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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ppc-scatterplots.R
\name{PPC-scatterplots}
\alias{PPC-scatterplots}
\alias{ppc_scatter}
\alias{ppc_scatter_avg}
\alias{ppc_scatter_avg_grouped}
\alias{ppc_scatter_data}
\alias{ppc_scatter_avg_data}
\title{PPC scatterplots}
\usage{
ppc_scatter(
y,
yrep,
...,
facet_args = list(),
size = 2.5,
alpha = 0.8,
ref_line = TRUE
)
ppc_scatter_avg(
y,
yrep,
...,
stat = "mean",
size = 2.5,
alpha = 0.8,
ref_line = TRUE
)
ppc_scatter_avg_grouped(
y,
yrep,
group,
...,
stat = "mean",
facet_args = list(),
size = 2.5,
alpha = 0.8,
ref_line = TRUE
)
ppc_scatter_data(y, yrep)
ppc_scatter_avg_data(y, yrep, group = NULL, stat = "mean")
}
\arguments{
\item{y}{A vector of observations. See \strong{Details}.}
\item{yrep}{An \code{S} by \code{N} matrix of draws from the posterior (or prior)
predictive distribution. The number of rows, \code{S}, is the size of the
posterior (or prior) sample used to generate \code{yrep}. The number of columns,
\code{N} is the number of predicted observations (\code{length(y)}). The columns of
\code{yrep} should be in the same order as the data points in \code{y} for the plots
to make sense. See the \strong{Details} and \strong{Plot Descriptions} sections for
additional advice specific to particular plots.}
\item{...}{Currently unused.}
\item{facet_args}{A named list of arguments (other than \code{facets}) passed
to \code{\link[ggplot2:facet_wrap]{ggplot2::facet_wrap()}} or \code{\link[ggplot2:facet_grid]{ggplot2::facet_grid()}}
to control faceting. Note: if \code{scales} is not included in \code{facet_args}
then \strong{bayesplot} may use \code{scales="free"} as the default (depending
on the plot) instead of the \strong{ggplot2} default of \code{scales="fixed"}.}
\item{size, alpha}{Arguments passed to \code{\link[ggplot2:geom_point]{ggplot2::geom_point()}} to control the
appearance of the points.}
\item{ref_line}{If \code{TRUE} (the default) a dashed line with intercept 0 and
slope 1 is drawn behind the scatter plot.}
\item{stat}{A function or a string naming a function for computing the
posterior average. In both cases, the function should take a vector input
and return a scalar statistic. The function name is displayed in the
axis-label, and the underlying \verb{$rep_label} for \code{ppc_scatter_avg_data()}
includes the function name. Defaults to \code{"mean"}.}
\item{group}{A grouping variable of the same length as \code{y}.
Will be coerced to \link[base:factor]{factor} if not already a factor.
Each value in \code{group} is interpreted as the group level pertaining
to the corresponding observation.}
}
\value{
The plotting functions return a ggplot object that can be further
customized using the \strong{ggplot2} package. The functions with suffix
\verb{_data()} return the data that would have been drawn by the plotting
function.
}
\description{
Scatterplots of the observed data \code{y} vs. simulated/replicated data
\code{yrep} from the posterior predictive distribution. See the
\strong{Plot Descriptions} and \strong{Details} sections, below.
}
\details{
For Binomial data, the plots may be more useful if
the input contains the "success" \emph{proportions} (not discrete
"success" or "failure" counts).
}
\section{Plot Descriptions}{
\describe{
\item{\code{ppc_scatter()}}{
For each dataset (row) in \code{yrep} a scatterplot is generated showing \code{y}
against that row of \code{yrep}. For this plot \code{yrep} should only contain a
small number of rows.
}
\item{\code{ppc_scatter_avg()}}{
A single scatterplot of \code{y} against the average values of \code{yrep}, i.e.,
the points \verb{(x,y) = (average(yrep[, n]), y[n])}, where each \code{yrep[, n]} is
a vector of length equal to the number of posterior draws and \code{average()}
is a summary statistic. Unlike for \code{ppc_scatter()}, for
\code{ppc_scatter_avg()} \code{yrep} should contain many draws (rows).
}
\item{\code{ppc_scatter_avg_grouped()}}{
The same as \code{ppc_scatter_avg()}, but a separate plot is generated for
each level of a grouping variable.
}
}
}
\examples{
y <- example_y_data()
yrep <- example_yrep_draws()
p1 <- ppc_scatter_avg(y, yrep)
p1
# don't draw line x=y
ppc_scatter_avg(y, yrep, ref_line = FALSE)
p2 <- ppc_scatter(y, yrep[20:23, ], alpha = 0.5, size = 1.5)
p2
# give x and y axes the same limits
lims <- ggplot2::lims(x = c(0, 160), y = c(0, 160))
p1 + lims
p2 + lims
# "average" function is customizable
ppc_scatter_avg(y, yrep, stat = "median", ref_line = FALSE)
# for ppc_scatter_avg_grouped the default is to allow the facets
# to have different x and y axes
group <- example_group_data()
ppc_scatter_avg_grouped(y, yrep, group)
# let x-axis vary but force y-axis to be the same
ppc_scatter_avg_grouped(y, yrep, group, facet_args = list(scales = "free_x"))
}
\references{
Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari,
A., and Rubin, D. B. (2013). \emph{Bayesian Data Analysis.} Chapman & Hall/CRC
Press, London, third edition. (Ch. 6)
}
\seealso{
Other PPCs:
\code{\link{PPC-censoring}},
\code{\link{PPC-discrete}},
\code{\link{PPC-distributions}},
\code{\link{PPC-errors}},
\code{\link{PPC-intervals}},
\code{\link{PPC-loo}},
\code{\link{PPC-overview}},
\code{\link{PPC-test-statistics}}
}
\concept{PPCs}
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