The SEVERITY Procedure

proc severity data=input;
   loss lossclaim;
   dist pareto;
   outscorelib outlib=sasuser.fitdist;
run;

data prob;
   set work.limits;
   exceedance_probability = sev_sdf(limit);
run;

proc severity data=input;
   loss lossclaim;
   dist exp pareto weibull;
   outscorelib outlib=sasuser.fitdist commonpackage;
run;

data prob;
   set work.limits;
   exceedance_exp = sev_sdf_exp(limit);
   exceedance_pareto = sev_sdf_pareto(limit);
   exceedance_weibull = sev_sdf_weibull(limit);
run;

PROC SEVERITY creates a scoring function for each distribution function. A distribution function is defined as any function named dist_suffix, where dist is the name of a distribution that you specify in the DIST statement and the function’s signature is identical to the signature of the required distribution function such as dist_CDF or dist_LOGCDF. For example, for the function 'FOO_BAR' to be a distribution function, you must specify the distribution 'FOO' in the DIST statement and you must define 'FOO_BAR' in the following manner if the distribution 'FOO' has parameters named 'P1' and 'P2':

   function FOO_BAR(y, P1, P2);
      /* Code to compute BAR by using y, P1, and P2 */
      R = <computed BAR>;
      return (R);
   endsub;

For more information about the signature that defines a distribution function, see the description of the dist_CDF function in the section Defining a Severity Distribution Model with the FCMP Procedure.

The name and package of the scoring function of a distribution function depend on whether you specify the COMMONPACKAGE option in the OUTSCORELIB statement.

When you do not specify the COMMONPACKAGE option, the scoring function that corresponds to the distribution function dist_suffix is named SEV_suffix, where SEV_ is the standard prefix of all scoring functions. The scoring function is created in a package named dist. Each scoring function accepts only one argument, the value of the loss variable, and returns the same value as the value returned by the corresponding distribution function for the final estimates of the distribution’s parameters. For example, for the preceding 'FOO_BAR' distribution function, the scoring function named 'SEV_BAR' is created in the package named 'FOO' and 'SEV_BAR' has the following signature:

   function SEV_BAR(y);
      /* returns value of FOO_BAR for the supplied value 
         of y and fitted values of P1, P2 */
   endsub;

If you specify the COMMONPACKAGE option in the OUTSCORELIB statement, then the scoring function that corresponds to the distribution function dist_suffix is named SEV_suffix_dist, where SEV_ is the standard prefix of all scoring functions. The scoring function is created in a package named sevfit. For example, for the preceding 'FOO_BAR' distribution function, if you specify the COMMONPACKAGE option, the scoring function named 'SEV_BAR_FOO' is created in the sevfit package and 'SEV_BAR_FOO' has the following signature:

   function SEV_BAR_FOO(y);
      /* returns value of FOO_BAR for the supplied value 
         of y and fitted values of P1, P2 */
   endsub;

If you use the SCALEMODEL statement to specify a scale regression model, then PROC SEVERITY generates the scoring functions when you specify only singleton continuous effects. If you specify interaction or classification effects, then scoring functions are not generated.

For a scale regression model, the estimate of the scale parameter or the log-transformed scale parameter of the distribution depends on the values of the regressors. So PROC SEVERITY creates a scoring function that has the following signature, where x{*} represents the array of regressors:

   function SEV_BAR(y, x{*});
      /* returns value of FOO_BAR for the supplied value of x and fitted values of P1, P2 */
   endsub;

As an illustration of using this form, assume that you submit the following PROC SEVERITY step to create the scoring library Sasuser.Scalescore:

proc severity data=input;
   loss lossclaim;
   scalemodel x1-x3;
   dist pareto;
   outscorelib outlib=sasuser.scalescore;
run;

Your scoring data set must contain all the regressors that you specify in the SCALEMODEL statement. You can submit the following DATA step to score observations by using the scale regression model:

data prob;
   array regvals{*} x1-x3;
   set work.limits;
   exceedance_probability = sev_sdf(limit, regvals);
run;

PROC SEVERITY creates two utility functions, SEV_NUMREG and SEV_REGNAME, in the OUTLIB= library that return the number of regressors and name of a given regressor, respectively. They are described in detail in the next section. These utility functions are useful when you do not have easy access to the regressor names in the SCALEMODEL statement. You can use the utility functions as follows:

data prob;
   array regvals{10} _temporary_;
   set work.limits;
   do i = 1 to sev_numreg();
      regvals(i) = input(vvaluex(sev_regname(i)), best12.);
   end;
   exceedance_probability = sev_sdf(limit, regvals);
run;

The dimension of the regressor values array that you supply to the scoring function must be equal to , where K is the number of regressors that you specify in the SCALEMODEL statement irrespective of whether PROC SEVERITY deems any of those regressors to be redundant. L is 1 if you specify an OFFSET= variable in the SCALEMODEL statement, and 0 otherwise.

In addition to creating the scoring functions for all distribution functions, PROC SEVERITY creates the following utility functions and subroutines in the OUTLIB= library.

If you use the SCALEMODEL statement to specify a scale regression model, and if you specify only singleton continuous effects, then the following helper functions and subroutines are also created in the OUTLIB= library.