prabclus: regpop.sar – R documentation

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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.

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en

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.

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))

prabclus

Functions for Clustering and Testing of Presence-Absence, Abundance and Multilocus Genetic Data

v2.3-2

GPL

Authors

Christian Hennig <christian.hennig@unibo.it>, Bernhard Hausdorf <Hausdorf@zoologie.uni-hamburg.de>

Initial release

2020-01-06