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Returns the requested auxiliary parameters from models, like dispersion, sigma, or beta...


  type = "sigma",
  summary = TRUE,
  centrality = "mean",
  verbose = TRUE,



A model.


The name of the auxiliary parameter that should be retrieved. "sigma" is available for most models, "dispersion" for models of class glm, glmerMod or glmmTMB as well as brmsfit. "beta" and other parameters are currently only returned for brmsfit models. See 'Details'.


Logical, indicates whether the full posterior samples (summary = FALSE)) or the summarized centrality indices of the posterior samples (summary = TRUE)) should be returned as estimates.


Only for models with posterior samples, and when summary = TRUE. In this case, centrality = "mean" would calculate means of posterior samples for each parameter, while centrality = "median" would use the more robust median value as measure of central tendency.


Toggle warnings.


Currently not used.


The requested auxiliary parameter, or NULL if this information could not be accessed.


Currently, only sigma and the dispersion parameter are returned, and only for a limited set of models.

Sigma Parameter

See get_sigma().

Dispersion Parameter

There are many different definitions of "dispersion", depending on the context. get_auxiliary() returns the dispersion parameters that usually can be considered as variance-to-mean ratio for generalized (linear) mixed models. Exceptions are models of class glmmTMB, where the dispersion equals σ2. In detail, the computation of the dispersion parameter for generalized linear models is the ratio of the sum of the squared working-residuals and the residual degrees of freedom. For mixed models of class glmer, the dispersion parameter is also called φ and is the ratio of the sum of the squared Pearson-residuals and the residual degrees of freedom. For models of class glmmTMB, dispersion is σ2.

brms models

For models of class brmsfit, there are different options for the type argument. See a list of supported auxiliary parameters here: find_parameters.BGGM().


# from ?glm
clotting <- data.frame(
  u = c(5, 10, 15, 20, 30, 40, 60, 80, 100),
  lot1 = c(118, 58, 42, 35, 27, 25, 21, 19, 18),
  lot2 = c(69, 35, 26, 21, 18, 16, 13, 12, 12)
model <- glm(lot1 ~ log(u), data = clotting, family = Gamma())
get_auxiliary(model, type = "dispersion") # same as summary(model)$dispersion
#> [1] 0.002446059