Description Usage Arguments Details Value Note Author(s) Examples
A little nullmodel 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 ttest (with all its assumptions).
1  null.t.test(web, N = 30, ...)

web 
A matrix representing the interactions observed between higher trophic level species (columns) and lower trophic level species (rows). 
N 
Number of null models to be produced; see ‘Note’ below! 
... 
Optional parameters to be passed on to the functions

This is only a very rough nullmodel 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 ttest 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).
Returns a table with one row per index, and columns giving
obs 
observed value 
null mean 
mean null model value 
lower CI 
lower 95% confidence interval (or whatever level is specified in the function's call) 
upper CI 
upper 95% confidence interval (or whatever level is specified in the function's call) 
t 
tstatistic 
P 
Pvalue of t statistic 
This function is rather slow. Using large replications in combination with iterative indices (degree distribution, compartment diversity, extinction slope, H2) may lead to rather long runtimes!
Carsten F. Dormann [email protected]
1 2 3  data(mosquin1967)
null.t.test(mosquin1967, index=c("generality", "vulnerability",
"cluster coefficient", "H2", "ISA", "SA"), nrep=2, N=10)

Loading required package: vegan
Loading required package: permute
Loading required package: lattice
This is vegan 2.43
Loading required package: sna
Loading required package: statnet.common
Loading required package: network
network: Classes for Relational Data
Version 1.13.0 created on 20150831.
copyright (c) 2005, Carter T. Butts, University of CaliforniaIrvine
Mark S. Handcock, University of California  Los Angeles
David R. Hunter, Penn State University
Martina Morris, University of Washington
Skye BenderdeMoll, University of Washington
For citation information, type citation("network").
Type help("networkpackage") to get started.
sna: Tools for Social Network Analysis
Version 2.4 created on 20160723.
copyright (c) 2005, Carter T. Butts, University of CaliforniaIrvine
For citation information, type citation("sna").
Type help(package="sna") to get started.
This is bipartite 2.08
For latest changes see versionlog in ?"bipartitepackage".
For citation see: citation("bipartite").
Have a nice time plotting and analysing twomode networks.
Attaching package: 'bipartite'
The following object is masked from 'package:vegan':
nullmodel
obs null mean lower CI upper CI
cluster coefficient 0.1363636 0.25454545 0.22313180 0.28595911
interaction strength asymmetry 0.1607192 0.05760505 0.04699869 0.06821141
specialisation asymmetry 0.1761567 0.14381383 0.16976778 0.11785987
H2 0.4975192 0.13672429 0.11316892 0.16027967
cluster.coefficient.HL 0.3398915 0.50203528 0.48170211 0.52236845
cluster.coefficient.LL 0.3254561 0.56417910 0.53478707 0.59357114
generality.HL 2.6773063 4.25622995 4.12421959 4.38824031
vulnerability.LL 4.1143452 7.30852747 7.02103950 7.59601543
t P
cluster coefficient 8.510498 1.345896e05
interaction strength asymmetry 21.992513 3.923171e09
specialisation asymmetry 2.819017 2.007920e02
H2 34.649193 6.860614e11
cluster.coefficient.HL 18.039234 2.251276e08
cluster.coefficient.LL 18.373311 1.916149e08
generality.HL 27.056767 6.232709e10
vulnerability.LL 25.134069 1.200522e09
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