The class implements a method for selection of optimal number of components in PLS1 regression
based on the randomization test [1]. The basic idea is that for each component from 1 to
ncomp
a statistic T, which is a covariance between t-score (X score, derived from a PLS
model) and the reference Y values, is calculated. By repeating this for randomly permuted
Y-values a distribution of the statistic is obtained. A parameter alpha
is computed to
show how often the statistic T, calculated for permuted Y-values, is the same or higher than
the same statistic, calculated for original data without permutations.
If a component is important, then the covariance for unpermuted data should be larger than the
covariance for permuted data and therefore the value for alpha
will be quie small (there
is still a small chance to get similar covariance). This makes alpha
very similar to
p-value in a statistical test.
The randtest
procedure calculates alpha for each component, the values can be observed
using summary
or plot
functions. There are also several function, allowing e.g.
to show distribution of statistics and the critical value for each component.