https://github.com/cran/bayestestR
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Tip revision: 23ea3229abe72a5f23dcf3a4cfcd3478d744b536 authored by Dominique Makowski on 20 June 2019, 11:50 UTC
version 0.2.2
Tip revision: 23ea322
ci.Rd
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ci.R
\name{ci}
\alias{ci}
\alias{ci.numeric}
\alias{ci.data.frame}
\alias{ci.emmGrid}
\alias{ci.stanreg}
\alias{ci.brmsfit}
\alias{ci.BFBayesFactor}
\title{Confidence/Credible Interval (CI)}
\usage{
ci(x, ...)

\method{ci}{numeric}(x, ci = 0.89, verbose = TRUE, ...)

\method{ci}{data.frame}(x, ci = 0.89, verbose = TRUE, ...)

\method{ci}{emmGrid}(x, ci = 0.89, verbose = TRUE, ...)

\method{ci}{stanreg}(x, ci = 0.89, effects = c("fixed", "random",
  "all"), parameters = NULL, verbose = TRUE, ...)

\method{ci}{brmsfit}(x, ci = 0.89, effects = c("fixed", "random",
  "all"), component = c("conditional", "zi", "zero_inflated", "all"),
  parameters = NULL, verbose = TRUE, ...)

\method{ci}{BFBayesFactor}(x, ci = 0.89, verbose = TRUE, ...)
}
\arguments{
\item{x}{A \code{stanreg} or \code{brmsfit} model, or a vector representing a posterior distribution.}

\item{...}{Currently not used.}

\item{ci}{Value or vector of probability of the interval (between 0 and 1)
to be estimated. Named Credible Interval (CI) for consistency.}

\item{verbose}{Toggle off warnings.}

\item{effects}{Should results for fixed effects, random effects or both be returned?
Only applies to mixed models. May be abbreviated.}

\item{parameters}{Regular expression pattern that describes the parameters that
should be returned. Meta-parameters (like \code{lp__} or \code{prior_}) are
filtered by default, so only parameters that typically appear in the
\code{summary()} are returned. Use \code{parameters} to select specific parameters
for the output.}

\item{component}{Should results for all parameters, parameters for the conditional model
or the zero-inflated part of the model be returned? May be abbreviated. Only
applies to \pkg{brms}-models.}
}
\value{
A data frame with following columns:
  \itemize{
    \item \code{Parameter} The model parameter(s), if \code{x} is a model-object. If \code{x} is a vector, this column is missing.
    \item \code{CI} The probability of the credible interval.
    \item \code{CI_low}, \code{CI_high} The lower and upper credible interval limits for the parameters.
  }
}
\description{
Compute Confidence/Credible Intervals (CI) for Bayesian (using quantiles) and frequentist models.
}
\details{
Documentation is accessible for:
\itemize{
 \item \href{https://easystats.github.io/bayestestR/reference/ci.html}{Bayesian models}
 \item \href{https://easystats.github.io/parameters/reference/ci.merMod.html}{Frequentist models}
}
\strong{Bayesian models}
\cr \cr
This functions returns, by default, the quantile interval, i.e., an
equal-tailed interval (ETI). A 90\% ETI has 5\% of the distribution on either
side of its limits. It indicates the 5th percentile and the 95h percentile.
In symmetric distributions, the two methods of computing credible intervals,
the ETI and the \link[=hdi]{HDI}, return similar results.
\cr \cr
This is not the case for skewed distributions. Indeed, it is possible that
parameter values in the ETI have lower credibility (are less probable) than
parameter values outside the ETI. This property seems undesirable as a summary
of the credible values in a distribution.
\cr \cr
On the other hand, the ETI range does change when transformations are applied
to the distribution (for instance, for a log odds scale to probabilities):
the lower and higher bounds of the transformed distribution will correspond
to the transformed lower and higher bounds of the original distribution.
On the contrary, applying transformations to the distribution will change
the resulting HDI.
\cr \cr
\strong{Frequentist models}
\cr \cr
 This function is implemented in the \href{https://github.com/easystats/parameters}{parameters} package and attemps to retrieve, or compute, the Confidence Interval (default \code{ci} level: \code{.95}).
}
\examples{
library(bayestestR)

posterior <- rnorm(1000)
ci(posterior)
ci(posterior, ci = c(.80, .89, .95))

df <- data.frame(replicate(4, rnorm(100)))
ci(df)
ci(df, ci = c(.80, .89, .95))

library(rstanarm)
model <- stan_glm(mpg ~ wt + gear, data = mtcars, chains = 2, iter = 200)
ci(model)
ci(model, ci = c(.80, .89, .95))

library(emmeans)
ci(emtrends(model, ~1, "wt"))
\dontrun{
library(brms)
model <- brms::brm(mpg ~ wt + cyl, data = mtcars)
ci(model)
ci(model, ci = c(.80, .89, .95))

library(BayesFactor)
bf <- ttestBF(x = rnorm(100, 1, 1))
ci(bf)
ci(bf, ci = c(.80, .89, .95))
}

}
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