```/* nag_rank_regsn (g08rac) Example Program.
*
* NAGPRODCODE Version.
*
* Copyright 2016 Numerical Algorithms Group.
*
* Mark 26, 2016.
*/

#include <stdio.h>
#include <nag.h>
#include <nag_stdlib.h>
#include <nagg08.h>

int main(void)
{

/* Scalars */
double tol;
Integer exit_status, i, idist, p, j, nmax, ns, nsum;
Integer pdx, pdparvar;
NagError fail;
Nag_OrderType order;

/* Arrays */
double *eta = 0, *parest = 0, *parvar = 0, *vapvec = 0, *x = 0;
double *y = 0, *zin = 0;
Integer *irank = 0, *nv = 0;

#ifdef NAG_COLUMN_MAJOR
#define X(I, J)      x[(J-1)*pdx + I - 1]
#define PARVAR(I, J) parvar[(J-1)*pdparvar + I - 1]
order = Nag_ColMajor;
#else
#define X(I, J)      x[(I-1)*pdx + J - 1]
#define PARVAR(I, J) parvar[(I-1)*pdparvar + J - 1]
order = Nag_RowMajor;
#endif

INIT_FAIL(fail);

exit_status = 0;
printf("nag_rank_regsn (g08rac) Example Program Results\n");

/* Skip heading in data file */
scanf("%*[^\n] ");

/* Read number of samples, number of parameters to be fitted,
* error distribution parameter and tolerance criterion for ties.
*/
scanf("%" NAG_IFMT "%" NAG_IFMT "%" NAG_IFMT "%lf%*[^\n] ", &ns, &p, &idist,
&tol);

/* Allocate memory to nv only */
if (!(nv = NAG_ALLOC(ns, Integer)))
{
printf("Allocation failure\n");
exit_status = -1;
goto END;
}

printf("\n");
printf("Number of samples =%2" NAG_IFMT "\n", ns);
printf("Number of parameters fitted =%2" NAG_IFMT "\n", p);
printf("Distribution =%2" NAG_IFMT "\n", idist);
printf("Tolerance for ties =%8.5f\n", tol);

/* Read the number of observations in each sample. */

for (i = 1; i <= ns; ++i)
scanf("%" NAG_IFMT "", &nv[i - 1]);
scanf("%*[^\n] ");

nmax = 0;
nsum = 0;
for (i = 1; i <= ns; ++i) {
nsum += nv[i - 1];
nmax = MAX(nmax, nv[i - 1]);
}
if (nmax > 0 && nmax <= 100 && nsum > 0 && nsum <= 100) {
/* Allocate memory */
if (!(eta = NAG_ALLOC(nmax, double)) ||
!(parest = NAG_ALLOC(4 * p + 1, double)) ||
!(parvar = NAG_ALLOC((p + 1) * p, double)) ||
!(vapvec = NAG_ALLOC(nmax * (nmax + 1) / 2, double)) ||
!(x = NAG_ALLOC(nsum * p, double)) ||
!(y = NAG_ALLOC(nsum, double)) ||
!(zin = NAG_ALLOC(nmax, double)) ||
!(irank = NAG_ALLOC(nmax, Integer)))
{
printf("Allocation failure\n");
exit_status = -1;
goto END;
}
#ifdef NAG_COLUMN_MAJOR
pdx = nsum;
pdparvar = p + 1;
#else
pdx = p;
pdparvar = p;
#endif

/* Read in observations and design matrices for each sample. */
for (i = 1; i <= nsum; ++i) {
scanf("%lf", &y[i - 1]);
for (j = 1; j <= p; ++j)
scanf("%lf", &X(i, j));
}
scanf("%*[^\n] ");

/* nag_rank_regsn (g08rac).
* Regression using ranks, uncensored data
*/
nag_rank_regsn(order, ns, nv, y, p, x, pdx, idist, nmax, tol,
parvar, pdparvar, irank, zin, eta, vapvec, parest, &fail);
if (fail.code != NE_NOERROR) {
printf("Error from nag_rank_regsn (g08rac).\n%s\n", fail.message);
exit_status = 1;
goto END;
}

printf("\n");
printf("Score statistic\n");
for (i = 1; i <= p; ++i)
printf("%9.3f%s", parest[i - 1], i % 2 == 0 || i == p ? "\n" : " ");
printf("\n");

printf("Covariance matrix of score statistic\n");
for (j = 1; j <= p; ++j) {
for (i = 1; i <= j; ++i)
printf("%9.3f%s", PARVAR(i, j), i % 2 == 0 || i == j ? "\n" : " ");
}
printf("\n");

printf("Parameter estimates\n");
for (i = 1; i <= p; ++i)
printf("%9.3f%s", parest[p + i - 1], i % 2 == 0 || i == p ? "\n" : " ");
printf("\n");

printf("Covariance matrix of parameter estimates\n");
for (i = 1; i <= p; ++i)
{
printf(" ");

for (j = 1; j <= i; ++j)
printf("%9.3f%s", PARVAR(i + 1, j),
j % 2 == 0 || j == i ? "\n" : " ");
}
printf("\n");

printf("Chi-squared statistic =%9.3f with%2" NAG_IFMT " d.f.\n",
parest[p * 2], p);
printf("\n");
printf("Standard errors of estimates and\n");
printf("approximate z-statistics\n");
for (i = 1; i <= p; ++i)
printf("%9.3f%14.3f\n", parest[2 * p + 1 + i - 1],
parest[p * 3 + 1 + i - 1]);
printf("\n");
}
END:
NAG_FREE(eta);
NAG_FREE(parest);
NAG_FREE(parvar);
NAG_FREE(vapvec);
NAG_FREE(x);
NAG_FREE(y);
NAG_FREE(zin);
NAG_FREE(irank);
NAG_FREE(nv);

return exit_status;
}
```