% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/selectX_create.r
\name{retrieveOutliers}
\alias{retrieveOutliers}
\title{Identify outlier null-model matrices}
\usage{
retrieveOutliers(obj, nSim = 1000)
}
\arguments{
\item{obj}{SelectX object (list with al and null fields)}

\item{nSim}{Number of simulations in the null model}
}
\value{
Logical vector; TRUE for each null matrix flagged as an outlier.
}
\description{
Flags simulations whose mean per-gene and per-sample deviation from the
observed counts falls in the top 10\%, indicating numerical instability.
These are removed before computing effect sizes to prevent them from
inflating the null distribution.
}
\examples{
\donttest{
data(luad_run_data, package = "SelectSim")
result <- selectX(M = luad_run_data$M,
                  sample.class = luad_run_data$sample.class,
                  alteration.class = luad_run_data$alteration.class,
                  n.cores = 1, min.freq = 10, n.permut = 10,
                  verbose = FALSE)
outliers <- retrieveOutliers(result$obj, nSim = result$obj$nSim)
sum(outliers)
}

}
