M/PhudZddlZddlmZddlmcmZddlmZddl m Z dZ dZ dZ Gd d ZGd d ejZGd dejZGddZdS)a Implementation of proportional hazards regression models for duration data that may be censored ("Cox models"). References ---------- T Therneau (1996). Extending the Cox model. Technical report. http://www.mayo.edu/research/documents/biostat-58pdf/DOC-10027288 G Rodriguez (2005). Non-parametric estimation in survival models. http://data.princeton.edu/pop509/NonParametricSurvival.pdf B Gillespie (2006). Checking the assumptions in the Cox proportional hazards model. http://www.mwsug.org/proceedings/2006/stats/MWSUG-2006-SD08.pdf N)model)cache_readonly)Appendera Returns predicted values from the proportional hazards regression model. Parameters ----------%(params_doc)s exog : array_like Data to use as `exog` in forming predictions. If not provided, the `exog` values from the model used to fit the data are used.%(cov_params_doc)s endog : array_like Duration (time) values at which the predictions are made. Only used if pred_type is either 'cumhaz' or 'surv'. If using model `exog`, defaults to model `endog` (time), but may be provided explicitly to make predictions at alternative times. strata : array_like A vector of stratum values used to form the predictions. Not used (may be 'None') if pred_type is 'lhr' or 'hr'. If `exog` is None, the model stratum values are used. If `exog` is not None and pred_type is 'surv' or 'cumhaz', stratum values must be provided (unless there is only one stratum). offset : array_like Offset values used to create the predicted values. pred_type : str If 'lhr', returns log hazard ratios, if 'hr' returns hazard ratios, if 'surv' returns the survival function, if 'cumhaz' returns the cumulative hazard function. pred_only : bool If True, returns only an array of predicted values. Otherwise returns a bunch containing the predicted values and standard errors. Returns ------- A bunch containing two fields: `predicted_values` and `standard_errors`. Notes ----- Standard errors are only returned when predicting the log hazard ratio (pred_type is 'lhr'). Types `surv` and `cumhaz` require estimation of the cumulative hazard function. zK params : array_like The proportional hazards model parameters.z cov_params : array_like The covariance matrix of the estimated `params` vector, used to obtain prediction errors if pred_type='lhr', otherwise optional.c&eZdZ ddZdZdZdS)PHSurvivalTimeNc |-tjt|tj}|!tjt|}||||tj|}d|Dt |D] \}} | |!fd|D|fdt D} t|_fd| Dfd| Dt} | |_ t D]\\} } t|| | dkfd t || D} | | | <]t D]\\} } t|| | dkfd t || D} | | | <]t D]0\} } tj || } | | | <1|@g|_ t| D](} |j || )nd|_ td D|_|_|_|||_|||_||_|||_ggggf\|_|_|_|_t|j D]'} tj|j| dk}|j| |}tj|}t|}d t |D}d t|D}t;||D]&\} }|||| 'dt|D}t |j| D]?