model_parameters.averaging: Parameters from special models

In parameters: Processing of Model Parameters

Parameters from special models

Description

Parameters from special regression models not listed under one of the previous categories yet.

Usage

## S3 method for class 'glimML'
model_parameters(
  model,
  ci = 0.95,
  bootstrap = FALSE,
  iterations = 1000,
  component = c("conditional", "random", "dispersion", "all"),
  standardize = NULL,
  exponentiate = FALSE,
  p_adjust = NULL,
  summary = getOption("parameters_summary", FALSE),
  include_info = getOption("parameters_info", FALSE),
  keep = NULL,
  drop = NULL,
  verbose = TRUE,
  ...
)

## S3 method for class 'averaging'
model_parameters(
  model,
  ci = 0.95,
  component = c("conditional", "full"),
  exponentiate = FALSE,
  p_adjust = NULL,
  summary = getOption("parameters_summary", FALSE),
  include_info = getOption("parameters_info", FALSE),
  keep = NULL,
  drop = NULL,
  verbose = TRUE,
  ...
)

## S3 method for class 'betareg'
model_parameters(
  model,
  ci = 0.95,
  bootstrap = FALSE,
  iterations = 1000,
  component = c("conditional", "precision", "all"),
  standardize = NULL,
  exponentiate = FALSE,
  p_adjust = NULL,
  summary = getOption("parameters_summary", FALSE),
  include_info = getOption("parameters_info", FALSE),
  keep = NULL,
  drop = NULL,
  verbose = TRUE,
  ...
)

## S3 method for class 'emm_list'
model_parameters(
  model,
  ci = 0.95,
  exponentiate = FALSE,
  p_adjust = NULL,
  verbose = TRUE,
  ...
)

## S3 method for class 'glmx'
model_parameters(
  model,
  ci = 0.95,
  bootstrap = FALSE,
  iterations = 1000,
  component = c("all", "conditional", "extra"),
  standardize = NULL,
  exponentiate = FALSE,
  p_adjust = NULL,
  keep = NULL,
  drop = NULL,
  verbose = TRUE,
  ...
)

## S3 method for class 'marginaleffects'
model_parameters(model, ci = 0.95, exponentiate = FALSE, ...)

## S3 method for class 'metaplus'
model_parameters(
  model,
  ci = 0.95,
  bootstrap = FALSE,
  iterations = 1000,
  standardize = NULL,
  exponentiate = FALSE,
  include_studies = TRUE,
  keep = NULL,
  drop = NULL,
  verbose = TRUE,
  ...
)

## S3 method for class 'meta_random'
model_parameters(
  model,
  ci = 0.95,
  ci_method = "eti",
  exponentiate = FALSE,
  include_studies = TRUE,
  verbose = TRUE,
  ...
)

## S3 method for class 'meta_bma'
model_parameters(
  model,
  ci = 0.95,
  ci_method = "eti",
  exponentiate = FALSE,
  include_studies = TRUE,
  verbose = TRUE,
  ...
)

## S3 method for class 'betaor'
model_parameters(
  model,
  ci = 0.95,
  bootstrap = FALSE,
  iterations = 1000,
  component = c("conditional", "precision", "all"),
  standardize = NULL,
  exponentiate = FALSE,
  p_adjust = NULL,
  verbose = TRUE,
  ...
)

## S3 method for class 'betamfx'
model_parameters(
  model,
  ci = 0.95,
  bootstrap = FALSE,
  iterations = 1000,
  component = c("all", "conditional", "precision", "marginal"),
  standardize = NULL,
  exponentiate = FALSE,
  p_adjust = NULL,
  keep = NULL,
  drop = NULL,
  verbose = TRUE,
  ...
)

## S3 method for class 'mjoint'
model_parameters(
  model,
  ci = 0.95,
  effects = "fixed",
  component = c("all", "conditional", "survival"),
  exponentiate = FALSE,
  p_adjust = NULL,
  keep = NULL,
  drop = NULL,
  verbose = TRUE,
  ...
)

## S3 method for class 'mvord'
model_parameters(
  model,
  ci = 0.95,
  component = c("all", "conditional", "thresholds", "correlation"),
  standardize = NULL,
  exponentiate = FALSE,
  p_adjust = NULL,
  summary = getOption("parameters_summary", FALSE),
  include_info = getOption("parameters_info", FALSE),
  keep = NULL,
  drop = NULL,
  verbose = TRUE,
  ...
)

## S3 method for class 'selection'
model_parameters(
  model,
  ci = 0.95,
  component = c("all", "selection", "outcome", "auxiliary"),
  bootstrap = FALSE,
  iterations = 1000,
  standardize = NULL,
  exponentiate = FALSE,
  p_adjust = NULL,
  summary = getOption("parameters_summary", FALSE),
  include_info = getOption("parameters_info", FALSE),
  keep = NULL,
  drop = NULL,
  verbose = TRUE,
  ...
)

