M/PhdZddlmZddlmZddlZddlZddlZ ddl m Z ddl mZddlmZmZmZddlmZGd d ZGd d eZGd deZGddeZGddeZGddeZGddeZGddeZGddeZGddeZGddeZGdd eZ dS)!z Covariance models and estimators for GEE. Some details for the covariance calculations can be found in the Stata docs: http://www.stata.com/manuals13/xtxtgee.pdf )Appender defaultdictN)linalg) cov_nearest)ConvergenceWarningNotImplementedWarning OutputWarning) bool_likec8eZdZdZd dZdZdZdZdZdZ d S) CovStructa Base class for correlation and covariance structures. An implementation of this class takes the residuals from a regression model that has been fit to grouped data, and uses them to estimate the within-group dependence structure of the random errors in the model. The current state of the covariance structure is represented through the value of the `dep_params` attribute. The default state of a newly-created instance should always be the identity correlation matrix. clippedc0d|_g|_||_dSN) dep_params cov_adjustcov_nearest_method)selfrs ]/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/statsmodels/genmod/cov_struct.py__init__zCovStruct.__init__+s$#5c||_dS)z Called by GEE, used by implementations that need additional setup prior to running `fit`. Parameters ---------- model : GEE class A reference to the parent GEE class instance. N)model)rrs r initializezCovStruct.initialize8s rct)z Update the association parameter values based on the current regression coefficients. Parameters ---------- params : array_like Working values for the regression parameters. NotImplementedErrorrparamss rupdatezCovStruct.updateDs "!rct)a Returns the working covariance or correlation matrix for a given cluster of data. Parameters ---------- endog_expval : array_like The expected values of endog for the cluster for which the covariance or correlation matrix will be returned index : int The index of the cluster for which the covariance or correlation matrix will be returned Returns ------- M : matrix The covariance or correlation matrix of endog is_cor : bool True if M is a correlation matrix, False if M is a covariance matrix r)r endog_expvalindexs rcovariance_matrixzCovStruct.covariance_matrixPs ,"!rcv |||\}}|r|tj||z}d}d}d} tdD]p} t j| d}nV#tjj$r?t||j |}|dz}| dz } d } | d z } tj | YmwxYw|j | |sTtj d ttjtj|}t j| fd |D} | S) a~ Solves matrix equations of the form `covmat * soln = rhs` and returns the values of `soln`, where `covmat` is the covariance matrix represented by this class. Parameters ---------- expval : array_like The expected value of endog for each observed value in the group. index : int The group index. stdev : array_like The standard deviation of endog for each observation in the group. rhs : list/tuple of array_like A set of right-hand sides; each defines a matrix equation to be solved. Returns ------- soln : list/tuple of array_like