Cluster analysis additive trees

Exploratory modeling using modem statistical modeling techniques such as generalized additive modeling (GAM) (15), cluster analysis, and tree-based modehng (TBM) to reveal structure in the data and initially select explanatory covariates. 

Broadly speaking, the statistical strategies of analysis can be classified into two families of methods, namely (i) factor analytical methods including, in particular, multidimensional scaling (MDS) and multiple correspondence analysis (MCA) and (ii) methods pertaining to cluster analysis and additive trees. As is usually the case, the choice of one method over another depends on several factors (i) the domain of application (i.e. traditionally, some methods are more popular than others in each particular domain of application) (ii) the individual preferences and background of each practitioner and (iii) the availability of appropriate (and user-friendly) software. 

FIGURE 16.4 Results of a similarity-based compaiison producing a distance square matrix (lower window) on which two different clustering methods were applied. The top-left window represents a UPGMA tree where one additional criterion was printed (bands of one-dimensional electrophoresis profiles). The top-right window is a three-dimensional representation of the OTUs presented on the distance matrix below that have been reordered using the principal coordinate analysis ordination method. 

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