SOCPROG is a set of programs for analyzing the social systems (as well as movements and
populations) of animals. It has been used often for cetacean studies.
A new and updated version of SOCPROG, SOCPROG2.6 (both compiled
and uncompiled downloads) is available at:
http://myweb.dal.ca/hwhitehe/social.htm.
This version includes new a network diagram drawing routine, and calculates and analyzes
generalized affiliation indices, which I think may be of value to some users (see below). The
new version is compatible with MATLAB2015a and also fixes a few bugs.
I hope this helps. Let me know about any problems that remain, or have been introduced.
Thanks
Hal
Hal Whitehead (hwhit...@dal.ca)
Dalhousie University
Whitehead, H. and R. James. 2015. Generalized affiliation indices extract affiliations from
social network data. Methods in Ecology and Evolution
Available at: http://whitelab.biology.dal.ca/labpub.htm
Summary
1. In the analysis of animal social networks, a common challenge has been distinguishing
affiliations - active preferences of pairs of individuals to interact or associate with one another
- from other, structural, causes of association or interaction. Such structural factors can
include patterns of use of the habitat in time and space, gregariousness and differential
association rates among age/sex classes.
2. In an approach with similarities to the multiple regression quadratic assignment
procedures test, we suggest calculating generalized affiliation indices as the residuals from a
regression of the measures of association or interaction on structural predictor variables,
such as gregariousness and spatiotemporal overlap. If the original data are association
indices or counts of interactions, then generalized linear models with binomial or Poisson
error structures, respectively, can be used in place of linear regression. Anscombe or
deviance residuals can be used to assess the significance of particular affiliation indices.
3. Generalized affiliation indices can be used as the weights of links in a social network
representation. They can then be portrayed in network diagrams or cluster diagrams and
used to calculate network statistics, to delineate communities by maximizing modularity and
to test for overall affiliation using data-stream permutation tests.
4. We evaluate the effectiveness of such generalized affiliation indices using simulated and
real association data, finding that the method removes much of the effect of structural
variables on association patterns, revealing real affiliations. While the approach is very
promising, it is limited by the extent to which the input predictor variables represent important
structural factors.
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