Description Usage Arguments Details Value Author(s) References See Also Examples
Parametric bootstrap test of a null model of i.i.d., but spatially
autocorrelated species against clustering of the species' population
patterns. Note that most relevant functionality of prabtest
(except of the use of the geco distance) is
also included in abundtest
, so that abundtest
can also
be used on binary presenceabsence data.
In spite of the lots of
parameters, a standard execution (for the default test statistics, see
parameter teststat
below) will be
prabmatrix < prabinit(file="path/abundmatrixfile",
neighborhood="path/neighborhoodfile")
test < abundtest(prabmatrix)
summary(test)
Note: Data formats are described
on the prabinit
help page. You may also consider the example datasets
kykladspecreg.dat
and nb.dat
. Take care of the
parameter rows.are.species
of prabinit
.
1 2 3 4 5 6 7 8  abundtest(prabobj, teststat = "distratio", tuning = 0.25,
times = 1000, p.nb = NULL,
prange = c(0, 1), nperp = 4, step = 0.1, step2 = 0.01,
twostep = TRUE, species.fixed=TRUE, prab01=NULL,
groupvector=NULL,
sarestimate=prab.sarestimate(prabobj),
dist = prabobj$distance,
n.species = prabobj$n.species)

prabobj 
an object of class 
teststat 
string, indicating the test statistics. 
tuning 
integer or (if 
times 
integer. Number of simulation runs. 
p.nb 
numerical between 0 and 1. The probability that a new
region is drawn from the nonneighborhood of the previous regions
belonging to a species under generation. If 
prange 
numerical range vector, lower value not smaller than 0, larger
value not larger than 1. Range where 
nperp 
integer. Number of simulations per 
step 
numerical between 0 and 1. Interval length between
subsequent choices of 
step2 
numerical between 0 and 1. Interval length between
subsequent choices of 
twostep 
logical. If 
species.fixed 
logical. Indicates if the range sizes of the species
are held fixed
in the test simulation ( 
prab01 

groupvector 
integer vector. For every species, a number
indicating the species' group membership. Needed only if

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

dist 
One of 
n.species 
number of species. By default this is taken from

For presenceabsence data, the routine is described in
prabtest
. For abundance data, the first step under the
null model is to
simulated presenceabsence patterns as in prabtest
. The second
step is to fit a simultaneous autoregression (SAR) model (Ripley 1981,
section 5.2) to the logabundances, see
prab.sarestimate
. The simulation from the null model is
implemented in regpop.sar
.
For more details see Hennig
and Hausdorf (2004) for presenceabsence data and Hausdorf and Hennig
(2007) for abundance data and the test statistics "mean"
and
"groups"
, which can also be applied to binary data.
If p.nb=NA
was
specified, a diagnostic plot
for the estimation of pd
is plotted by autoconst
.
For details see Hennig
and Hausdorf (2004) and the help pages of the cited functions.
An object of class prabtest
, which is a list with components
results 
vector of test statistic values for all simulated
populations. For 
p.above 
pvalue against an alternative that generates large
values of the test statistic (usually reasonable for

p.below 
pvalue against an alternative that generates small
values of the test statistic (usually reasonable for

datac 
test statistic value for the original
data. ( 
tuning 
see above. 
distance 

teststat 
see above. 
pd 

abund 

sarlambda 
Estimator of the autocorrelation
parameter 
sarestimate 
the output object of 
groupinfo 
list containing information from

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en
Hausdorf, B. and Hennig, C. (2007) Null model tests of clustering of species, negative cooccurrence patterns and nestedness in metacommunities. Oikos 116, 818828.
Hennig, C. and Hausdorf, B. (2004) Distancebased parametric bootstrap tests for clustering of species ranges. Computational Statistics and Data Analysis 45, 875896. http://stat.ethz.ch/ResearchReports/110.html.
Ripley, B. D. (1981) Spatial Statistics. Wiley.
prabinit
generates objects of class prab
.
autoconst
estimates pd
from such objects.
prabtest
(analogous function for presenceabsence data).
regpop.sar
generates populations from the null model.
prab.sarestimate
(parameter estimators for simultaneous
autoregression model). This calls
errorsarlm
(original estimation function from
package spdep
).
Some more information on the test statistics is given in
homogen.test
, lcomponent
,
distratio
, nn
,
incmatrix
.
Summary and print methods: summary.prabtest
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14  # Note: NOT RUN.
# This needs package spdep and a bunch of packages that are
# called by spdep!
# data(siskiyou)
# set.seed(1234)
# x < prabinit(prabmatrix=siskiyou, neighborhood=siskiyou.nb,
# distance="logkulczynski")
# a1 < abundtest(x, times=5, p.nb=0.0465)
# a2 < abundtest(x, times=5, p.nb=0.0465, teststat="groups",
# groupvector=siskiyou.groups)
# These settings are chosen to make the example execution
# faster; usually you will use abundtest(x).
# summary(a1)
# summary(a2)

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