# 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 The implementation of MANOVA is based on multivariate regression and does not assume that the explanatory variables are categorical.

## 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