R: Simulation of abundance matrices (non-clustered)

regpop.sar {prabclus} | R Documentation

## 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.

### Author(s)

Christian Hennig chrish@stats.ucl.ac.uk http://www.homepages.ucl.ac.uk/~ucakche

### 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

    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))

[Package prabclus version 2.2-6 Index]