pca.nipals: NIPALS based PCA algorithm
In mdatools: Multivariate Data Analysis for Chemometrics
pca.nipals R Documentation
NIPALS based PCA algorithm
Description
Calculates principal component space using non-linear iterative partial least squares algorithm
(NIPALS)
Usage
pca.nipals(x, ncomp = min(ncol(x), nrow(x) - 1), tol = 10^-10)
Arguments
x
a matrix with data values (preprocessed)
ncomp
number of components to calculate
tol
tolerance (if difference in eigenvalues is smaller - convergence achieved)
Value
a list with scores, loadings and eigenvalues for the components
References
Geladi, Paul; Kowalski, Bruce (1986), "Partial Least Squares
Regression:A Tutorial", Analytica Chimica Acta 185: 1-17
mdatools documentation built on Sept. 11, 2024, 7:59 p.m.
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| pca.nipals | R Documentation |
NIPALS based PCA algorithm
Description
Calculates principal component space using non-linear iterative partial least squares algorithm (NIPALS)
Usage
pca.nipals(x, ncomp = min(ncol(x), nrow(x) - 1), tol = 10^-10)
Arguments
x |
a matrix with data values (preprocessed) |
ncomp |
number of components to calculate |
tol |
tolerance (if difference in eigenvalues is smaller - convergence achieved) |
Value
a list with scores, loadings and eigenvalues for the components
References
Geladi, Paul; Kowalski, Bruce (1986), "Partial Least Squares Regression:A Tutorial", Analytica Chimica Acta 185: 1-17
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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