The solutions to the matrix equations. Notes ----- Returns None if the solver fails. Some dependence structures do not use `expval` and/or `index` to determine the correlation matrix. Some families (e.g. binomial) do not use the `stdev` parameter when forming the covariance matrix. If the covariance matrix is singular or not SPD, it is projected to the nearest such matrix. These projection events are recorded in the fit_history attribute of the GEE model. Systems of linear equations with the covariance matrix as the left hand side (LHS) are solved for different right hand sides (RHS); the LHS is only factorized once to save time. This is a default implementation, it can be reimplemented in subclasses to optimize the linear algebra according to the structure of the covariance matrix. g{Gz?FrT)method thresholdz+At least one covariance matrix was not PSD zand required projection.zHUnable to condition covariance matrix to an SPD matrix using cov_nearestc:g|]}tj|S)spl cho_solve).0xvcos r z5CovStruct.covariance_matrix_solve..s%333! c1%%333r)r$npouterranger- cho_factorr LinAlgErrorrrwarningswarnrappendrdiag)rexpvalr#stdevrhsvmatis_corr(successritrmsgsolnr1s @rcovariance_matrix_solvez!CovStruct.covariance_matrix_solvehs~^--fe<< f  + BHUE** *D  99 # #C #nT**9( # # #"40G-6888Q a C11 c""""" # z*** ' M+,> @ @ @7274==))D.&&C3333s333 s A%%AB87B8ct)ze Returns a text summary of the current estimate of the dependence structure. rrs rsummaryzCovStruct.summarys "!rNr) __name__ __module__ __qualname____doc__rrr r$rErHr,rrr r s   5 5 5 5    " " """"0QQQf"""""rr ceZdZdZeejjdZeejjdZeejjdZdZ dS) Independencez7 An independence working dependence structure. cdSrr,rs rr zIndependence.updates  rcdt|}tj|tjdfSNdtypeT)lenr3eyefloat64)rr<r#dims rr$zIndependence.covariance_matrixs)&kkvc,,,d22rc|dz}g}|D]H}|jdkr|||z &|||dddfz I|S)Nr)r*)ndimr:)rr<r#r=r>vrsltr0s rrEz$Independence.covariance_matrix_solvesm QJ , ,Av{{ AE"""" A!!!T' N++++ rcdS)Nz?Observations within a cluster are modeled as being independent.r,rGs rrHzIndependence.summarys ((rN) rJrKrLrMrr r r$rErHr,rrrOrOsXi&''('Xi)12233323Xi/78898)))))rrOceZdZdZdfd ZdZeejjdZeej jdZ dZ xZ S) Unstructureda4 An unstructured dependence structure. To use the unstructured dependence structure, a `time` argument must be provided when creating the GEE. The time argument must be of integer dtype, and indicates which position in a complete data vector is occupied by each observed value. rcJt|dSr)superr)rr __class__s rrzUnstructured.