simple_pca: Calculate the PCA rotation matrix The implementation is based...
In edahelsinki/slise: Sparse Linear Subset Explanations
simple_pca R Documentation
Calculate the PCA rotation matrix
The implementation is based on stats::prcomp.
Assumes the data has already been centered and scaled (if that is desired).
Description
Calculate the PCA rotation matrix
The implementation is based on stats::prcomp.
Assumes the data has already been centered and scaled (if that is desired).
Usage
simple_pca(X, dimensions, tolerance = 1e-10)
Arguments
X
the matrix to reduce
dimensions
the number of dimensions after PCA
tolerance
remove components with variance less than the tolerance
Value
pca rotation matrix
edahelsinki/slise documentation built on Aug. 24, 2023, 11:03 p.m.
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| simple_pca | R Documentation |
Calculate the PCA rotation matrix The implementation is based on stats::prcomp. Assumes the data has already been centered and scaled (if that is desired).
Description
Calculate the PCA rotation matrix The implementation is based on stats::prcomp. Assumes the data has already been centered and scaled (if that is desired).
Usage
simple_pca(X, dimensions, tolerance = 1e-10)
Arguments
X |
the matrix to reduce |
dimensions |
the number of dimensions after PCA |
tolerance |
remove components with variance less than the tolerance |
Value
pca rotation matrix
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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