sandbox.tools.tools_pca.pcasvd()

statsmodels.sandbox.tools.tools_pca.pcasvd

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.