/* nag_correg_linregm_fit_stepwise (g02efc) Example Program.
 *
 * Copyright 2023 Numerical Algorithms Group.
 *
 * Mark 29.3, 2023.
 */

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

int main(void) {
  /* Scalars */
  double fin, fout, rms, rsq, sw, tau;
  Integer df, exit_status, i, j, m, n, pdx;

  /* Arrays */
  double *b = 0, *c = 0, *se = 0, *wmean = 0, *x = 0;
  Integer *isx = 0;

  /* Nag types */
  Nag_OrderType order;
  Nag_SumSquare mean;
  Nag_Comm comm;
  NagError fail;

#ifdef NAG_COLUMN_ORDER
#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_correg_linregm_fit_stepwise (g02efc) Example Program Results\n\n");

  /* Skip heading in data file */
  scanf("%*[^\n]");
  scanf("%" NAG_IFMT " %" NAG_IFMT " %lf %lf %lf", &n, &m, &fin, &fout, &tau);
  scanf("%*[^\n]");

  if (n > 1 && m > 1) {
    /* Allocate memory */
    if (!(b = NAG_ALLOC(m + 1, double)) ||
        !(c = NAG_ALLOC((m + 1) * (m + 2) / 2, double)) ||
        !(se = NAG_ALLOC(m + 1, double)) ||
        !(wmean = NAG_ALLOC(m + 1, double)) ||
        !(x = NAG_ALLOC(n * (m + 1), double)) ||
        !(isx = NAG_ALLOC(m, Integer))) {
      printf("Allocation failure\n");
      exit_status = -1;
      goto END;
    }
  } else {
    printf("Invalid n or m.\n");
    exit_status = 1;
    return exit_status;
  }

#ifdef NAG_COLUMN_ORDER
  pdx = n;
#else
  pdx = m + 1;
#endif

  for (i = 1; i <= n; ++i) {
    for (j = 1; j <= m + 1; ++j) {
      scanf("%lf", &X(i, j));
    }
  }
  scanf("%*[^\n]");

  for (j = 1; j <= m; ++j) {
    scanf("%" NAG_IFMT "", &isx[j - 1]);
  }
  scanf("%*[^\n]");

  /* nag_correg_ssqmat (g02buc).
   * Computes sums of squares and cross-products of deviations
   * from the mean for the augmented matrix
   */
  mean = Nag_AboutMean;
  nag_correg_ssqmat(order, mean, n, m + 1, x, pdx, 0, &sw, wmean, c, &fail);
  if (fail.code != NE_NOERROR) {
    printf("Error from nag_correg_ssqmat (g02buc).\n%s\n.", fail.message);
    exit_status = 1;
    goto END;
  }

  fflush(stdout);

  /* Perform stepwise selection of variables using
   * nag_correg_linregm_fit_stepwise (g02efc):
   *   Stepwise linear regression.
   */
  nag_correg_linregm_fit_stepwise(
      m, n, wmean, c, sw, isx, fin, fout, tau, b, se, &rsq, &rms, &df,
      nag_correg_linregm_fit_stepwise_sample_monfun, &comm, &fail);
  if (fail.code != NE_NOERROR) {
    printf("Error from nag_correg_linregm_fit_stepwise (g02efc).\n%s\n.",
           fail.message);
    exit_status = 1;
    goto END;
  }

  /* Display summary information for fitted model */
  printf("\n");
  printf("Fitted Model Summary\n");
  printf("%-10s   %-10s%19s\n", "Term", " Estimate", "Standard Error");
  printf("%-10s   %11.3e%17.3e\n", "Intercept:", b[0], se[0]);
  for (i = 1; i <= m; ++i) {
    j = isx[i - 1];
    if (j == 1 || j == 2) {
      printf("%-10s%3" NAG_IFMT "%11.3e%17.3e\n", "Variable:", i, b[i], se[i]);
    }
  }
  printf("\n");
  printf("RMS: %-12.3e\n\n", rms);

END:
  NAG_FREE(b);
  NAG_FREE(c);
  NAG_FREE(se);
  NAG_FREE(wmean);
  NAG_FREE(x);
  NAG_FREE(isx);

  return exit_status;
}