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\name{regpop.sar}
\alias{regpop.sar}
%- Also NEED an `\alias' for EACH other topic documented here.
\title{Simulation of abundance matrices (non-clustered)}
\description{
Generates a simulated matrix where the rows are interpreted as regions
and the columns as species, and the entries are abundances.
Species are generated i.i.d. in two steps. In the first step, a
presence-absence matrix is generated as in \code{randpop.nb}. In the
second step, conditionally on presence in the first step, abundance
values are generated according to a simultaneous autoregression (SAR)
model for the log-abundances (see \code{\link[spdep]{errorsarlm}} for
the model; estimates are provided by the parameter
\code{sarestimate}). Spatial autocorrelation of a species' presences
is governed by the parameter \code{p.nb}, \code{sarestimate} and a
list of neighbors for each region.
}
\usage{
regpop.sar(abmat, prab01=NULL, sarestimate=prab.sarestimate(abmat),
p.nb=NULL,
vector.species=prab01$regperspec,
pdf.regions=prab01$specperreg/(sum(prab01$specperreg)),
count=FALSE)
}
%- maybe also `usage' for other objects documented here.
\arguments{
\item{abmat}{object of class \code{prab}, containing the abundance or
presence/absence data.}
\item{prab01}{presence-absence matrix of same dimensions than the
abundance matrix of \code{prabobj}. This specifies the presences and
absences on which the presence/absence step of abundance-based tests
is based (see details). If \code{NULL} (which is usually the only
reasonable choice), \code{prab01} is computed in order to indicate
the nonzeroes of \code{prabobj$prab}.}
\item{sarestimate}{Estimator of the parameters of a simultaneous
autoregression model corresponding to the null model for abundance
data from Hausdorf and Hennig (2007) as generated by
\code{prab.sarestimate}. This requires package \code{spdep}. If
\code{sarestimate$sar=FALSE}, spatial structure is ignored for
generating the abundance values.}
\item{p.nb}{numeric between 0 and 1. The probability that a new
region is drawn from the non-neighborhood of the previous regions
belonging to a species under generation. If \code{NULL}, the spatial
structure of the regions is ignored. Note that for a given
presence-absence matrix, this parameter can be estimated by
\code{autoconst} (called \code{pd} there).}
\item{vector.species}{vector of integers. \code{vector.species} gives
the sizes (i.e., numbers of regions) of
the species to generate..}
\item{pdf.regions}{numerical vector of length \code{n.species}. The
entries must sum up to 1 and give probabilities for the regions to
be drawn during the generation of a species. These probabilities are
used conditional on the new region being a neighbor or a
non-neighbor of the previous regions of the species, see
\code{p.nb}.}
\item{count}{logical. If \code{TRUE}, the number of the currently
generated species is printed.}
}
\value{
A matrix of abundance values, rows are regions, columns are species.
}
\references{
Hausdorf, B. and Hennig, C. (2007) Null model tests of clustering of
species, negative co-occurrence patterns and nestedness in meta-communities.
\emph{Oikos} 116, 818-828.
}
\author{Christian Hennig
\email{christian.hennig@unibo.it}
\url{https://www.unibo.it/sitoweb/christian.hennig/en}}
\seealso{
\code{\link{autoconst}} estimates \code{p.nb} from matrices of class
\code{prab}. These are generated by \code{\link{prabinit}}.
\code{\link{abundtest}} uses \code{regpop.sar} as a null model for
tests of clustering.
\code{\link{randpop.nb}} (analogous function for simulating
presence-absence data)
}
\examples{
options(digits=4)
data(siskiyou)
set.seed(1234)
x <- prabinit(prabmatrix=siskiyou, neighborhood=siskiyou.nb,
distance="none")
# Not run; this needs package spdep.
# regpop.sar(x, p.nb=0.046)
regpop.sar(x, p.nb=0.046, sarestimate=prab.sarestimate(x,sar=FALSE))
}
\keyword{spatial}% at least one, from doc/KEYWORDS
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