/* nag_regsn_mult_linear_addrem_obs (g02dcc) Example Program.
 *
 * Copyright 1991 Numerical Algorithms Group.
 *
 * Mark 2, 1991.
 * Mark 8 revised, 2004.
 */

#include <nag.h>
#include <nagx04.h>
#include <stdio.h>
#include <nag_stdlib.h>
#include <nagg02.h>

#define XM(I, J) xm[(I) *tdxm + J]
#define Q(I, J)  q[(I) *tdq + J]

int main(int argc, char *argv[])
{
  FILE             *fpin, *fpout;
  Integer          exit_status = 0, i, ip, j, m, n, rank, *sx = 0, tdq, tdxm;
  double           *b = 0, *com_ar = 0, *cov = 0, df, *h = 0, *p = 0, *q = 0;
  double           *res = 0, rss, *se = 0, tol, *wt = 0, *wtptr, *xm = 0;
  double           *y = 0;
  char             nag_enum_arg[40];
  Nag_Boolean      svd, weight;
  Nag_IncludeMean  mean;
  Nag_UpdateObserv update;
  NagError         fail;

  INIT_FAIL(fail);

  /* Check for command-line IO options */
  fpin = nag_example_file_io(argc, argv, "-data", NULL);
  fpout = nag_example_file_io(argc, argv, "-results", NULL);

  fprintf(fpout, "nag_regsn_mult_linear_addrem_obs (g02dcc) Example Program "
          "Results\n");
  /* Skip heading in data file */
  fscanf(fpin, "%*[^\n]");
  fscanf(fpin, "%ld %ld", &n, &m);
  fscanf(fpin, " %s", nag_enum_arg);
  /* nag_enum_name_to_value(x04nac).
   * Converts NAG enum member name to value
   */
  weight = (Nag_Boolean) nag_enum_name_to_value(nag_enum_arg);
  fscanf(fpin, " %s", nag_enum_arg);
  mean = (Nag_IncludeMean) nag_enum_name_to_value(nag_enum_arg);

  if (weight)
    wtptr = wt;
  else
    wtptr = (double *) 0;

  if (n >= 2 && m >= 1)
    {
      if (!(b = NAG_ALLOC(m, double)) ||
          !(h = NAG_ALLOC(n, double)) ||
          !(res = NAG_ALLOC(n, double)) ||
          !(wt = NAG_ALLOC(n, double)) ||
          !(xm = NAG_ALLOC(n*m, double)) ||
          !(y = NAG_ALLOC(n, double)) ||
          !(sx = NAG_ALLOC(m, Integer)))
        {
          fprintf(fpout, "Allocation failure\n");
          exit_status = -1;
          goto END;
        }
      tdxm = m;
    }
  else
    {
      fprintf(fpout, "Invalid n or m.\n");
      exit_status = 1;
      return exit_status;
    }
  if (wtptr)
    {
      for (i = 0; i < n; i++)
        {
          for (j = 0; j < m; j++)
            fscanf(fpin, "%lf", &XM(i, j));
          fscanf(fpin, "%lf%lf", &y[i], &wt[i]);
        }
    }
  else
    {
      for (i = 0; i < n; i++)
        {
          for (j = 0; j < m; j++)
            fscanf(fpin, "%lf", &XM(i, j));
          fscanf(fpin, "%lf", &y[i]);
        }
    }
  for (j = 0; j < m; ++j)
    fscanf(fpin, "%ld", &sx[j]);
  fscanf(fpin, "%ld", &ip);

  if (!(cov = NAG_ALLOC(ip*(ip+1)/2, double)) ||
      !(p = NAG_ALLOC(ip*(ip+2), double)) ||
      !(q = NAG_ALLOC((n)*(ip+1), double)) ||
      !(com_ar = NAG_ALLOC(5*(ip-1)+ip*ip, double)) ||
      !(se = NAG_ALLOC(ip, double)))
    {
      fprintf(fpout, "Allocation failure\n");
      exit_status = -1;
      goto END;
    }
  tdq = ip+1;
  /* Set tolerance */
  tol = 0.00001e0;

  /* Fit initial model using nag_regsn_mult_linear (g02dac) */
  /* nag_regsn_mult_linear (g02dac).
   * Fits a general (multiple) linear regression model
   */
  nag_regsn_mult_linear(mean, n, xm, tdxm, m, sx, ip, y, wtptr, &rss,
                        &df, b, se, cov, res, h, q, tdq, &svd, &rank,
                        p, tol, com_ar, &fail);
  if (fail.code != NE_NOERROR)
    {
      fprintf(fpout, "Error from nag_regsn_mult_linear (g02dac).\n%s\n",
              fail.message);
      exit_status = 1;
      goto END;
    }

  fprintf(fpout, "Results from g02dac\n\n");
  if (svd)
    fprintf(fpout, "Model not of full rank\n");
  fprintf(fpout, "Residual sum of squares = %13.4e\n", rss);
  fprintf(fpout, "Degrees of freedom = %3.1f\n\n", df);
  fprintf(fpout, "Variable    Parameter estimate   Standard error\n\n");
  for (j = 0; j < ip; j++)
    fprintf(fpout, "%6ld%20.4e%20.4e\n", j+1, b[j], se[j]);
  fprintf(fpout, "\n");
  update = Nag_ObservDel;
  /* nag_regsn_mult_linear_addrem_obs (g02dcc).
   * Add/delete an observation to/from a general linear
   * regression model
   */
  nag_regsn_mult_linear_addrem_obs(update, mean, m, sx, q, tdq, ip,
                                   xm, n, tdxm, n, y[11], wtptr, &rss, &fail);
  if (fail.code != NE_NOERROR)
    {
      fprintf(fpout,
              "Error from nag_regsn_mult_linear_addrem_obs (g02dcc).\n%s\n",
              fail.message);
      exit_status = 1;
      goto END;
    }

  fprintf(fpout, "Results from dropping an observation using\n"
          "nag_regsn_mult_linear_addrem_obs (g02dcc)\n");
  n = n - 1;
  /* nag_regsn_mult_linear_upd_model (g02ddc).
   * Estimates of regression parameters from an updated model
   */
  nag_regsn_mult_linear_upd_model(n, ip, q, tdq, &rss, &df, b, se, cov,
                                  &svd, &rank, p, tol, &fail);
  if (fail.code != NE_NOERROR)
    {
      fprintf(fpout,
              "Error from nag_regsn_mult_linear_upd_model (g02ddc).\n%s\n",
              fail.message);
      exit_status = 1;
      goto END;
    }


  fprintf(fpout, "Residual sum of squares = %13.4e\n", rss);
  fprintf(fpout, "Degrees of freedom = %3.1f\n\n", df);
  fprintf(fpout, "Variable    Parameter estimate   Standard error\n\n");
  for (j = 0; j < ip; j++)
    fprintf(fpout, "%6ld%20.4e%20.4e\n", j+1, b[j], se[j]);
 END:
  if (fpin != stdin) fclose(fpin);
  if (fpout != stdout) fclose(fpout);
  if (b) NAG_FREE(b);
  if (h) NAG_FREE(h);
  if (res) NAG_FREE(res);
  if (wt) NAG_FREE(wt);
  if (xm) NAG_FREE(xm);
  if (y) NAG_FREE(y);
  if (sx) NAG_FREE(sx);
  if (cov) NAG_FREE(cov);
  if (p) NAG_FREE(p);
  if (q) NAG_FREE(q);
  if (com_ar) NAG_FREE(com_ar);
  if (se) NAG_FREE(se);
  return exit_status;
}