prabtest {prabclus} | R Documentation |
Parametric bootstrap test of a null model of i.i.d., but spatially
autocorrelated species against clustering of the species' occupied
areas (or alternatively nestedness). 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/prabmatrixfile",
neighborhood="path/neighborhoodfile")
test <- prabtest(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
.
prabtest(x, teststat = "distratio", tuning=switch(teststat,distratio=0.25, lcomponent=floor(3*ncol(x$distmat)/4), isovertice=ncol(x$distmat),nn=4,NA), times = 1000, pd = NULL, prange = c(0, 1), nperp = 4, step = 0.1, step2=0.01, twostep = TRUE, sf.sim = FALSE, sf.const = sf.sim, pdfnb=FALSE)
x |
an object of class prab (presence-absence data), as
generated by prabinit . |
teststat |
string, indicating the test statistics. "isovertice" :
number of isolated vertices in the graph of tuning
smallest distances
between species. "lcomponent" : size of largest connectivity
component in this graph. "distratio" : ratio between tuning
smallest and largest distances. "nn" : average distance of species to
tuning th nearest neighbor.
"inclusions" : number of inclusions between areas of different
species (tests for nestedness structure, not for clustering). |
tuning |
integer or (if teststat="distratio" ) numerical
between 0 and 1. Tuning constant for test statistics, see
teststat . |
times |
integer. Number of simulation runs. |
pd |
numerical 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 NA (the default),
prabtest estimates this by function
autoconst . Otherwise the next five parameters have no effect. |
prange |
numerical range vector, lower value not smaller than 0, larger
value not larger than 1. Range where pd is to be found. Used
by function autoconst . |
nperp |
integer. Number of simulations per pd -value. Used
by function autoconst . |
step |
numerical between 0 and 1. Interval length between
subsequent choices of pd for the first simulation. Used
by function autoconst . |
step2 |
numerical between 0 and 1. Interval length between
subsequent choices of pd for the second simulation (see
parameter twostep ). Used
by function autoconst . |
twostep |
logical. If TRUE , a first estimation step for
pd 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. Used
by function autoconst . |
sf.sim |
logical. Indicates if the range sizes of the species
are held fixed
in the test simulation (TRUE ) or generated from their empirical
distribution in x (FALSE ). See function randpop.nb . |
sf.const |
logical. Same as sf.sim , but for estimation of
pd by autoconst . |
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. This is
usually no improvement. |
From the original data, the distribution of the
range sizes of the species, the autocorrelation parameter pd
(estimated by autoconst
) and the distribution on the regions
induced by the relative species numbers are taken. With these
parameters, times
populations according to the null model
implemented in randpop.nb
are generated and the test statistic
is evaluated. The resulting p-value is number of simulated statistic
values more extreme than than the value of the original data+1
divided by times+1
. "More extreme" means smaller for
"lcomponent"
, "distratio"
, "nn"
, larger for
"inclusions"
, and
twice the smaller number between the original statistic value and the
"border", i.e., a two-sided test for "isovertice"
.
If pd=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. |
datac |
test statistic value for the original data.' |
p.value |
the p-value. |
tuning |
see above. |
pd |
see above. |
reg |
regression coefficients from autoconst . |
teststat |
see above. |
distance |
the distance measure chosen, see prabinit . |
gtf |
the geco-distance tuning parameter (only informative if
distance="geco" ), see prabinit . |
times |
see above. |
pdfnb |
see above. |
Christian Hennig chrish@stats.ucl.ac.uk http://www.homepages.ucl.ac.uk/~ucakche
Hennig, C. and Hausdorf, B. (2004) Distance-based parametric bootstrap tests for clustering of species ranges. Computational Statistics and Data Analysis 45, 875-896. http://stat.ethz.ch/Research-Reports/110.html.
Hausdorf, B. and Hennig, C. (2003) Biotic Element Analysis in Biogeography. Systematic Biology 52, 717-723.
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.
prabinit
generates objects of class prab
.
autoconst
estimates pd
from such objects.
randpop.nb
generates populations from the null model.
An alternative model is given by cluspop.nb
.
Some more information on the test statistics is given in
homogen.test
, lcomponent
,
distratio
, nn
,
incmatrix
.
The simulations are computed by pop.sim
.
Summary and print methods: summary.prabtest
.
data(kykladspecreg) data(nb) set.seed(1234) x <- prabinit(prabmatrix=kykladspecreg, neighborhood=nb) # If you want to use your own ASCII data files, use # x <- prabinit(file="path/prabmatrixfile", # neighborhood="path/neighborhoodfile") prabtest(x, times=5, pd=0.35) # These settings are chosen to make the example execution # a bit faster; usually you will use prabtest(kprab).