statsmodels.multivariate.manova.MANOVA

class statsmodels.multivariate.manova.MANOVA(endog, exog, missing='none', hasconst=None, **kwargs)

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

endogarray_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.

exogarray_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.

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

*

ftp://public.dhe.ibm.com/software/analytics/spss/documentation/ statistics/20.0/en/client/Manuals/IBM_SPSS_Statistics_Algorithms.pdf

Attributes

endogndarray

See Parameters.

exogndarray

See Parameters.

Methods

fit()	Fit a model to data.
from_formula(formula, data[, subset, drop_cols])	Create a Model from a formula and dataframe.
mv_test([hypotheses])	Linear hypotheses testing
predict(params[, exog])	After a model has been fit predict returns the fitted values.

Methods

fit()	Fit a model to data.
from_formula(formula, data[, subset, drop_cols])	Create a Model from a formula and dataframe.
mv_test([hypotheses])	Linear hypotheses testing
predict(params[, exog])	After a model has been fit predict returns the fitted values.

Properties

endog_names	Names of endogenous variables.
exog_names	Names of exogenous variables.