__init__s" +,,,,,rc||_ddl}t|jjjj|jsd}t||jjdddfdz}tj ||_ dS)Nrz1time must be provided and must have integer dtyper*) rnumbers issubclasstimerTtypeIntegral ValueErrormaxr3rVr)rrrdrCqs rrzUnstructured.initializes| $*//4g6FGG "ECS// ! JOAAAqD ! % % ' '! +&))rct|jdr@|jj}||dddf}|jt j||dfS|jdfS)NrfrT)hasattrrtime_lirr3ix_)rr"r#rnixs rr$zUnstructured.covariance_matrixsa 4:v & & 8j(G1%B?26"b>>247 7$$rch|jj}|jj}|jjj}|jj}|jjdu}|jj}|jj}|jj dz} tj | | f} d} tj | | f} d} t|jj D]}||\}}tj||}|||z |z }||dddf}tj||}tjtj|}|r||nd}| tj||xx|z cc<| |t'|zz } | tj||xx||zz cc<| ||zz } |jj}| | ||z zt+|z z} | | |z z} tjtj| }| tj||z} | |_dS)Nr*r?)rendog_linobsfamilyvariance cached_meansweightsrnrfrjr3zerosr5 num_groupsqrtr4sumr;rorU ddof_scalefloatr)rrendogruvarfuncrx has_weights weights_lirnrkcsumwsumcovscaleir<_r=residrpmssrwddofsds rr zUnstructured.updates" #z*#,z. j(4 Z' *$ JO   ! !A %xAh1vtz+,,  A%QIFAGGGFOO,,E1X&%/EAAAqD!B&&A&$$C!,4 1 "A B A % ABK D r2   1q5 (    QW EEz$ %d 33 t  WRWS\\ " " rxBrcLtdt|jdS)NzEstimated covariance structure:)printrrGs rrHzUnstructured.summary1s' /000 dorrI) rJrKrLrMrrrr r$r rH __classcell__rbs@rr_r_s------ $ $ $Xi)122%%32%Xi&''''(''Rrr_ceZdZdZfdZeejjdZeejjdZeej jdZ dZ xZ S) Exchangeablez7 An exchangeable working dependence structure. cVtd|_dSNrr)rarr)rrbs rrzExchangeable.__init__;s& rc|jj}|jj}|jjj}|jj}|jjdu}|jj}d\}} d\} } } t|jjD]} || \}}tj ||}|| |z |z }|r|| nd}tj ||z}| ||zz } | |t|| zz } ||| dz|z zdz z }t|}d|z|dz z}| ||zz } | |z } |jj }| | ||z zt|z z} || z}|| | |z zt| z z |_dS)Nrrrrrrrrrsr)?r*)rrtrurvrwrxryr5r{r3r|r}rUr~rr)rrrrurrxrr residsq_sumrfsum1fsum2n_pairsrr<rr=rfrngrpnprrs rr zExchangeable.updateBs #z*#,z. j(4 Z' ! U *ugtz+,,  A$QIFAGGGFOO,,E1X&%/E!,4 1 "A&''C QW E QU1X& &E 1 q 03 67!; ;Ku::D*q)C QW E sNGGz$ %4$;'%++56u % gn %g 68rct|}|jtj||ftjz}tj|d|dfS)NrSr*T)rUrr3onesrW fill_diagonal)rr<r#rXdps rr$zExchangeable.covariance_matrixhsJ&kk _rwSzDDD D Q4xrct|}|jd|jz z }|d|j|dz zzz}g}|D]}|jdkr-||z } | d|jz z } | |t| zz} | |z} nF||dddfz } | d|jz z } | || dzz} | |dddfz} || |S)Nrsr*r)rUrrZr}r:) rr<r#r=r>kcr\r0x1ys rrEz$Exchangeable.covariance_matrix_solveos KK OrDO3 4 R$/QU+ ++  Av{{Y"t./QR[ U qqq$w'"t./Q]"U111d7^# KKNNNN rcdd|jzzS)Nz0The correlation between two observations in the zsame cluster is %.3frrGs rrHzExchangeable.summarysB&89 :r) rJrKrLrMrrr r r$rErHrrs@rrr6sXi&''#8#8('#8JXi)12232 Xi/78898,:::::::rrceZdZdZfdZeejjdZeejjdZdZ xZ S)Nesteda* A nested working dependence structure. A nested working dependence structure captures unique variance associated with each level in a hierarchy of partitions of the cases. For each level of the hierarchy, there is a set of iid random effects with mean zero, and with variance that is specific to the level. These variance parameters are estimated from the data using the method of moments. The top level of the hierarchy is always defined by the required `groups` argument to GEE. The `dep_data` argument used to create the GEE defines the remaining levels of the hierarchy. it should be either an array, or if using the formula interface, a string that contains a formula. If an array, it should contain a `n_obs x k` matrix of