% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plotSCCpanel.R
\name{plotSCCpanel}
\alias{plotSCCpanel}
\title{Plot SCC bands and mean estimate for a single group}
\usage{
plotSCCpanel(scc, title = NULL, zlim = "auto", palette = "nih")
}
\arguments{
\item{scc}{A list-like SCC result object, typically returned by
\code{ImageSCC::scc.image()}, containing at least the components
\code{Z}, \code{ind.inside.cover}, \code{scc}, and \code{Yhat}.}

\item{title}{\code{character}, title shown above the panel. If \code{NULL},
the default title is \code{"SCC FOR GROUP 1"}.}

\item{zlim}{Either \code{"auto"} to compute color limits from the plotted values,
or a numeric vector of length 2 specifying the lower and upper limits of the
fill scale.}

\item{palette}{\code{character}, color palette used in the heatmaps.
Supported values are \code{"nih"}, \code{"viridis"}, and \code{"gray"}.}
}
\value{
A patchwork/ggplot object containing three aligned panels:
\itemize{
  \item lower SCC band,
  \item mean estimate,
  \item upper SCC band.
}
}
\description{
This function visualizes the lower band, mean estimate, and upper band
from a single-group Simultaneous Confidence Corridor (SCC) result.
It returns a three-panel plot showing the estimated mean function and
its confidence bands over the neuroimaging domain.
}
\details{
The three panels are displayed using a shared color scale so that the lower band,
mean estimate, and upper band can be compared directly.

The function assumes that the second slice of \code{scc$scc[, , 2]} corresponds
to the SCC bands for the default significance level used in the package workflow.
}
\examples{
data("sccOneGroup", package = "neuroSCC")
plotSCCpanel(sccOneGroup, title = "SCC FOR CONTROL GROUP")

}
\seealso{
\code{\link{sccOneGroup}} for the example single-group SCC object used in the examples. \cr
\code{\link{SCCcomp}} for a two-group SCC example object. \cr
\code{\link{plotSCCcomparisonPanel}} for two-group SCC visualization. \cr
\code{\link{plotValidationPanel}} for SCC versus SPM performance visualization.
}
