[pspp] / src / regression.q

/* PSPP - linear regression.
   Copyright (C) 2005 Free Software Foundation, Inc.
   Written by Jason H Stover <jason@sakla.net>.

   This program is free software; you can redistribute it and/or
   modify it under the terms of the GNU General Public License as
   published by the Free Software Foundation; either version 2 of the
   License, or (at your option) any later version.

   This program is distributed in the hope that it will be useful, but
   WITHOUT ANY WARRANTY; without even the implied warranty of
   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
   General Public License for more details.

   You should have received a copy of the GNU General Public License
   along with this program; if not, write to the Free Software
   Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA
   02110-1301, USA. */

#include <config.h>
#include <stdlib.h>
#include <gsl/gsl_cdf.h>
#include <gsl/gsl_vector.h>
#include <gsl/gsl_matrix.h>
#include "alloc.h"
#include "case.h"
#include "casefile.h"
#include "cat.h"
#include "cat-routines.h"
#include "command.h"
#include "design-matrix.h"
#include "dictionary.h"
#include "error.h"
#include "file-handle.h"
#include "gettext.h"
#include "lexer.h"
#include <linreg/pspp_linreg.h>
#include "missing-values.h"
#include "reg_export_comments.h"
#include "tab.h"
#include "var.h"
#include "vfm.h"

#define REG_LARGE_DATA 1000

/* (headers) */

/* (specification)
   "REGRESSION" (regression_):
   *variables=varlist;
   statistics[st_]=r,
   coeff,
   anova,
   outs,
   zpp,
   label,
   sha,
   ci,
   bcov,
   ses,
   xtx,
   collin,
   tol,
   selection,
   f,
   defaults,
   all;
   export=custom;
   ^dependent=varlist;
   ^method=enter.
*/
/* (declarations) */
/* (functions) */
static struct cmd_regression cmd;

/*
  Array holding the subscripts of the independent variables.
 */
size_t *indep_vars;

/*
  File where the model will be saved if the EXPORT subcommand
  is given. 
 */
struct file_handle *model_file;

/*
  Return value for the procedure.
 */
int pspp_reg_rc = CMD_SUCCESS;

static void run_regression (const struct casefile *, void *);
/* 
   STATISTICS subcommand output functions.
 */
static void reg_stats_r (pspp_linreg_cache *);
static void reg_stats_coeff (pspp_linreg_cache *);
static void reg_stats_anova (pspp_linreg_cache *);
static void reg_stats_outs (pspp_linreg_cache *);
static void reg_stats_zpp (pspp_linreg_cache *);
static void reg_stats_label (pspp_linreg_cache *);
static void reg_stats_sha (pspp_linreg_cache *);
static void reg_stats_ci (pspp_linreg_cache *);
static void reg_stats_f (pspp_linreg_cache *);
static void reg_stats_bcov (pspp_linreg_cache *);
static void reg_stats_ses (pspp_linreg_cache *);
static void reg_stats_xtx (pspp_linreg_cache *);
static void reg_stats_collin (pspp_linreg_cache *);
static void reg_stats_tol (pspp_linreg_cache *);
static void reg_stats_selection (pspp_linreg_cache *);
static void statistics_keyword_output (void (*)(pspp_linreg_cache *),
                                       int, pspp_linreg_cache *);

static void
reg_stats_r (pspp_linreg_cache * c)
{
  struct tab_table *t;
  int n_rows = 2;
  int n_cols = 5;
  double rsq;
  double adjrsq;
  double std_error;

  assert (c != NULL);
  rsq = c->ssm / c->sst;
  adjrsq = 1.0 - (1.0 - rsq) * (c->n_obs - 1.0) / (c->n_obs - c->n_indeps);
  std_error = sqrt ((c->n_indeps - 1.0) / (c->n_obs - 1.0));
  t = tab_create (n_cols, n_rows, 0);
  tab_dim (t, tab_natural_dimensions);
  tab_box (t, TAL_2, TAL_2, -1, TAL_1, 0, 0, n_cols - 1, n_rows - 1);
  tab_hline (t, TAL_2, 0, n_cols - 1, 1);
  tab_vline (t, TAL_2, 2, 0, n_rows - 1);
  tab_vline (t, TAL_0, 1, 0, 0);

