get_parameters {insight} | R Documentation |
Get model parameters
Description
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. |
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.
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
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 classnlmerMod
ornls
), returns staring estimates for the nonlinear parameters. -
"correlation"
: for models with correlation-component, likegls
, the variables used to describe the correlation structure are returned. -
"location"
: returns location parameters such asconditional
,zero_inflated
,smooth_terms
, orinstruments
(everything that are fixed or random effects - depending on theeffects
argument - but no auxiliary parameters). -
"distributional"
(or"auxiliary"
): components likesigma
,dispersion
,beta
orprecision
(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. It can be any pre-defined or arbitrary distributional parameter, like
mu
, ndt
, kappa
, etc.
Examples
data(mtcars)
m <- lm(mpg ~ wt + cyl + vs, data = mtcars)
get_parameters(m)