View source: R/methods_averaging.R
model_parameters.glimML  R Documentation 
Parameters from special regression models not listed under one of the previous categories yet.
## 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),
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),
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),
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),
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),
keep = NULL,
drop = NULL,
verbose = TRUE,
...
)
model 
Model object. 
ci 
Confidence Interval (CI) level. Default to 
bootstrap 
Should estimates be based on bootstrapped model? If

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 
standardize 
The method used for standardizing the parameters. Can be

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 
p_adjust 
Character vector, if not 
summary 
Logical, if 
keep 
Character containing a regular expression pattern that
describes the parameters that should be included (for 
drop 
See 
verbose 
Toggle warnings and messages. 
... 
Arguments passed to or from other methods. For instance, when

include_studies 
Logical, if 
ci_method 
Method for computing degrees of freedom for
confidence intervals (CI) and the related pvalues. Allowed are following
options (which vary depending on the model class): 
effects 
Should results for fixed effects, random effects or both be returned? Only applies to mixed models. May be abbreviated. 
A data frame of indices related to the model's parameters.
insight::standardize_names()
to rename
columns into a consistent, standardized naming scheme.
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)
}
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.