labels, corresponding to the k levels of partitioning that are nested under the top-level `groups` of the GEE instance. These subgroups should be nested from left to right, so that two observations with the same label for column j of `dep_data` should also have the same label for all columns j' < j (this only applies to observations in the same top-level cluster given by the `groups` argument to GEE). If `dep_data` is a formula, it should usually be of the form `0 + a + b + ...`, where `a`, `b`, etc. contain labels defining group membership. The `0 + ` should be included to prevent creation of an intercept. The variable values are interpreted as labels for group membership, but the variables should not be explicitly coded as categorical, i.e. use `0 + a` not `0 + C(a)`. Notes ----- The calculations for the nested structure involve all pairs of observations within the top level `group` passed to GEE. Large group sizes will result in slow iterations. cHt||jjt jdt tj|jj }|j dkr |dddf}||_ |jj }gg}}|j j d}t|jjD]m}t!||}|jj|} |jj| } tj|d\} } |j | | ddf|j | | ddfkd} tj||ftj}| dz|| | f<| dz|| | f<||tjt!| |dzftj}d|dddf<tj| D]&}tj| |k}d||d|dzf<'||otj|d|_||_tj|jd}|d|_ |d|_!|dj"|_#dS) a Called on the first call to update `ilabels` is a list of n_i x n_i matrices containing integer labels that correspond to specific correlation parameters. Two elements of ilabels[i] with the same label share identical variance components. `designx` is a matrix, with each row containing dummy variables indicating which variance components are associated with the corresponding element of QY. NzSweights not implemented for nested cov_struct, using unweighted covariance estimater*rSraxisr))$rarrryr8r9r r3asarraydep_datarZ id_matrixrtshaper5r{rU group_labels group_indices tril_indicesr}rzint32r:rWunique flatnonzero concatenatedesignxilabelsrsvd designx_u designx_sT designx_v)rrrrrrn_nestrrglabrixix1ix2ncmilabeldsxriirrbs rrzNested.initializes 5!!! :  ) MA/ 1 1 1 Jtz233 >Q  !!!!T'*I" #r%a(tz+,,  AuQx==D:*1-D**40CtR00HC>#c(AAA+.>#c(AAA+./03A  XtTl"(;;;F!$qFC: !$qFC:  NN6 " " "(CHHfqj1DDDCC1IYs^^ % %^C1H--#$B!a%K  NN3    ~gA666  immDL!,,QQQrc|jj}|jj}t|}|j||j|jj}|jjj}g}d}t|jj D]} || \} } tj || } || | z | z } tj t| d\}}|| || |z|tj| dzz }tj|}|||z z}tj|jtj|jj||jz }tj|dtj|_||_|j|_dS)Nrrrr)r)rrtrurUr_compute_designrxrvrwr5r{r3r|rr:r}rdotrrrrclipinf vcomp_coeffrcopyr)rrrrurXrxrdvmatrrr<rr=rrrrs rr z Nested.updates #z&kk <    , , ,z. *#,tz+,, ( (A$QIFAGGGFOO,,E1X&%/Es5zz266HC LLseCj0 1 1 1 RVEQJ'' 'EEu%% $*fT^RVDN4D49.;.;=A^.LMM 7;26:: *//11rct|}|j"tj|tjdfS|j|}tj|jtj|j f}||}||jz}|dfSrR) rUrr3rVrWrr_rcumsumr)rr<r#rXrrr?s rr$zNested.covariance_matrix s&kk ? "6#RZ000$6 6e$ E$*bi(8999 :y  Tzrc*dg}t|jdr ||jjnA|dt t |jdz Dt|jdr|jj|d<|d|j }||j tj |z tjd|i| }|S) zd Returns a summary string describing the state of the dependence structure. Groups_dep_data_namescg|] }d|dzz S)z Component %d:r*r,)r/rs rr2z"Nested.summary..:s!bbbAoQ7bbbrr* _groups_namerResidualVariance)r#)rmrextendrr5rUrrr:tolistrr3r}pd DataFrame)r dep_namesvcsmrys rrHzNested.summary0s J 4:0 1 1 d   TZ7 8 8 8 8   bbs4K[G\G\_`G`AaAabbb c c c 4:~ . . 