Source code for statsmodels.multivariate.manova

# -*- coding: utf-8 -*-

"""Multivariate analysis of variance

author: Yichuan Liu
from __future__ import division

import numpy as np

from statsmodels.base.model import Model
from .multivariate_ols import MultivariateTestResults
from .multivariate_ols import _multivariate_ols_fit
from .multivariate_ols import _multivariate_ols_test, _hypotheses_doc

__docformat__ = 'restructuredtext en'

[docs]class MANOVA(Model): """ Multivariate analysis of variance The implementation of MANOVA is based on multivariate regression and does not assume that the explanatory variables are categorical. Any type of variables as in regression is allowed. Parameters ---------- endog : array_like Dependent variables. A nobs x k_endog array where nobs is the number of observations and k_endog is the number of dependent variables. exog : array_like Independent variables. A nobs x k_exog array where nobs is the number of observations and k_exog is the number of independent variables. An intercept is not included by default and should be added by the user. Models specified using a formula include an intercept by default. Attributes ---------- endog : array See Parameters. exog : array See Parameters. Notes ----- MANOVA is used though the `mv_test` function, and `fit` is not used. The ``from_formula`` interface is the recommended method to specify a model and simplifies testing without needing to manually configure the contrast matrices. References ---------- .. [*] """ def __init__(self, endog, exog, missing='none', hasconst=None, **kwargs): if len(endog.shape) == 1 or endog.shape[1] == 1: raise ValueError('There must be more than one dependent variable' ' to fit MANOVA!') super(MANOVA, self).__init__(endog, exog, missing=missing, hasconst=hasconst, **kwargs) self._fittedmod = _multivariate_ols_fit(self.endog, self.exog)
[docs] def fit(self): raise NotImplementedError('fit is not needed to use MANOVA. Call' 'mv_test directly on a MANOVA instance.')
[docs] def mv_test(self, hypotheses=None): if hypotheses is None: if (hasattr(self, 'data') and is not None and hasattr(, 'design_info')): terms = hypotheses = [] for key in terms: L_contrast = np.eye(self.exog.shape[1])[terms[key], :] hypotheses.append([key, L_contrast, None]) else: hypotheses = [] for i in range(self.exog.shape[1]): name = 'x%d' % (i) L = np.zeros([1, self.exog.shape[1]]) L[0, i] = 1 hypotheses.append([name, L, None]) results = _multivariate_ols_test(hypotheses, self._fittedmod, self.exog_names, self.endog_names) return MultivariateTestResults(results, self.endog_names, self.exog_names)
mv_test.__doc__ = ( """ Linear hypotheses testing Parameters ---------- """ + _hypotheses_doc + """ Returns ------- results: MultivariateTestResults Notes ----- Testing the linear hypotheses L * params * M = 0 where `params` is the regression coefficient matrix for the linear model y = x * params If the model is not specified using the formula interfact, then the hypotheses test each included exogenous variable, one at a time. In most applications with categorical variables, the ``from_formula`` interface should be preferred when specifying a model since it provides knowledge about the model when specifying the hypotheses. """)