% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/methods_htest.R, R/methods_lmtest.R
\name{model_parameters.htest}
\alias{model_parameters.htest}
\alias{model_parameters.pairwise.htest}
\alias{model_parameters.coeftest}
\title{Parameters from hypothesis tests}
\usage{
\method{model_parameters}{htest}(
  model,
  ci = 0.95,
  alternative = NULL,
  bootstrap = FALSE,
  effectsize_type = NULL,
  verbose = TRUE,
  cramers_v = NULL,
  phi = NULL,
  standardized_d = NULL,
  hedges_g = NULL,
  omega_squared = NULL,
  eta_squared = NULL,
  epsilon_squared = NULL,
  cohens_g = NULL,
  rank_biserial = NULL,
  rank_epsilon_squared = NULL,
  kendalls_w = NULL,
  ...
)

\method{model_parameters}{pairwise.htest}(model, verbose = TRUE, ...)

\method{model_parameters}{coeftest}(model, ci = 0.95, ci_method = "wald", verbose = TRUE, ...)
}
\arguments{
\item{model}{Object of class \code{htest} or \code{pairwise.htest}.}

\item{ci}{Level of confidence intervals for effect size statistic. Currently
only applies to objects from \code{chisq.test()} or \code{oneway.test()}.}

\item{alternative}{A character string specifying the alternative hypothesis;
Controls the type of CI returned: \code{"two.sided"} (default, two-sided CI),
\code{"greater"} or \code{"less"} (one-sided CI). Partial matching is allowed
(e.g., \code{"g"}, \code{"l"}, \code{"two"}...). See section \emph{One-Sided CIs} in
the \href{https://easystats.github.io/effectsize/}{effectsize_CIs vignette}.}

\item{bootstrap}{Should estimates be bootstrapped?}

\item{effectsize_type}{The effect size of interest. Not that possibly not all
effect sizes are applicable to the model object. See 'Details'. For Anova
models, can also be a character vector with multiple effect size names.}

\item{verbose}{Toggle warnings and messages.}

\item{cramers_v, phi, cohens_g, standardized_d, hedges_g, omega_squared, eta_squared, epsilon_squared, rank_biserial, rank_epsilon_squared, kendalls_w}{Deprecated. Please use \code{effectsize_type}.}

\item{...}{Arguments passed to or from other methods. For instance, when
\code{bootstrap = TRUE}, arguments like \code{type} or \code{parallel} are
passed down to \code{bootstrap_model()}.}

\item{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): \code{"residual"},
\code{"normal"}, \code{"likelihood"}, \code{"satterthwaite"}, \code{"kenward"}, \code{"wald"},
\code{"profile"}, \code{"boot"}, \code{"uniroot"}, \code{"ml1"}, \code{"betwithin"}, \code{"hdi"},
\code{"quantile"}, \code{"ci"}, \code{"eti"}, \code{"si"}, \code{"bci"}, or \code{"bcai"}. See section
\emph{Confidence intervals and approximation of degrees of freedom} in
\code{\link[=model_parameters]{model_parameters()}} for further details. When \code{ci_method=NULL}, in most
cases \code{"wald"} is used then.}
}
\value{
A data frame of indices related to the model's parameters.
}
\description{
Parameters of h-tests (correlations, t-tests, chi-squared, ...).
}
\details{
\itemize{
\item For an object of class \code{htest}, data is extracted via \code{\link[insight:get_data]{insight::get_data()}}, and passed to the relevant function according to:
\itemize{
\item A \strong{t-test} depending on \code{type}: \code{"cohens_d"} (default), \code{"hedges_g"}, or one of \code{"p_superiority"}, \code{"u1"}, \code{"u2"}, \code{"u3"}, \code{"overlap"}.
\item A \strong{Chi-squared tests of independence} or \strong{Fisher's Exact Test}, depending on \code{type}: \code{"cramers_v"} (default), \code{"tschuprows_t"}, \code{"phi"}, \code{"cohens_w"}, \code{"pearsons_c"}, \code{"cohens_h"}, \code{"oddsratio"}, or \code{"riskratio"}.
\item A \strong{Chi-squared tests of goodness-of-fit}, depending on \code{type}: \code{"fei"} (default) \code{"cohens_w"}, \code{"pearsons_c"}
\item A \strong{One-way ANOVA test}, depending on \code{type}: \code{"eta"} (default), \code{"omega"} or \code{"epsilon"} -squared, \code{"f"}, or \code{"f2"}.
\item A \strong{McNemar test} returns \emph{Cohen's g}.
\item A \strong{Wilcoxon test} depending on \code{type}: returns "\code{rank_biserial}" correlation (default) or one of \code{"p_superiority"}, \code{"vda"}, \code{"u2"}, \code{"u3"}, \code{"overlap"}.
\item A \strong{Kruskal-Wallis test} depending on \code{type}: \code{"epsilon"} (default) or \code{"eta"}.
\item A \strong{Friedman test} returns \emph{Kendall's W}.
(Where applicable, \code{ci} and \code{alternative} are taken from the \code{htest} if not otherwise provided.)
}
\item For an object of class \code{BFBayesFactor}, using \code{\link[bayestestR:describe_posterior]{bayestestR::describe_posterior()}},
\itemize{
\item A \strong{t-test} depending on \code{type}: \code{"cohens_d"} (default) or one of \code{"p_superiority"}, \code{"u1"}, \code{"u2"}, \code{"u3"}, \code{"overlap"}.
\item A \strong{correlation test} returns \emph{r}.
\item A \strong{contingency table test}, depending on \code{type}: \code{"cramers_v"} (default), \code{"phi"}, \code{"tschuprows_t"}, \code{"cohens_w"}, \code{"pearsons_c"}, \code{"cohens_h"}, \code{"oddsratio"}, or \code{"riskratio"}.
\item A \strong{proportion test} returns \emph{p}.
}
\item Objects of class \code{anova}, \code{aov}, \code{aovlist} or \code{afex_aov}, depending on \code{type}: \code{"eta"} (default), \code{"omega"} or \code{"epsilon"} -squared, \code{"f"}, or \code{"f2"}.
\item Other objects are passed to \code{\link[parameters:standardize_parameters]{parameters::standardize_parameters()}}.
}

\strong{For statistical models it is recommended to directly use the listed
functions, for the full range of options they provide.}
}
\examples{

model <- cor.test(mtcars$mpg, mtcars$cyl, method = "pearson")
model_parameters(model)

model <- t.test(iris$Sepal.Width, iris$Sepal.Length)
model_parameters(model, effectsize_type = "hedges_g")

model <- t.test(mtcars$mpg ~ mtcars$vs)
model_parameters(model, effectsize_type = "hedges_g")

model <- t.test(iris$Sepal.Width, mu = 1)
model_parameters(model, effectsize_type = "cohens_d")

data(airquality)
airquality$Month <- factor(airquality$Month, labels = month.abb[5:9])
model <- pairwise.t.test(airquality$Ozone, airquality$Month)
model_parameters(model)

smokers <- c(83, 90, 129, 70)
patients <- c(86, 93, 136, 82)
model <- suppressWarnings(pairwise.prop.test(smokers, patients))
model_parameters(model)

model <- suppressWarnings(chisq.test(table(mtcars$am, mtcars$cyl)))
model_parameters(model, effectsize_type = "cramers_v")

}
