prabclus: autoconst – R documentation

Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!

autoconst

Spatial autocorrelation parameter estimation

Description

Monte Carlo estimation of the disjunction/spatial autocorrelation parameter pd for the simulation model used in randpop.nb, used for tests for clustering of presence-absence data.

autoconst is the main function; autoreg performs the simulation and is executed within autoconst.

Usage

autoconst(x, prange = c(0, 1), twostep = TRUE, step1 = 0.1,
step2 = 0.01, plot = TRUE, nperp = 4, ejprob = NULL,
species.fixed = TRUE, pdfnb=FALSE, ignore.richness=FALSE)

autoreg(x, probs, ejprob, plot = TRUE, nperp = 4, species.fixed = TRUE,
pdfnb=FALSE, ignore.richness=FALSE)

Arguments

`x`	object of class `prab` as generated by `prabinit`. Presence-absence data to be analyzed.
`prange`	numerical range vector, lower value not smaller than 0, larger value not larger than 1. Range where the parameter is to be found.
`twostep`	logical. If `TRUE`, a first estimation step is carried out in the whole `prange`, and then the final estimation is determined between the preliminary estimator `-5step2` and `+5step2`. Else, the first simulation determines the final estimator.
`step1`	numerical between 0 and 1. Interval length between subsequent choices of `pd` for the first simulation.
`step2`	numerical between 0 and 1. Interval length between subsequent choices of `pd` for the second simulation in case of `twostep=TRUE`.
`plot`	logical. If `TRUE`, a scatterplot of `pd`-values against resulting `ejprob` values (see below), with regression line and data value of `ejprob` is shown.
`nperp`	integer. Number of simulations per `pd`-value.
`ejprob`	numerical between 0 and 1. Observed disjunction probability for data `x`; if not specified in advance, it will be computed by `autoconst`.
`species.fixed`	logical. If `TRUE`, sizes of generated species match the species sizes in `x`, else they are generated from the empirical distribution of species sizes in `x`.
`probs`	vector of numericals between 0 and 1. `pd` values for the simulation.
`pdfnb`	logical. If `TRUE`, the probabilities of the regions are modified according to the number of neighboring regions in `randpop.nb`, see Hennig and Hausdorf (2002), p. 5.
`ignore.richness`	logical. If `TRUE`, there is no assumption of species richnesses to differ between regions in the null model. Regionwise probabilities don't differ in the generation of null data.

Details

The spatial autocorrelation parameter pd of the model for the generation of presence-absence data sets used by randpop.nb can be estimated by use of the observed disjuction probability ejprob which is the sum of all species' connectivity components minus the number of species divided by the number of "presence" entries minus the number of species. This is done by a simulation of artificial data sets with characteristics of x and different pd-values, governed by prange, step1, step2 and nperp. ejprob is then calculated for all simulated populations. A linear regression of ejprob on pd is performed and the estimator of pd is determined by computing the inverse of the regression function for the ejprob-value of x.

Value

autoconst produces the same list as autoreg with additional component ejprob. The components are

`pd`	(eventually) estimated parameter `pd`.
`coef`	(eventually) estimated regression coefficients.
`ejprob`	see above.

Author(s)

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

References

Hausdorf, B. and Hennig, C. (2003) Biotic Element Analysis in Biogeography. To appear in Systematic Biology.

Hausdorf, B. and Hennig, C. (2003) Nestedness of north-west European land snail ranges as a consequence of differential immigration from Pleistocene glacial refuges. Oecologia 135, 102-109.

Hennig, C. and Hausdorf, B. (2004) Distance-based parametric bootstrap tests for clustering of species ranges. Computational Statistics and Data Analysis 45, 875-896.

Examples

options(digits=4)
data(kykladspecreg)
data(nb)
set.seed(1234)
x <- prabinit(prabmatrix=kykladspecreg, neighborhood=nb)
ax <- autoconst(x,nperp=2,step1=0.3,twostep=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