Returns the coefficients (or posterior samples for Bayesian models) from a model. See the documentation for your object's class:

## Usage

get_parameters(x, ...)

# S3 method for default
get_parameters(x, verbose = TRUE, ...)

## Arguments

x

A fitted model.

...

Currently not used.

verbose

Toggle messages and warnings.

## Value

• for non-Bayesian models, a data frame with two columns: the parameter names and the related point estimates.

• for Anova (aov()) with error term, a list of parameters for the conditional and the random effects parameters

## Details

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

get_parameters() is comparable to coef(), however, the coefficients are returned as data frame (with columns for names and point estimates of coefficients). For Bayesian models, the posterior samples of parameters are returned.

## Model components

Possible values for the component argument depend on the model class. Following are valid options:

• "all": returns all model components, applies to all models, but will only have an effect for models with more than just the conditional model component.

• "conditional": only returns the conditional component, i.e. "fixed effects" terms from the model. Will only have an effect for models with more than just the conditional model component.

• "smooth_terms": returns smooth terms, only applies to GAMs (or similar models that may contain smooth terms).

• "zero_inflated" (or "zi"): returns the zero-inflation component.

• "dispersion": returns the dispersion model component. This is common for models with zero-inflation or that can model the dispersion parameter.

• "instruments": for instrumental-variable or some fixed effects regression, returns the instruments.

• "location": returns location parameters such as conditional, zero_inflated, smooth_terms, or instruments (everything that are fixed or random effects - depending on the effects argument - but no auxiliary parameters).

• "distributional" (or "auxiliary"): components like sigma, dispersion, beta or precision (and other auxiliary parameters) are returned.

## Examples

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