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