find_parameters.BGGM: Find names of model parameters from Bayesian models

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

Find names of model parameters from Bayesian models

Description

Returns the names of model parameters, like they typically appear in the summary() output. For Bayesian models, the parameter names equal the column names of the posterior samples after coercion from as.data.frame().

Usage

## S3 method for class 'BGGM'
find_parameters(
  x,
  component = c("correlation", "conditional", "intercept", "all"),
  flatten = FALSE,
  ...
)

## S3 method for class 'BFBayesFactor'
find_parameters(
  x,
  effects = c("all", "fixed", "random"),
  component = c("all", "extra"),
  flatten = FALSE,
  ...
)

## S3 method for class 'MCMCglmm'
find_parameters(x, effects = c("all", "fixed", "random"), flatten = FALSE, ...)

## S3 method for class 'bamlss'
find_parameters(
  x,
  flatten = FALSE,
  component = c("all", "conditional", "location", "distributional", "auxiliary"),
  parameters = NULL,
  ...
)

## S3 method for class 'brmsfit'
find_parameters(
  x,
  effects = "all",
  component = "all",
  flatten = FALSE,
  parameters = NULL,
  ...
)

## S3 method for class 'bayesx'
find_parameters(
  x,
  component = c("all", "conditional", "smooth_terms"),
  flatten = FALSE,
  parameters = NULL,
  ...
)

## S3 method for class 'stanreg'
find_parameters(
  x,
  effects = c("all", "fixed", "random"),
  component = c("location", "all", "conditional", "smooth_terms", "sigma",
    "distributional", "auxiliary"),
  flatten = FALSE,
  parameters = NULL,
  ...
)

## S3 method for class 'stanmvreg'
find_parameters(
  x,
  effects = c("all", "fixed", "random"),
  component = c("location", "all", "conditional", "smooth_terms", "sigma",
    "distributional", "auxiliary"),
  flatten = FALSE,
  parameters = NULL,
  ...
)

## S3 method for class 'sim.merMod'
find_parameters(
  x,
  effects = c("all", "fixed", "random"),
  flatten = FALSE,
  parameters = NULL,
  ...
)

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.
flatten	Logical, if TRUE, the values are returned as character vector, not as list. Duplicated values are removed.
...	Currently not used.
effects	Should parameters for fixed effects, random effects or both be returned? Only applies to mixed models. May be abbreviated.
parameters	Regular expression pattern that describes the parameters that should be returned.

Value

A list of parameter names. For simple models, only one list-element, conditional, is returned. For more complex models, the returned list may have following elements:

• conditional, the "fixed effects" part from the model

• random, the "random effects" part from the model

• zero_inflated, the "fixed effects" part from the zero-inflation component of the model

• zero_inflated_random, the "random effects" part from the zero-inflation component of the model

• smooth_terms, the smooth parameters

Furthermore, some models, especially from brms, can also return auxiliary parameters. These may be one of the following:

• sigma, the residual standard deviation (auxiliary parameter)

• dispersion, the dispersion parameters (auxiliary parameter)

• beta, the beta parameter (auxiliary parameter)

• simplex, simplex parameters of monotonic effects (brms only)

• mix, mixture parameters (brms only)

• shiftprop, shifted proportion parameters (brms only)

Examples

data(mtcars)
m <- lm(mpg ~ wt + cyl + vs, data = mtcars)
find_parameters(m)

insight documentation built on Oct. 2, 2024, 9:07 a.m.