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

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 carsten.dormann@biom.uni-freiburg.de

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
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':

    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.