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Returns the coefficients from a model.


# S3 method for glmm
get_parameters(x, effects = c("all", "fixed", "random"), ...)

# S3 method for coxme
get_parameters(x, effects = c("fixed", "random"), ...)

# S3 method for merMod
get_parameters(x, effects = c("fixed", "random"), ...)

# S3 method for glmmTMB
  effects = c("fixed", "random"),
  component = c("all", "conditional", "zi", "zero_inflated", "dispersion"),

# S3 method for glimML
get_parameters(x, effects = c("fixed", "random", "all"), ...)



A fitted model.


Should parameters for fixed effects, random effects or both be returned? Only applies to mixed models. May be abbreviated.


Currently not used.


Which type of parameters to return, such as parameters for the conditional model, the zero-inflated part of the model or the dispersion term? Applies to models with zero-inflated and/or dispersion formula. Note that the conditional component is also called count or mean component, depending on the model. There are three convenient shortcuts: component = "all" returns all possible parameters. If component = "location", location parameters such as conditional or zero_inflated are returned (everything that are fixed or random effects - depending on the effects argument - but no auxiliary parameters). For component = "distributional" (or "auxiliary"), components like sigma or dispersion (and other auxiliary parameters) are returned.


If effects = "fixed", a data frame with two columns: the parameter names and the related point estimates. If effects = "random", a list of data frames with the random effects (as returned by ranef()), unless the random effects have the same simplified structure as fixed effects (e.g. for models from MCMCglmm).


In most cases when models either return different "effects" (fixed, random) or "components" (conditional, zero-inflated, ...), the arguments effects and component can be used.


m <- lm(mpg ~ wt + cyl + vs, data = mtcars)
#>     Parameter   Estimate
#> 1 (Intercept) 38.7460642
#> 2          wt -3.2463673
#> 3         cyl -1.3641033
#> 4          vs  0.5241721