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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 class 'BGGM'
get_parameters(
  x,
  component = "correlation",
  summary = FALSE,
  centrality = "mean",
  ...
)

# S3 method for class 'BFBayesFactor'
get_parameters(
  x,
  effects = "all",
  component = "all",
  iterations = 4000,
  progress = FALSE,
  verbose = TRUE,
  summary = FALSE,
  centrality = "mean",
  ...
)

# S3 method for class 'brmsfit'
get_parameters(
  x,
  effects = "fixed",
  component = "all",
  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). See details in section Model Components .May be abbreviated. Note that the conditional component also refers to the count or mean component - names may differ, depending on the modeling package. There are three convenient shortcuts (not applicable to all model classes):

  • component = "all" returns all possible parameters.

  • If component = "location", location parameters such as conditional, zero_inflated, smooth_terms, or instruments are returned (everything that are fixed or random effects - depending on the effects argument - but no auxiliary parameters).

  • For component = "distributional" (or "auxiliary"), components like sigma, dispersion, beta or precision (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, while centrality = "median" would use the more robust median value as measure of central tendency.

...

Currently only used for models of class brmsfit, where a variable argument can be used, which is directly passed to the as.data.frame() method (i.e., as.data.frame(x, variable = variable)).

effects

Should variables for fixed effects ("fixed"), random effects ("random") or both ("all") be returned? Only applies to mixed models. May be abbreviated.

For models of from packages brms or rstanarm there are additional options:

  • "fixed" returns fixed effects.

  • "random_variance" return random effects parameters (variance and correlation components, e.g. those parameters that start with sd_ or cor_).

  • "grouplevel" returns random effects group level estimates, i.e. those parameters that start with r_.

  • "random" returns both "random_variance" and "grouplevel".

  • "all" returns fixed effects and random effects variances.

  • "full" returns all parameters.

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().

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 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. 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)
#>     Parameter   Estimate
#> 1 (Intercept) 38.7460642
#> 2          wt -3.2463673
#> 3         cyl -1.3641033
#> 4          vs  0.5241721