\}}tj||ddz } | dkr|| |@dt|D}t |j| D]5\}}tj||} || |6|j||jd|D|jd|D|jd|D)dS)a Represent a collection of survival times with possible stratification and left truncation. Parameters ---------- time : array_like The times at which either the event (failure) occurs or the observation is censored. status : array_like Indicates whether the event (failure) occurs at `time` (`status` is 1), or if `time` is a censoring time (`status` is 0). exog : array_like The exogeneous (covariate) data matrix, cases are rows and variables are columns. strata : array_like Grouping variable defining the strata. If None, all observations are in a single stratum. entry : array_like Entry (left truncation) times. The observation is not part of the risk set for times before the entry time. If None, the entry time is treated as being zero, which gives no left truncation. The entry time must be less than or equal to `time`. offset : array_like An optional array of offsets Ndtypeci|]}|gSr .0xs f/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/statsmodels/duration/hazard_regression.py z+PHSurvivalTime.__init__..s"""q""""c\g|](}tj|tj)Sr npasarrayint32)rksths r z+PHSurvivalTime.__init__..s.HHHq 3q6:::HHHrcVg|]%\}}|dk#|&S)r)sum)riixstatuss rrz+PHSurvivalTime.__init__..s5 L L LDAbvbz~~7G7G!7K7Ka7K7K7Krc g|] }| Sr r )rr stratum_rowss rrz+PHSurvivalTime.__init__..s444A Q444rc g|] }| Sr r )rr stratum_namess rrz+PHSurvivalTime.__init__..s666aq)666rc&g|] \}}|k |Sr r )rrt last_failures rrz+PHSurvivalTime.__init__..s1%%%!|#####rc&g|] \}}|k |Sr r )rrr' first_failures rrz+PHSurvivalTime.__init__..s1&&&!}$$$$$rc,g|]}t|Sr )len)rrs rrz+PHSurvivalTime.__init__..s999b#b''999rci|]\}}|| Sr r )rrrs rrz+PHSurvivalTime.__init__..s666!q!666rcg|]}gSr r rrs rrz+PHSurvivalTime.__init__..s...Qb...rcg|]}gSr r r/s rrz+PHSurvivalTime.__init__..s333!2333rrightrcg|]}gSr r r/s rrz+PHSurvivalTime.__init__..s222"222rcNg|]"}tj|tj#Srrr s rrz+PHSurvivalTime.__init__..s9#4#4#4'($&:arx#@#@#@#4#4#4rcNg|]"}tj|tj#Srrr s rrz+PHSurvivalTime.__init__..s9$:$:$:()%'Jq$A$A$A$:$:$:rcNg|]"}tj|tj#Srrr s rrz+PHSurvivalTime.__init__..s9#8#8#8'($&:arx#@#@#@#8#8#8r)rzerosr,r_checkunique enumerateappend nstrat_orignstratmaxminargsortoffset_srangern_obsr"r$_splittime_sexog_sstatus_sentry_s ufailt_ix risk_enter risk_exitufailt flatnonzerozip searchsorted)selftimer exogstrataentryoffsetsturrrr<stxiiiftftuftnuftuft_mapuft_ixti risk_enter1r' risk_exit1r*r(rr$r"s ` @@@@@r__init__zPHSurvivalTime.__init__Ws@ >Xc$iirx888F =HSYY''E D&&%000i""c"""V$$  CAa FMM!    HHHHCHHH  M L L LIl33 L L L|,,4444444 66662666 \""  -- 6 6FCtBxr a899L%%%%yr33%%%B ,S 1" 5L   -- 6 6FCRq 9::M&&&&yb22&&&B ,S 1" 5L   -- 6 6FCDH%%B ,S 1" 5L    DMV}} @ @ $$VL,=%>???? @!DM99L999:: (*kk$'' kk$''  F++ {{5)) " BN E%%& 9& 9C.s!3q!899CS!#&B)B--Cs88D76y~~666G..%++...FS / /2wr{#**2....43uT{{333K S!122 . .!_S!W55977O**1---32eDkk222J c!233 ) )!_S!,,2%%a(((( K  s # # # N ! !#4#4,2#4#4#4 5 5 5 O " "$:$:-8$:$:$: ; ; ; N ! !#8#8,6#8#8#8 9 9 9 9K& 9& 9rcg}|jdkr&|jD]}|||n)|jD]!}|||ddf"|S)Nr%)ndimr"r:)rOrvrs rrCzPHSurvivalTime._splitsx  6Q;;'  2 ' # #2qqq5""""rct|t|t|t|f\}}}}||||g} t| t| krtdt|dkrtdt|dkrtdt j||krtddS)Nz>endog, status, strata, and entry must all have the same lengthrzendog must be non-negativezentry time must be non-negativez8entry times may not occur after event or censoring times)r,r=r> ValueErrorrany) rOrPr rRrSn1n2n3n4nvs rr7zPHSurvivalTime._checksTCKKV JJBB"b"  r77c"gg  CDD D t99q==9:: : u::>>>?? ? 6%$,   ?