% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/model_parameters_default.R
\name{model_parameters.default}
\alias{model_parameters.default}
\alias{model_parameters.betareg}
\alias{model_parameters.clm2}
\alias{model_parameters.glmx}
\title{Parameters from (General) Linear Models}
\usage{
\method{model_parameters}{default}(
  model,
  ci = 0.95,
  bootstrap = FALSE,
  iterations = 1000,
  standardize = NULL,
  exponentiate = FALSE,
  robust = FALSE,
  ...
)

\method{model_parameters}{betareg}(
  model,
  ci = 0.95,
  bootstrap = FALSE,
  iterations = 1000,
  component = c("conditional", "precision", "all"),
  standardize = NULL,
  exponentiate = FALSE,
  ...
)

\method{model_parameters}{clm2}(
  model,
  ci = 0.95,
  bootstrap = FALSE,
  iterations = 1000,
  component = c("all", "conditional", "scale"),
  standardize = NULL,
  exponentiate = FALSE,
  ...
)

\method{model_parameters}{glmx}(
  model,
  ci = 0.95,
  bootstrap = FALSE,
  iterations = 1000,
  component = c("all", "conditional", "extra"),
  standardize = NULL,
  exponentiate = FALSE,
  ...
)
}
\arguments{
\item{model}{Model object.}

\item{ci}{Confidence Interval (CI) level. Default to 0.95 (95\%).}

\item{bootstrap}{Should estimates be based on bootstrapped model? If \code{TRUE}, then arguments of \link[=model_parameters.stanreg]{Bayesian regressions} apply (see also \code{\link[=parameters_bootstrap]{parameters_bootstrap()}}).}

\item{iterations}{The number of bootstrap replicates. This only apply in the case of bootstrapped frequentist models.}

\item{standardize}{The method used for standardizing the parameters. Can be \code{"refit"}, \code{"posthoc"}, \code{"smart"}, \code{"basic"} or \code{NULL} (default) for no standardization. See 'Details' in \code{\link[effectsize]{standardize_parameters}}. Note that robust estimation (i.e. \code{robust=TRUE}) of standardized parameters only works when \code{standardize="refit"}.}

\item{exponentiate}{Logical, indicating whether or not to exponentiate the the coefficients (and related confidence intervals). This is typical for, say, logistic regressions, or more generally speaking: for models with log or logit link.}

\item{robust}{Logical, if \code{TRUE}, robust standard errors are calculated (if possible), and confidence intervals and p-values are based on these robust standard errors. Additional arguments like \code{vcov_estimation} or \code{vcov_type} are passed down to other methods, see \code{\link[=standard_error_robust]{standard_error_robust()}} for details.}

\item{...}{Arguments passed to or from other methods. For instance, when \code{bootstrap = TRUE}, arguments like \code{ci_method} are passed down to \code{\link[bayestestR]{describe_posterior}}.}

\item{component}{Model component for which parameters should be shown. May be one of \code{"conditional"}, \code{"precision"} (\pkg{betareg}), \code{"scale"} (\pkg{ordinal}), \code{"extra"} (\pkg{glmx}) or \code{"all"}.}
}
\value{
A data frame of indices related to the model's parameters.
}
\description{
Extract and compute indices and measures to describe parameters of (general) linear models (GLMs).
}
\examples{
library(parameters)
model <- lm(mpg ~ wt + cyl, data = mtcars)

model_parameters(model)

# bootstrapped parameters
model_parameters(model, bootstrap = TRUE)

# standardized parameters
model_parameters(model, standardize = "refit")

# different p-value style in output
model_parameters(model, p_digits = 5)
model_parameters(model, digits = 3, ci_digits = 4, p_digits = "scientific")

# logistic regression model
model <- glm(vs ~ wt + cyl, data = mtcars, family = "binomial")
model_parameters(model)

# show odds ratio / exponentiated coefficients
model_parameters(model, exponentiate = TRUE)
}
\seealso{
\code{\link[=standardize_names]{standardize_names()}} to rename
  columns into a consistent, standardized naming scheme.
}