  tab_text (t, 1, 0, TAB_CENTER | TAT_TITLE, _("R"));
  tab_text (t, 2, 0, TAB_CENTER | TAT_TITLE, _("R Square"));
  tab_text (t, 3, 0, TAB_CENTER | TAT_TITLE, _("Adjusted R Square"));
  tab_text (t, 4, 0, TAB_CENTER | TAT_TITLE, _("Std. Error of the Estimate"));
  tab_float (t, 1, 1, TAB_RIGHT, sqrt (rsq), 10, 2);
  tab_float (t, 2, 1, TAB_RIGHT, rsq, 10, 2);
  tab_float (t, 3, 1, TAB_RIGHT, adjrsq, 10, 2);
  tab_float (t, 4, 1, TAB_RIGHT, std_error, 10, 2);
  tab_title (t, 0, _("Model Summary"));
  tab_submit (t);
}

/*
  Table showing estimated regression coefficients.
 */
static void
reg_stats_coeff (pspp_linreg_cache * c)
{
  size_t i;
  size_t j;
  int n_cols = 7;
  int n_rows;
  double t_stat;
  double pval;
  double coeff;
  double std_err;
  double beta;
  const char *label;
  struct tab_table *t;

  assert (c != NULL);
  n_rows = c->n_coeffs + 2;

  t = tab_create (n_cols, n_rows, 0);
  tab_headers (t, 2, 0, 1, 0);
  tab_dim (t, tab_natural_dimensions);
  tab_box (t, TAL_2, TAL_2, -1, TAL_1, 0, 0, n_cols - 1, n_rows - 1);
  tab_hline (t, TAL_2, 0, n_cols - 1, 1);
  tab_vline (t, TAL_2, 2, 0, n_rows - 1);
  tab_vline (t, TAL_0, 1, 0, 0);

  tab_text (t, 2, 0, TAB_CENTER | TAT_TITLE, _("B"));
  tab_text (t, 3, 0, TAB_CENTER | TAT_TITLE, _("Std. Error"));
  tab_text (t, 4, 0, TAB_CENTER | TAT_TITLE, _("Beta"));
  tab_text (t, 5, 0, TAB_CENTER | TAT_TITLE, _("t"));
  tab_text (t, 6, 0, TAB_CENTER | TAT_TITLE, _("Significance"));
  tab_text (t, 1, 1, TAB_LEFT | TAT_TITLE, _("(Constant)"));
  coeff = c->coeff[0].estimate;
  tab_float (t, 2, 1, 0, coeff, 10, 2);
  std_err = sqrt (gsl_matrix_get (c->cov, 0, 0));
  tab_float (t, 3, 1, 0, std_err, 10, 2);
  beta = coeff / c->depvar_std;
  tab_float (t, 4, 1, 0, beta, 10, 2);
  t_stat = coeff / std_err;
  tab_float (t, 5, 1, 0, t_stat, 10, 2);
  pval = 2 * gsl_cdf_tdist_Q (fabs (t_stat), 1.0);
  tab_float (t, 6, 1, 0, pval, 10, 2);
  for (j = 1; j <= c->n_indeps; j++)
    {
      i = indep_vars[j];
      label = var_to_string (c->coeff[j].v);
      tab_text (t, 1, j + 1, TAB_CENTER, label);
      /*
         Regression coefficients.
       */
      coeff = c->coeff[j].estimate;
      tab_float (t, 2, j + 1, 0, coeff, 10, 2);
      /*
         Standard error of the coefficients.
       */
      std_err = sqrt (gsl_matrix_get (c->cov, j, j));
      tab_float (t, 3, j + 1, 0, std_err, 10, 2);
      /*
         'Standardized' coefficient, i.e., regression coefficient
         if all variables had unit variance.
       */
      beta = gsl_vector_get (c->indep_std, j);
      beta *= coeff / c->depvar_std;
      tab_float (t, 4, j + 1, 0, beta, 10, 2);

      /*
         Test statistic for H0: coefficient is 0.
       */
      t_stat = coeff / std_err;
      tab_float (t, 5, j + 1, 0, t_stat, 10, 2);
      /*
         P values for the test statistic above.
       */
      pval = 2 * gsl_cdf_tdist_Q (fabs (t_stat), 1.0);
      tab_float (t, 6, j + 1, 0, pval, 10, 2);
    }
  tab_title (t, 0, _("Coefficients"));
  tab_submit (t);
}

