ó áp7]c@ s’dZddlmZddlmZddlZddlmZddl m Z ddl m Z d efd „ƒYZ d e fd „ƒYZdS( sÿ 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. iÿÿÿÿ(tdivision(trangeNi(t _OptFuncts(toptimize(tchi2t _ANOVAOptcB seZdZd„ZRS(sX Class containing functions that are optimized over when conducting ANOVA cC s)|j}|j}|j}tj||fƒ}d}xRtt|ƒƒD]>}|t||ƒ}||||||…|f<|}qIW|} tj| jdƒd| jd} |j tj|ƒ| | ƒ} dtj | | j ƒ} d|d| |_ tj tj||j ƒƒ} d| S(s4 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 igð?iþÿÿÿ(tnobstendogt num_groupstnptzerosRtlentonestshapet _modif_newtontdottTt new_weightstsumtlog(tselftmuRRRt endog_asarraytobs_numtarr_numt new_obs_numtest_vecttwtsteta_startdenomtllr((s:lib/python2.7/site-packages/statsmodels/emplike/elanova.pyt_opt_common_mus"    %(t__name__t __module__t__doc__R(((s:lib/python2.7/site-packages/statsmodels/emplike/elanova.pyRstANOVAcB s)eZdZd„Zdddd„ZRS(së 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. cC sR||_t|jƒ|_d|_x'|jD]}|jt|ƒ|_q.WdS(Ni(RR RR(RRti((s:lib/python2.7/site-packages/statsmodels/emplike/elanova.pyt__init__Js   icC sã|dk ra|j|ƒ}dtj||jdƒ}|rQ||||jfS|||fSn~tj|j|dddtƒ}|d}t |dƒ}dtj||jdƒ}|rÒ||||jfS|||fSdS(s& 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. it full_outputtdispiN( tNoneRRtcdfRRRt fmin_powelltFalsetfloat(RRtmu_starttreturn_weightsRtpvaltrest mu_common((s:lib/python2.7/site-packages/statsmodels/emplike/elanova.pyt compute_ANOVAQs  N(R R!R"R%R(R2(((s:lib/python2.7/site-packages/statsmodels/emplike/elanova.pyR#>s  (R"t __future__Rtstatsmodels.compat.pythonRtnumpyR t descriptiveRtscipyRt scipy.statsRRR#(((s:lib/python2.7/site-packages/statsmodels/emplike/elanova.pyt s )