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.
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)
autoconst produces the same list as autoreg with
additional component ejprob. The components are
(eventually) estimated parameter pd.
(eventually) estimated regression coefficients.
see above.
object of class prab as generated by prabinit.
Presence-absence data to be analyzed.
numerical range vector, lower value not smaller than 0, larger value not larger than 1. Range where the parameter is to be found.
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
-5*step2 and +5*step2. Else, the first simulation
determines the final estimator.
numerical between 0 and 1. Interval length between
subsequent choices of pd for the first simulation.
numerical between 0 and 1. Interval length between
subsequent choices of pd for the second simulation in case of
twostep=TRUE.
logical. If TRUE, a scatterplot of pd-values
against resulting ejprob values (see below), with regression
line and data value of ejprob is shown.
integer. Number of simulations per pd-value.
numerical between 0 and 1. Observed disjunction
probability for data x; if not specified in advance,
it will be computed by autoconst.
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.
vector of numericals between 0 and 1. pd values
for the simulation.
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.
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.
Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en
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.
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.
randpop.nb, prabinit, con.comp
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)
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