Get model parameters

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

• Bayesian models (rstanarm, brms, MCMCglmm, ...)

• Estimated marginal means (emmeans)

• Generalized additive models (mgcv, VGAM, ...)

• Marginal effects models (mfx)

• Mixed models (lme4, glmmTMB, GLMMadaptive, ...)

• Zero-inflated and hurdle models (pscl, ...)

• Models with special components (betareg, MuMIn, ...)

• Hypothesis tests (htest)

Usage

get_parameters(x, ...)

# Default S3 method
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.

• "nonlinear": for non-linear models (like models of class nlmerMod or nls), returns staring estimates for the nonlinear parameters.

• "correlation": for models with correlation-component, like gls, the variables used to describe the correlation structure are returned.

• "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.

Special models

Some model classes also allow rather uncommon options. These are:

• mhurdle: "infrequent_purchase", "ip", and "auxiliary"

• BGGM: "correlation" and "intercept"

• BFBayesFactor, glmx: "extra"

• averaging:"conditional" and "full"

• mjoint: "survival"

• mfx: "precision", "marginal"

• betareg, DirichletRegModel: "precision"

• mvord: "thresholds" and "correlation"

• clm2: "scale"

• selection: "selection", "outcome", and "auxiliary"

For models of class brmsfit (package brms), even more options are possible for the component argument, which are not all documented in detail here.

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