\name{generate-methods}
\docType{methods}

\alias{generate}
\alias{generate-methods}
\alias{generate,numeric,numeric-method}

\title{Method for generating a random mutagenetic trees mixture model}
\description{
Function for generating a random mutagenetic mixture model. Each tree component
from the model is drawn uniformly at random from the tree topology
space by using the Pr\"ufer encoding of trees.
The number of tree components and the number of genetic events
have to be specified.
}
\usage{
generate(K, no.events, \dots)
}
\arguments{
\item{K}{An \code{integer} larger than 0 specifying the number of
branchings in the mixture model.}
\item{no.events}{An \code{integer} larger than 0 specifying the number of
genetic events in the mixture model.}
\item{\dots}{
\code{noise.tree} is a \code{logical} indicating the presence of a noise
(star) component in the random mixture model. The default value is
\code{TRUE}.
\code{equal.edgeweights} is a \code{logical} specifying whether to use
equal edge weights in the noise component. The default value is
\code{TRUE}.
\code{prob} is a \code{numeric} vector of length 2 specifying the
boundaries for the edge weights of the randomly generated trees. The
first component of the vector (the lower boundary) must be smaller
than the second component (the upper boundary). The default value
is (0.0, 1.0).
\code{seed} is a positive \code{integer} specifying the random generator
seed. The default value is (-1) and then the time is used as a
random generator.
}
}
\value{
The method returns an \code{RtreemixModel} object that represents the
randomly generated K-trees mixture model.
}
\references{Beweis eines Satzes \"uber Permutationen, H. Pr\"ufer; Learning multiple evolutionary pathways from cross-sectional
data, N. Beerenwinkel et al.; Model Selection for Mixtures of
Mutagenetic Trees, Yin et al. }

\author{Jasmina Bogojeska}

\seealso{