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)
get_parameters(x, ...)# S3 method for default
get_parameters(x, verbose = TRUE, ...)
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
A fitted model.
Currently not used.
Toggle messages and warnings.
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.
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.
data(mtcars)
m <- lm(mpg ~ wt + cyl + vs, data = mtcars)
get_parameters(m)
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