>?? ? ? ?r)NNN)__name__ __module__ __qualname__rarCr7r rrrrUsQ>B^9^9^9^9@?????rrceZdZdZ dfd Ze d fd Zd!fd Z d"d Zd Z d Z dZ dZ dZ dZdZdZdZdZdZdZdZdZeeeedz d#dZd$dZxZS)%PHRega Cox Proportional Hazards Regression Model The Cox PH Model is for right censored data. Parameters ---------- endog : array_like The observed times (event or censoring) exog : 2D array_like The covariates or exogeneous variables status : array_like The censoring status values; status=1 indicates that an event occurred (e.g. failure or death), status=0 indicates that the observation was right censored. If None, defaults to status=1 for all cases. entry : array_like The entry times, if left truncation occurs strata : array_like Stratum labels. If None, all observations are taken to be in a single stratum. ties : str The method used to handle tied times, must be either 'breslow' or 'efron'. offset : array_like Array of offset values missing : str The method used to handle missing data Notes ----- Proportional hazards regression models should not include an explicit or implicit intercept. The effect of an intercept is not identified using the partial likelihood approach. `endog`, `event`, `strata`, `entry`, and the first dimension of `exog` all must have the same length Nbreslowdropc |!tjt|}tj||f|||||d| |jtj|j|_|jtj|j|_|jtj|j|_|j tj|j |_ t|j |j|j |j|j|j |_ t|j |_d|_||_t#|j jdtj|j z |_t#tj|j |_|}|dvrt1d||_dS)N)r rSrRrTmissingr)efronrrz*`ties` must be either `efron` or `breslow`)ronesr,superrar rrSrRrTrendogrQsurvnobsgroupsrufloatshapelinalg matrix_rankdf_residdf_modellowerrfties) rOryrQr rSrRrTrrukwargs __class__s rrazPHReg.__init__;s >WSZZ((F .V*/+17 . .'- . . . ; "*T[11DK : !DJ//DJ ; "*T[11DK ; "*T[11DK"4:t{$(It{$(J ==  OO   dioa0 i33DI>>?@@ bi33DI>>?? zz|| + + +)** * rc t|tr||}t|tr||}t|tr||}t|tr||}ddl} | d|} | D]3}|}|dvrddl}|d4tj||g| R||||||| dgd| }|S)aK Create a proportional hazards regression model from a formula and dataframe. Parameters ---------- formula : str or generic Formula object The formula specifying the model data : array_like The data for the model. See Notes. status : array_like The censoring status values; status=1 indicates that an event occurred (e.g. failure or death), status=0 indicates that the observation was right censored. If None, defaults to status=1 for all cases. entry : array_like The entry times, if left truncation occurs strata : array_like Stratum labels. If None, all observations are taken to be in a single stratum. offset : array_like Array of offset values subset : array_like An array-like object of booleans, integers, or index values that indicate the subset of df to use in the model. Assumes df is a `pandas.DataFrame` ties : str The method used to handle tied times, must be either 'breslow' or 'efron'. missing : str The method used to handle missing data args : extra arguments These are passed to the model kwargs : extra keyword arguments These are passed to the model with one exception. The ``eval_env`` keyword is passed to patsy. It can be either a :class:`patsy:patsy.EvalEnvironment` object or an integer indicating the depth of the namespace to use. For example, the default ``eval_env=0`` uses the calling namespace. If you wish to use a "clean" environment set ``eval_env=-1``. Returns ------- model : PHReg model instance rNz[+\-~])01z6PHReg formulas should not include any '0' or '1' terms Intercept)r rSrRrTsubsetrru drop_cols) isinstancestrresplitstripwarningswarnrx from_formula)clsformuladatar rSrRrTrrruargsrrtermstermrmodrs rrzPHReg.from_formulags1f fc " " "&\F eS ! ! KE fc " " "&\F fc " " "&\F G,, X XD::<& >C(-H^C(Fv;;D[%FfVV,,G}(4=-- w{{}} $GwIHC4[[2& > >_S)!,r77Q;;r!!!t A9R=,,...CIbM!!!D&1A5::1===CAY111c 166q999^C(+r77Q;;r!!!t A9R=,,...CIbM!!!D&1A5::1===C% >( rc|j}d}t|jD]}|j|}|j|}t j||}|j||j|z }||z}t j |}d\} } |j |} t| } t| dddD]} |j || }t|dkrT||ddf}| || z } | ||dddf|z dz } | | }t|dkr||ddf}|| }||dddf|z d}|| dz }t|}t j|t j|z }| t j||z }| t j||z }||z }| d}||z}|j|| }t|dkrT||ddf}| || z} | ||dddf|z dz} |S)z Returns the gradient of the log partial likelihood evaluated at `params`, using the Efron method to handle tied times. rNrrrr )rzrAr<r"rErrr@r=rrHr,rIrrrouterrJ)rOrrzrrVrrErrrrr]r[rrrdixfrxp1frrnumerdenomratiorsums rrzPHReg.efron_gradients y%%0 >0 >C(-H[%FfVV,,G}(4=-- w{{}} $GwIHC^C(Fv;;D4[[2& > >_S)!,r77Q;;r!!!t A9R=,,...CIbM!!!D&1A5::1===CQis88a<<s111u A$S>--//D%cN111T62Q6;;A>>DAEE!HH$DCA !2:666:A"(1d"3"33E"(1d"3"33E!EME 99Q<@ rc z|j}d}t|jD]}|j|}t |}|j|}t j||}|j||j|z }|| z}t j |} d\} } } t|dddD]w} |j || }t |dkrv| | | z } ||ddf}| | |dddf|z dz } | |}| t j d|||z } t || }||| | z t j| | | dzz z zz }|j|| }t |dkrv| | | z} ||ddf}| | |dddf|z dz} | |}| t j d|||z} y| S)z Returns the Hessian of the log partial likelihood evaluated at `params`, using the Breslow method to handle tied times. rNrrrrr ij,ik,i->jk)rzrAr<rHr,rErrr@r=rrIreinsumrrJ)rOrrzhessrVr]r[rErrrrxp2rrrdelxrs rrzPHReg.breslow_hessiansM y%%& ?& ?C^C(Fv;;D[%FfVV,,G}(4=-- w{{}} $GwI&MCc4[[2& ? ?_S)!,r77Q;;9R=,,...Cr!!!t AIbM!!!D&1A5::1===C#B-C29]Aq#>>>Cq NN39c(:(:S!V(CCDD^C(+r77Q;;9R=,,...Cr!!!t AIbM!!!D&1A5::1===C#B-C29]Aq#>>>C- ?.u rc |j}d}t|jD](}|j|}t j||}|j||j|z }||z}t j|}d\}} } |j |} t| } t| dddD]} |j || }t|dkrv||| z }||ddf}| ||dddf|z dz } ||}| t j d|||z } | | }t|dkrm||ddf}|| }||dddf|z d}||}t j d|||}t| | }t j|tj|z }|||zz }|| t j d|z zz }||t j ||z zz}| dddft j||z |dddfz }|t j d ||z}|j|| }t|dkrv||| z}||ddf}| ||dddf|z dz} ||}| t j d|||z} *| S) z Returns the Hessian matrix of the partial log-likelihood evaluated at `params`, using the Efron method to handle tied times. rNrrrrr r%z ij,ik->jk)rzrAr<rErrr@r=rrHr,rIrrrrrrJ)rOrrzrrVrErrrrrr]r[rrrdrrrrxp2frrc0mats rrzPHReg.efron_hessianHsPy%%2 ?2 ?C[%FfVV,,G}(4=-- w{{}} $GwI&MCc^C(Fv;;D4[[2&# ?# ?_S)!,r77Q;;9R=,,...Cr!!!t AIbM!!!D&1A5::1===C#B-C29]Aq#>>>CQis88a<<s111u A$S>--//D%cN111T62Q6;;A>>D#C.C9]Aq#>>Dq NNIarz222Q61T6\bfQVnn,,rva"f~~--47|bhq$&7&772aaag;F +sC888^C(+r77Q;;9R=,,...Cr!!!t AIbM!!!D&1A5::1===C#B-C29]Aq#>>>CG# ?Ju rcT|jtd||}||}i}t |jD](\}}||vrd||<||xx||ddfz cc<)t jt|}|dddddf}|j |z}| d}t j |}t j |t j ||} | S)a Returns a covariance matrix for the proportional hazards model regresion coefficient estimates that is robust to certain forms of model misspecification. Parameters ---------- params : ndarray The parameter vector at which the covariance matrix is calculated. Returns ------- The robust covariance matrix as a square ndarray. Notes ----- This function uses the `groups` argument to determine groups within which observations may be dependent. The covariance matrix is calculated