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 _multivariate_ols_test, _hypotheses_doc
from .multivariate_ols import _multivariate_ols_fit
from .multivariate_ols import MultivariateTestResults
__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.
References
----------
.. [*] ftp://public.dhe.ibm.com/software/analytics/spss/documentation/statistics/20.0/en/client/Manuals/IBM_SPSS_Statistics_Algorithms.pdf
"""
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 mv_test(self, hypotheses=None):
if hypotheses is None:
if (hasattr(self, 'data') and self.data is not None and
hasattr(self.data, 'design_info')):
terms = self.data.design_info.term_name_slices
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[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
""")