null.t.test: Compares observed pattern to random webs.

Description Usage Arguments Details Value Note Author(s) Examples

View source: R/null.t.test.r


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, ...)



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.


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).


Returns a table with one row per index, and columns giving


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)




P-value 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]


null.t.test(mosquin1967, index=c("generality", "vulnerability",
    "cluster coefficient", "H2", "ISA", "SA"), nrep=2, N=10)

Example output

Loading required package: vegan
Loading required package: permute
Loading required package: lattice
This is vegan 2.4-3
Loading required package: sna
Loading required package: statnet.common
Loading required package: network
network: Classes for Relational Data
Version 1.13.0 created on 2015-08-31.
copyright (c) 2005, Carter T. Butts, University of California-Irvine
                    Mark S. Handcock, University of California -- Los Angeles
                    David R. Hunter, Penn State University
                    Martina Morris, University of Washington
                    Skye Bender-deMoll, University of Washington
 For citation information, type citation("network").
 Type help("network-package") to get started.

sna: Tools for Social Network Analysis
Version 2.4 created on 2016-07-23.
copyright (c) 2005, Carter T. Butts, University of California-Irvine
 For citation information, type citation("sna").
 Type help(package="sna") to get started.

 This is bipartite 2.08
 For latest changes see versionlog in  ?"bipartite-package".
 For citation see: citation("bipartite").
 Have a nice time plotting and analysing two-mode networks.

Attaching package: 'bipartite'

The following object is masked from 'package:vegan':


                                      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 1.345896e-05
interaction strength asymmetry -21.992513 3.923171e-09
specialisation asymmetry         2.819017 2.007920e-02
H2                             -34.649193 6.860614e-11
cluster.coefficient.HL          18.039234 2.251276e-08
cluster.coefficient.LL          18.373311 1.916149e-08
generality.HL                   27.056767 6.232709e-10
vulnerability.LL                25.134069 1.200522e-09

bipartite documentation built on May 30, 2017, 1:25 a.m.