Thank you for your reply! I am using survival data mining node in SAS E Miner. I used the score node to socre the data after developing the model. The score code is the following: *************************************; *** begin scoring code for regression; *************************************; length _WARN_ $4; label _WARN_ = 'Warnings' ; length I__g_ $ 12; label I__g_ = 'Into: _g_' ; *** Target Values; array SURV4DRF [2] $12 _temporary_ ('1' '0' ); label U__g_ = 'Unnormalized Into: _g_' ; *** Unnormalized target values; ARRAY SURV4DRU[2] _TEMPORARY_ (1 0); drop _DM_BAD; _DM_BAD=0; /*-------------------------------------------------*/ /*Survival Score Code*/ /*-------------------------------------------------*/ label EM_SURVIVAL = "Survival Probability at Censoring Time"; label EM_SURVFCST = "Survival Probability at Future Time"; label EM_SURVEVENT = "Event Probability before or at the Future Time"; label EM_HAZARD = "Hazard Function at Censoring Time"; label EM_HZRDFCST = "Hazard Function at Future Time"; BadObs = 0; if _T_ ne . and BadObs=0 then do; T_FCST=_T_+4 ; /*----------Generate Cubic Spline Basis Functions-------------*/ if _T_ > 10 then _csb1 =(_T_-10)**3 - _T_**3 + 30*_T_**2 - 300*_T_; else _csb1=-_T_**3 + 30*_T_**2 - 300*_T_; if _T_ > 20 then _csb2 =(_T_-20)**3 - _T_**3 + 60*_T_**2 - 1200*_T_; else _csb2=-_T_**3 + 60*_T_**2 - 1200*_T_; if _T_ > 30 then _csb3 =(_T_-30)**3 - _T_**3 + 90*_T_**2 - 2700*_T_; else _csb3=-_T_**3 + 90*_T_**2 - 2700*_T_; if _T_ > 40 then _csb4 =(_T_-40)**3 - _T_**3 + 120*_T_**2 - 4800*_T_; else _csb4=-_T_**3 + 120*_T_**2 - 4800*_T_; if _T_ > 50 then _csb5 =(_T_-50)**3 - _T_**3 + 150*_T_**2 - 7500*_T_; else _csb5=-_T_**3 + 150*_T_**2 - 7500*_T_; *** Check _t_ for missing values ; if missing( _t_ ) then do; substr(_warn_,1,1) = 'M'; _DM_BAD = 1; end; *** Check _csb1 for missing values ; if missing( _csb1 ) then do; substr(_warn_,1,1) = 'M'; _DM_BAD = 1; end; *** Check _csb2 for missing values ; if missing( _csb2 ) then do; substr(_warn_,1,1) = 'M'; _DM_BAD = 1; end; *** Check _csb3 for missing values ; if missing( _csb3 ) then do; substr(_warn_,1,1) = 'M'; _DM_BAD = 1; end; *** Check _csb4 for missing values ; if missing( _csb4 ) then do; substr(_warn_,1,1) = 'M'; _DM_BAD = 1; end; *** Check _csb5 for missing values ; if missing( _csb5 ) then do; substr(_warn_,1,1) = 'M'; _DM_BAD = 1; end; *** Check Age for missing values ; if missing( Age ) then do; substr(_warn_,1,1) = 'M'; _DM_BAD = 1; end; *** Check CAP for missing values ; if missing( CAP ) then do; substr(_warn_,1,1) = 'M'; _DM_BAD = 1; end; *** Generate dummy variables for Agey_c ; drop _1_0 _1_1 _1_2 _1_3 _1_4 ; *** encoding is sparse, initialize to zero; _1_0 = 0; _1_1 = 0; _1_2 = 0; _1_3 = 0; _1_4 = 0; if missing( Agey_c ) then do; _1_0 = .; _1_1 = .; _1_2 = .; _1_3 = .; _1_4 = .; substr(_warn_,1,1) = 'M'; _DM_BAD = 1; end; else do; length _dm12 $ 12; drop _dm12 ; _dm12 = put( Agey_c , BEST12. ); %DMNORMIP( _dm12 ) if _dm12 = '0' then do; _1_0 = 1; end; else if _dm12 = '1' then do; _1_1 = 1; end; else if _dm12 = '3' then do; _1_3 = 1; end; else if _dm12 = '2' then do; _1_2 = 1; end; else if _dm12 = '4' then do; _1_4 = 1; end; else do; _1_0 = .; _1_1 = .; _1_2 = .; _1_3 = .; _1_4 = .; substr(_warn_,2,1) = 'U'; _DM_BAD = 1; end; end; *** Generate dummy variables for EduLevel ; drop _3_0 _3_1 _3_2 _3_3 _3_4 ; *** encoding is sparse, initialize to zero; _3_0 = 0; _3_1 = 0; _3_2 = 0; _3_3 = 0; _3_4 = 0; if missing( EduLevel ) then do; _3_0 = .; _3_1 = .; _3_2 = .; _3_3 = .; _3_4 = .; substr(_warn_,1,1) = 'M'; _DM_BAD = 1; end; else do; length _dm12 $ 12; drop _dm12 ; _dm12 = put( EduLevel , BEST12. ); %DMNORMIP( _dm12 ) _dm_find = 0; drop _dm_find; if _dm12 <= '3' then do; if _dm12 <= '2' then do; if _dm12 = '1' then do; _3_0 = 1; _dm_find = 1; end; else do; if _dm12 = '2' then do; _3_1 = 1; _dm_find = 1; end; end; end; else do; if _dm12 = '3' then do; _3_2 = 1; _dm_find = 1; end; end; end; else do; if _dm12 = '4' then do; _3_3 = 1; _dm_find = 1; end; else do; if _dm12 = '5' then do; _3_4 = 1; _dm_find = 1; end; end; end; if not _dm_find then do; _3_0 = .; _3_1 = .; _3_2 = .; _3_3 = .; _3_4 = .; substr(_warn_,2,1) = 'U'; _DM_BAD = 1; end; end; *** Generate dummy variables for Emlevel ; drop _4_0 _4_1 _4_2 _4_3 ; *** encoding is sparse, initialize to zero; _4_0 = 0; _4_1 = 0; _4_2 = 0; _4_3 = 0; if missing( Emlevel ) then do; _4_0 = .; _4_1 = .; _4_2 = .; _4_3 = .; substr(_warn_,1,1) = 'M'; _DM_BAD = 1; end; else do; length _dm12 $ 12; drop _dm12 ; _dm12 = put( Emlevel , BEST12. ); %DMNORMIP( _dm12 ) if _dm12 = '1' then do; _4_0 = 1; end; else if _dm12 = '2' then do; _4_1 = 1; end; else if _dm12 = '3' then do; _4_2 = 1; end; else if _dm12 = '4' then do; _4_3 = 1; end; else do; _4_0 = .; _4_1 = .; _4_2 = .; _4_3 = .; substr(_warn_,2,1) = 'U'; _DM_BAD = 1; end; end; *** Generate dummy variables for Gender ; drop _6_0 _6_1 ; if missing( Gender ) then do; _6_0 = .; _6_1 = .; substr(_warn_,1,1) = 'M'; _DM_BAD = 1; end; else do; length _dm12 $ 12; drop _dm12 ; _dm12 = put( Gender , BEST12. ); %DMNORMIP( _dm12 ) if _dm12 = '0' then do; _6_0 = 1; _6_1 = 0; end; else if _dm12 = '1' then do; _6_0 = 0; _6_1 = 1; end; else do; _6_0 = .; _6_1 = .; substr(_warn_,2,1) = 'U'; _DM_BAD = 1; end; end; *** Generate dummy variables for HiPo ; drop _7_0 _7_1 _7_2 _7_3 _7_4 ; *** encoding is sparse, initialize to zero; _7_0 = 0; _7_1 = 0; _7_2 = 0; _7_3 = 0; _7_4 = 0; if missing( HiPo ) then do; _7_0 = .; _7_1 = .; _7_2 = .; _7_3 = .; _7_4 = .; substr(_warn_,1,1) = 'M'; _DM_BAD = 1; end; else do; length _dm12 $ 12; drop _dm12 ; _dm12 = put( HiPo , BEST12. ); %DMNORMIP( _dm12 ) if _dm12 = '0' then do; _7_0 = 1; end; else if _dm12 = '2' then do; _7_2 = 1; end; else if _dm12 = '3' then do; _7_3 = 1; end; else if _dm12 = '1' then do; _7_1 = 1; end; else if _dm12 = '4' then do; _7_4 = 1; end; else do; _7_0 = .; _7_1 = .; _7_2 = .; _7_3 = .; _7_4 = .; substr(_warn_,2,1) = 'U'; _DM_BAD = 1; end; end; *** Generate dummy variables for Promoted ; drop _9_0 _9_1 ; if missing( Promoted ) then do; _9_0 = .; _9_1 = .; substr(_warn_,1,1) = 'M'; _DM_BAD = 1; end; else do; length _dm12 $ 12; drop _dm12 ; _dm12 = put( Promoted , BEST12. ); %DMNORMIP( _dm12 ) if _dm12 = '0' then do; _9_0 = 1; _9_1 = 0; end; else if _dm12 = '1' then do; _9_0 = 0; _9_1 = 1; end; else do; _9_0 = .; _9_1 = .; substr(_warn_,2,1) = 'U'; _DM_BAD = 1; end; end; *** Generate dummy variables for Spouse ; drop _10_0 _10_1 ; if missing( Spouse ) then do; _10_0 = .; _10_1 = .; substr(_warn_,1,1) = 'M'; _DM_BAD = 1; end; else do; length _dm12 $ 12; drop _dm12 ; _dm12 = put( Spouse , BEST12. ); %DMNORMIP( _dm12 ) if _dm12 = '1' then do; _10_0 = 0; _10_1 = 1; end; else if _dm12 = '0' then do; _10_0 = 1; _10_1 = 0; end; else do; _10_0 = .; _10_1 = .; substr(_warn_,2,1) = 'U'; _DM_BAD = 1; end; end; *** If missing inputs, use averages; if _DM_BAD > 0 then do; _P0 = 0.0226759359; _P1 = 0.9773240641; goto SURV4DR1; end; *** Compute Linear Predictor; drop _TEMP; drop _LP0 ; _LP0 = 0; *** Effect: _t_ ; _TEMP = _t_ ; _LP0 = _LP0 + ( -1.42842396283266 * _TEMP); *** Effect: _csb1 ; _TEMP = _csb1 ; _LP0 = _LP0 + ( -0.01535754286207 * _TEMP); *** Effect: _csb2 ; _TEMP = _csb2 ; _LP0 = _LP0 + ( 0.01118630965477 * _TEMP); *** Effect: _csb3 ; _TEMP = _csb3 ; _LP0 = _LP0 + ( -0.01194256527271 * _TEMP); *** Effect: _csb4 ; _TEMP = _csb4 ; _LP0 = _LP0 + ( 0.01022061466094 * _TEMP); *** Effect: _csb5 ; _TEMP = _csb5 ; _LP0 = _LP0 + ( -0.00371669297902 * _TEMP); *** Effect: Agey_c ; _TEMP = 1; _LP0 = _LP0 + ( 4.0275846578654) * _TEMP * _1_0; _LP0 = _LP0 + ( 4.22606526886279) * _TEMP * _1_1; _LP0 = _LP0 + ( 3.64271459200542) * _TEMP * _1_2; _LP0 = _LP0 + ( 3.36781118290354) * _TEMP * _1_3; _LP0 = _LP0 + ( 0) * _TEMP * _1_4; *** Effect: EduLevel ; _TEMP = 1; _LP0 = _LP0 + ( 0.08857144518881) * _TEMP * _3_0; _LP0 = _LP0 + ( 0.14704421842113) * _TEMP * _3_1; _LP0 = _LP0 + ( 0.34104973636078) * _TEMP * _3_2; _LP0 = _LP0 + ( 0.66474244705976) * _TEMP * _3_3; _LP0 = _LP0 + ( 0) * _TEMP * _3_4; *** Effect: Emlevel ; _TEMP = 1; _LP0 = _LP0 + ( 0.68145919313593) * _TEMP * _4_0; _LP0 = _LP0 + ( 0.29754298008235) * _TEMP * _4_1; _LP0 = _LP0 + ( -0.66171686655645) * _TEMP * _4_2; _LP0 = _LP0 + ( 0) * _TEMP * _4_3; *** Effect: Gender ; _TEMP = 1; _LP0 = _LP0 + ( 0.19470623601398) * _TEMP * _6_0; _LP0 = _LP0 + ( 0) * _TEMP * _6_1; *** Effect: HiPo ; _TEMP = 1; _LP0 = _LP0 + ( 4.16393327855287) * _TEMP * _7_0; _LP0 = _LP0 + ( -1.58016075023945) * _TEMP * _7_1; _LP0 = _LP0 + ( 2.34369735819832) * _TEMP * _7_2; _LP0 = _LP0 + ( 2.73316541811577) * _TEMP * _7_3; _LP0 = _LP0 + ( 0) * _TEMP * _7_4; *** Effect: Promoted ; _TEMP = 1; _LP0 = _LP0 + ( 1.55628770969706) * _TEMP * _9_0; _LP0 = _LP0 + ( 0) * _TEMP * _9_1; *** Effect: Spouse ; _TEMP = 1; _LP0 = _LP0 + ( 0.66291205484103) * _TEMP * _10_0; _LP0 = _LP0 + ( 0) * _TEMP * _10_1; *** Effect: Age ; _TEMP = Age ; _LP0 = _LP0 + ( -0.03816383026071 * _TEMP); *** Effect: CAP ; _TEMP = CAP ; _LP0 = _LP0 + ( 0.16072692688469 * _TEMP); *** Naive Posterior Probabilities; drop _MAXP _IY _P0 _P1; drop _LPMAX; _LPMAX= 0; _LP0 = -13.5255493090752 + _LP0; if _LPMAX < _LP0 then _LPMAX = _LP0; _LP0 = exp(_LP0 - _LPMAX); _LPMAX = exp(-_LPMAX); _P1 = 1 / (_LPMAX + _LP0); _P0 = _LP0 * _P1; _P1 = _LPMAX * _P1; SURV4DR1: *** Posterior Probabilities and Predicted Level; label P__g_1 = 'Predicted: _g_=1' ; label P__g_0 = 'Predicted: _g_=0' ; P__g_1 = _P0; _MAXP = _P0; _IY = 1; P__g_0 = _P1; if (_P1 > _MAXP + 1E-8) then do; _MAXP = _P1; _IY = 2; end; I__g_ = SURV4DRF[_IY]; U__g_ = SURV4DRU[_IY]; *************************************; ***** end scoring code for regression; *************************************; EM_SUBHZRD1 = P__g_1; EM_SUBHZRD0 =1-(EM_SUBHZRD1); EM_HAZARD=EM_SUBHZRD1; /*---------- Survival Function Estimation -------------*/ EM_SURVFCST=1; _T0_ = _T_; t0_fcst = t_fcst; _t_ = 0; do while (_t_ <= t0_fcst); /*----------Generate Cubic Spline Basis Functions-------------*/ if _T_ > 10 then _csb1 =(_T_-10)**3 - _T_**3 + 30*_T_**2 - 300*_T_; else _csb1=-_T_**3 + 30*_T_**2 - 300*_T_; if _T_ > 20 then _csb2 =(_T_-20)**3 - _T_**3 + 60*_T_**2 - 1200*_T_; else _csb2=-_T_**3 + 60*_T_**2 - 1200*_T_; if _T_ > 30 then _csb3 =(_T_-30)**3 - _T_**3 + 90*_T_**2 - 2700*_T_; else _csb3=-_T_**3 + 90*_T_**2 - 2700*_T_; if _T_ > 40 then _csb4 =(_T_-40)**3 - _T_**3 + 120*_T_**2 - 4800*_T_; else _csb4=-_T_**3 + 120*_T_**2 - 4800*_T_; if _T_ > 50 then _csb5 =(_T_-50)**3 - _T_**3 + 150*_T_**2 - 7500*_T_; else _csb5=-_T_**3 + 150*_T_**2 - 7500*_T_; *** Check _t_ for missing values ; if missing( _t_ ) then do; substr(_warn_,1,1) = 