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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/hrg.R
\name{hrg.predict}
\alias{hrg.predict}
\title{Predict edges based on a hierarchical random graph model}
\usage{
hrg.predict(
graph,
hrg = NULL,
start = FALSE,
num.samples = 10000,
num.bins = 25
)
}
\arguments{
\item{graph}{The graph to fit the model to. Edge directions are ignored in
directed graphs.}
\item{hrg}{A hierarchical random graph model, in the form of an
\code{igraphHRG} object. \code{predict_edges()} allow this to be
\code{NULL} as well, then a HRG is fitted to the graph first, from a
random starting point.}
\item{start}{Logical, whether to start the fitting/sampling from the
supplied \code{igraphHRG} object, or from a random starting point.}
\item{num.samples}{Number of samples to use for consensus generation or
missing edge prediction.}
\item{num.bins}{Number of bins for the edge probabilities. Give a higher
number for a more accurate prediction.}
}
\description{
\ifelse{html}{\href{https://lifecycle.r-lib.org/articles/stages.html#deprecated}{\figure{lifecycle-deprecated.svg}{options: alt='[Deprecated]'}}}{\strong{[Deprecated]}}
\code{hrg.predict()} was renamed to \code{predict_edges()} to create a more
consistent API.
}
\keyword{internal}
|
