M/PhrdZddlZddlmZddlmZddlmZddlm Z ddlm Z m Z d Z Gd d eZ dS) z7Multivariate analysis of variance author: Yichuan Liu N) Substitution)Model)MultivariateTestResults)_multivariate_ols_fit)_multivariate_ols_test_hypotheses_doczrestructuredtext encZeZdZdZdZd fd ZdZeed dZ xZ S) MANOVAaN 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 : ndarray See Parameters. exog : ndarray 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 Nnonec t|jdks|jddkrtdtj||f||d|t |j|j|_dS)Nrz=There must be more than one dependent variable to fit MANOVA!)missinghasconst) lenshape ValueErrorsuper__init__rendogexog _fittedmod)selfrrrrkwargs __class__s _/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/statsmodels/multivariate/manova.pyrzMANOVA.__init__;s u{  q EKNa$7$7/00 0 Bg.6 B B:@ B B B/ DIFFc td)NzKfit is not needed to use MANOVA. Callmv_test directly on a MANOVA instance.)NotImplementedError)rs rfitz MANOVA.fitCs!#KLL Lr)hypotheses_docFc|t|dr|jt|jdrn|jjj}g}|D]W}|r|dkr t j|jjd||ddf}|||dgXnng}t|jjdD]L}d|z}t j d|jjdg}d|d|f<|||dgMt||j |j |j} t| |j|j S)aT Linear hypotheses testing Parameters ---------- %(hypotheses_doc)s skip_intercept_test : bool If true, then testing the intercept is skipped, the model is not changed. Note: If a term has a numerically insignificant effect, then an exception because of emtpy arrays may be raised. This can happen for the intercept if the data has been demeaned. Returns ------- results: MultivariateTestResults Notes ----- Testing the linear hypotheses L * params * M = 0 where `params` is the regression coefficient matrix for the linear model y = x * params If the model is not specified using the formula interfact, then the hypotheses test each included exogenous variable, one at a time. In most applications with categorical variables, the ``from_formula`` interface should be preferred when specifying a model since it provides knowledge about the model when specifying the hypotheses. Ndata design_info Interceptrzx%dr)hasattrr"r#term_name_slicesnpeyerrappendrangezerosrr exog_names endog_namesr) r hypothesesskip_intercept_testtermskey L_contrastinameLresultss rmv_testzMANOVA.mv_testGssD  f%% 7$)*? =99+@ ->  ??C*!sk/A/A !# (:!;!;E#JM!JJ%%sJ&=>>>> ?  tyq12277A A;D!TY_Q%7!899AAadG%%tQo6666(T_)-$:JLL'w0@'+88 8r)r N)NF) __name__ __module__ __qualname____doc___formula_max_endogrrrr r7 __classcell__)rs@rr r s''PGGGGGGLLL\111787878217878787878rr )r;numpyr'statsmodels.compat.pandasrstatsmodels.base.modelrmultivariate_olsrrrr __docformat__r rrrDs222222((((((555555333333EEEEEEEE% o8o8o8o8o8Uo8o8o8o8o8r