using the Huber-White "sandwich" approach. NzD`groups` must be specified to calculate the robust covariance matrixrr%)r|rfrscore_residualsr9rrlistvaluesTrrinvr) rOrr score_obsgradsrgrhess_invcmats rrzPHReg.robust_covariances. ; cdd d||F##((00 T[)) ( (CAa~~a !HHH !QQQ$ 'HHHH 4 //00D!!!QQQJeckggajj9==&&vhsH 5 566 rc |j}tj|jjtj}tj|jjdtj}||}t|j D]}|j |}|j |}t|} |j |} d} tj||} |j| |j|z } | | z} tj| } t%}t| dddD]9}|j||}|t%|z}| | |z } t+|}||ddf|||ddfz }tj|jd}d|||<t||| z }||| ||zz }| |}||ddfxx||dddfzz cc<d||<|j||}|t%|z}| | |z} ;tj|dk}t|dkrtj||ddf<|S)a Returns the score residuals calculated at a given vector of parameters. Parameters ---------- params : ndarray The parameter vector at which the score residuals are calculated. Returns ------- The score residuals, returned as a ndarray having the same shape as `exog`. Notes ----- Observations in a stratum with no observed events have undefined score residuals, and contain NaN in the returned matrix. r rrNrr%)rzrr6rQr~rrweighted_covariate_averagesrAr<rHrEr,r"rr@r=rsetrIrrrJrLnan)rOrrz score_residmaskw_avgrVr]rEr[rrrr at_risk_ixrratr_ixleverageddchazmrprWjjs rrzPHReg.score_residualss,yhtybjAAA x *"(;;;0088%%. +. +C^C(F[%Fv;;D(-HCfVV,,G}(4=-- w{{}} $GwIJ4[[2& + +_S)!,c"gg% y}((***j))!&!!!),uSz!QQQ$/??HV\!_-- &) F1I,i)F"3e";;f%BqqqD!!!XAAAtG %<<!!!R^C(+c"gg% y}((***9 +<^DAI & & r77Q;;!#KAAA rc |j}g}d\}}t|jD]}|j|}|j|}t |} t jt ||jdft j } t j ||} |j | |j |z } | | z} t j | } t| dddD]} |j|| }|| |z }|t j | |||ddfz }||z | | ddf<|j|| }|| |z}|t j | |||ddfz}|| |S)a Returns the hazard-weighted average of covariate values for subjects who are at-risk at a particular time. Parameters ---------- params : ndarray Parameter vector Returns ------- averages : list of ndarrays averages[stx][i,:] is a row vector containing the weighted average values (for all the covariates) of at-risk subjects a the i^th largest observed failure time in stratum `stx`, using the hazard multipliers as weights. Notes ----- Used to calculate leverages and score residuals. rr%r Nr)rzrAr<rHrEr,rr6r~rrr@r=rrIrrJr:)rOrrzaveragesrrrVr]rEr[ average_srrrrs rrz!PHReg.weighted_covariate_averagess.yS%% ' 'C^C(F[%Fv;;D#f++v|A!?(* 444IfVV,,G}(4=-- w{{}} $GwI4[[2& < <_S)!,y}((***rvimVBE];;;"%) !QQQ$^C(+y}((***rvimVBE];;; OOI & & & &rc2|j}g}t|jD]w}|j|}|j|}|j|}t |}tj||} |j | |j |z } tj | } d} tj |tj } t|dddD]} |j || }| | |z } || }t || z | | <|j|| }| | |z} tj| | z }tj | }||||gy|S)a Estimate the baseline cumulative hazard and survival functions. Parameters ---------- params : ndarray The model parameters. Returns ------- A list of triples (time, hazard, survival) containing the time values and corresponding cumulative hazard and survival function values for each stratum. Notes ----- Uses the Nelson-Aalen estimator. Nrr r)rzrAr<rKrHrEr,rrr@rr6rrIrrJcumsumr:)rOrrzrsltrVrZr]rEr[rrrh0rrcumhazcurrent_strata_survs rbaseline_cumulative_hazardz PHReg.baseline_cumulative_hazardNs0y%% < .bunchN)rmrnropredicted_valuesstandard_errorsr rrbunchr"s# "OOOrr%TFrr%r z/If `exog` is provided `endog` must be provided.z`strata` must be providedrr r rz)rrfrQrrrTr#rsqrtr$rryrzr<rRr$r,rrwrr8rrL)rOrrQrryrRrT pred_type pred_onlyrr%ret_val exog_providedrrvar r stvbhazrVrrs rpredictz PHReg.predictsOO%% ; ; ;6BCS// ! # # # # # # # #%''  <9D!MfT6""   6MCC [ $] $ 4; C   '*G $%fT:..Dj%%a((*,'"++' 0//N VC[[   ')G $ 0//N =]=CCS// ! ]=]JE > >!1A!5!5 !