The method uses full distance for decomposition of X-data and squared Y-residuals of PLS results
from res
with critical limits computed for the PLS model and categorizes the
corresponding objects as "regular", "extreme" or "outlier".
# S3 method for pls
categorize(obj, res = obj$res$cal, ncomp = obj$ncomp.selected, ...)
vector (factor) with results of categorization.
object with PCA model
object with PCA results
number of components to use for the categorization
other parameters
The method does not categorize hidden values if any. It is based on the approach described in [1] and works only if data driven approach is used for computing critical limits.
1. Rodionova O. Ye., Pomerantsev A. L. Detection of Outliers in Projection-Based Modeling. Analytical Chemistry (2020, in publish). doi: 10.1021/acs.analchem.9b04611