Extend and reuse SAS own procedures within data step code. Using PROC FCMP, we show you can create reusable code in the data step to pull together the power of possibly many procedures and getting a much cleaner programming model.

1. Adding Statistical Functionality to the DATA Step with PROC FCMP Stacey Christian and Jacques Rioux SAS Institute Inc., Cary, NC Paper 326-2010

10. Linear Approximation Subroutine function linear_approximation (ds_in $, var $, first_obs, last_obs); rc = run_macro(‘linear_approximation_macro’, ds_in, first_obs, last_obs, var, error); return(error); endsub;

12. Recursive Technique: Results data _NULL_; call segment_topdown("sasuser.snp", "work.segds_20", "close", 1, 15116, 0.2); call segment_topdown("sasuser.snp", "work.segds_15", "close", 1, 15116, 0.15); run;

13. Recursive Technique: Graphic Results 42 Piecewise Linear Segments

14. Recursive Technique: Graphic Results 113 Piecewise Linear Segments

18. Run_Regression Subroutine subroutine run_regression ( data $, dependent $, independent $, weight $, parmData $, parmArray[*]); outargs parmArray; array tmpArray[1] _temporary_; rc = RUN_MACRO ('run_regression_macro', data, parmData , dependent, independent, weight) ; rc = read_array(parmData, tmpArray); do i = 1 to dim(parmArray); parmArray[i] = tmpArray[1,i]; end; endsub;

22. Simulation Results

23. Simulation Graphs

25. Score Function Macro %macro create_score_func_macro; proc transpose data =&paramds out=&paramds._t; var &independent; run; proc sql noprint; select trim(_NAME_) || &quot; * &quot; || strip(put(col1,BEST12.)) into: theScore separated by &quot; + &quot; from &paramds._t; select trim(_NAME_) into: theArgs separated by &quot; , &quot; from &paramds._t; quit; data _NULL_; set &paramds; call symputX (&quot;Intercept&quot;,intercept); run; <continued>

26. Score Function Macro - continued proc fcmp outlib=&library..score; function &scoreFunc(&theArgs); return(&Intercept + &theScore); endsub; quit; %mend create_score_func_macro;

27. Run Create Score Function data _NULL_; call create_score(&quot;work.mroz&quot;, &quot;lwage&quot;, &quot;educ exper age kidslt6 kidsge6&quot;, &quot;PredLWage_Full&quot;, &quot;sasuser.score&quot;); call create_score(&quot;work.mroz&quot;, &quot;lwage&quot;, &quot;educ exper age&quot;, &quot;PredLWage_NoKids&quot;, &quot;sasuser.score&quot;); run; data _NULL_; educ = 15; exper = 5; age = 30; kidslt6 = 2; kidsge6 = 1; PredWage_Full = exp(PredLWage_Full(educ, exper, age, kidslt6, kidsge6)); put PredWage_Full=; PredWage_NoKids = exp(PredLWage_NoKids(educ, exper, age)); put PredWage_NoKids=; run; PredWage_Full=3.4199679212 PredWage_NoKids=3.787216653

30. Adding Statistical Functionality to the DATA Step with PROC FCMP Paper 326-2010