Multivariate Statistics multivariate

This section includes methods and algorithms from multivariate statistics.

Principal Component Analysis

PCA(data[, ncomp, standardize, demean, …]) Principal Component Analysis
pca(data[, ncomp, standardize, demean, …]) Principal Component Analysis

Factor Analysis

Factor([endog, n_factor, corr, method, smc, …]) Factor analysis
FactorResults(factor) Factor results class

Factor Rotation

rotate_factors(A, method, *method_args, …) Subroutine for orthogonal and oblique rotation of the matrix \(A\).
target_rotation(A, H[, full_rank]) Analytically performs orthogonal rotations towards a target matrix, i.e., we minimize:
procrustes(A, H) Analytically solves the following Procrustes problem:
promax(A[, k]) Performs promax rotation of the matrix \(A\).

Canonical Correlation

CanCorr(endog, exog[, tolerance, missing, …]) Canonical correlation analysis using singluar value decomposition


MANOVA(endog, exog[, missing, hasconst]) Multivariate analysis of variance The implementation of MANOVA is based on multivariate regression and does not assume that the explanatory variables are categorical.


_MultivariateOLS is a model class with limited features. Currently it supports multivariate hypothesis tests and is used as backend for MANOVA.

_MultivariateOLS(endog, exog[, missing, …]) Multivariate linear model via least squares
_MultivariateOLSResults(fitted_mv_ols) _MultivariateOLS results class
MultivariateTestResults(mv_test_df, …) Multivariate test results class Returned by mv_test method of _MultivariateOLSResults class