##### https://github.com/cran/bayestestR

Tip revision:

**d8462ad2168ad7ee61c0d7e679174e775f01a9be**authored by**Dominique Makowski**on**18 January 2020, 07:10 UTC****version 0.5.0** Tip revision:

**d8462ad** 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.sim.merMod}
\alias{ci.sim}
\alias{ci.stanreg}
\alias{ci.brmsfit}
\alias{ci.BFBayesFactor}
\alias{ci.MCMCglmm}
\title{Confidence/Credible/Compatibility Interval (CI)}
\usage{
ci(x, ...)
\method{ci}{numeric}(x, ci = 0.89, method = "ETI", verbose = TRUE, BF = 1, ...)
\method{ci}{data.frame}(x, ci = 0.89, method = "ETI", verbose = TRUE, BF = 1, ...)
\method{ci}{emmGrid}(x, ci = 0.89, method = "ETI", verbose = TRUE, BF = 1, ...)
\method{ci}{sim.merMod}(
x,
ci = 0.89,
method = "ETI",
effects = c("fixed", "random", "all"),
parameters = NULL,
verbose = TRUE,
...
)
\method{ci}{sim}(x, ci = 0.89, method = "ETI", parameters = NULL, verbose = TRUE, ...)
\method{ci}{stanreg}(
x,
ci = 0.89,
method = "ETI",
effects = c("fixed", "random", "all"),
parameters = NULL,
verbose = TRUE,
BF = 1,
...
)
\method{ci}{brmsfit}(
x,
ci = 0.89,
method = "ETI",
effects = c("fixed", "random", "all"),
component = c("conditional", "zi", "zero_inflated", "all"),
parameters = NULL,
verbose = TRUE,
BF = 1,
...
)
\method{ci}{BFBayesFactor}(x, ci = 0.89, method = "ETI", verbose = TRUE, BF = 1, ...)
\method{ci}{MCMCglmm}(x, ci = 0.89, method = "ETI", 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 CI (between 0 and 1)
to be estimated. Default to \code{.89} (89\%) for Bayesian models and \code{.95} (95\%) for frequentist models.}
\item{method}{Can be \link[=eti]{'ETI'} (default), \link[=hdi]{'HDI'} or \link[=si]{'SI'}.}
\item{verbose}{Toggle off warnings.}
\item{BF}{The amount of support required to be included in the support interval.}
\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/Compatibility Intervals (CI) or Support Intervals (SI) for Bayesian and frequentist models. The Documentation is accessible for:
}
\details{
\itemize{
\item \href{https://easystats.github.io/bayestestR/articles/credible_interval.html}{Bayesian models}
\item \href{https://easystats.github.io/parameters/reference/ci.merMod.html}{Frequentist models}
}
}
\note{
When it comes to interpretation, we recommend thinking of the CI in terms of
an "uncertainty" or "compatibility" interval, the latter being defined as
\dQuote{Given any value in the interval and the background assumptions,
the data should not seem very surprising} (\cite{Gelman & Greenland 2019}).
}
\examples{
library(bayestestR)
posterior <- rnorm(1000)
ci(posterior, method = "ETI")
ci(posterior, method = "HDI")
df <- data.frame(replicate(4, rnorm(100)))
ci(df, method = "ETI", ci = c(.80, .89, .95))
ci(df, method = "HDI", ci = c(.80, .89, .95))
library(rstanarm)
model <- stan_glm(mpg ~ wt, data = mtcars, chains = 2, iter = 200, refresh = 0)
ci(model, method = "ETI", ci = c(.80, .89))
ci(model, method = "HDI", ci = c(.80, .89))
ci(model, method = "SI")
\dontrun{
library(brms)
model <- brms::brm(mpg ~ wt + cyl, data = mtcars)
ci(model, method = "ETI")
ci(model, method = "HDI")
ci(model, method = "SI")
library(BayesFactor)
bf <- ttestBF(x = rnorm(100, 1, 1))
ci(bf, method = "ETI")
ci(bf, method = "HDI")
library(emmeans)
model <- emtrends(model, ~1, "wt")
ci(model, method = "ETI")
ci(model, method = "HDI")
ci(model, method = "SI")
}
}
\references{
Gelman A, Greenland S. Are confidence intervals better termed "uncertainty intervals"? BMJ 2019;l5381. \doi{10.1136/bmj.l5381}
}
```