categorize.pls: Categorize data rows based on PLS results and critical limits...
In mdatools: Multivariate Data Analysis for Chemometrics
categorize.pls R Documentation
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$ncomp.selected, ...)
Arguments
obj
object with PCA model
res
object with PCA results
ncomp
number of components to use for the categorization
...
other parameters
Details
The method does not categorize hidden values if any. It is based on the approach described in
[1] and works only if data driven approach is used for computing critical limits.
Value
vector (factor) with results of categorization.
References
1. Rodionova O. Ye., Pomerantsev A. L. Detection of Outliers in Projection-Based Modeling.
Analytical Chemistry (2020, in publish). doi: 10.1021/acs.analchem.9b04611
mdatools documentation built on Sept. 11, 2024, 7:59 p.m.
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| categorize.pls | R Documentation |
Categorize data rows based on PLS results and critical limits for total distance.
Description
The method uses full distance for decomposition of X-data and squared Y-residuals of PLS results
from res with critical limits computed for the PLS model and categorizes the
corresponding objects as "regular", "extreme" or "outlier".
Usage
## S3 method for class 'pls'
categorize(obj, res = obj$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
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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