(s)PCA Build using RSpectra::svds()
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refactor: import and utilise RSpectra::svds() instead of base::svd(). svds() only calculates the required number of left and right singular vector as eigenvalues. svd() calculates an excessive amount which can cause runtime inflation for large datasets
refactor: import and utilise RSpectra::svds() instead of base::svd(). This is now also applied to spca()
docs: updated NAMESPACE and DESCRIPTION to include new dependency
docs: updated DESCRIPTION with new import
refactor: import and utilise RSpectra::svds() instead of base::svd(). svds() only calculates the required number of left and right singular vector as eigenvalues. svd() calculates an excessive amount which can cause runtime inflation for large datasets
refactor: import and utilise RSpectra::svds() instead of base::svd(). This is now also applied to spca()
docs: updated NAMESPACE and DESCRIPTION to include new dependency
docs: updated DESCRIPTION with new import
fix: when ILR/CLR was used, X was of invalid class. Added check to ensure `svds()` can operate on X
fix: when ILR/CLR was used, X was of invalid class. Added check to ensure `svds()` can operate on X. Implemented this for `spca()`
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Really weird issue arising preventing the checks from passing. Honestly, not worth the effort in trying to resolve as this is a PR generated for a single user from the MIG lab. |
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Imported the
RSpectra::svds()function to bothpca()andspca().spca()function being highly inefficient generally. The SVD calculations are only one aspect of this inefficiency.