U mdI@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 ZdropsZixykofs __class__`/home/sam/Atlas/atlas_env/lib/python3.8/site-packages/statsmodels/discrete/conditional_models.pyrs              z_ConditionalModel.__init__cCs&ddlm}|||j}t|}|S)Nr) approx_fprime)Zstatsmodels.tools.numdiffr7scorerZ atleast_2d)r*paramsr7Zhessr5r5r6hessianbs   z_ConditionalModel.hessianNBFGSdTFr5c  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)rrfitConditionalResultsr9Z cov_paramsr?rr&n_groupsminrmaxrZmean _group_statsConditionalResultsWrapper) r*r>r?r@rArBfargscallbackretallrCr,rsltZcrsltr3r5r6rDis$   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_elasticnetrOz.method for fit_regularized must be elastic_net2r g|=)r@ZL1_wtZ cnvrg_tolZzero_tol)r?alphar>refit)Zstatsmodels.base.elastic_netrQr update)r*r?rSr>rTr,rQdefaultsr5r5r6fit_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,rmodelr3r5r6r^s(     z_ConditionalModel.from_formula)r) Nr;r<TFr5NFF)rOrPNF)NN) __name__ __module__ __qualname__rr:rDrW classmethodr^ __classcell__r5r5r3r6rs*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 ) rrgrranyuniquer+Zr_r rr K)r*r+rrr,r-r3r5r6rs zConditionalLogit.__init__cCs,d}tt|jD]}||||7}q|SNr)r)rr loglike_grp)r*r9llr/r5r5r6loglikeszConditionalLogit.loglikecCs(d}t|jD]}||||7}q|Srk)r)r& score_grp)r*r9r8r/r5r5r6r8szConditionalLogit.scoreNcsR|dkr d}tt|j|||ifdd|j||j|S)Nrcsx||kr dS|dkrdSz||fWStk r:YnX|d||d|d|d}|||f<|S)Nrr )rZ)tr1vexbfmemor5r6rts, z"ConditionalLogit._denom..f)rexpr%rrr(r*grpr9r2r5rrr6_denoms zConditionalLogit._denomcsZ|dkr d}j|tt||ifddj|j|S)Nrc s||krdtjfS|dkr$dSz||fWStk rFYnX|d}|d|\}}|d|d\}}|||dddf}|||||||}} || f||f<|| fS)Nr)r rr )rzerosrrZ) rpr1habcedurqexrsrusr*r5r6r.s z'ConditionalLogit._denom_grad..s)rrrvr%rr(rwr5rr6 _denom_grad"s zConditionalLogit._denom_gradcCs\d}t|dr|j|}t|j||}|dk r@||j|7}|t||||8}|S)Nr )hasattrrrr%r'r$logry)r*rxr9r2Zllgr5r5r6rlFs  zConditionalLogit.loglike_grpcCs<d}t|dr|j|}||||\}}|j|||S)Nrr )rrrr')r*rxr9r2rr{r5r5r6roUs   zConditionalLogit.score_grp)r)N)N) rbrcrd__doc__rrnr8ryrrlrorfr5r5r3r6rgs   $rgc@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 rP) rrr)rrrr%rrvr rr) r*r9r2rmr.xbrsr0rr5r5r6rnrs    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%rvr r) r*r9r2r8r.xrrsrr0r5r5r6r8s       zConditionalPoisson.scoreN)rbrcrdrrnr8r5r5r5r6r_srcs&eZdZfddZdddZZS)rEcstt|j||||ddS)N)normalized_cov_paramsscale)rrEr)r*rar9rrr3r5r6rs  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)rrrSuse_t)r?rFrIZstatsmodels.iolib.summaryrZadd_table_2colsZadd_table_paramsr) r*rrrrStop_left top_rightrZsmryr5r5r6summarysB    zConditionalResults.summary)NNNr)rbrcrdrrrfr5r5r3r6rEs rEc 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. 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