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.
