Partial Least Squares (PLS) includes a comprehensive selection of algorithms for univariate and
multivariate partial least squares problems. PLS will compute all the standard results for a
partial least squares analysis; in addition, it offers a large number of results options and
in particular graphics options that are usually not available in other implementations; for
example, graphs of parameter values as a function of the number of components, two-dimensional
plots for all output statistics (parameters, factor loadings, etc.), two-dimensional plots for
all residuals statistics, etc. Because PLS offers an identical selection of flexible user
interfaces to that of GLM, GRM and GLZ, it is very easy to set up models in one module and
quickly analyze the data using the same model in PLS. This unique flexibility allows even
novice users to apply these powerful techniques to their analysis problems. The partial least
squares method is a powerful data mining technique, particularly well suited for determining
a smaller number of dimensions in a large number of predictors and response variables. These
methods for analyzing linear systems have become popular only in the last few years; thus,
many of the algorithms and statistics are still the subject of ongoing research.
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