X-Git-Url: https://pintos-os.org/cgi-bin/gitweb.cgi?a=blobdiff_plain;f=src%2Flanguage%2Fstats%2Fregression.q;h=2c259d0f904c1834c7d7e1614fe4f47084bab5ee;hb=2cf38ce51a9f34961d68a75e0b312a591b5c9abf;hp=595e7e750c54d4d996b85b71eb141f87b4ba8d52;hpb=a1efcf97ca2f75f4be6a0389ff2372c03ed2d4e1;p=pspp-builds.git

diff --git a/src/language/stats/regression.q b/src/language/stats/regression.q
index 595e7e75..2c259d0f 100644
--- a/src/language/stats/regression.q
+++ b/src/language/stats/regression.q
@@ -148,9 +148,9 @@ reg_stats_r (pspp_linreg_cache * c)
   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));
+  std_error = sqrt (pspp_linreg_mse (c));
   t = tab_create (n_cols, n_rows, 0);
-  tab_dim (t, tab_natural_dimensions);
+  tab_dim (t, tab_natural_dimensions, NULL);
   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);
@@ -160,10 +160,10 @@ reg_stats_r (pspp_linreg_cache * c)
   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_double (t, 1, 1, TAB_RIGHT, sqrt (rsq), NULL);
+  tab_double (t, 2, 1, TAB_RIGHT, rsq, NULL);
+  tab_double (t, 3, 1, TAB_RIGHT, adjrsq, NULL);
+  tab_double (t, 4, 1, TAB_RIGHT, std_error, NULL);
   tab_title (t, _("Model Summary"));
   tab_submit (t);
 }
@@ -193,7 +193,7 @@ reg_stats_coeff (pspp_linreg_cache * c)
 
   t = tab_create (n_cols, n_rows, 0);
   tab_headers (t, 2, 0, 1, 0);
-  tab_dim (t, tab_natural_dimensions);
+  tab_dim (t, tab_natural_dimensions, NULL);
   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);
@@ -205,14 +205,14 @@ reg_stats_coeff (pspp_linreg_cache * c)
   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)"));
-  tab_float (t, 2, 1, 0, c->intercept, 10, 2);
+  tab_double (t, 2, 1, 0, c->intercept, NULL);
   std_err = sqrt (gsl_matrix_get (c->cov, 0, 0));
-  tab_float (t, 3, 1, 0, std_err, 10, 2);
-  tab_float (t, 4, 1, 0, 0.0, 10, 2);
+  tab_double (t, 3, 1, 0, std_err, NULL);
+  tab_double (t, 4, 1, 0, 0.0, NULL);
   t_stat = c->intercept / std_err;
-  tab_float (t, 5, 1, 0, t_stat, 10, 2);
+  tab_double (t, 5, 1, 0, t_stat, NULL);
   pval = 2 * gsl_cdf_tdist_Q (fabs (t_stat), 1.0);
-  tab_float (t, 6, 1, 0, pval, 10, 2);
+  tab_double (t, 6, 1, 0, pval, NULL);
   for (j = 0; j < c->n_coeffs; j++)
     {
       struct string tstr;
@@ -240,32 +240,32 @@ reg_stats_coeff (pspp_linreg_cache * c)
       /*
          Regression coefficients.
        */
-      tab_float (t, 2, this_row, 0, c->coeff[j]->estimate, 10, 2);
+      tab_double (t, 2, this_row, 0, c->coeff[j]->estimate, NULL);
       /*
          Standard error of the coefficients.
        */
       std_err = sqrt (gsl_matrix_get (c->cov, j + 1, j + 1));
-      tab_float (t, 3, this_row, 0, std_err, 10, 2);
+      tab_double (t, 3, this_row, 0, std_err, NULL);
       /*
          Standardized coefficient, i.e., regression coefficient
          if all variables had unit variance.
        */
       beta = pspp_coeff_get_sd (c->coeff[j]);
       beta *= c->coeff[j]->estimate / c->depvar_std;
-      tab_float (t, 4, this_row, 0, beta, 10, 2);
+      tab_double (t, 4, this_row, 0, beta, NULL);
 
       /*
          Test statistic for H0: coefficient is 0.
        */
       t_stat = c->coeff[j]->estimate / std_err;
-      tab_float (t, 5, this_row, 0, t_stat, 10, 2);
+      tab_double (t, 5, this_row, 0, t_stat, NULL);
       /*
          P values for the test statistic above.
        */
       pval =
 	2 * gsl_cdf_tdist_Q (fabs (t_stat),
 			     (double) (c->n_obs - c->n_coeffs));
-      tab_float (t, 6, this_row, 0, pval, 10, 2);
+      tab_double (t, 6, this_row, 0, pval, NULL);
       ds_destroy (&tstr);
     }
   tab_title (t, _("Coefficients"));
@@ -281,7 +281,7 @@ 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 mse = pspp_linreg_mse (c);
   const double F = msm / mse;
   const double pval = gsl_cdf_fdist_Q (F, c->dfm, c->dfe);
 
