View source: R/get_parameters.R
get_parameters | R Documentation |
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, ...)
## Default S3 method:
get_parameters(x, verbose = TRUE, ...)
x |
A fitted model. |
... |
Currently not used. |
verbose |
Toggle messages and warnings. |
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.
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
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.
data(mtcars)
m <- lm(mpg ~ wt + cyl + vs, data = mtcars)
get_parameters(m)
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