3:2IaL$$$   $ $ & & $*rvbzz)***|Z,I>>> r) rJrKrLrMrrr r r$rHrrs@rrrs%%NA"A"A"A"A"FXi&'''2'2(''2RXi)122  32 rrceZdZdZd fd ZfdZeejjdZdZ dZ eej jd Z d Z eej jfd Z d ZxZS) Stationarya A stationary covariance structure. The correlation between two observations is an arbitrary function of the distance between them. Distances up to a given maximum value are included in the covariance model. Parameters ---------- max_lag : float The largest distance that is included in the covariance model. grid : bool If True, the index positions in the data (after dropping missing values) are used to define distances, and the `time` variable is ignored. r*Nctt|dd}|tjdt ||_t||_tj |dz|_ dS)NgridToptional1grid=True will become default in a future versionr*) rarr r8r9 FutureWarningmax_lagboolrr3rzr)rrrrbs rrzStationary.__init__Ys{ v555 < MC     JJ (7Q;//rct||jsT|jjdddft j}|j||_dSdS)Nr) rarrrrfastyper3r cluster_list)rrrfrbs rrzStationary.initializegsr 5!!!y 6:?111a4(//99D //55DIII 6 6rcl|jr||dS||dSr)r update_grid update_nogridrs rr zStationary.updatepsB 9 '   V $ $ $ $ $   v & & & & &rc |jj}|jj}|jjj}t j|jdz}t|jj D]}||\}}t j ||} |||z | z } |dxxt j | | zt| z z cc<td|jdzD]J} | | d} || xxt j | d| | zt| z z cc<K||dz}||_ dS)Nr*r)rrtrxrvrwr3rzrr5r{r|r}rUr) rrrrxrrrr<rr=rjr[s rrzStationary.update_gridxsH #z. *#,XdlQ.// tz+,, B BA$QIFAGGGFOO,,E1X&%/E qMMMRVEEM22SZZ? ?MMM1dlQ.// B B!""I1 ad a!8!83q66!AA  B jm# $rc|jj}|jj}|jjj}t j|jdz}t j|jdz}d}d}t|jj D]} || \} } t j || } || | z | z } t j t| d\}}t j |j| ||j| |z }t j||jk}||}||}||}t j|| || |z|jdz}t j||jdz}|t j| dzz }|t| z }t j|dk}t|dkr/||xxdz cc<||xx||||z z cc<t j|dk}||xx||zcc<||z }||z}||_dS)Nr*rr)ry minlength)rr))rrtrxrvrwr3rzrr5r{r|rrUabsrfrbincountr}r)rrrrxrrdn resid_ssq resid_ssq_nrr<rr=rj1j2dxrvsvdi0 resid_msqs rrzStationary.update_nogrids< #z. *#,XdlQ.// XdlQ& ' '  tz+,, 2 2A$QIFAGGGFOO,,E1X&%/E_S[["55FB ! R(49Q<+;;<'+|a'7999BR4rrr\r0rrbs rrEz"Stationary.covariance_matrix_solvesy +7722uc++ + >===== HS[[ ! ! OABB/!DL. E EAv{{I ,,Q22U:;;;;aaag& ,,Q22U111d7^CDDDD rcrtj|jdz}tj||jdS)Nr*)LagCov)r3arangerrrr)rlags rrHzStationary.summarys2i q())|C@@AAAr)r*N)rJrKrLrMrrrr r rrr$r rErHrrs@rrrGs2" 0 0 0 0 0 066666Xi&''''(''%%%*'%'%'%RXi)12232$!!!Xi/78898*BBBBBBBrrceZdZdZd fd ZeejjdZdZdZ eej jdZ eej jdZ d Z xZ S) Autoregressivea A first-order autoregressive working dependence structure. The dependence is defined in terms of the `time` component of the parent GEE class, which defaults to the index position of each value within its cluster, based on the