statsmodels.sandbox.tools.tools_pca.pcasvd
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statsmodels.sandbox.tools.tools_pca.pcasvd(data, keepdim=0, demean=True)
[source] -
principal components with svd
Parameters: data : ndarray, 2d
data with observations by rows and variables in columns
keepdim : integer
number of eigenvectors to keep if keepdim is zero, then all eigenvectors are included
demean : boolean
if true, then the column mean is subtracted from the data
Returns: xreduced : ndarray, 2d, (nobs, nvars)
projection of the data x on the kept eigenvectors
factors : ndarray, 2d, (nobs, nfactors)
factor matrix, given by np.dot(x, evecs)
evals : ndarray, 2d, (nobs, nfactors)
eigenvalues
evecs : ndarray, 2d, (nobs, nfactors)
eigenvectors, normalized if normalize is true
See Also :
??- :
pca : principal component analysis using eigenvector decomposition
Notes
This doesn?t have yet the normalize option of pca.
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