statsmodels.multivariate.pca.pca¶

statsmodels.multivariate.pca.
pca
(data, ncomp=None, standardize=True, demean=True, normalize=True, gls=False, weights=None, method='svd')[source]¶ Principal Component Analysis
 Parameters
 dataarray
Variables in columns, observations in rows.
 ncompint, optional
Number of components to return. If None, returns the as many as the smaller to the number of rows or columns of data.
 standardize: bool, optional
Flag indicating to use standardized data with mean 0 and unit variance. standardized being True implies demean.
 demeanbool, optional
Flag indicating whether to demean data before computing principal components. demean is ignored if standardize is True.
 normalizebool , optional
Indicates whether th normalize the factors to have unit inner product. If False, the loadings will have unit inner product.
 weightsarray, optional
Series weights to use after transforming data according to standardize or demean when computing the principal components.
 glsbool, optional
Flag indicating to implement a twostep GLS estimator where in the first step principal components are used to estimate residuals, and then the inverse residual variance is used as a set of weights to estimate the final principal components
 methodstr, optional
Determines the linear algebra routine uses. ‘eig’, the default, uses an eigenvalue decomposition. ‘svd’ uses a singular value decomposition.
 Returns
 factorsarray or DataFrame
nobs by ncomp array of of principal components (also known as scores)
 loadingsarray or DataFrame
ncomp by nvar array of principal component loadings for constructing the factors
 projectionarray or DataFrame
nobs by var array containing the projection of the data onto the ncomp estimated factors
 rsquarearray or Series
ncomp array where the element in the ith position is the Rsquare of including the fist i principal components. The values are calculated on the transformed data, not the original data.
 icarray or DataFrame
ncomp by 3 array containing the Bai and Ng (2003) Information criteria. Each column is a different criteria, and each row represents the number of included factors.
 eigenvalsarray or Series
nvar array of eigenvalues
 eigenvecsarray or DataFrame
nvar by nvar array of eigenvectors
Notes
This is a simple function wrapper around the PCA class. See PCA for more information and additional methods.