# 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.