null.t.test: Compares observed pattern to random webs. In bipartite: Visualising Bipartite Networks and Calculating Some (Ecological) Indices

Description

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).

Usage

 `1` ```null.t.test(web, N = 30, ...) ```

Arguments

 `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 `networklevel` and `t.test`.

Details

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).

Value

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` t-statistic `P` P-value of t statistic

Note

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!

Author(s)

Carsten F. Dormann [email protected]

Examples

 ```1 2 3``` ```data(mosquin1967) null.t.test(mosquin1967, index=c("generality", "vulnerability", "cluster coefficient", "H2", "ISA", "SA"), nrep=2, N=10) ```

Example output

```Loading required package: vegan
This is vegan 2.4-3
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 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':

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 1.345896e-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.