/*
  Display the ANOVA table.
 */
static void
reg_stats_anova (pspp_linreg_cache * c)
{
  int n_cols = 7;
  int n_rows = 4;
  const double msm = c->ssm / c->dfm;
  const double mse = c->sse / c->dfe;
  const double F = msm / mse;
  const double pval = gsl_cdf_fdist_Q (F, c->dfm, c->dfe);

  struct tab_table *t;

  assert (c != NULL);
  t = tab_create (n_cols, n_rows, 0);
  tab_headers (t, 2, 0, 1, 0);
  tab_dim (t, tab_natural_dimensions);

  tab_box (t, TAL_2, TAL_2, -1, TAL_1, 0, 0, n_cols - 1, n_rows - 1);

  tab_hline (t, TAL_2, 0, n_cols - 1, 1);
  tab_vline (t, TAL_2, 2, 0, n_rows - 1);
  tab_vline (t, TAL_0, 1, 0, 0);

  tab_text (t, 2, 0, TAB_CENTER | TAT_TITLE, _("Sum of Squares"));
  tab_text (t, 3, 0, TAB_CENTER | TAT_TITLE, _("df"));
  tab_text (t, 4, 0, TAB_CENTER | TAT_TITLE, _("Mean Square"));
  tab_text (t, 5, 0, TAB_CENTER | TAT_TITLE, _("F"));
  tab_text (t, 6, 0, TAB_CENTER | TAT_TITLE, _("Significance"));

  tab_text (t, 1, 1, TAB_LEFT | TAT_TITLE, _("Regression"));
  tab_text (t, 1, 2, TAB_LEFT | TAT_TITLE, _("Residual"));
  tab_text (t, 1, 3, TAB_LEFT | TAT_TITLE, _("Total"));

  /* Sums of Squares */
  tab_float (t, 2, 1, 0, c->ssm, 10, 2);
  tab_float (t, 2, 3, 0, c->sst, 10, 2);
  tab_float (t, 2, 2, 0, c->sse, 10, 2);


  /* Degrees of freedom */
  tab_float (t, 3, 1, 0, c->dfm, 4, 0);
  tab_float (t, 3, 2, 0, c->dfe, 4, 0);
  tab_float (t, 3, 3, 0, c->dft, 4, 0);

  /* Mean Squares */

  tab_float (t, 4, 1, TAB_RIGHT, msm, 8, 3);
  tab_float (t, 4, 2, TAB_RIGHT, mse, 8, 3);

  tab_float (t, 5, 1, 0, F, 8, 3);

  tab_float (t, 6, 1, 0, pval, 8, 3);

  tab_title (t, 0, _("ANOVA"));
  tab_submit (t);
}
static void
reg_stats_outs (pspp_linreg_cache * c)
{
  assert (c != NULL);
}
static void
reg_stats_zpp (pspp_linreg_cache * c)
{
  assert (c != NULL);
}
static void
reg_stats_label (pspp_linreg_cache * c)
{
  assert (c != NULL);
}
static void
reg_stats_sha (pspp_linreg_cache * c)
{
  assert (c != NULL);
}
static void
reg_stats_ci (pspp_linreg_cache * c)
{
  assert (c != NULL);
}
static void
reg_stats_f (pspp_linreg_cache * c)
{
  assert (c != NULL);
}
static void
reg_stats_bcov (pspp_linreg_cache * c)
{
  int n_cols;
  int n_rows;
  int i;
  int j;
  int k;
  int row;
  int col;
  const char *label;
  struct tab_table *t;

