Fuses, or combines, dissimilarity objects in a very flexible way to
create a single dissimilarity object that incorporates the separate
dissimilarities. In analogue matching, we may wish to combine
information from two or more proxies, such as diatoms and cladocera,
or from biological and chemical or physical data in the case of
matching modern samples.
The function can also be used to fuse dissimilarity objects created
from a single data set but using different dissimilarity
coefficients. In this way one could create a new dissimilarity object
combining dissimilarity based on abundance data and presence absence
data into a single measure.
fuse uses the method of Melssen et al. (2006) to combine
dissimilarities. The dissimilarities in each dissimilarity object are
scaled so that the maximum dissimilarity in each object is 1. The
scaled dissimilarity objects are then weighted according to the
supplied weights. If no weights are supplied (the default) the
dissimilarity objects are weighted equally; weights = rep(1/N,
N), where N is the number of dissimilarity objects fused.
$$D_{fused}(j, k) = \sum_{i = 1}^N w_i D_{ijk}$$
where \(D_{fused}(j, k)\) is the fused dissimilarity
between samples \(j\) and \(k\), \(w_i\) is the weight
assigned to the \(i\)th dissimilarity object and
\(D_{ijk}\) is the dissimilarity between \(j\) and
\(k\) for the \(i\)th dissimilarity object.