predict.sparseprcomp: Fast PCA using the irlba library for SVD
In zachmayer/r2vec: Text data to numeric vectors
Description
Usage
Arguments
Value
Description
This functions performs principle components analysis via SVD,
using the irlba library. Note that it is not possible to
center and scale a sparse matrix. Folding in the
eigenvalues might help compensate for this.
Usage
1
2
Arguments
object
a sparse.prcomp object
newdata
a sparse matrix of new data
...
ignored
Value
a sparse matrix
zachmayer/r2vec documentation built on May 4, 2019, 9:05 p.m.
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Description Usage Arguments Value
Description
This functions performs principle components analysis via SVD, using the irlba library. Note that it is not possible to center and scale a sparse matrix. Folding in the eigenvalues might help compensate for this.
Usage
1 2 |
Arguments
object |
a sparse.prcomp object |
newdata |
a sparse matrix of new data |
... |
ignored |
Value
a sparse 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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