@@ -290,7 +290,7 @@ reg_stats_anova (pspp_linreg_cache * c)
   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_dim (t, tab_natural_dimensions, NULL);
 
   tab_box (t, TAL_2, TAL_2, -1, TAL_1, 0, 0, n_cols - 1, n_rows - 1);
 
@@ -309,23 +309,23 @@ reg_stats_anova (pspp_linreg_cache * c)
   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);
+  tab_double (t, 2, 1, 0, c->ssm, NULL);
+  tab_double (t, 2, 3, 0, c->sst, NULL);
+  tab_double (t, 2, 2, 0, c->sse, NULL);
 
 
   /* Degrees of freedom */
-  tab_text (t, 3, 1, TAB_RIGHT | TAT_PRINTF, "%g", c->dfm);
-  tab_text (t, 3, 2, TAB_RIGHT | TAT_PRINTF, "%g", c->dfe);
-  tab_text (t, 3, 3, TAB_RIGHT | TAT_PRINTF, "%g", c->dft);
+  tab_text_format (t, 3, 1, TAB_RIGHT, "%g", c->dfm);
+  tab_text_format (t, 3, 2, TAB_RIGHT, "%g", c->dfe);
+  tab_text_format (t, 3, 3, TAB_RIGHT, "%g", c->dft);
 
   /* Mean Squares */
-  tab_float (t, 4, 1, TAB_RIGHT, msm, 8, 3);
-  tab_float (t, 4, 2, TAB_RIGHT, mse, 8, 3);
+  tab_double (t, 4, 1, TAB_RIGHT, msm, NULL);
+  tab_double (t, 4, 2, TAB_RIGHT, mse, NULL);
 
-  tab_float (t, 5, 1, 0, F, 8, 3);
+  tab_double (t, 5, 1, 0, F, NULL);
 
-  tab_float (t, 6, 1, 0, pval, 8, 3);
+  tab_double (t, 6, 1, 0, pval, NULL);
 
   tab_title (t, _("ANOVA"));
   tab_submit (t);
@@ -381,7 +381,7 @@ reg_stats_bcov (pspp_linreg_cache * c)
   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_dim (t, tab_natural_dimensions, NULL);
   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);
@@ -398,8 +398,8 @@ reg_stats_bcov (pspp_linreg_cache * c)
 	{
 	  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_double (t, k + 2, i, TAB_CENTER,
+		     gsl_matrix_get (c->cov, row, col), NULL);
 	}
     }
   tab_title (t, _("Coefficient Correlations"));
@@ -689,17 +689,21 @@ subcommand_save (struct dataset *ds, int save, pspp_linreg_cache ** models)
 
       for (lc = models; lc < models + cmd.n_dependent; lc++)
 	{
-	  assert (*lc != NULL);
-	  assert ((*lc)->depvar != NULL);
-	  if (cmd.a_save[REGRESSION_SV_RESID])
-	    {
-	      reg_save_var (ds, "RES", regression_trns_resid_proc, *lc,
-			    &(*lc)->resid, n_trns);
-	    }
-	  if (cmd.a_save[REGRESSION_SV_PRED])
+	  if (*lc != NULL)
 	    {
-	      reg_save_var (ds, "PRED", regression_trns_pred_proc, *lc,
-			    &(*lc)->pred, n_trns);
+	      if ((*lc)->depvar != NULL)
+		{
+		  if (cmd.a_save[REGRESSION_SV_RESID])
+		    {
+		      reg_save_var (ds, "RES", regression_trns_resid_proc, *lc,
+				    &(*lc)->resid, n_trns);
+		    }
+		  if (cmd.a_save[REGRESSION_SV_PRED])
+		    {
+		      reg_save_var (ds, "PRED", regression_trns_pred_proc, *lc,
+				    &(*lc)->pred, n_trns);
+		    }
+		}
 	    }
 	}
     }
@@ -943,7 +947,7 @@ run_regression (struct casereader *input, struct cmd_regression *cmd,
 	      lopts.get_indep_mean_std[i] = 1;
 	    }
 	  models[k] = pspp_linreg_cache_alloc (dep_var, (const struct variable **) indep_vars,
-					       X->m->size1, X->m->size2);
+					       X->m->size1, n_indep);
 	  models[k]->depvar = dep_var;
 	  /*
 	     For large data sets, use QR decomposition.