PCA: Principal Components Analysis
In stevexniu/FuseNet: Non-Parametric Network Fusion
PCA R Documentation
Principal Components Analysis
Description
Perform principal components analysis on scaled and centered data (z-scores).
Usage
PCA(scaled_data, n_dims = 10, seed = 1)
Arguments
scaled_data
Scaled and centered data (z-scores).
n_dims
Number of dimensions to return. Default is 10.
seed
Random seed number. Default is 1.
Value
Returns a list contains outputs from irlba.
stevexniu/FuseNet documentation built on May 16, 2022, 12:23 p.m.
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| PCA | R Documentation |
Principal Components Analysis
Description
Perform principal components analysis on scaled and centered data (z-scores).
Usage
PCA(scaled_data, n_dims = 10, seed = 1)
Arguments
scaled_data |
Scaled and centered data (z-scores). |
n_dims |
Number of dimensions to return. Default is 10. |
seed |
Random seed number. Default is 1. |
Value
Returns a list contains outputs from irlba.
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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