<==={")1!46UC"'#e**BJ????i77?? 2 2C# ..B9DeBi2b61F2JJ  '-G $ $ & ')vvgG $  ,+ +r?c |j}||}gg}}||j}n|j|}t |jjD]8} || } t j| |} |j| |j| z } t j | } || d} t j | || d}t j | }t j |j ddf}t j ||fd}t j|d }|||t j t j|j d| :t#d|D}t |jjD]}||j d|krt j ||j d|f}t j ||j d|f}|||ddd||j df<|||ddd||j df<||c||<||<t jt jt'|j|fz}t jt'|j|ft j|z }t |jjD]2} |jj| }|| ||ddf<|| ||ddf<3t/||}|S)a Returns a scipy distribution object corresponding to the distribution of uncensored endog (duration) values for each case. Parameters ---------- params : array_like The proportional hazards model parameters. scale : float Present for compatibility, not used. exog : array_like A design matrix, defaults to model.exog. Returns ------- A list of objects of type scipy.stats.distributions.rv_discrete Notes ----- The distributions are obtained from a simple discrete estimate of the survivor function that puts all mass on the observed failure times within a stratum. Nrr%axisc(g|]}|jdS)r%)r~r s rrz*PHReg.get_distribution..Ks***!171:***rr )rzrrErCrAr<rrr@rrr6r~ concatenatediffr:rwr=rr,ryrr"rv_discrete_float)rOrscalerQrzr-pkxk exog_splitrVrErrptsichazusurvzprobsmxcrxk1pk1xkapkardists rget_distributionzPHReg.get_distributions4y..v66RB <JJ))$//J)** > >C_FfVV,,G}(4=--wIs)A,CHYS ! 55EFE6NNE%+a.!,--ANE1:A666EWUA&&&E IIe    IIbhrwu{1~66<< = = = =**r***++ty'(( ( (A!u{1~##h1 A455h1 A455+-a5AAAqAQ''(+-a5AAAqAQ''("C 1r!ufrwDJ5666gs4:,BJ???#E)** ! !C',BCCAAAJCCAAAJJ c** r)NNNNrrrs)NNNNNrrrsN)rrNF)NNNNNrF)r/N)rmrnro__doc__ra classmethodrrrrrrrrrrrrrrrrrr_predict_docstring_predict_params_doc_predict_cov_params_docstringr.rF __classcell__rs@rrqrqs"%%N8<09******X<@6:-3IIIII[IV''''''R;=16F*F*F*F*R . . ./// . . .)))V...`333j===~222h@@@D---^TTTl===~>>>@>X )7$9$99::AEEJVVV::VpVVVVVVVVrrqc&eZdZdZdfd ZedZedZdZe e ddd z dfd Z dZ edZ edZedZedZedZedZddZxZS)ra Class to contain results of fitting a Cox proportional hazards survival model. PHregResults inherits from statsmodels.LikelihoodModelResults Parameters ---------- See statsmodels.LikelihoodModelResults Attributes ---------- model : class instance PHreg model instance that called fit. normalized_cov_params : ndarray The sampling covariance matrix of the estimates params : ndarray The coefficients of the fitted model. Each coefficient is the log hazard ratio corresponding to a 1 unit difference in a single covariate while holding the other covariates fixed. bse : ndarray The standard errors of the fitted parameters. See Also -------- statsmodels.LikelihoodModelResults r/naivec||_|j|_|j|_t||d|dS)Nr/)r7normalized_cov_params)covariance_typerrrxra)rOrrrr7rSrs rrazPHRegResults.__init__~sS /   b!+  - - - - -rcrtjtj|SzI Returns the standard errors of the parameter estimates. )rr&diagrrOs rr$zPHRegResults.standard_errorss( wrwt0011222rc|jSrU)r$rWs rbsezPHRegResults.bses ##rc@|j|jS)a Returns a scipy distribution object corresponding to the distribution of uncensored endog (duration) values for each case. Returns ------- A list of objects of type scipy.stats.distributions.rv_discrete Notes ----- The distributions are obtained from a simple discrete estimate of the survivor function that puts all mass on the observed failure times within a stratum. )rrFrrWs rrFzPHRegResults.get_distributions"z**4;777rrNTrc xt|||||||S)N)rQ transformrryrRrTr')rxr.r)rOryrQrRrTr]r'rs rr.zPHRegResults.predictsEwwD;D<@OO8>;D FF Frctj|d}|d}|||t |fS)z7 Descriptive statistics of the groups. T) return_countsr%)rr8r>r=meanr,)rOr|gsizess r _group_statszPHRegResults._group_statssN6666zz||VZZ\\6;;==#f++EErc@|j|jS)z{ The average covariate values within the at-risk set at each event time point, weighted by hazard. )rrrrWs rrz(PHRegResults.weighted_covariate_averagess z55dkBBBrc@|j|jS)z: A matrix containing the score residuals. )rrrrWs rrzPHRegResults.score_residualss z))$+666rc@|j|jS)z A list (corresponding to the strata) containing the baseline cumulative hazard function evaluated at the event points. )rrrrWs rrz'PHRegResults.baseline_cumulative_hazards z44T[AAArc@|j|jS)z A list (corresponding to the strata) containing function objects that calculate the cumulative hazard function. )rrrrWs rrz0PHRegResults.baseline_cumulative_hazard_functions z==dkJJJrc||jj}|j}tjtj|jjjtjz}t|j D]}|j |}|j |}|j |}|j|}tj||} tj| t#|k} || ddf||| | ddfz ||| ddf<tj|jjdk} tj|| ddf<|S)z A matrix containing the Schoenfeld residuals. Notes ----- Schoenfeld residuals for censored observations are set to zero. r Nr)rrzrrrrwrQr~rrAr<rKrErDr"rNrLr,r ) rOrzr sch_residrVrZrErDrrWrs rschoenfeld_residualsz!PHRegResults.schoenfeld_residualss'z0F274:?#8 KKKK %% O OC+c"C[%F[%F(-Hf--B SXX ..B)/AAAsBrFAAAI9N)NIhrlAAAo & & ^DJ-2 3 36 "aaa%rc|jj}tjtjt |jjtjz}|j}t|j D]}||}|j |}|j |}tj ||j}|j||j|z }tj|} |j|} ||} |jj| | | zz || <|S)z+ The martingale residuals. r )rrzrrrwr,ryrrrAr<rErDrrr@rr"r ) rOrz mart_resid cumhaz_f_listrVrrErDrrrWchazs rmartingale_residualsz!PHRegResults.martingale_residualss zVBGC (8$9$9LLLL @ %% F FC$S)H[%F[%FfVT[11G}(4=--wI"3'B8F##D!Z.r2Y5EEJrNNr皙?c ddlm}|}d}i}d|d<| |jj}||d<|jj|d<t|jjj |d <ttt|jj |d <|jj D||jj \} } } } d | z|d <d | z|d <d | z|d<d| z|d<|jjD||jj\} } } } d | z|d<d | z|d<d | z|d<d| z|d<||d||||} | ddd} | ddt)j| dd|dz z}t)j| jdd|f| jdd|f<dd |dz z z}t)j| jdd|f| jdd|f<||| _|| |!|||"|d#|jjj|jjjz }|dkr4|d kr|d$n|d%|z|jjQt|jjdk}|d kr|d&n|d'|z|jj |d(t=|d)r|d*|S)+ay Summarize the proportional hazards regression results. Parameters ---------- yname : str, optional Default is `y` xname : list[str], optional Names for the exogenous variables, default is `x#` for ## in p the number of regressors. 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.summary2.Summary : class to hold summary results r)summary2z%8.3fzPH RegzModel:NzDependent variable:zTies:z Sample size:z Num. events:z%.0fz Num groups:zMin group size:zMax group size:z%.1fzAvg group size:z Num strata:zMin stratum size:zMax stratum size:zAvg stratum size:l)align float_format)rzlog HRz log HR SE)zCoef.zStd.Err.)columnsrHRz[%.3fz%.3f]r%)rt)titlerz.Confidence intervals are for the hazard ratiosz&1 stratum dropped for having no eventsz&%d strata dropped for having no eventsz'1 observation has a positive entry timez)%d observations have positive entry timesz4Standard errors account for dependence within groups regularizedz5Standard errors do not account for the regularization)statsmodels.iolibrqSummaryr endog_namesr capitalizerrzrBintrr r|rbrRadd_dictsummary_paramsrenameinsertrrlocindexadd_df add_titleadd_textr;r<rShasattr)rOynamexnamerwrrqsmryrtinfomnmxavgnumparamadstratn_entrys rsummaryzPHRegResults.summary%s: /.....!! !X =J*E&+ "# 2244W "4:?