ó áp7]c@ sˆdZddlmZddlZddlmZddlmZddlm Z ddlm Z m Z d Z d efd „ƒYZ dS( s7Multivariate analysis of variance author: Yichuan Liu iÿÿÿÿ(tdivisionN(tModeli(tMultivariateTestResults(t_multivariate_ols_fit(t_multivariate_ols_testt_hypotheses_docsrestructuredtext entMANOVAcB sCeZdZddd„Zd„Zdd„Zdede_RS(sB 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 ---------- .. [*] ftp://public.dhe.ibm.com/software/analytics/spss/documentation/statistics/20.0/en/client/Manuals/IBM_SPSS_Statistics_Algorithms.pdf tnonecK s{t|jƒdks(|jddkr7tdƒ‚ntt|ƒj||d|d||t|j|jƒ|_ dS(Nis=There must be more than one dependent variable to fit MANOVA!tmissingthasconst( tlentshapet ValueErrortsuperRt__init__Rtendogtexogt _fittedmod(tselfRRRR tkwargs((s>lib/python2.7/site-packages/statsmodels/multivariate/manova.pyR;s ( cC stdƒ‚dS(NsKfit is not needed to use MANOVA. Callmv_test directly on a MANOVA instance.(tNotImplementedError(R((s>lib/python2.7/site-packages/statsmodels/multivariate/manova.pytfitCsc C sU|dkr!t|dƒr¨|jdk r¨t|jdƒr¨|jjj}g}xÊ|D]I}tj|jjdƒ||dd…f}|j ||dgƒqXWq!g}xpt |jjdƒD]U}d|}tj d|jjdgƒ}d|d|f<|j ||dgƒqÅWnt ||j |j|jƒ}t||j|jƒS(Ntdatat design_infoisx%di(tNonethasattrRRtterm_name_slicestnpteyeRR tappendtrangetzerosRRt exog_namest endog_namesR( Rt hypothesesttermstkeyt L_contrasttitnametLtresults((s>lib/python2.7/site-packages/statsmodels/multivariate/manova.pytmv_testGs$  -   s2 Linear hypotheses testing Parameters ---------- s 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. N(t__name__t __module__t__doc__RRRR*R(((s>lib/python2.7/site-packages/statsmodels/multivariate/manova.pyRs '  (R-t __future__RtnumpyRtstatsmodels.base.modelRtmultivariate_olsRRRRt __docformat__R(((s>lib/python2.7/site-packages/statsmodels/multivariate/manova.pyts