  assert (c != NULL);
  n_cols = c->n_indeps + 1 + 2;
  n_rows = 2 * (c->n_indeps + 1);
  t = tab_create (n_cols, n_rows, 0);
  tab_headers (t, 2, 0, 1, 0);
  tab_dim (t, tab_natural_dimensions);
  tab_box (t, TAL_2, TAL_2, -1, TAL_1, 0, 0, n_cols - 1, n_rows - 1);
  tab_hline (t, TAL_2, 0, n_cols - 1, 1);
  tab_vline (t, TAL_2, 2, 0, n_rows - 1);
  tab_vline (t, TAL_0, 1, 0, 0);
  tab_text (t, 0, 0, TAB_CENTER | TAT_TITLE, _("Model"));
  tab_text (t, 1, 1, TAB_CENTER | TAT_TITLE, _("Covariances"));
  for (i = 1; i < c->n_indeps + 1; i++)
    {
      j = indep_vars[(i - 1)];
      struct variable *v = cmd.v_variables[j];
      label = var_to_string (v);
      tab_text (t, 2, i, TAB_CENTER, label);
      tab_text (t, i + 2, 0, TAB_CENTER, label);
      for (k = 1; k < c->n_indeps + 1; k++)
        {
          col = (i <= k) ? k : i;
          row = (i <= k) ? i : k;
          tab_float (t, k + 2, i, TAB_CENTER,
                     gsl_matrix_get (c->cov, row, col), 8, 3);
        }
    }
  tab_title (t, 0, _("Coefficient Correlations"));
  tab_submit (t);
}
static void
reg_stats_ses (pspp_linreg_cache * c)
{
  assert (c != NULL);
}
static void
reg_stats_xtx (pspp_linreg_cache * c)
{
  assert (c != NULL);
}
static void
reg_stats_collin (pspp_linreg_cache * c)
{
  assert (c != NULL);
}
static void
reg_stats_tol (pspp_linreg_cache * c)
{
  assert (c != NULL);
}
static void
reg_stats_selection (pspp_linreg_cache * c)
{
  assert (c != NULL);
}

static void
statistics_keyword_output (void (*function) (pspp_linreg_cache *),
                           int keyword, pspp_linreg_cache * c)
{
  if (keyword)
    {
      (*function) (c);
    }
}

static void
subcommand_statistics (int *keywords, pspp_linreg_cache * c)
{
  /* 
     The order here must match the order in which the STATISTICS 
     keywords appear in the specification section above.
   */
  enum
  { r,
    coeff,
    anova,
    outs,
    zpp,
    label,
    sha,
    ci,
    bcov,
    ses,
    xtx,
    collin,
    tol,
    selection,
    f,
    defaults,
    all
  };
  int i;
  int d = 1;

  if (keywords[all])
    {
      /*
         Set everything but F.
       */
      for (i = 0; i < f; i++)
        {
          keywords[i] = 1;
        }
    }
  else
    {
      for (i = 0; i < all; i++)
        {
          if (keywords[i])
            {
              d = 0;
            }
        }
      /*
         Default output: ANOVA table, parameter estimates,
         and statistics for variables not entered into model,
         if appropriate.
       */
      if (keywords[defaults] | d)
        {
          keywords[anova] = 1;
          keywords[outs] = 1;
          keywords[coeff] = 1;
          keywords[r] = 1;
        }
    }
  statistics_keyword_output (reg_stats_r, keywords[r], c);
  statistics_keyword_output (reg_stats_anova, keywords[anova], c);
  statistics_keyword_output (reg_stats_coeff, keywords[coeff], c);
  statistics_keyword_output (reg_stats_outs, keywords[outs], c);
  statistics_keyword_output (reg_stats_zpp, keywords[zpp], c);
  statistics_keyword_output (reg_stats_label, keywords[label], c);
  statistics_keyword_output (reg_stats_sha, keywords[sha], c);
  statistics_keyword_output (reg_stats_ci, keywords[ci], c);
  statistics_keyword_output (reg_stats_f, keywords[f], c);
  statistics_keyword_output (reg_stats_bcov, keywords[bcov], c);
  statistics_keyword_output (reg_stats_ses, keywords[ses], c);
  statistics_keyword_output (reg_stats_xtx, keywords[xtx], c);
  statistics_keyword_output (reg_stats_collin, keywords[collin], c);
  statistics_keyword_output (reg_stats_tol, keywords[tol], c);
  statistics_keyword_output (reg_stats_selection, keywords[selection], c);
}
static void
reg_print_depvars (FILE *fp, pspp_linreg_cache *c)
{
  int i;
  struct pspp_linreg_coeff coeff;

