
Get model parameters from Bayesian models
Source:R/get_parameters_bayesian.R
get_parameters.BGGM.Rd
Returns the coefficients (or posterior samples for Bayesian models) from a model.
Usage
# S3 method for BGGM
get_parameters(
x,
component = c("correlation", "conditional", "intercept", "all"),
summary = FALSE,
centrality = "mean",
...
)
# S3 method for MCMCglmm
get_parameters(
x,
effects = c("fixed", "random", "all"),
summary = FALSE,
centrality = "mean",
...
)
# S3 method for BFBayesFactor
get_parameters(
x,
effects = c("all", "fixed", "random"),
component = c("all", "extra"),
iterations = 4000,
progress = FALSE,
verbose = TRUE,
summary = FALSE,
centrality = "mean",
...
)
# S3 method for stanmvreg
get_parameters(
x,
effects = c("fixed", "random", "all"),
parameters = NULL,
summary = FALSE,
centrality = "mean",
...
)
# S3 method for brmsfit
get_parameters(
x,
effects = "fixed",
component = "all",
parameters = NULL,
summary = FALSE,
centrality = "mean",
...
)
# S3 method for stanreg
get_parameters(
x,
effects = c("fixed", "random", "all"),
component = c("location", "all", "conditional", "smooth_terms", "sigma",
"distributional", "auxiliary"),
parameters = NULL,
summary = FALSE,
centrality = "mean",
...
)
# S3 method for bayesx
get_parameters(
x,
component = c("conditional", "smooth_terms", "all"),
summary = FALSE,
centrality = "mean",
...
)
# S3 method for bamlss
get_parameters(
x,
component = c("all", "conditional", "smooth_terms", "location", "distributional",
"auxiliary"),
parameters = NULL,
summary = FALSE,
centrality = "mean",
...
)
# S3 method for sim.merMod
get_parameters(
x,
effects = c("fixed", "random", "all"),
parameters = NULL,
summary = FALSE,
centrality = "mean",
...
)
# S3 method for sim
get_parameters(x, parameters = NULL, summary = FALSE, centrality = "mean", ...)
Arguments
- x
A fitted model.
- component
Which type of parameters to return, such as parameters for the conditional model, the zero-inflated part of the model, the dispersion term, the instrumental variables or marginal effects be returned? Applies to models with zero-inflated and/or dispersion formula, or to models with instrumental variables (so called fixed-effects regressions), or models with marginal effects from mfx. May be abbreviated. Note that the conditional component is also called count or mean component, depending on the model. There are three convenient shortcuts:
component = "all"
returns all possible parameters. Ifcomponent = "location"
, location parameters such asconditional
,zero_inflated
,smooth_terms
, orinstruments
are returned (everything that are fixed or random effects - depending on theeffects
argument - but no auxiliary parameters). Forcomponent = "distributional"
(or"auxiliary"
), components likesigma
,dispersion
,beta
orprecision
(and other auxiliary parameters) are returned.- summary
Logical, indicates whether the full posterior samples (
summary = FALSE
)) or the summarized centrality indices of the posterior samples (summary = TRUE
)) should be returned as estimates.- centrality
Only for models with posterior samples, and when
summary = TRUE
. In this case,centrality = "mean"
would calculate means of posterior samples for each parameter, whilecentrality = "median"
would use the more robust median value as measure of central tendency.- ...
Currently not used.
- effects
Should parameters for fixed effects, random effects or both be returned? Only applies to mixed models. May be abbreviated.
- iterations
Number of posterior draws.
- progress
Display progress.
- verbose
Toggle messages and warnings.
- parameters
Regular expression pattern that describes the parameters that should be returned.
Value
The posterior samples from the requested parameters as data frame.
If summary = TRUE
, returns a data frame with two columns: the
parameter names and the related point estimates (based on centrality
).
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.
BFBayesFactor Models
Note that for BFBayesFactor
models (from the BayesFactor
package), posteriors are only extracted from the first numerator model (i.e.,
model[1]
). If you want to apply some function foo()
to another
model stored in the BFBayesFactor
object, index it directly, e.g.
foo(model[2])
, foo(1/model[5])
, etc.
See also bayestestR::weighted_posteriors()
.
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