prabclus (version 2.2-7)

regpop.sar: 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 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 errorsarlm for the model; estimates are provided by the parameter sarestimate). Spatial autocorrelation of a species' presences is governed by the parameter p.nb, 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)

Arguments

abmat

object of class prab, containing the abundance or presence/absence data.

prab01

presence-absence matrix of same dimensions than the abundance matrix of prabobj. This specifies the presences and absences on which the presence/absence step of abundance-based tests is based (see details). If NULL (which is usually the only reasonable choice), prab01 is computed in order to indicate the nonzeroes of prabobj$prab.

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 prab.sarestimate. This requires package spdep. If sarestimate$sar=FALSE, spatial structure is ignored for generating the abundance values.

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 NULL, the spatial structure of the regions is ignored. Note that for a given presence-absence matrix, this parameter can be estimated by autoconst (called pd there).

vector.species

vector of integers. vector.species gives the sizes (i.e., numbers of regions) of the species to generate..

pdf.regions

numerical vector of length 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 p.nb.

count

logical. If 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. Oikos 116, 818-828.

See Also

autoconst estimates p.nb from matrices of class prab. These are generated by prabinit.

abundtest uses regpop.sar as a null model for tests of clustering.

randpop.nb (analogous function for simulating presence-absence data)

Examples

# NOT RUN {
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))
# }