>h`SrSSKrSSKJr SSKJr SSKJr "SS\5r "S S \ 5r g) a This script contains empirical likelihood ANOVA. Currently the script only contains one feature that allows the user to compare means of multiple groups. General References ------------------ Owen, A. B. (2001). Empirical Likelihood. Chapman and Hall. N) _OptFuncts)optimize)chi2c\rSrSrSrSrSrg) _ANOVAOptzL Class containing functions that are optimized over when conducting ANOVA. cURnURnURn[R"X$45nSn[ [ U55H"nU[ X75-nX7U- XVU2U4'UnM$ Un [R"U RS5SU RS- -n UR[R"U5X5n S[R"XR5-n SU- S-U - Ul [R"[R"X R-55n SU -$)z Optimizes the likelihood under the null hypothesis that all groups have mean mu. Parameters ---------- mu : float The common mean. Returns ------- llr : float -2 times the llr ratio, which is the test statistic. rg?)nobsendog num_groupsnpzerosrangelenonesshape _modif_newtondotT new_weightssumlog)selfmur r r endog_asarrayobs_numarr_num new_obs_numest_vectwtseta_stardenomllrs nC:\Users\julio\OneDrive\Documentos\Trabajo\Ideas Frescas\venv\Lib\site-packages\statsmodels/emplike/elanova.py_opt_common_mu_ANOVAOpt._opt_common_musyy __ $!34 SZ(G!C$77K;@><M;.7 8!G ) !gghnnQ'(B(..2C,DE%%bhhz&:HJRVVHjj119r>E1ffRVVD#3#3345Cx)rN)__name__ __module__ __qualname____firstlineno____doc__r'__static_attributes__r)r&rrs  r)rc(\rSrSrSrSrSSjrSrg)ANOVA:z A class for ANOVA and comparing means. Parameters ---------- endog : list of arrays endog should be a list containing 1 dimensional arrays. Each array is the data collected from a certain group. cXl[UR5UlSUlURH nUR[U5-UlM" g)Nr)r rrr )rr is r&__init__ANOVA.__init__Fs? djj/ A CF*DIr)NcUbPURU5nS[R"X@RS- 5- nU(aXEXR4$XEU4$[ R "URUSSS9nUSn[[R"US55nS[R"X@RS- 5- nU(aXEXpR4$XEU4$)a Returns -2 log likelihood, the pvalue and the maximum likelihood estimate for a common mean. Parameters ---------- mu : float If a mu is specified, ANOVA is conducted with mu as the common mean. Otherwise, the common mean is the maximum empirical likelihood estimate of the common mean. Default is None. mu_start : float Starting value for commean mean if specific mu is not specified. Default = 0. return_weights : bool if TRUE, returns the weights on observations that maximize the likelihood. Default is FALSE. Returns ------- res: tuple The log-likelihood, p-value and estimate for the common mean. rF) full_outputdispr) r'rcdfrrr fmin_powellfloatrsqueeze)rrmu_startreturn_weightsr%pvalres mu_commons r& compute_ANOVAANOVA.compute_ANOVAMs8 >%%b)Ctxx__q%899D"&6&666"}$&&t':':H345BCa&CbjjQ01Itxx__q%899D)-=-===)++r))r r r)Nrr)r*r+r,r-r.r6rDr/r0r)r&r2r2:s +,,r)r2) r.numpyr descriptiverscipyr scipy.statsrrr2r0r)r&rJs1 #% %P?,I?,r)