get_parameters.BGGM: Get model parameters from Bayesian models

In insight: Easy Access to Model Information for Various Model Objects

Get model parameters from Bayesian models

Description

Returns the coefficients (or posterior samples for Bayesian models) from a model.

Usage

## S3 method for class 'BGGM'
get_parameters(
  x,
  component = c("correlation", "conditional", "intercept", "all"),
  summary = FALSE,
  centrality = "mean",
  ...
)

## S3 method for class 'MCMCglmm'
get_parameters(
  x,
  effects = c("fixed", "random", "all"),
  summary = FALSE,
  centrality = "mean",
  ...
)

## S3 method for class '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 class 'stanmvreg'
get_parameters(
  x,
  effects = c("fixed", "random", "all"),
  parameters = NULL,
  summary = FALSE,
  centrality = "mean",
  ...
)

## S3 method for class 'brmsfit'
get_parameters(
  x,
  effects = "fixed",
  component = "all",
  parameters = NULL,
  summary = FALSE,
  centrality = "mean",
  ...
)

## S3 method for class '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 class 'bayesx'
get_parameters(
  x,
  component = c("conditional", "smooth_terms", "all"),
  summary = FALSE,
  centrality = "mean",
  ...
)

## S3 method for class 'bamlss'
get_parameters(
  x,
  component = c("all", "conditional", "smooth_terms", "location", "distributional",
    "auxiliary"),
  parameters = NULL,
  summary = FALSE,
  centrality = "mean",
  ...
)

## S3 method for class 'sim.merMod'
get_parameters(
  x,
  effects = c("fixed", "random", "all"),
  parameters = NULL,
  summary = FALSE,
  centrality = "mean",
  ...
)

## S3 method for class 'sim'
get_parameters(x, parameters = NULL, summary = FALSE, centrality = "mean", ...)

Arguments

x	A fitted model.
component	Should all predictor variables, predictor variables for the conditional model, the zero-inflated part of the model, the dispersion term or the instrumental variables be returned? Applies to models with zero-inflated and/or dispersion formula, or to models with instrumental variable (so called fixed-effects regressions). May be abbreviated. Note that the conditional component is also called count or mean component, depending on the model.
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, while centrality = "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.

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.

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).

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)

insight documentation built on Nov. 26, 2023, 5:08 p.m.