Arguments

model	Model object.
ci	Confidence Interval (CI) level. Default to 0.95 (⁠95%⁠).
bootstrap	Should estimates be based on bootstrapped model? If TRUE, then arguments of Bayesian regressions apply (see also bootstrap_parameters()).
iterations	The number of bootstrap replicates. This only apply in the case of bootstrapped frequentist models.
component	Model component for which parameters should be shown. May be one of "conditional", "precision" (betareg), "scale" (ordinal), "extra" (glmx), "marginal" (mfx), "conditional" or "full" (for MuMIn::model.avg()) or "all".
standardize	The method used for standardizing the parameters. Can be NULL (default; no standardization), "refit" (for re-fitting the model on standardized data) or one of "basic", "posthoc", "smart", "pseudo". See 'Details' in standardize_parameters(). Importantly: The "refit" method does not standardize categorical predictors (i.e. factors), which may be a different behaviour compared to other R packages (such as lm.beta) or other software packages (like SPSS). to mimic such behaviours, either use standardize="basic" or standardize the data with datawizard::standardize(force=TRUE) before fitting the model. For mixed models, when using methods other than "refit", only the fixed effects will be standardized. Robust estimation (i.e., vcov set to a value other than NULL) of standardized parameters only works when standardize="refit".
exponentiate	Logical, indicating whether or not to exponentiate the coefficients (and related confidence intervals). This is typical for logistic regression, or more generally speaking, for models with log or logit links. It is also recommended to use exponentiate = TRUE for models with log-transformed response values. Note: Delta-method standard errors are also computed (by multiplying the standard errors by the transformed coefficients). This is to mimic behaviour of other software packages, such as Stata, but these standard errors poorly estimate uncertainty for the transformed coefficient. The transformed confidence interval more clearly captures this uncertainty. For compare_parameters(), exponentiate = "nongaussian" will only exponentiate coefficients from non-Gaussian families.
p_adjust	Character vector, if not NULL, indicates the method to adjust p-values. See stats::p.adjust() for details. Further possible adjustment methods are "tukey", "scheffe", "sidak" and "none" to explicitly disable adjustment for emmGrid objects (from emmeans).
summary	Deprecated, please use info instead.
include_info	Logical, if TRUE, prints summary information about the model (model formula, number of observations, residual standard deviation and more).
keep	Character containing a regular expression pattern that describes the parameters that should be included (for keep) or excluded (for drop) in the returned data frame. keep may also be a named list of regular expressions. All non-matching parameters will be removed from the output. If keep is a character vector, every parameter name in the "Parameter" column that matches the regular expression in keep will be selected from the returned data frame (and vice versa, all parameter names matching drop will be excluded). Furthermore, if keep has more than one element, these will be merged with an OR operator into a regular expression pattern like this: "(one|two|three)". If keep is a named list of regular expression patterns, the names of the list-element should equal the column name where selection should be applied. This is useful for model objects where model_parameters() returns multiple columns with parameter components, like in model_parameters.lavaan(). Note that the regular expression pattern should match the parameter names as they are stored in the returned data frame, which can be different from how they are printed. Inspect the ⁠$Parameter⁠ column of the parameters table to get the exact parameter names.
drop	See keep.
verbose	Toggle warnings and messages.
...	Arguments passed to or from other methods. For instance, when bootstrap = TRUE, arguments like type or parallel are passed down to bootstrap_model(). Further non-documented arguments are digits, p_digits, ci_digits and footer_digits to set the number of digits for the output. If s_value = TRUE, the p-value will be replaced by the S-value in the output (cf. Rafi and Greenland 2020). pd adds an additional column with the probability of direction (see bayestestR::p_direction() for details). groups can be used to group coefficients. It will be passed to the print-method, or can directly be used in print(), see documentation in print.parameters_model(). Furthermore, see 'Examples' for this function. For developers, whose interest mainly is to get a "tidy" data frame of model summaries, it is recommended to set pretty_names = FALSE to speed up computation of the summary table.
include_studies	Logical, if TRUE (default), includes parameters for all studies. Else, only parameters for overall-effects are shown.
ci_method	Method for computing degrees of freedom for confidence intervals (CI) and the related p-values. Allowed are following options (which vary depending on the model class): "residual", "normal", "likelihood", "satterthwaite", "kenward", "wald", "profile", "boot", "uniroot", "ml1", "betwithin", "hdi", "quantile", "ci", "eti", "si", "bci", or "bcai". See section Confidence intervals and approximation of degrees of freedom in model_parameters() for further details. When ci_method=NULL, in most cases "wald" is used then.
effects	Should results for fixed effects, random effects or both be returned? Only applies to mixed models. May be abbreviated.

Value

A data frame of indices related to the model's parameters.

See Also

insight::standardize_names() to rename columns into a consistent, standardized naming scheme.

Examples

library(parameters)
if (require("brglm2", quietly = TRUE)) {
  data("stemcell")
  model <- bracl(
    research ~ as.numeric(religion) + gender,
    weights = frequency,
    data = stemcell,
    type = "ML"
  )
  model_parameters(model)
}

parameters documentation built on Oct. 18, 2024, 5:13 p.m.