'M'; _DM_BAD = 1; end; *** Check _csb1 for missing values ; if missing( _csb1 ) then do; substr(_warn_,1,1) = 'M'; _DM_BAD = 1; end; *** Check _csb2 for missing values ; if missing( _csb2 ) then do; substr(_warn_,1,1) = 'M'; _DM_BAD = 1; end; *** Check _csb3 for missing values ; if missing( _csb3 ) then do; substr(_warn_,1,1) = 'M'; _DM_BAD = 1; end; *** Check _csb4 for missing values ; if missing( _csb4 ) then do; substr(_warn_,1,1) = 'M'; _DM_BAD = 1; end; *** Check _csb5 for missing values ; if missing( _csb5 ) then do; substr(_warn_,1,1) = 'M'; _DM_BAD = 1; end; *** Check Age for missing values ; if missing( Age ) then do; substr(_warn_,1,1) = 'M'; _DM_BAD = 1; end; *** Check CAP for missing values ; if missing( CAP ) then do; substr(_warn_,1,1) = 'M'; _DM_BAD = 1; end; *** Generate dummy variables for Agey_c ; drop _1_0 _1_1 _1_2 _1_3 _1_4 ; *** encoding is sparse, initialize to zero; _1_0 = 0; _1_1 = 0; _1_2 = 0; _1_3 = 0; _1_4 = 0; if missing( Agey_c ) then do; _1_0 = .; _1_1 = .; _1_2 = .; _1_3 = .; _1_4 = .; substr(_warn_,1,1) = 'M'; _DM_BAD = 1; end; else do; length _dm12 $ 12; drop _dm12 ; _dm12 = put( Agey_c , BEST12. ); %DMNORMIP( _dm12 ) if _dm12 = '0' then do; _1_0 = 1; end; else if _dm12 = '1' then do; _1_1 = 1; end; else if _dm12 = '3' then do; _1_3 = 1; end; else if _dm12 = '2' then do; _1_2 = 1; end; else if _dm12 = '4' then do; _1_4 = 1; end; else do; _1_0 = .; _1_1 = .; _1_2 = .; _1_3 = .; _1_4 = .; substr(_warn_,2,1) = 'U'; _DM_BAD = 1; end; end; *** Generate dummy variables for EduLevel ; drop _3_0 _3_1 _3_2 _3_3 _3_4 ; *** encoding is sparse, initialize to zero; _3_0 = 0; _3_1 = 0; _3_2 = 0; _3_3 = 0; _3_4 = 0; if missing( EduLevel ) then do; _3_0 = .; _3_1 = .; _3_2 = .; _3_3 = .; _3_4 = .; substr(_warn_,1,1) = 'M'; _DM_BAD = 1; end; else do; length _dm12 $ 12; drop _dm12 ; _dm12 = put( EduLevel , BEST12. ); %DMNORMIP( _dm12 ) _dm_find = 0; drop _dm_find; if _dm12 <= '3' then do; if _dm12 <= '2' then do; if _dm12 = '1' then do; _3_0 = 1; _dm_find = 1; end; else do; if _dm12 = '2' then do; _3_1 = 1; _dm_find = 1; end; end; end; else do; if _dm12 = '3' then do; _3_2 = 1; _dm_find = 1; end; end; end; else do; if _dm12 = '4' then do; _3_3 = 1; _dm_find = 1; end; else do; if _dm12 = '5' then do; _3_4 = 1; _dm_find = 1; end; end; end; if not _dm_find then do; _3_0 = .; _3_1 = .; _3_2 = .; _3_3 = .; _3_4 = .; substr(_warn_,2,1) = 'U'; _DM_BAD = 1; end; end; *** Generate dummy variables for Emlevel ; drop _4_0 _4_1 _4_2 _4_3 ; *** encoding is sparse, initialize to zero; _4_0 = 0; _4_1 = 0; _4_2 = 0; _4_3 = 0; if missing( Emlevel ) then do; _4_0 = .; _4_1 = .; _4_2 = .; _4_3 = .; substr(_warn_,1,1) = 'M'; _DM_BAD = 1; end; else do; length _dm12 $ 12; drop _dm12 ; _dm12 = put( Emlevel , BEST12. ); %DMNORMIP( _dm12 ) if _dm12 = '1' then do; _4_0 = 1; end; else if _dm12 = '2' then do; _4_1 = 1; end; else if _dm12 = '3' then do; _4_2 = 1; end; else if _dm12 = '4' then do; _4_3 = 1; end; else do; _4_0 = .; _4_1 = .; _4_2 = .; _4_3 = .; substr(_warn_,2,1) = 'U'; _DM_BAD = 1; end; end; *** Generate dummy variables for Gender ; drop _6_0 _6_1 ; if missing( Gender ) then do; _6_0 = .; _6_1 = .; substr(_warn_,1,1) = 'M'; _DM_BAD = 1; end; else do; length _dm12 $ 12; drop _dm12 ; _dm12 = put( Gender , BEST12. ); %DMNORMIP( _dm12 ) if _dm12 = '0' then do; _6_0 = 1; _6_1 = 0; end; else if _dm12 = '1' then do; _6_0 = 0; _6_1 = 1; end; else do; _6_0 = .; _6_1 = .; substr(_warn_,2,1) = 'U'; _DM_BAD = 1; end; end; *** Generate dummy variables for HiPo ; drop _7_0 _7_1 _7_2 _7_3 _7_4 ; *** encoding is sparse, initialize to zero; _7_0 = 0; _7_1 = 0; _7_2 = 0; _7_3 = 0; _7_4 = 0; if missing( HiPo ) then do; _7_0 = .; _7_1 = .; _7_2 = .; _7_3 = .; _7_4 = .; substr(_warn_,1,1) = 'M'; _DM_BAD = 1; end; else do; length _dm12 $ 12; drop _dm12 ; _dm12 = put( HiPo , BEST12. ); %DMNORMIP( _dm12 ) if _dm12 = '0' then do; _7_0 = 1; end; else if _dm12 = '2' then do; _7_2 = 1; end; else if _dm12 = '3' then do; _7_3 = 1; end; else if _dm12 = '1' then do; _7_1 = 1; end; else if _dm12 = '4' then do; _7_4 = 1; end; else do; _7_0 = .; _7_1 = .; _7_2 = .; _7_3 = .; _7_4 = .; substr(_warn_,2,1) = 'U'; _DM_BAD = 1; end; end; *** Generate dummy variables for Promoted ; drop _9_0 _9_1 ; if missing( Promoted ) then do; _9_0 = .; _9_1 = .; substr(_warn_,1,1) = 'M'; _DM_BAD = 1; end; else do; length _dm12 $ 12; drop _dm12 ; _dm12 = put( Promoted , BEST12. ); %DMNORMIP( _dm12 ) if _dm12 = '0' then do; _9_0 = 1; _9_1 = 0; end; else if _dm12 = '1' then do; _9_0 = 0; _9_1 = 1; end; else do; _9_0 = .; _9_1 = .; substr(_warn_,2,1) = 'U'; _DM_BAD = 1; end; end; *** Generate dummy variables for Spouse ; drop _10_0 _10_1 ; if missing( Spouse ) then do; _10_0 = .; _10_1 = .; substr(_warn_,1,1) = 'M'; _DM_BAD = 1; end; else do; length _dm12 $ 12; drop _dm12 ; _dm12 = put( Spouse , BEST12. ); %DMNORMIP( _dm12 ) if _dm12 = '1' then do; _10_0 = 0; _10_1 = 1; end; else if _dm12 = '0' then do; _10_0 = 1; _10_1 = 0; end; else do; _10_0 = .; _10_1 = .; substr(_warn_,2,1) = 'U'; _DM_BAD = 1; end; end; *** If missing inputs, use averages; if _DM_BAD > 0 then do; _P0 = 0.0226759359; _P1 = 0.9773240641; goto SURV4DR4; end; *** Compute Linear Predictor; drop _TEMP; drop _LP0 ; _LP0 = 0; *** Effect: _t_ ; _TEMP = _t_ ; _LP0 = _LP0 + ( -1.42842396283266 * _TEMP); *** Effect: _csb1 ; _TEMP = _csb1 ; _LP0 = _LP0 + ( -0.01535754286207 * _TEMP); *** Effect: _csb2 ; _TEMP = _csb2 ; _LP0 = _LP0 + ( 0.01118630965477 * _TEMP); *** Effect: _csb3 ; _TEMP = _csb3 ; _LP0 = _LP0 + ( -0.01194256527271 * _TEMP); *** Effect: _csb4 ; _TEMP = _csb4 ; _LP0 = _LP0 + ( 0.01022061466094 * _TEMP); *** Effect: _csb5 ; _TEMP = _csb5 ; _LP0 = _LP0 + ( -0.00371669297902 * _TEMP); *** Effect: Agey_c ; _TEMP = 1; _LP0 = _LP0 + ( 4.0275846578654) * _TEMP * _1_0; _LP0 = _LP0 + ( 4.22606526886279) * _TEMP * _1_1; _LP0 = _LP0 + ( 3.64271459200542) * _TEMP * _1_2; _LP0 = _LP0 + ( 3.36781118290354) * _TEMP * _1_3; _LP0 = _LP0 + ( 0) * _TEMP * _1_4; *** Effect: EduLevel ; _TEMP = 1; _LP0 = _LP0 + ( 0.08857144518881) * _TEMP * _3_0; _LP0 = _LP0 + ( 0.14704421842113) * _TEMP * _3_1; _LP0 = _LP0 + ( 0.34104973636078) * _TEMP * _3_2; _LP0 = _LP0 + ( 0.66474244705976) * _TEMP * _3_3; _LP0 = _LP0 + ( 0) * _TEMP * _3_4; *** Effect: Emlevel ; _TEMP = 1; _LP0 = _LP0 + ( 0.68145919313593) * _TEMP * _4_0; _LP0 = _LP0 + ( 0.29754298008235) * _TEMP * _4_1; _LP0 = _LP0 + ( -0.66171686655645) * _TEMP * _4_2; _LP0 = _LP0 + ( 0) * _TEMP * _4_3; *** Effect: Gender ; _TEMP = 1; _LP0 = _LP0 + ( 0.19470623601398) * _TEMP * _6_0; _LP0 = _LP0 + ( 0) * _TEMP * _6_1; *** Effect: HiPo ; _TEMP = 1; _LP0 = _LP0 + ( 4.16393327855287) * _TEMP * _7_0; _LP0 = _LP0 + ( -1.58016075023945) * _TEMP * _7_1; _LP0 = _LP0 + ( 2.34369735819832) * _TEMP * _7_2; _LP0 = _LP0 + ( 2.73316541811577) * _TEMP * _7_3; _LP0 = _LP0 + ( 0) * _TEMP * _7_4; *** Effect: Promoted ; _TEMP = 1; _LP0 = _LP0 + ( 1.55628770969706) * _TEMP * _9_0; _LP0 = _LP0 + ( 0) * _TEMP * _9_1; *** Effect: Spouse ; _TEMP = 1; _LP0 = _LP0 + ( 0.66291205484103) * _TEMP * _10_0; _LP0 = _LP0 + ( 0) * _TEMP * _10_1; *** Effect: Age ; _TEMP = Age ; _LP0 = _LP0 + ( -0.03816383026071 * _TEMP); *** Effect: CAP ; _TEMP = CAP ; _LP0 = _LP0 + ( 0.16072692688469 * _TEMP); *** Naive Posterior Probabilities; drop _MAXP _IY _P0 _P1; drop _LPMAX; _LPMAX= 0; _LP0 = -13.5255493090752 + _LP0; if _LPMAX < _LP0 then _LPMAX = _LP0; _LP0 = exp(_LP0 - _LPMAX); _LPMAX = exp(-_LPMAX); _P1 = 1 / (_LPMAX + _LP0); _P0 = _LP0 * _P1; _P1 = _LPMAX * _P1; SURV4DR4: *** Posterior Probabilities and Predicted Level; label P__g_1 = 'Predicted: _g_=1' ; label P__g_0 = 'Predicted: _g_=0' ; P__g_1 = _P0; _MAXP = _P0; _IY = 1; P__g_0 = _P1; if (_P1 > _MAXP + 1E-8) then do; _MAXP = _P1; _IY = 2; end; I__g_ = SURV4DRF[_IY]; U__g_ = SURV4DRU[_IY]; *************************************; ***** end scoring code for regression; *************************************;
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