U Kv fI@sdZddlZddlmmZddlmmZ ddl mm Z ddl mZmZddlZddlZddlZGdddejZGdddeZGdd d eZGd d d ejZGd d d eZGddde jZe eedS)zA Conditional logistic, Poisson, and multinomial logit regression N)MultinomialResultsMultinomialResultsWrapperc sPeZdZdfdd ZddZdfd d ZdddZedfdd ZZ S)_ConditionalModelnonec sd|krtd|d}|j|jkr0d}t||jd|jkrLd}t|tt|j||fd|i||jjdk rd}t||j}|jd|_ i}t |D]&\}} | |krg|| <||  |qt |t |}}|d } g|_g|_g|_| dk rt | } g|_g|_g|_d|_ddg} |D]\} } || j} t | dkr| dd7<| dt| 7<q6|jt| 7_|j | | dk r|j | | |j t| |j || ddf|j t | q6| ddkrd t| }t|| dk rZg|_t |jD]$\}}|j t |j||q4t|j|_ g|_!g|_"t#|j D]>} |j! t |j| |j| |j" t |j| q|dS) Ngroups'groups' is a required argumentz4'endog' and 'groups' should have the same dimensionsrzBThe leading dimension of 'exog' should equal the length of 'endog'missingzDConditional models should not have an intercept in the design matrixoffsetzIDropped %d groups and %d observations for having no within-group variance)$ ValueErrorsizeshapesuperr__init__dataZ const_idxexogk_params enumerateappendnpZasarrayget _endog_grp _exog_grp _groupsize _offset_grp_offset_sumynobsitemsZflatZstdlensumtuplewarningswarn_endofsdot _n_groups_xy_n1range)selfendogrrkwargsrmsgZrow_ixigr Zdropsixykofs __class__K/tmp/pip-unpacked-wheel-2v6byqio/statsmodels/discrete/conditional_models.pyrs              z_ConditionalModel.__init__cCs&ddlm}|||j}t|}|S)Nr) approx_fprime)Zstatsmodels.tools.numdiffr8scorerZ atleast_2d)r*paramsr8Zhessr6r6r7hessianbs   z_ConditionalModel.hessianNBFGSdTFr6c  s~tt|j|||||| d} t|| j| d} || _|j| _|j| _ dt |j dt |j dt |j g| _t| } | S)N start_paramsmethodmaxiter full_outputdisp skip_hessianr z%dz%.1f)rrfitConditionalResultsr:Z cov_paramsr@rr&n_groupsminrmaxrZmean _group_statsConditionalResultsWrapper) r*r?r@rArBrCfargscallbackretallrDr,rsltZcrsltr4r6r7rEis$   z_ConditionalModel.fit elastic_netcKsNddlm}|dkrtdddddd}||||f||||d |S) a Return a regularized fit to a linear regression model. Parameters ---------- method : {'elastic_net'} Only the `elastic_net` approach is currently implemented. alpha : scalar or array_like The penalty weight. If a scalar, the same penalty weight applies to all variables in the model. If a vector, it must have the same length as `params`, and contains a penalty weight for each coefficient. start_params : array_like Starting values for `params`. refit : bool If True, the model is refit using only the variables that have non-zero coefficients in the regularized fit. The refitted model is not regularized. **kwargs Additional keyword argument that are used when fitting the model. Returns ------- Results A results instance. r)fit_elasticnetrPz.method for fit_regularized must be elastic_net2r g|=)rAZL1_wtZ cnvrg_tolZzero_tol)r@alphar?refit)Zstatsmodels.base.elastic_netrRr update)r*r@rTr?rUr,rRdefaultsr6r6r7fit_regularizeds!  z!