M/PhZndZddlZddlmZddlmZddlmZGddeZ Gd d e Z dS) 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)chi2ceZdZdZdZdS) _ANOVAOptzX Class containing functions that are optimized over when conducting ANOVA. ch|j}|j}|j}tj||f}d}t t |D].}|t ||z}|||z ||||f<|}/|} tj| jdd| jdz z} | tj|| | } dtj | | j z} d|z dz| z |_ tj tj||j z} d| zS)a7 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 [/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/statsmodels/emplike/elanova.py_opt_common_muz_ANOVAOpt._opt_common_mus0y _ $ !344 SZZ(( " "G!Cg$7$77K;@><M';.7 8!GG ghnQ'((B(.2C,DE%%bhz&:&:HcJJRVHhj1119r>E1fRVD4#334455CxN)__name__ __module__ __qualname____doc__r&r'r%rrs- r'rc eZdZdZdZddZdS)ANOVAz A class for ANOVA and comparing means. Parameters ---------- endog : list of arrays endog should be a list containing 1 dimensional arrays. Each array is the data collected from a certain group. c||_t|j|_d|_|jD]}|jt|z|_dS)Nr)r rr r )rr is r%__init__zANOVA.__init__FsP dj//  + +A CFF*DII + +r'Nrc|G||}dtj||jdz z }|r ||||jfS|||fSt j|j|dd}|d}ttj |d}dtj||jdz z }|r ||||jfS|||fS)a( Returns -2 log likelihood, the pvalue and the maximum likelihood estimate for a common mean. Parameters ---------- mu : float If a mu is specified, ANOVA is conducted with mu as the common mean. Otherwise, the common mean is the maximum empirical likelihood estimate of the common mean. Default is None. mu_start : float Starting value for commean mean if specific mu is not specified. Default = 0. return_weights : bool if TRUE, returns the weights on observations that maximize the likelihood. Default is FALSE. Returns ------- res: tuple The log-likelihood, p-value and estimate for the common mean. NrF) full_outputdispr) r&rcdfr rr fmin_powellfloatrsqueeze)rrmu_startreturn_weightsr$pvalres mu_commons r% compute_ANOVAzANOVA.compute_ANOVAMs8 >%%b))CtxT_q%8999D %D"d&666D"}$&t':H345BBBCa&CbjQ0011ItxT_q%8999D ,D)T-===D)++r')Nrr)r(r)r*r+r1r>r,r'r%r.r.:sA  +++,,,,,,,,,,,,r'r.) r+numpyr descriptiverscipyr scipy.statsrrr.r,r'r%rCs  ######%%%%% %%%P?,?,?,?,?,I?,?,?,?,?,r'