% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/convert_stat_to_anova.R
\name{F_to_eta2}
\alias{F_to_eta2}
\alias{t_to_eta2}
\alias{F_to_epsilon2}
\alias{t_to_epsilon2}
\alias{F_to_eta2_adj}
\alias{t_to_eta2_adj}
\alias{F_to_omega2}
\alias{t_to_omega2}
\alias{F_to_f}
\alias{t_to_f}
\alias{F_to_f2}
\alias{t_to_f2}
\title{Convert \emph{F} and \emph{t} Statistics to \strong{partial}-\eqn{\eta^2} and Other ANOVA Effect Sizes}
\usage{
F_to_eta2(f, df, df_error, ci = 0.95, alternative = "greater", ...)

t_to_eta2(t, df_error, ci = 0.95, alternative = "greater", ...)

F_to_epsilon2(f, df, df_error, ci = 0.95, alternative = "greater", ...)

t_to_epsilon2(t, df_error, ci = 0.95, alternative = "greater", ...)

F_to_eta2_adj(f, df, df_error, ci = 0.95, alternative = "greater", ...)

t_to_eta2_adj(t, df_error, ci = 0.95, alternative = "greater", ...)

F_to_omega2(f, df, df_error, ci = 0.95, alternative = "greater", ...)

t_to_omega2(t, df_error, ci = 0.95, alternative = "greater", ...)

F_to_f(
  f,
  df,
  df_error,
  squared = FALSE,
  ci = 0.95,
  alternative = "greater",
  ...
)

t_to_f(t, df_error, squared = FALSE, ci = 0.95, alternative = "greater", ...)

F_to_f2(
  f,
  df,
  df_error,
  squared = TRUE,
  ci = 0.95,
  alternative = "greater",
  ...
)

t_to_f2(t, df_error, squared = TRUE, ci = 0.95, alternative = "greater", ...)
}
\arguments{
\item{df, df_error}{Degrees of freedom of numerator or of the error estimate
(i.e., the residuals).}

\item{ci}{Confidence Interval (CI) level}

\item{alternative}{a character string specifying the alternative hypothesis;
Controls the type of CI returned: \code{"greater"} (default) or \code{"less"}
(one-sided CI), or \code{"two.sided"} (default, two-sided CI). Partial matching
is allowed (e.g., \code{"g"}, \code{"l"}, \code{"two"}...). See \emph{One-Sided CIs} in
\link{effectsize_CIs}.}

\item{...}{Arguments passed to or from other methods.}

\item{t, f}{The t or the F statistics.}

\item{squared}{Return Cohen's \emph{f} or Cohen's \emph{f}-squared?}
}
\value{
A data frame with the effect size(s) between 0-1 (\code{Eta2_partial},
\code{Epsilon2_partial}, \code{Omega2_partial}, \code{Cohens_f_partial} or
\code{Cohens_f2_partial}), and their CIs (\code{CI_low} and \code{CI_high}).
}
\description{
These functions are convenience functions to convert F and t test statistics
to \strong{partial} Eta- (\eqn{\eta}), Omega- (\eqn{\omega}) Epsilon-
(\eqn{\epsilon}) squared (an alias for the adjusted Eta squared) and Cohen's
f. These are useful in cases where the various Sum of Squares and Mean
Squares are not easily available or their computation is not straightforward
(e.g., in liner mixed models, contrasts, etc.). For test statistics derived
from \code{lm} and \code{aov} models, these functions give exact results. For all other
cases, they return close approximations.
\cr
See \href{https://easystats.github.io/effectsize/articles/from_test_statistics.html}{Effect Size from Test Statistics vignette.}
}
\details{
These functions use the following formulae:
\cr
\deqn{\eta_p^2 = \frac{F \times df_{num}}{F \times df_{num} + df_{den}}}{\eta^2_p = F * df1 / (F * df1 + df2)}
\cr
\deqn{\epsilon_p^2 = \frac{(F - 1) \times df_{num}}{F \times df_{num} + df_{den}}}{\epsilon^2_p = (F - 1) * df1 / (F * df1 + df2)}
\cr
\deqn{\omega_p^2 = \frac{(F - 1) \times df_{num}}{F \times df_{num} + df_{den} + 1}}{\omega^2_p=(F - 1) * df1 / (F * df1 + df2 + 1)}
\cr
\deqn{f_p = \sqrt{\frac{\eta_p^2}{1-\eta_p^2}}}{f = \eta^2 / (1 - \eta^2)}
\cr\cr
For \emph{t}, the conversion is based on the equality of \eqn{t^2 = F} when \eqn{df_{num}=1}{df1 = 1}.
\subsection{Choosing an Un-Biased Estimate}{