  fprintf (fp, "char *model_depvars[%d] = {", c->n_indeps);
  for (i = 1; i < c->n_indeps; i++)
    {
      coeff = c->coeff[i];
      fprintf (fp, "\"%s\",\n\t\t", coeff.v->name);
    }
  coeff = c->coeff[i];
  fprintf (fp, "\"%s\"};\n\t", coeff.v->name);
}
static void
reg_print_getvar (FILE *fp, pspp_linreg_cache *c)
{
  fprintf (fp, "static int\npspp_reg_getvar (char *v_name)\n{\n\t");
  fprintf (fp, "int i;\n\tint n_vars = %d;\n\t",c->n_indeps);
  reg_print_depvars (fp, c);
  fprintf (fp, "for (i = 0; i < n_vars; i++)\n\t{\n\t\t");
  fprintf (fp, "if (strcmp (v_name, model_depvars[i]) == 0)\n\t\t{\n\t\t\t");
  fprintf (fp, "return i;\n\t\t}\n\t}\n}\n");
}
static void
subcommand_export (int export, pspp_linreg_cache *c)
{
  FILE *fp;
  size_t i;
  struct pspp_linreg_coeff coeff;

  if (export)
    {
      assert (c != NULL);
      assert (model_file != NULL);
      assert (fp != NULL);
      fp = fopen (handle_get_filename (model_file), "w");
      fprintf (fp, "%s", reg_preamble);
      fprintf (fp, "#include <string.h>\n\n");
      reg_print_getvar (fp, c);
      fprintf (fp, "%s", reg_mean_cmt);
      fprintf (fp, "double\npspp_reg_estimate (const double *var_vals,");
      fprintf (fp, "const char *var_names[])\n{\n\t");
      fprintf (fp, "double model_coeffs[%d] = {", c->n_indeps);
      for (i = 1; i < c->n_indeps; i++)
        {
          coeff = c->coeff[i];
          fprintf (fp, "%.15e,\n\t\t", coeff.estimate);
        }
      coeff = c->coeff[i];
      fprintf (fp, "%.15e};\n\t", coeff.estimate);
      coeff = c->coeff[0];
      fprintf (fp, "double estimate = %.15e;\n\t", coeff.estimate);
      fprintf (fp, "int i;\n\tint j;\n\n\t");
      fprintf (fp, "for (i = 0; i < %d; i++)\n\t", c->n_indeps);
      fprintf (fp, "{\n\t\tj = pspp_reg_getvar (var_names[i]);\n");
      fprintf (fp, "\t\testimate += var_vals[j] * model_coeffs[j];\n");
      fprintf (fp, "\t}\n\t\n\treturn estimate;\n}\n\n");
      fprintf (fp, "double\npspp_reg_standard_error (const double *var_vals,");
      fprintf (fp, "const char *var_names[])\n{\n\t");
      fclose (fp);
    }
}
static int
regression_custom_export (struct cmd_regression *cmd)
{
  /* 0 on failure, 1 on success, 2 on failure that should result in syntax error */
  if (!lex_force_match ('('))
    return 0;
  
  if (lex_match ('*'))
    model_file = NULL;
  else 
    {
      model_file = fh_parse ();
      if (model_file == NULL)
        return 0; 
    }
  
  if (!lex_force_match (')'))
    return 0;

  return 1;
}

int
cmd_regression (void)
{
  if (!parse_regression (&cmd))
    {
      return CMD_FAILURE;
    }
  multipass_procedure_with_splits (run_regression, &cmd);

  return pspp_reg_rc;
}

/*
  Is variable k one of the dependent variables?
 */
static int
is_depvar (size_t k)
{
  size_t j = 0;
  for (j = 0; j < cmd.n_dependent; j++)
    {
      /*
         compare_var_names returns 0 if the variable
         names match.
       */
      if (!compare_var_names (cmd.v_dependent[j], cmd.v_variables[k], NULL))
        return 1;
    }
  return 0;
}

static void
run_regression (const struct casefile *cf, void *cmd_ UNUSED)
{
  size_t i;
  size_t n_data = 0;
  size_t row;
  size_t case_num;
  int n_indep;
  int j = 0;
  /*
     Keep track of the missing cases.
   */
  int *is_missing_case;
  const union value *val;
  struct casereader *r;
  struct casereader *r2;
  struct ccase c;
  struct variable *v;
  struct variable **indep_vars;
  struct design_matrix *X;
  gsl_vector *Y;
  pspp_linreg_cache *lcache;
  pspp_linreg_opts lopts;

  n_data = casefile_get_case_cnt (cf);

  is_missing_case = xnmalloc (n_data, sizeof (*is_missing_case));
  for (i = 0; i < n_data; i++)
    is_missing_case[i] = 0;

  n_indep = cmd.n_variables - cmd.n_dependent;
  indep_vars = xnmalloc (n_indep, sizeof *indep_vars);

  lopts.get_depvar_mean_std = 1;
  lopts.get_indep_mean_std = xnmalloc (n_indep, sizeof (int));