_ConditionalModel.fit_regularizedc sz|d}|d=Wntk r.tdYnXt|trB||}d|ddkr\tdtt|j |f|||d|}|S)Nrrz0+ z2Conditional models should not include an intercept)rr) KeyErrorr isinstancestrreplacer"r#rr from_formula) clsZformularZsubsetZ drop_colsargsr,rmodelr4r6r7r_s(     z_ConditionalModel.from_formula)r) Nr<r=TFr6NFF)rPrQNF)NN) __name__ __module__ __qualname__rr;rErX classmethodr_ __classcell__r6r6r4r7rs*P! 1rcsVeZdZdZdfdd ZddZddZdd d Zdd d ZddZ ddZ Z S)ConditionalLogita Fit a conditional logistic regression model to grouped data. Every group is implicitly given an intercept, but the model is fit using a conditional likelihood in which the intercepts are not present. Thus, intercept estimates are not given, but the other parameter estimates can be interpreted as being adjusted for any group-level confounders. Parameters ---------- endog : array_like The response variable, must contain only 0 and 1. exog : array_like The array of covariates. Do not include an intercept in this array. groups : array_like Codes defining the groups. This is a required keyword parameter. rc sXtt|j||fd|i|tt|jtjdkrFd}t||j j d|_ dS)Nr)rr zendog must be coded as 0, 1r ) rrhrranyuniquer+Zr_r rr K)r*r+rrr,r-r4r6r7rs zConditionalLogit.__init__cCs,d}tt|jD]}||||7}q|SNr)r)rr loglike_grp)r*r:llr/r6r6r7loglikeszConditionalLogit.loglikecCs(d}t|jD]}||||7}q|Srl)r)r& score_grp)r*r:r9r/r6r6r7r9szConditionalLogit.scoreNcsR|dkr d}tt|j|||ifdd|j||j|S)Nrcsx||kr dS|dkrdSz||fWStk r:YnX|d||d|d|d}|||f<|S)Nrr )r[)tr2vexbfmemor6r7rus, z"ConditionalLogit._denom..f)rexpr%rrr(r*grpr:r3r6rsr7_denoms zConditionalLogit._denomcsZ|dkr d}j|tt||ifddj|j|S)Nrc s||krdtjfS|dkr$dSz||fWStk rFYnX|d}|d|\}}|d|d\}}|||dddf}|||||||}} || f||f<|| fS)Nr)r rr )rzerosrr[) rqr2habcedurrexrtrvsr*r6r7r.s z'ConditionalLogit._denom_grad..s)rrrwr%rr(rxr6rr7 _denom_grad"s zConditionalLogit._denom_gradcCs\d}t|dr|j|}t|j||}|dk r@||j|7}|t||||8}|S)Nr )hasattrrrr%r'r$logrz)r*ryr:r3Zllgr6r6r7rmFs  zConditionalLogit.loglike_grpcCs<d}t|dr|j|}||||\}}|j|||S)Nrr )rrrr')r*ryr:r3rr|r6r6r7rpUs   zConditionalLogit.score_grp)r)N)N) rcrdre__doc__rror9rzrrmrprgr6r6r4r7rhs   $rhc@s eZdZdZddZddZdS)ConditionalPoissonaU Fit a conditional Poisson regression model to grouped data. Every group is implicitly given an intercept, but the model is fit using a conditional likelihood in which the intercepts are not present. Thus, intercept estimates are not given, but the other parameter estimates can be interpreted as being adjusted for any group-level confounders. Parameters ---------- endog : array_like The response variable exog : array_like The covariates groups : array_like Codes defining the groups. This is a required keyword parameter. c Csd}t|dr|j}d}tt|jD]n}t|j||}|dk rP|||7}t|}|j|}|t||7}| }||j |t |8}q&|SNr rQ) rrr)rrrr%rrwr rr) r*r:r3rnr.xbrtr1rr6r6r7rors    zConditionalPoisson.loglikec Csd}t|dr|j}d}tt|jD]x}|j|}t||}|dk rT|||7}t|}| }|j|} |t| |7}||j |t|||8}q&|Sr) rrr)rrrrr%rwr r) r*r:r3r9r.xrrtrr1r6r6r7r9s       zConditionalPoisson.scoreN)rcrdrerror9r6r6r6r7r_srcs&eZdZfddZdddZZS)rFcstt|j||||ddS)N)normalized_cov_paramsscale)rrFr)r*rbr:rrr4r6r7rs  zConditionalResults.