Both Omega and Epsilon are unbiased estimators of the population Eta. But
which to choose? Though Omega is the more popular choice, it should be noted
that:
\enumerate{
\item The formula given above for Omega is only an approximation for complex
designs.
\item Epsilon has been found to be less biased (Carroll & Nordholm, 1975).
}
}
}
\note{
Adjusted (partial) Eta-squared is an alias for (partial) Epsilon-squared.
}
\section{Confidence (Compatibility) Intervals (CIs)}{
Unless stated otherwise, confidence (compatibility) intervals (CIs) are
estimated using the noncentrality parameter method (also called the "pivot
method"). This method finds the noncentrality parameter ("\emph{ncp}") of a
noncentral \emph{t}, \emph{F}, or \eqn{\chi^2} distribution that places the observed
\emph{t}, \emph{F}, or \eqn{\chi^2} test statistic at the desired probability point of
the distribution. For example, if the observed \emph{t} statistic is 2.0, with 50
degrees of freedom, for which cumulative noncentral \emph{t} distribution is \emph{t} =
2.0 the .025 quantile (answer: the noncentral \emph{t} distribution with \emph{ncp} =
.04)? After estimating these confidence bounds on the \emph{ncp}, they are
converted into the effect size metric to obtain a confidence interval for the
effect size (Steiger, 2004).
\cr\cr
For additional details on estimation and troubleshooting, see \link{effectsize_CIs}.
}

\section{CIs and Significance Tests}{
"Confidence intervals on measures of effect size convey all the information
in a hypothesis test, and more." (Steiger, 2004). Confidence (compatibility)
intervals and p values are complementary summaries of parameter uncertainty
given the observed data. A dichotomous hypothesis test could be performed
with either a CI or a p value. The 100 (1 - \eqn{\alpha})\% confidence
interval contains all of the parameter values for which \emph{p} > \eqn{\alpha}
for the current data and model. For example, a 95\% confidence interval
contains all of the values for which p > .05.
\cr\cr
Note that a confidence interval including 0 \emph{does not} indicate that the null
(no effect) is true. Rather, it suggests that the observed data together with
the model and its assumptions combined do not provided clear evidence against
a parameter value of 0 (same as with any other value in the interval), with
the level of this evidence defined by the chosen \eqn{\alpha} level (Rafi &
Greenland, 2020; Schweder & Hjort, 2016; Xie & Singh, 2013). To infer no
effect, additional judgments about what parameter values are "close enough"
to 0 to be negligible are needed ("equivalence testing"; Bauer & Kiesser,
1996).
}

\section{Plotting with \code{see}}{

The \code{see} package contains relevant plotting functions. See the \href{https://easystats.github.io/see/articles/effectsize.html}{plotting vignette in the \code{see} package}.
}

\examples{
mod <- aov(mpg ~ factor(cyl) * factor(am), mtcars)
anova(mod)
(etas <- F_to_eta2(
  f = c(44.85, 3.99, 1.38),
  df = c(2, 1, 2),
  df_error = 26
))

if (require(see)) plot(etas)

# Compare to:
eta_squared(mod)

\dontshow{if (require(lmerTest) && interactive()) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
fit <- lmerTest::lmer(extra ~ group + (1 | ID), sleep)
# anova(fit)
# #> Type III Analysis of Variance Table with Satterthwaite's method
# #>       Sum Sq Mean Sq NumDF DenDF F value   Pr(>F)
# #> group 12.482  12.482     1     9  16.501 0.002833 **
# #> ---
# #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

F_to_eta2(16.501, 1, 9)
F_to_omega2(16.501, 1, 9)
F_to_epsilon2(16.501, 1, 9)
F_to_f(16.501, 1, 9)
\dontshow{\}) # examplesIf}
\dontshow{if (require(emmeans)) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
## Use with emmeans based contrasts
## --------------------------------
warp.lm <- lm(breaks ~ wool * tension, data = warpbreaks)

jt <- emmeans::joint_tests(warp.lm, by = "wool")
F_to_eta2(jt$F.ratio, jt$df1, jt$df2)
\dontshow{\}) # examplesIf}
}
\references{
\itemize{
\item Albers, C., & Lakens, D. (2018). When power analyses based on pilot data
are biased: Inaccurate effect size estimators and follow-up bias. Journal of
experimental social psychology, 74, 187-195. \doi{10.31234/osf.io/b7z4q}
\item Carroll, R. M., & Nordholm, L. A. (1975). Sampling Characteristics of
Kelley's epsilon and Hays' omega. Educational and Psychological Measurement,
35(3), 541-554.
\item Cumming, G., & Finch, S. (2001). A primer on the understanding, use, and
calculation of confidence intervals that are based on central and noncentral
distributions. Educational and Psychological Measurement, 61(4), 532-574.
\item Friedman, H. (1982). Simplified determinations of statistical power,
magnitude of effect and research sample sizes. Educational and Psychological
Measurement, 42(2), 521-526. \doi{10.1177/001316448204200214}
\item Mordkoff, J. T. (2019). A Simple Method for Removing Bias From a Popular
Measure of Standardized Effect Size: Adjusted Partial Eta Squared. Advances
in Methods and Practices in Psychological Science, 2(3), 228-232.
\doi{10.1177/2515245919855053}
\item Morey, R. D., Hoekstra, R., Rouder, J. N., Lee, M. D., & Wagenmakers, E. J.
(2016). The fallacy of placing confidence in confidence intervals.
Psychonomic bulletin & review, 23(1), 103-123.
\item Steiger, J. H. (2004). Beyond the F test: Effect size confidence intervals
and tests of close fit in the analysis of variance and contrast analysis.
Psychological Methods, 9, 164-182.
}
}
\seealso{
\code{\link[=eta_squared]{eta_squared()}} for more details.

Other effect size from test statistic: 
\code{\link{chisq_to_phi}()},
\code{\link{t_to_d}()}
}
\concept{effect size from test statistic}
