statsmodels.multivariate.pca.pca¶
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statsmodels.multivariate.pca.pca(data, ncomp=None, standardize=True, demean=True, normalize=True, gls=False, weights=None, method='svd')[source]¶ Perform Principal Component Analysis (PCA).
- 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.
- standardizebool,
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.
- glsbool,
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
- weights
array,optional Series weights to use after transforming data according to standardize or demean when computing the principal components.
- method
str,optional Determines the linear algebra routine uses. ‘eig’, the default, uses an eigenvalue decomposition. ‘svd’ uses a singular value decomposition.
- data
- Returns
- factors{
ndarray,DataFrame} Array (nobs, ncomp)of of principal components (also known as scores).
- loadings{
ndarray,DataFrame} Array (ncomp, nvar) of principal component loadings for constructing the factors.
- projection{
ndarray,DataFrame} Array (nobs, nvar) containing the projection of the data onto the ncomp estimated factors.
- rsquare{
ndarray,Series} Array (ncomp,) 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{
ndarray,DataFrame} Array (ncomp, 3) containing the Bai and Ng (2003) Information criteria. Each column is a different criteria, and each row represents the number of included factors.
- eigenvals{
ndarray,Series} Array of eigenvalues (nvar,).
- eigenvecs{
ndarray,DataFrame} Array of eigenvectors. (nvar, nvar).
- factors{
Notes
This is a simple function wrapper around the PCA class. See PCA for more information and additional methods.