statsmodels.tools.pca.pca¶
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statsmodels.tools.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.