How to fit 2-D vectors in PAW ?

Here is an example showing how to proceed:

 MACRO FIT2D
 *
 * Macro to test 2-D fits of functions F(x1,x2) = Y to data.
 *
 ZONE 2 2
 OPT NSTA

 H/DEL *
 VECT/DEL *

 NP= 3
 VECT/CREA PAR([NP])  R 100.0 1.0 1.0
 VECT/CREA STEP([NP]) R 1.0 1.0 1.0
 VECT/CREA PMIN([NP]) R 1.0 0.0 1.0
 VECT/CREA PMAX([NP]) R 1.0 1.0 1.0
 *
 * Define number of bins in X and Y, then book vectors:
 *
 NX= 6
 NY= 8
 MINX=  0.0
 MAXX=  3.0
 MINY=  0.0
 MAXY=  4.0
 N= [NX]*[NY]
 VECT/CREA Y([N])
 VECT/CREA EY([N])
 VECT/CREA X([N],2)
 *
 * Make dummy data using random function HRNDM2. Observe that here the values to
 * be expected from the fits are defined:
 *                    |          |
 FUN2 20 EXP(-0.5*(((1.5-X)**2+(2.0-Y)**2)/(3.**2))) 20 [MINX] [MAXX] _
 20 [MINY] [MAXY]
 2D 30 'DATA'  [NX] [MINX] [MAXX] [NY] [MINY] [MAXY]
 APPLICATION COMIS QUIT
       SUBROUTINE RNDM
       DO 10 I=1,10000
         CALL HRNDM2(20,RX,RY)
         CALL HFILL(30,RX,RY,1.0)
  10   CONTINUE
       END
       CALL RNDM
       END
 QUIT
 H/PL 30 LEGO
 GET/CON 30 Y
 GET/ERR 30 EY
 *
 * Put coordinates x1 and x2 into vector X:
 *
 BINWX= ([MAXX]-[MINX])/[NX]
 BINWY= ([MAXY]-[MINY])/[NY]
 DO I=1,[NY]
   DO J=1,[NX]
     ZX= $SIGMA( [MINX]+[BINWX]*([J]-0.5) )
     ZY= $SIGMA( [MINY]+[BINWY]*([I]-0.5) )
     N= $SIGMA( ([I]-1)*[NX]+[J] )
     VECT/INP X([N],1) [ZX]
     VECT/INP X([N],2) [ZY]
   ENDDO
 ENDDO
 *
 * Do the fit:
 *
 V/IN PAR 100.0 1.0 1.0
 V/FIT X Y EY FUNXY.FOR 0 [NP] PAR STEP PMIN PMAX

 | Plot result:
 P1= PAR(1)
 P2= PAR(2)
 P3= PAR(3)
 ZONE 1 2 2 S
 FUN2 50 [P1]*EXP(-0.5*((([P2]-X)**2+([P3]-Y)**2)/(3.**2))) [NX] [MINX] [MAXX] _
 [NY] [MINY] [MAXY] ' '
 H/PL 50 LEGO

 RETURN

Where the COMIS function FUNXY.FOR is:

       FUNCTION FUNXY(X)
       DIMENSION X(2)
       COMMON/PAWPAR/PAR(3)
       FUNXY= PAR(1)
      &      *EXP( -0.5*(((PAR(2)-X(1))**2+(PAR(3)-X(2))**2)/(3.**2)) )
       END

       PAW > exe fit2d
            **********************************************
            *                                            *
            * Function minimization by SUBROUTINE HFITV  *
            * Variable-metric method                     *
            * ID =          0  CHOPT = 0                 *
            *                                            *
            **********************************************
        Convergence when estimated distance to minimum (EDM) .LT.  0.10E+01

        FCN=   44.38704     FROM MIGRAD    STATUS=CONVERGED     70 CALLS       71 TOTAL
                            EDM=  0.92E-04    STRATEGY= 1      ERROR MATRIX ACCURATE

         EXT PARAMETER                                   STEP         FIRST
         NO.   NAME        VALUE          ERROR          SIZE      DERIVATIVE
          1      P1        232.45        2.4176       0.58806       0.23439E-04
          2      P2        1.7379       0.10665       0.24700E-01   0.10854
          3      P3        2.0745       0.81073E-01   0.19395E-01  -0.86093E-01

        CHISQUARE = 0.9864E+00  NPFIT =    48

       PAW >