/* nag_robust_m_regsn_wts (g02hbc) Example Program.
*
* Copyright 2014 Numerical Algorithms Group.
*
* Mark 7, 2002.
* Mark 7b revised, 2004.
*/
#include <math.h>
#include <stdio.h>
#include <nag.h>
#include <nag_stdlib.h>
#include <nagg02.h>
#include <nags.h>
#include <nagx01.h>
#include <nagx02.h>
#ifdef __cplusplus
extern "C" {
#endif
static double NAG_CALL ucv(double t, Nag_Comm *comm);
#ifdef __cplusplus
}
#endif
int main(void)
{
/* Scalars */
double bd, bl, tol;
Integer exit_status, i, j, k, l1, l2, m, maxit, mm, n, nit, nitmon;
Integer pdx;
NagError fail;
Nag_OrderType order;
Nag_Comm comm;
/* Arrays */
static double ruser[1] = {-1.0};
double *a = 0, *x = 0, *z = 0;
#ifdef NAG_COLUMN_MAJOR
#define X(I, J) x[(J-1)*pdx + I - 1]
order = Nag_ColMajor;
#else
#define X(I, J) x[(I-1)*pdx + J - 1]
order = Nag_RowMajor;
#endif
INIT_FAIL(fail);
exit_status = 0;
printf("nag_robust_m_regsn_wts (g02hbc) Example Program Results\n");
/* For communication with user-supplied functions: */
comm.user = ruser;
/* Skip heading in data file */
scanf("%*[^\n] ");
/* Read in the dimensions of X */
scanf("%ld%ld%*[^\n] ", &n, &m);
/* Allocate memory */
if (!(a = NAG_ALLOC(m*(m+1)/2, double)) ||
!(x = NAG_ALLOC(n * m, double)) ||
!(z = NAG_ALLOC(n, double)))
{
printf("Allocation failure\n");
exit_status = -1;
goto END;
}
#ifdef NAG_COLUMN_MAJOR
pdx = n;
#else
pdx = m;
#endif
/* Read in the X matrix */
for (i = 1; i <= n; ++i)
{
for (j = 1; j <= m; ++j)
scanf("%lf", &X(i, j));
scanf("%*[^\n] ");
}
/* Read in the initial value of A */
mm = (m + 1) * m / 2;
for (j = 1; j <= mm; ++j)
scanf("%lf", &a[j - 1]);
scanf("%*[^\n] ");
/* Set the values remaining parameters */
bl = 0.9;
bd = 0.9;
maxit = 50;
tol = 5e-5;
/* Change nitmon to a positive value if monitoring information
* is required
*/
nitmon = 0;
/* nag_robust_m_regsn_wts (g02hbc).
* Robust regression, compute weights for use with
* nag_robust_m_regsn_user_fn (g02hdc)
*/
nag_robust_m_regsn_wts(order, ucv, n, m, x, pdx, a, z, bl, bd, tol, maxit,
nitmon, 0, &nit, &comm, &fail);
if (fail.code != NE_NOERROR)
{
printf("Error from nag_robust_m_regsn_wts (g02hbc).\n%s\n",
fail.message);
exit_status = 1;
goto END;
}
printf(
"nag_robust_m_regsn_wts (g02hbc) required %4ld iterations to "
"converge\n\n", nit);
printf("Matrix A\n");
l2 = 0;
for (j = 1; j <= m; ++j)
{
l1 = l2 + 1;
l2 += j;
for (k = l1; k <= l2; ++k)
printf("%9.4f%s", a[k - 1], k%6 == 0 || k == l2?"\n":" ");
}
printf("\n");
printf("Vector Z\n");
for (i = 1; i <= n; ++i)
printf("%9.4f\n", z[i - 1]);
/* Calculate Krasker-Welsch weights */
printf("\n");
printf("Vector of weights\n");
for (i = 1; i <= n; ++i)
{
z[i - 1] = 1.0 / z[i - 1];
printf("%9.4f\n", z[i - 1]);
}
END:
NAG_FREE(a);
NAG_FREE(x);
NAG_FREE(z);
return exit_status;
}
static double NAG_CALL ucv(double t, Nag_Comm *comm)
{
/* Scalars */
double pc, pd, q, q2;
double ret_val;
/* ucv function for Krasker-Welsch weights */
if (comm->user[0] == -1.0)
{
printf("(User-supplied callback ucv, first invocation.)\n");
comm->user[0] = 0.0;
}
ret_val = 1.0;
if (t != 0.0)
{
q = 2.5 / t;
q2 = q * q;
/* nag_cumul_normal (s15abc).
* Cumulative Normal distribution function P(x)
*/
pc = nag_cumul_normal(q);
/* nag_real_smallest_number (x02akc).
* The smallest positive model number
*/
if (q2 < -log(nag_real_smallest_number))
/* nag_pi (x01aac).
* pi
*/
pd = exp(-q2 / 2.0) / sqrt(nag_pi * 2.0);
else
pd = 0.0;
ret_val = (pc * 2.0 - 1.0) * (1.0 - q2) + q2 - q * 2.0 * pd;
}
return ret_val;
}