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:

data : array

Variables in columns, observations in rows.

ncomp : int, 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.

demean : bool, optional

Flag indicating whether to demean data before computing principal components. demean is ignored if standardize is True.

normalize : bool , optional

Indicates whether th normalize the factors to have unit inner product. If False, the loadings will have unit inner product.

weights : array, optional

Series weights to use after transforming data according to standardize or demean when computing the principal components.

gls : bool, optional

Flag indicating to implement a two-step 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

method : str, optional

Determines the linear algebra routine uses. ‘eig’, the default, uses an eigenvalue decomposition. ‘svd’ uses a singular value decomposition.

Returns:

factors : array or DataFrame

nobs by ncomp array of of principal components (also known as scores)

loadings : array or DataFrame

ncomp by nvar array of principal component loadings for constructing the factors

projection : array or DataFrame

nobs by var array containing the projection of the data onto the ncomp estimated factors

rsquare : array or Series

ncomp array where the element in the ith position is the R-square of including the fist i principal components. The values are calculated on the transformed data, not the original data.

ic : array 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.

eigenvals : array or Series

nvar array of eigenvalues

eigenvecs : array 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.