categorize.pls {mdatools}

Categorize data rows based on PLS results and critical limits for total distance.

Description

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".

Usage

## S3 method for class 'pls'
categorize(obj, res = obj$res$cal, ncomp = obj$ncomp.selected, ...)

Arguments

obj

object with PCA model

res

object with PCA results

ncomp

number of components to use for the categorization

...

other parameters

Details

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.

Value

vector (factor) with results of categorization.

References

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