A severity distribution model consists of a set of functions and subroutines that are defined using the FCMP procedure. The FCMP procedure is part of Base SAS software. Each function or subroutine must be named as distributionname_keyword, where distributionname is the identifying short name of the distribution and keyword identifies one of the functions or subroutines. The total length of the name should not exceed 32. Each function or subroutine must have a specific signature, which consists of the number of arguments, sequence and types of arguments, and return value type. The summary of all the recognized function and subroutine names and their expected behavior is given in Table 23.4.
Consider following points when you define a distribution model:
When you define a function or subroutine requiring parameter arguments, the names and order of those arguments must be the same. Arguments other than the parameter arguments can have any name, but they must satisfy the requirements on their type and order.
When the SEVERITY procedure invokes any function or subroutine, it provides the necessary input values according to the specified signature, and expects the function or subroutine to prepare the output and return it according to the specification of the return values in the signature.
You can typically use most of the SAS programming statements and SAS functions that you can use in a DATA step for defining the FCMP functions and subroutines. However, there are a few differences in the capabilities of the DATA step and the FCMP procedure. Refer to the documentation of the FCMP procedure to learn more.
You must specify either the PDF or the LOGPDF function. Similarly, you must specify either the CDF or the LOGCDF function. All other functions are optional, except when necessary for correct definition of the distribution. It is strongly recommended that you define the PARMINIT subroutine to provide a good set of initial values for the parameters. The information provided by PROC SEVERITY to the PARMINIT subroutine enables you to use popular initialization approaches based on the method of moments and the method of percentile matching, but you can implement any algorithm to initialize the parameters by using the values of the response variable and the estimate of its empirical distribution function.
The LOWERBOUNDS subroutines should be defined if the lower bound on at least one distribution parameter is different from the default lower bound of 0. If you define a LOWERBOUNDS subroutine but do not set a lower bound for some parameter inside the subroutine, then that parameter is assumed to have no lower bound (or a lower bound of ). Hence, it is recommended that you explicitly return the lower bound for each parameter when you define the LOWERBOUNDS subroutine.
The UPPERBOUNDS subroutines should be defined if the upper bound on at least one distribution parameter is different from the default upper bound of . If you define an UPPERBOUNDS subroutine but do not set an upper bound for some parameter inside the subroutine, then that parameter is assumed to have no upper bound (or a upper bound of ). Hence, it is recommended that you explicitly return the upper bound for each parameter when you define the UPPERBOUNDS subroutine.
If you want to use the distribution in a model with regression effects, then make sure that the first parameter of the distribution is the scale parameter itself or a logtransformed scale parameter. If the first parameter is a logtransformed scale parameter, then you must define the SCALETRANSFORM function.
In general, it is not necessary to define the gradient and Hessian functions, because PROC SEVERITY uses an internal system to evaluate the required derivatives. The internal system typically computes the derivatives analytically. But it might not be able to do so if your function definitions use other functions that it cannot differentiate analytically. In such cases, derivatives are approximated using a finite difference method and a note is written to the SAS log to indicate the components that are differentiated using such approximations. PROC SEVERITY does reasonably well with these finite difference approximations. But, if you know of a way to compute the derivatives of such components analytically, then you should define the gradient and Hessian functions.
In order to use your distribution with PROC SEVERITY, you need to record the FCMP library that contains the functions and subroutines for your distribution and other FCMP libraries that contain FCMP functions or subroutines used within your distribution’s functions and subroutines. Specify all those libraries in the CMPLIB= system option by using the OPTIONS global statement. For more information about the OPTIONS statement, see the SAS Statements: Reference. For more information about the CMPLIB= system option, see the SAS System Options: Reference.
Each predefined distribution mentioned in the section Predefined Distributions has a distribution model associated with it. The functions and subroutines of all those models are available in the Sashelp.Svrtdist
library. The order of the parameters in the signatures of the functions and subroutines is the same as listed in Table 23.2. You do not need to use the CMPLIB= option in order to use the predefined distributions with PROC SEVERITY. However, if you
need to use the functions or subroutines of the predefined distributions in SAS statements other than the PROC SEVERITY step
(such as in a DATA step), then specify the Sashelp.Svrtdist
library in the CMPLIB= system option by using the OPTIONS global statement prior to using them.
Table 23.4 shows functions and subroutines that define a distribution model, and subsections after the table provide more detail. The functions are listed in alphabetical order of the keyword suffix.
Table 23.4: List of Functions and Subroutines That Define a Distribution Model
Name 
Type 
Required 
Expected to Return 

Function 
YES 
Cumulative distribution 

function value 

Subroutine 
NO 
Gradient of the CDF 

Subroutine 
NO 
Hessian of the CDF 

Subroutine 
NO 
Constant parameters 

Function 
NO 
Description of the distribution 

Function 
YES 
Log of cumulative distribution 

function value 

Subroutine 
NO 
Gradient of the LOGCDF 

Subroutine 
NO 
Hessian of the LOGCDF 

Function 
YES 
Log of probability density 

function value 

Subroutine 
NO 
Gradient of the LOGPDF 

Subroutine 
NO 
Hessian of the LOGPDF 

Function 
NO 
Log of survival 

function value 

Subroutine 
NO 
Gradient of the LOGSDF 

Subroutine 
NO 
Hessian of the LOGSDF 

Subroutine 
NO 
Lower bounds on parameters 

Subroutine 
NO 
Initial values 

for parameters 

Function 
YES 
Probability density 

function value 

Subroutine 
NO 
Gradient of the PDF 

Subroutine 
NO 
Hessian of the PDF 

Function 
NO 
Quantile for a given CDF value 

Function 
NO 
Type of relationship between 

the first distribution parameter 

and the scale parameter 

Function 
NO 
Survival function value 

Subroutine 
NO 
Gradient of the SDF 

Subroutine 
NO 
Hessian of the SDF 

Subroutine 
NO 
Upper bounds on parameters 

Notes: 

1. Either the dist_CDF or the dist_LOGCDF function must be defined. 

2. Either the dist_PDF or the dist_LOGPDF function must be defined. 
The signature syntax and semantics of each function or subroutine are as follows: