_coefs.RdFixed effect + random effects.
extract_random_coefs(
model,
re = NULL,
ci_level = 0.95,
digits = 3,
component = NULL,
...
)
# S3 method for merMod
extract_random_coefs(model, re = NULL, ci_level = 0.95, digits = 3, ...)
# S3 method for glmmTMB
extract_random_coefs(
model,
re = NULL,
ci_level = 0.95,
digits = 3,
component = "cond",
...
)
# S3 method for lme
extract_random_coefs(model, re = NULL, ci_level = NULL, digits = 3, ...)
# S3 method for brmsfit
extract_random_coefs(
model,
re = NULL,
ci_level = 0.95,
digits = 3,
component = NULL,
...
)
# S3 method for stanreg
extract_random_coefs(
model,
re = NULL,
ci_level = 0.95,
digits = 3,
component = NULL,
...
)
# S3 method for gam
extract_random_coefs(model, re = NULL, ci_level = 0.95, digits = 3, ...)
extract_coef(
model,
re = NULL,
ci_level = 0.95,
digits = 3,
component = NULL,
...
)
extract_random_coefficients(
model,
re = NULL,
ci_level = 0.95,
digits = 3,
component = NULL,
...
)A merMod, nlme, brms, or glmmTMB object
The name of the grouping variable for the random effects.
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.
Rounding. Default is 3.
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 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').
Other arguments specific to the method. For example add_group_N
for extract_random_effects. Will not apply to brmsfit or stanreg models.
Experimental.
A data frame of the random coefficients and their standard errors.
Returns a data frame with random coefficients, a.k.a. random
intercepts and random slopes, and their standard errors.
r
Note that the standard errors assume independence of the conditional
variance and the fixed-effects variance, thus the standard errors are the
sum of variances for the respective fixed and random effects. See Bolker's
demo
here
and additional discussion at the
GLMM
FAQ. As noted there, this assumption may not be, and likely is not,
appropriate, and if you are really interested in an accurate uncertainty
estimate you should probably use brms.
Please realize that this functionality is likely only appropriate for
simpler GLMM type models, and is mostly just a shortcut for those settings.
It may work for more complicated situations also, but I don't make any
guarantees. For more complex models that include multiple
outcomes/categories or have other anomalies, this function likely will not
work even if the underlying extract_fixed_effects and
extract_random_effects do, as naming conventions are not consistent
enough within the relative packages to deal with this in a general way. I
will continue to look into its feasibility, but don't expect much.
The nlme package only provides the coefficients with no estimated
variance, so this function doesn't add to what you get from basic
functionality for those models. In addition, nlme adds all random
effects to the fixed effects, whereas lme4 and others only add the
effects requested.
For multicomponent glmmTMB models, e.g. zip, please specify the component
argument.
extract_coef and extract_random_coefficients are aliases.
Other extract:
extract_cor_structure(),
extract_fixed_effects(),
extract_het_var(),
extract_model_data(),
extract_random_effects(),
extract_vc()
library(lme4)
library(mixedup)
lmer_1 <- lmer(Reaction ~ Days + (1 | Subject), data = sleepstudy)
extract_random_coefs(lmer_1, re = 'Subject')
#> # A tibble: 18 × 7
#> group_var effect group value se lower_2.5 upper_97.5
#> <chr> <chr> <fct> <dbl> <dbl> <dbl> <dbl>
#> 1 Subject Intercept 308 292. 13.6 266. 319.
#> 2 Subject Intercept 309 174. 13.6 147. 200.
#> 3 Subject Intercept 310 188. 13.6 162. 215.
#> 4 Subject Intercept 330 256. 13.6 229. 282.
#> 5 Subject Intercept 331 262. 13.6 235. 288.
#> 6 Subject Intercept 332 260. 13.6 233. 286.
#> 7 Subject Intercept 333 268. 13.6 241. 295.
#> 8 Subject Intercept 334 248. 13.6 222. 275.
#> 9 Subject Intercept 335 206. 13.6 179. 233.
#> 10 Subject Intercept 337 324. 13.6 297. 350.
#> 11 Subject Intercept 349 230. 13.6 204. 257.
#> 12 Subject Intercept 350 266. 13.6 239. 292.
#> 13 Subject Intercept 351 244. 13.6 217. 270.
#> 14 Subject Intercept 352 288. 13.6 261. 314.
#> 15 Subject Intercept 369 258. 13.6 232. 285.
#> 16 Subject Intercept 370 245. 13.6 218. 272.
#> 17 Subject Intercept 371 248. 13.6 221. 275.
#> 18 Subject Intercept 372 270. 13.6 243. 296.
library(glmmTMB)
tmb_1 <- glmmTMB(Reaction ~ Days + (1 | Subject), data = sleepstudy)
extract_random_coefs(tmb_1, re = 'Subject')
#> # A tibble: 18 × 7
#> group_var effect group value se lower_2.5 upper_97.5
#> <chr> <chr> <fct> <dbl> <dbl> <dbl> <dbl>
#> 1 Subject Intercept 308 292. 15.7 261. 323.
#> 2 Subject Intercept 309 174. 15.8 143. 205.
#> 3 Subject Intercept 310 189. 15.8 158. 219.
#> 4 Subject Intercept 330 256. 15.7 225. 287.
#> 5 Subject Intercept 331 262. 15.7 231. 292.
#> 6 Subject Intercept 332 260. 15.7 229. 290.
#> 7 Subject Intercept 333 268. 15.7 237. 299.
#> 8 Subject Intercept 334 248. 15.7 218. 279.
#> 9 Subject Intercept 335 206. 15.7 175. 237.
#> 10 Subject Intercept 337 323. 15.8 292. 354.
#> 11 Subject Intercept 349 230. 15.7 200. 261.
#> 12 Subject Intercept 350 265. 15.7 235. 296.
#> 13 Subject Intercept 351 244. 15.7 213. 274.
#> 14 Subject Intercept 352 288. 15.7 257. 318.
#> 15 Subject Intercept 369 258. 15.7 228. 289.
#> 16 Subject Intercept 370 245. 15.7 214. 276.
#> 17 Subject Intercept 371 248. 15.7 217. 279.
#> 18 Subject Intercept 372 269. 15.7 239. 300.