A little null-model function to check, if the observed values actually are much different to what one would expect under random numbers given the observed row and column totals (i.e.~information in the structure of the web, not only in its species' abundances). Random matrices are based on the function r2dtable. The test itself is a t-test (with all its assumptions).
null.t.test(web, N = 30, ...)Returns a table with one row per index, and columns giving
observed value
mean null model value
lower 95% confidence interval (or whatever level is specified in the function's call)
upper 95% confidence interval (or whatever level is specified in the function's call)
t-statistic
P-value of t statistic
A matrix representing the interactions observed between higher trophic level species (columns) and lower trophic level species (rows).
Number of null models to be produced; see ‘Note’ below!
Optional parameters to be passed on to the functions
networklevel and t.test.
Carsten F. Dormann carsten.dormann@biom.uni-freiburg.de
This is only a very rough null-model test. There are various reasons why one may consider r2dtable as an incorrect way to construct null models (e.g.~because it yields very different connectance values compared to the original). It is merely used here to indicate into which direction a proper development of null models may start off. Also, if the distribution of null models is very skewed, a t-test is obviously not the test of choice.
Finally, not all indices will be reasonably testable (e.g.~number of species is fixed), or are returned by the function networklevel in a form that null.t.test can make use of (e.g.~degree distribution fits).
data(mosquin1967)
null.t.test(mosquin1967, index=c("generality", "vulnerability",
"cluster coefficient", "H2", "ISA", "SA"), nrep=2, N=10)
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