__init__N皙?c Csdddd|jgfddg}dd|jgfd |jd gfd |jd gfd |jdgfg}|dkr^d}d dlm}|}|j||||||d|j|||||jd|S)a< Summarize the fitted model. Parameters ---------- yname : str, optional Default is `y` xname : list[str], optional Names for the exogenous variables, default is "var_xx". Must match the number of parameters in the model title : str, optional Title for the top table. If not None, then this replaces the default title alpha : float Significance level for the confidence intervals Returns ------- smry : Summary instance This holds the summary tables and text, which can be printed or converted to various output formats. See Also -------- statsmodels.iolib.summary.Summary : class to hold summary results )zDep. Variable:N)zModel:N)zLog-Likelihood:NzMethod:)zDate:N)zTime:N)zNo. Observations:Nz No. groups:zMin group size:rzMax group size:r zMean group size:Nz*Conditional Logit Model Regression Results)Summary)ZgleftZgrightynamexnametitle)rrrTuse_t)r@rGrJZstatsmodels.iolib.summaryrZadd_table_2colsZadd_table_paramsr) r*rrrrTZtop_leftZ top_rightrZsmryr6r6r7summarysB    zConditionalResults.summary)NNNr)rcrdrerrrgr6r6r4r7rFs rFc s<eZdZdZdfdd Zdd d Zd dZddZZS)ConditionalMNLogita Fit a conditional multinomial logit model to grouped data. Parameters ---------- endog : array_like The dependent variable, must be integer-valued, coded 0, 1, ..., c-1, where c is the number of response categories. exog : array_like The independent variables. groups : array_like Codes defining the groups. This is a required keyword parameter. Notes ----- Equivalent to femlogit in Stata. References ---------- Gary Chamberlain (1980). Analysis of covariance with qualitative data. The Review of Economic Studies. Vol. 47, No. 1, pp. 225-238. rc stt|j||fd|i||jt|_|jd|_|jd|jj d|_ |j |j |_ ddt |jD|_|j|_|jj d|_|jdkrd}t|ttt|jD]\}}||qt|_|jfdd|jD|_dS) Nrr cSsi|]}|t|qSr6)r]).0jr6r6r7 sz/ConditionalMNLogit.__init__..rz%endog may not contain negative valuescsg|] }|qSr6r6)rr2Zgrxr6r7 sz/ConditionalMNLogit.__init__..)rrrr+ZastypeintrIk_catrr Zdf_modelrZdf_residr)Z _ynames_mapJrkrHr collections defaultdictlistrrrkeysZ _group_labelssort_grp_ix)r*r+rrr,r-r2rrr4rr7rs0   zConditionalMNLogit.__init__Nr<r=TFr6c  Ks|dkr0|jjd} |jd} tjj| | d}tjj||||||| d} | j |jjddf| _ t || } | j tj dt| S)Nr )r r>)Zllnull)rr rrrandomnormalbaseLikelihoodModelrEr:reshaperZset_null_optionsnanr)r*r?r@rArBrCrLrMrNrDr,qrrOr6r6r7rEs"     zConditionalMNLogit.fitc Cs|jjd}|jd}|||f}tjt|df|fdd}t|j|}d}|jD]~}||ddf}tj |jdt d} |j |} d} t | D]} | t|| | f7} q||| | ft| 7}qX|S)Nr ZaxisrQrZdtype)rr rrr concatenater{r%rarangerr+ itertools permutationsrwr r) r*r:rrpmatlprrniirjjr1denompr6r6r7ro?s     zConditionalMNLogit.loglikec Cs|jjd}|jd}|||f}tjt|df|fdd}t|j|}t||f}|jD]}||ddf}tj |jdt d} |j |} d} t||f} t | D]n} t|| | f}| |7} t| D]B\}}|dkr| dd|df||j||ddf7<qqt| D]B\}}|dkr*|dd|df|j||ddf7<q*|| | 8}qb|S)Nr rrrrQ)rr rrrrr{r%rrrr+rrrwr rflatten)r*r:rrrrZgradrrrr1rZdenomgrrrr.rr6r6r7r9Us.    6 2zConditionalMNLogit.score)r) Nr<r=TFr6NFF) rcrdrerrrEror9rgr6r6r4r7rs %rc@s eZdZdS)rKN)rcrdrer6r6r6r7rKvsrK)rZnumpyrZstatsmodels.base.modelrrbZ#statsmodels.regression.linear_modelZ regressionZ linear_modelZlmZstatsmodels.base.wrapperwrapperwrapZ#statsmodels.discrete.discrete_modelrrrr"rrrrhrZLikelihoodModelResultsrFrZRegressionResultsWrapperrKZpopulate_wrapperr6r6r6r7s$F ?I