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

MANOVA(endog, exog[, missing, hasconst])

Multivariate analysis of variance

MultivariateOLS

_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