order of values in the input data set. Time represents a potentially multidimensional index from which distances between pairs of observations can be determined. The correlation between two observations in the same cluster is dep_params^distance, where `dep_params` contains the (scalar) autocorrelation parameter to be estimated, and `distance` is the distance between the two observations, calculated from their corresponding time values. `time` is stored as an n_obs x k matrix, where `k` represents the number of dimensions in the time index. The autocorrelation parameter is estimated using weighted nonlinear least squares, regressing each value within a cluster on each preceding value in the same cluster. Parameters ---------- dist_func : function from R^k x R^k to R^+, optional A function that computes the distance between the two observations based on their `time` values. References ---------- B Rosner, A Munoz. Autoregressive modeling for the analysis of longitudinal data with unequally spaced examinations. Statistics in medicine. Vol 7, 59-71, 1988. Nctt|dd}| d|_n||_|t jdt t||_|jsd|_ d|_ dS)NrTrcTtj||z Sr)r3rr})r0rs rz)Autoregressive.__init__..s"&Q--*;*;*=*=rrrr) rarr dist_funcr8r9rrrrr)rrrrbs rrzAutoregressive.__init__s v555  ==DNN&DN < MC   JJ y DLrc|jjtjdt|jr||dS||dS)Nz[weights not implemented for autoregressive cov_struct, using unweighted covariance estimate)rryr8r9r r _update_grid_update_nogridrs rr zAutoregressive.update'sl :  ) MM/ 1 1 1 9 (   f % % % % %    ' ' ' ' 'rc|jj}|j}|jjj}|jj}d\}}t |jjD]}||\} } tj ||| z} ||| z | z } t| } | dkrM|tj | dd| ddz| dz z z }|tj | dz| z z }||z |_ dS)N)rrrrr*rrr)) rrxestimate_scalervrwrtr5r{r3r|rUr}r)rrrxrrrlag0lag1rr<rr=rns rrzAutoregressive._update_grid4s z.  ))++*#, # dtz+,, - -A$QIFAGEGGFOO344E1X&%/EE A1uuuQrT{U122Y6771q5AAuax((1,,+rc |jj}|jj}|j|jngt |jjD]}t ||}|dkrt |D]]}t |D]K}||||ddf|||ddfL^tj |_|j }|jj j } |jj} d|jdzzz } | d|jdzz z} d| z zgt |jjD]}| |\} } tj|| | z}||| z |z }t |}t |D]7}t |D]%}||||g&8tj fd}d|d}}d|d}}||kr%|dz}||}|dkr d|_dS||k%d|d}}||kr1|d|z dz z}||}|d krt%d ||k1dd lm}|||||g |_dS) Nrrsr)crdddf|zdddfzz }tj|dzS)Nrr*r))r3r)adifrresidmatwtss rfitfuncz.Autoregressive._update_nogrid..fitfunc{sD111a4.ALHQQQTN#BBC6#(C(( (rrrrg:0yE>g?g !?z,Autoregressive: unable to find right bracket)brent)brack)rrtrnrr5r{rUr:rr3arrayr!rvrwrxrr}r|riscipy.optimizer,)rrrrfrrrrrrrxvarr<rr=rr+b_lftf_lftb_ctrf_ctrb_rgtf_rgtr,rr)r*s @@@rrzAutoregressive._update_nogridIs| #z! < #lGGG4:/00 G G58}}199 ++GGB#BiiGGt~~d1gb!!!en6:1gb!!!en(F(FGGGGGG hw''G"DL ))++*#,z. 4?q7{33 rDOq(((3h swwyytz+,, < .s!000qA N000rr)rsFTrrr) rUrr3r.rZrr:rzrrsqueeze)rr<r#r=r>rrrDmatr0rc0c1c2flattenz0rhs1rhs2rs ` rrEz&Autoregressive.covariance_matrix_solves KK O 660000C000 0 66(QGqb!W-..C BaK C 6Q;;UBBU111d7^+BVC__6Q;;%KBB%4.