  /*
     Read from the active file. The first pass encodes categorical
     variables and drops cases with missing values.
   */
  j = 0;
  for (i = 0; i < cmd.n_variables; i++)
    {
      if (!is_depvar (i))
        {
          v = cmd.v_variables[i];
          indep_vars[j] = v;
          j++;
          if (v->type == ALPHA)
            {
              /* Make a place to hold the binary vectors 
                 corresponding to this variable's values. */
              cat_stored_values_create (v);
            }
          for (r = casefile_get_reader (cf);
               casereader_read (r, &c); case_destroy (&c))
            {
              row = casereader_cnum (r) - 1;
              
              val = case_data (&c, v->fv);
              cat_value_update (v, val);
              if (mv_is_value_missing (&v->miss, val))
                {
                  if (!is_missing_case[row])
                    {
                      /* Now it is missing. */
                      n_data--;
                      is_missing_case[row] = 1;
                    }
                }
            }
        }
    }

  Y = gsl_vector_alloc (n_data);
  X =
    design_matrix_create (n_indep, (const struct variable **) indep_vars,
                          n_data);
  lcache = pspp_linreg_cache_alloc (X->m->size1, X->m->size2);
  lcache->indep_means = gsl_vector_alloc (X->m->size2);
  lcache->indep_std = gsl_vector_alloc (X->m->size2);

  /*
     The second pass creates the design matrix.
   */
  row = 0;
  for (r2 = casefile_get_reader (cf); casereader_read (r2, &c);
       case_destroy (&c))
    /* Iterate over the cases. */
    {
      case_num = casereader_cnum (r2) - 1;
      if (!is_missing_case[case_num])
        {
          for (i = 0; i < cmd.n_variables; ++i) /* Iterate over the variables
                                                   for the current case. 
                                                 */
            {
              v = cmd.v_variables[i];
              val = case_data (&c, v->fv);
              /*
                 Independent/dependent variable separation. The
                 'variables' subcommand specifies a varlist which contains
                 both dependent and independent variables. The dependent
                 variables are specified with the 'dependent'
                 subcommand. We need to separate the two.
               */
              if (is_depvar (i))
                {
                  if (v->type != NUMERIC)
                    {
                      msg (SE,
                           gettext ("Dependent variable must be numeric."));
                      pspp_reg_rc = CMD_FAILURE;
                      return;
                    }
                  lcache->depvar = (const struct variable *) v;
                  gsl_vector_set (Y, row, val->f);
                }
              else
                {
                  if (v->type == ALPHA)
                    {
                      design_matrix_set_categorical (X, row, v, val);
                    }
                  else if (v->type == NUMERIC)
                    {
                      design_matrix_set_numeric (X, row, v, val);
                    }

                  lopts.get_indep_mean_std[i] = 1;
                }
            }
          row++;
        }
    }
  /*
     Now that we know the number of coefficients, allocate space
     and store pointers to the variables that correspond to the
     coefficients.
   */
  lcache->coeff = xnmalloc (X->m->size2 + 1, sizeof (*lcache->coeff));
  for (i = 0; i < X->m->size2; i++)
    {
      j = i + 1;                /* The first coeff is the intercept. */
      lcache->coeff[j].v =
        (const struct variable *) design_matrix_col_to_var (X, i);
      assert (lcache->coeff[j].v != NULL);
    }
  /*
    For large data sets, use QR decomposition.
   */
  if (n_data > sqrt (n_indep) && n_data > REG_LARGE_DATA)
    {
      lcache->method = PSPP_LINREG_SVD;
    }
  /* 
     Find the least-squares estimates and other statistics.
   */
  pspp_linreg ((const gsl_vector *) Y, X->m, &lopts, lcache);
  subcommand_statistics (cmd.a_statistics, lcache);
  subcommand_export (cmd.sbc_export, lcache);
  gsl_vector_free (Y);
  design_matrix_destroy (X);
  pspp_linreg_cache_free (lcache);
  free (lopts.get_indep_mean_std);
  free (indep_vars);
  free (is_missing_case);
  casereader_destroy (r);
  return;
}

/*
  Local Variables:   
  mode: c
  End:
*/