% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/cwi.R
\name{cwi}
\alias{cwi}
\alias{cwi.data.frame}
\title{Curvewise Intervals (CWI)}
\usage{
cwi(x, ...)

\method{cwi}{data.frame}(x, ci = 0.95, ...)
}
\arguments{
\item{x}{Vector representing a posterior distribution, or a data frame of such
vectors. Can also be a Bayesian model (\code{stanreg}, \code{brmsfit},
\code{MCMCglmm}, \code{mcmc} or \code{bcplm}) or a \code{BayesFactor} model.}

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

\item{ci}{Value or vector of probability of the (credible) interval - CI
(between 0 and 1) to be estimated. Default to \code{.95} (\verb{95\%}).}
}
\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 the \strong{Curvewise interval (CWI)} (also called the "simultaneous interval" or "joint interval") of posterior distributions using \code{ggdist::curve_interval()}.
Whereas the more typical "pointwise intervals" contain xx\% of the posterior for a single parameter,
joint/curvewise intervals contain xx\% of the posterior distribution for \strong{all} parameters.
}
\details{
Applied model predictions, pointwise intervals contain xx\% of the predicted response values \strong{conditional} on specific predictor values.
In contrast, curvewise intervals contain xx\% of the predicted response values across all predictor values.
Put another way, curvewise intervals contain xx\% of the full \strong{prediction lines} from the model.

For more details, see the \href{https://mjskay.github.io/ggdist/articles/lineribbon.html#curve-boxplots-aka-lineribbons-with-joint-intervals-or-curvewise-intervals-}{\emph{ggdist} documentation on curvewise intervals}.
}
\examples{
\donttest{
library(bayestestR)

if (require("ggplot2") && require("rstanarm") && require("ggdist")) {

# Generate data =============================================
k = 11 # number of curves (iterations)
n = 201 # number of rows
data <- data.frame(x = seq(-15,15,length.out = n))

# Simulate iterations as new columns
for(i in 1:k) {
 data[paste0("iter_", i)] <- dnorm(data$x, seq(-5,5, length.out = k)[i], 3)
}

# Note: first, we need to transpose the data to have iters as rows
iters <- datawizard::data_transpose(data[paste0("iter_", 1:k)])

# Compute Median
data$Median <- point_estimate(iters)[["Median"]]

# Compute Credible Intervals ================================

# Compute ETI (default type of CI)
data[c("ETI_low", "ETI_high")] <- eti(iters, ci = 0.5)[c("CI_low", "CI_high")]

# Compute CWI
# ggdist::curve_interval(reshape_iterations(data), iter_value .width = c(.5))

# Visualization =============================================
ggplot(data, aes(x = x, y = Median)) +
 geom_ribbon(aes(ymin = ETI_low, ymax = ETI_high), fill = "red", alpha = 0.3) +
 geom_line(size = 1) +
 geom_line(data = reshape_iterations(data),
           aes(y = iter_value, group = iter_group),
           alpha = 0.3)
}
}
}
\seealso{
Other ci: 
\code{\link{bci}()},
\code{\link{ci}()},
\code{\link{eti}()},
\code{\link{hdi}()},
\code{\link{si}()}
}
\concept{ci}