(B BK 16kb16k * 2Q; R2Q;   AGv{{aaagJU111d7^#B1bhqk*++B>2abb!!!e9b/:::D>2r!B$'{"3!<<.]s"""q1u"""rcg|]}|z Sr,r,)r/rwtsums rr2z5GlobalOddsRatio.pooled_odds_ratio.._s&&&Qq5y&&&rcg|] \}}||z Sr,r,)r/res rr2z5GlobalOddsRatio.pooled_odds_ratio..`s GGGtq!QUGGGr) rUr3logr:rrWr}zipexp) rtables log_oddsratior0tablelorr* log_pooled_orris @rpooled_odds_ratioz!GlobalOddsRatio.pooled_odds_ratioIs> v;;!  2 s  = =E&t%%uT{(;(;;uT{##$&(fU4[&9&9:C   % % % JJELL44499;; < < < <#"c"""C&&&&#&&&GGs3 /F/FGGGHH vm$$$rcf|||}|tj||z}|dfS)NF)get_eyyr3r4)rexpected_valuer#r?s rr$z!GlobalOddsRatio.covariance_matrixds7||NE22 888U{rc |j}|jj}i}|dD]%}t jdtj||<&tt|D]}||}t j ||}t j |d|z }t j d|z |} t j d|z d|z } ||} | D]} | | } || dxx|| dddf| dddff z cc<|| dxx|| dddf| dddff z cc<|| d xx| | dddf| dddff z cc<|| d xx| | dddf| dddff z cc<| t| S) a To obtain the crude (global) odds ratio, first pool all binary indicators corresponding to a given pair of cut points (c,c'), then calculate the odds ratio for this 2x2 table. The crude odds ratio is the inverse variance weighted average of these odds ratios. Since the covariate effects are ignored, this OR will generally be greater than the stratified OR. rr)r)rSrsrdNr*rfrer)rZrrtkeysr3rzrWr5rUr4r}rtlistvalues)rrZrrorryvecendog_11endog_10endog_01endog_00r`kyrps rr[z(GlobalOddsRatio.observed_crude_oddsratiokseh #a&++-- < R*_=EFGGD R0047D B*q.) )D B BCs1vc!f}-C)++J$$S#..SVCF]CF3q6M12268WS\\SVCF]CF3q6M122 rc |j}|jj}t|ddkrdSi}|dD]%}t jdtj||<&t|jjD]}}||\}}| ||} |dddf| z } | |z} d| | z| zz } ||} | D]}| |}||dxx| |dddf|dddff z cc<||dxx| |dddf|dddff z cc<||d xx| |dddf|dddff z cc<||d xx| |dddf|dddff z cc<| t|}|xj|j|z zc_t j|js#d|_t%jd t(dSdS) z\ Update the global odds ratio based on the current value of params. rNryrSrsrdr*rfrerz%dep_params became inf, resetting to 1)rZrrxrUr3rzrWr5r{rvrzr}rtr{r|rr\isfiniter8r9r)rrrZrxrorrr"remat_11emat_10emat_01emat_00r`rrp cor_expvals rr zGlobalOddsRatio.updateshz.  s1v;;!   Fa& < $?$C$C$E$EE   r 4   GBqqq!tHbAh,>$?$C$C$E$EE   r 4   GBqqq!tHbAh,>$?$C$C$E$EE   r 4   GBqqq!tHbAh,>$?$C$C$E$EE     F++D,A,ABB  4=:55{4?++ . DO MA, . . . . . . .rcd|jzS)NzGlobal odds ratio: %.3f rrGs rrHzGlobalOddsRatio.summarys*T_<> pairs = {0: {}, 1: {}} >> pairs[0][0] = (np.r_[0, 1, 2], np.r_[0, 1, 2]) >> pairs[0][1] = np.tril_indices(3, -1) >> pairs[1][0] = (np.r_[3, 4, 5], np.r_[3, 4, 5]) >> pairs[1][2] = 3 + np.tril_indices(3, -1) NFct||td||td|ddl}|||_|t j||_||_ dS)Nz:Equivalence cov_struct requires either `pairs` or `labels`z?Equivalence cov_struct accepts only one of `pairs` and `labels`r) rarrirdeepcopypairsr3rlabels return_cov)rrrrrrbs rrzEquivalence.