This example further illustrates the use of external data sets that are specified in the OBJECTIVE= option. For this example, a data set that contains randomly generated observations is used to estimate the parameters for the Johnson family of distributions (Bowman and Shenton 1983). The objective is the log likelihood for the family, which involves four variables, :
where
Here, denotes the value of d in the kth observation of the data set that is generated by the following DATA step.
data sudata; n=20000; theta=-1; sigma=1; delta=3; gamma=5; rngSeed=123; do i = 1 to n; z = rannor(rngSeed); a = exp( (z - gamma)/delta ); d = sigma * ( (a**2 - 1)/(2*a) ) + theta; output; end; keep d; run;
This generates a data set called sudata
that contains observations. You can modify n to increase or decrease the computational work per function evaluation. The following call to PROC FCMP defines the corresponding
FCMP function definition:
proc fcmp outlib=sasuser.myfuncs.mypkg; function jsu(x4,x2,f1); return (20000*(log(x4) - log(x2)) + f1); endsub; function jsu1(x1,x2,x3,x4,d); yk = (d - x1)/x2; zk = yk + sqrt(1 + yk**2); return (-0.5*(x3 + x4*log(zk))**2 -0.5*log(1 + yk**2)); endsub; run; options cmplib = sasuser.myfuncs;
In the following steps, the assumption for the definition of jsu
and jsu1
is that jsu1
is called once for each line of data (in this case 20,000 times) and cumulatively summed. The resulting value is then provided
to the function jsu
for a final calculation, which is called only once per evaluation of .
data objdata; input _id_ $ _function_ $ _sense_ $ _dataset_ $; datalines; f1 jsu1 . sudata f jsu max . ; data vardata; input _id_ $ _lb_ _ub_; datalines; x1 . . x2 1e-12 . x3 . . x4 1e-12 . ;
proc optlso primalout = solution objective = objdata variables = vardata logfreq = 100 maxgen = 1000; performance nodes=4 nthreads=8; run;
Output 3.8.1 shows the output from running these steps.
Output 3.8.1: Estimation for Johnson Family of Distributions
Problem Summary | |
---|---|
Problem Type | NLP |
Objective Definition Set | OBJDATA |
Variables | VARDATA |
Number of Variables | 4 |
Integer Variables | 0 |
Continuous Variables | 4 |
Number of Constraints | 0 |
Linear Constraints | 0 |
Nonlinear Constraints | 0 |
Objective Definition Source | OBJDATA |
Objective Sense | Maximize |
Objective Intermediate Functions | 1 |
Objective Data Set | sudata |