#899^"3s4:+<'='=#>#>??^ :  (#001BCC BC"(3,D &,rkD" #&,rkD" #&,slD" # :  (#001BCC BC"(3,D (. D$ %(. D$ %(. D$ % d#L AAA''E':: x2=&?&? @@ QbfU8_55666 uqy !&111a411 !!!Q$ q519} %&111a411 !!!Q$  EK E 555 UD111 FGGG,tz/EE A::{{ FGGGG FOPPP :  '$**a/00G!|| GHHHH IGSTTT :  ( MMP Q Q Q 4 ' ' S MMQ R R R r)r/rP)NNNNTr)NNNro)rmrnrorHrarr$rYrFrrJr.rbrrrrrirnrrMrNs@rrras8 - - - - - -33^3 $$^$ 888&X "#K#KKLL487<FFFFFMLFFFFCC^C77^7 BB^BKK^K$$^$L^@\\\\\\\\rrc2eZdZdZdZddZdZdZdZdS) r6a A class representing a collection of discrete distributions. Parameters ---------- xk : 2d array_like The support points, should be non-decreasing within each row. pk : 2d array_like The probabilities, should sum to one within each row. Notes ----- Each row of `xk`, and the corresponding row of `pk` describe a discrete distribution. `xk` and `pk` should both be two-dimensional ndarrays. Each row of `pk` should sum to 1. This class is used as a substitute for scipy.distributions. rv_discrete, since that class does not allow non-integer support points, or vectorized operations. Only a limited number of methods are implemented here compared to the other scipy distribution classes. cb||_||_tj|jd|_dS)Nr%r1)r9r8rr cpk)rOr9r8s rrazrv_discrete_float.__init__s,9TW1---rNc|jjd}tj|}|j|dddfkd}tj|tj}|j||fS)aD Returns a random sample from the discrete distribution. A vector is returned containing a single draw from each row of `xk`, using the probabilities of the corresponding row of `pk` Parameters ---------- n : not used Present for signature compatibility r)sizeNr%r ) r9r~rrandomuniformrrrr)rOnurrWs rrvszrv_discrete_float.rvssw GM!  I  1  % %h111d7# ( ( + + Yq ) ) )w2wrcF|j|jzdS)z Returns a vector containing the mean values of the discrete distributions. A vector is returned containing the mean value of each row of `xk`, using the probabilities in the corresponding row of `pk`. r%)r9r8rrWs rr`zrv_discrete_float.means!$'!&&q)))rc|}|j|dddfz }|j|j|z dzzdS)z Returns a vector containing the variances of the discrete distributions. A vector is returned containing the variance for each row of `xk`, using the probabilities in the corresponding row of `pk`. Nrr%)r`r9r8r)rOrxkcs rvarzrv_discrete_float.varsOYY[[g111d7 #47S=1,,11!444rcNtj|S)a  Returns a vector containing the standard deviations of the discrete distributions. A vector is returned containing the standard deviation for each row of `xk`, using the probabilities in the corresponding row of `pk`. )rr&rrWs rstdzrv_discrete_float.stdswtxxzz"""rrG) rmrnrorHrarr`rrr rrr6r6sn6...     ( * * * 5 5 5 # # # # #rr6)rHnumpyrstatsmodels.baserstatsmodels.base.modelrstatsmodels.tools.decoratorsrstatsmodels.compat.pandasrrJrKrLrLikelihoodModelrqLikelihoodModelResultsrr6r rrrs` """"""%%%%%%%%%777777.......`6!{?{?{?{?{?{?{?{?|KKKKKE !KKK\"`````4.```F [#[#[#[#[#[#[#[#[#[#r