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title: R: Cocopan
---
What does COCOPAN do ?
This program carries out a one-way analysis of variance for
spatially autocorrelated quantitative data when the classification
criterion is a subdivision of the study area into nonoverlapping
regions -- for instance countries, counties, language areas,
geomorphological subdivisions of the study area, and so on, as found in
numerous problems where the data points can be plotted on a map. The
method has been described by
Legendre, Oden, Sokal, Vaudor and Kim
(1990). The acronym COCOPAN stands for
Contiguity-constrained
permutational
ANOVA.
The principle of this permutation test is to keep the localities
fixed, each preserving its values for the variables under study, so as
to preserve the autocorrelation structure. The classification
criterion, which consists of the division of the map into subregions,
is permuted instead, with the following constraints: each pseudo-region
must contain the same number of localities as the original region that
it represents; each pseudo-region must remain connected, that is, it
must form a continuous surface on the pseudo-map; finally, the
pseudo-regions must occupy exactly the whole of the original map,
without omission or excess. The program contains two algorithms to
resolve the computation problem; the ring algorithm, created by Alain
Vaudor, and the random tree algorithm, developed by Junhyong Kim.
Several variables may be analyzed in a single run. The statistic
subjected to permutation testing is the sum, for all groups, of the
within-group sums of squares (SSQ). After each permutation, the SSQ
statistic is recomputed for that pseudo-map. Finally, the SSQ value for
the true map is compared to the distribution of the SSQ values obtained
for the pseudo-maps. So, this is a one-tailed test and the critical
area is the lower tail of the distribution.
Last updated on Saturday, March 30, 2013 by Philippe Casgrain