__init__\s  MLNN N  F$6      KKKu--DJ  *V,,DK$rcFtjt|t|zdftj}tjt|}tj||tj|dddf<tjt|}tj||tj|dddf<|d tjtj |jj |j dzf}tj | |}tj|d\}}n|#t$rotjd} tj|| |j d }tj|d\}}YnwxYw||ddf}|dddf|dddffS) z Create arrays containing all unique ordered pairs of i, j. The arrays i and j must be one-dimensional containing non-negative integers. r)rSNrr*T) return_indexi)size)r3rzrUrrkronrsortrTvoidrPrascontiguousarrayviewr TypeErrorrandom RandomStateruniform) rrrr=rrTbmatrr8rss r _make_pairszEquivalence._make_pairsrshAQ+28<<< GCFFOOGAqMM((22AAAqD GCFFOOGAqMM((22AAAqD    8Hbgsy'9CIaL'HIJJE',,11%88DYt$777FAss 8 8 8&&t,,B6#rzzsy|z<<==DYt$777FAsss  8 #qqq&k111a4y#aaad)##s A8FA6G<;G<cddlmfd}|j}tj|j|jd}|ddg}tj |j}t|j D]J\}}tt|D]&}t|dzD]} ||} || } |j|| f} |j|| f} n#t$rY?wxYw|| | \} } t!| dzt!| z}tj| | k}t|dkr|d z}| || |f|||<tj| | k}t|dkr| |} | |} | | f|||<(L||_dS) NrrcdS)NcdSrr,r,rrrzBEquivalence._pairs_from_labels....srr,rsrrz0Equivalence._pairs_from_labels..sKK $=$=r)rgroupsrrr*/z/v) collectionsrrrrrrgroupbyr3r enumeraterr5rUKeyErrorrstrrr)rrrdfgbulabelsg_ixg_lblx1lx2lb1lb2r^r_clabelrSclabelvrs @r_pairs_from_labelszEquivalence._pairs_from_labelss++++++ ====>>  \T[ELII J J ZZ8, - -)DK((#E$677 7 7JD$S\\** 7 7 q>>77C!#,C!#,C!Yc{3Yc{3#!!! !"--b"55FB XX^c#hh6Fb11B2ww{{"(4-0222/?d G,b11B2ww{{VV/12hd F+57 7: s C,, C9 8C9 c|t||jjt jdt t|ds|tt|_ t|_ |jjD]}|j|D]}|j||\}}t!j||krVt!j||kst jdt&|j |d|j |<dt!jt-|jjt jz}t3|jjD]G\}}|jj|} t!jt-| t j|| <H|jjD]X}|j|D]6}|j||\} } || || f|j||<7YdS)NzVweights not implemented for equalence cov_struct, using unweighted covariance estimaterzBequivalence class contains both variance and covariance parametersr*rrS)rarrryr8r9r rmrrrrset _var_classesrrr3anyallr addrrUrrrrrrz) rrgplbrrrxrrrr'rRrbs rrzEquivalence.initializes. 5!!! :  ) MA/ 1 1 1tW%% &  # # % % %&e,,EE*) , ,Bjn , ,B+B6"(##,6"(++H 89FHHH%))"---*+DOB' ,"'#dj.//rx@@@ @#DJ$;<< 8 8JD$)$/BYs2wwbh777BrFF*) 4 4Bjn))++ 4 4z"~b)1&(eRU^ 2r"" 4 4 4rc |jj}|jjj}|jj}t d}t t }t|}t|jj D]_\}} ||\} } tj || } ||| z | z } |j |  D]}|js ||jvr|j | |}||dxxtj| |d| |dzz cc<|jsn||dxxtj| |ddzz cc<||dxxtj| |ddzz cc<||xxt|dz cc<a|jr5| D]}||d|||z z ||< ni| D]E}tj ||d||dz}||d|z ||<F|jD]}d||<||_||_dS)Nc gdS)Nrr,r,rrrz$Equivalence.update..s rrr*r)rs)rrtrvrwrxrrYrUrrr3r|rrzrrr}rr)rrrrrxrrrXrrr<rr=rrrSdens rr zEquivalence.updates #*#,z.  !5!566 c""&kktz677 * *EAr$QIFAGGGFOO,,E1X&%/Ejn))++ * *R43D-D-DZ^B'2q!!!RVE"Q%L5A<,G%H%HH!!!CrN1%%%be 0A)B)BB%%%rN1%%%be 0A)B)BB%%% s2a5zz)  * ? $ oo'' I I!+B!2gbkC6G!H 2 I!oo'' 9 9gjnQ/*R.2CCDD!+B!2S!8 2' $ $!# 2$ rct|}tj||f}|jj|}|j|D]*}|j||\}}|j||||f<+||jz}tj || dz ||j fS)Nr)) rUr3rzrrrrzrrrdiagonalr) rr<r#rXr rrrrs rr$zEquivalence.covariance_matrix s&kkxc ##z&u-*T"'')) / /BZ%b)FB?2.DRLLdf} t}}2333(((r)NNF) rJrKrLrMrrrrrr r r$rrs@rrrs<<|%%%%%%,$$$@,,,\&4&4&4&4&4PXi&''""('"HXi)122 ) )32 ) ) ) ) )rr)!rMstatsmodels.compat.pandasrrrr8numpyr3pandasrscipyrr-#statsmodels.stats.correlation_toolsrstatsmodels.tools.sm_exceptionsrr r statsmodels.tools.validationr r rOr_rrrrrGrUrrrr,rrrs/.....######;;;;;; 322222e"e"e"e"e"e"e"e"P)))))9)))