X-Git-Url: https://pintos-os.org/cgi-bin/gitweb.cgi?a=blobdiff_plain;f=src%2Flanguage%2Fstats%2Fregression.q;h=4f6f0b862196b6a7365466147145714f3969bfa9;hb=e93857e0338c2a71794db2b57e3659bbe6946240;hp=92afb378cea85c45d308d7a38845d2640cb80d68;hpb=f1861ea4c715dfaddd84ff14be2ab92b13379014;p=pspp-builds.git

diff --git a/src/language/stats/regression.q b/src/language/stats/regression.q
index 92afb378..4f6f0b86 100644
--- a/src/language/stats/regression.q
+++ b/src/language/stats/regression.q
@@ -41,7 +41,7 @@
 #include <libpspp/taint.h>
 #include <math/design-matrix.h>
 #include <math/coefficient.h>
-#include <math/linreg/linreg.h>
+#include <math/linreg.h>
 #include <math/moments.h>
 #include <output/table.h>
 
@@ -180,7 +180,6 @@ reg_stats_coeff (pspp_linreg_cache * c)
   int this_row;
   double t_stat;
   double pval;
-  double coeff;
   double std_err;
   double beta;
   const char *label;
@@ -209,8 +208,7 @@ reg_stats_coeff (pspp_linreg_cache * c)
   tab_float (t, 2, 1, 0, c->intercept, 10, 2);
   std_err = sqrt (gsl_matrix_get (c->cov, 0, 0));
   tab_float (t, 3, 1, 0, std_err, 10, 2);
-  beta = c->intercept / c->depvar_std;
-  tab_float (t, 4, 1, 0, beta, 10, 2);
+  tab_float (t, 4, 1, 0, 0.0, 10, 2);
   t_stat = c->intercept / std_err;
   tab_float (t, 5, 1, 0, t_stat, 10, 2);
   pval = 2 * gsl_cdf_tdist_Q (fabs (t_stat), 1.0);
@@ -242,25 +240,24 @@ reg_stats_coeff (pspp_linreg_cache * c)
       /*
          Regression coefficients.
        */
-      coeff = c->coeff[j]->estimate;
-      tab_float (t, 2, this_row, 0, coeff, 10, 2);
+      tab_float (t, 2, this_row, 0, c->coeff[j]->estimate, 10, 2);
       /*
          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);
       /*
-         'Standardized' coefficient, i.e., regression coefficient
+         Standardized coefficient, i.e., regression coefficient
          if all variables had unit variance.
        */
-      beta = gsl_vector_get (c->indep_std, j + 1);
-      beta *= coeff / c->depvar_std;
+      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);
 
       /*
          Test statistic for H0: coefficient is 0.
        */
-      t_stat = coeff / std_err;
+      t_stat = c->coeff[j]->estimate / std_err;
       tab_float (t, 5, this_row, 0, t_stat, 10, 2);
       /*
          P values for the test statistic above.
@@ -318,12 +315,11 @@ reg_stats_anova (pspp_linreg_cache * c)
 
 
   /* 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);
+  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);
 
   /* Mean Squares */
-
   tab_float (t, 4, 1, TAB_RIGHT, msm, 8, 3);
   tab_float (t, 4, 2, TAB_RIGHT, mse, 8, 3);
 
@@ -334,21 +330,25 @@ reg_stats_anova (pspp_linreg_cache * c)
   tab_title (t, _("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)
 {
@@ -861,39 +861,6 @@ coeff_init (pspp_linreg_cache * c, struct design_matrix *dm)
   pspp_coeff_init (c->coeff, dm);
 }
 
-/*
-  Put the moments in the linreg cache.
- */
-static void
-compute_moments (pspp_linreg_cache * c, struct moments_var *mom,
-		 struct design_matrix *dm, size_t n)
-{
-  size_t i;
-  size_t j;
-  double weight;
-  double mean;
-  double variance;
-  double skewness;
-  double kurtosis;
-  /*
-     Scan the variable names in the columns of the design matrix.
-     When we find the variable we need, insert its mean in the cache.
-   */
-  for (i = 0; i < dm->m->size2; i++)
-    {
-      for (j = 0; j < n; j++)
-	{
-	  if (design_matrix_col_to_var (dm, i) == (mom + j)->v)
-	    {
-	      moments1_calculate ((mom + j)->m, &weight, &mean, &variance,
-				  &skewness, &kurtosis);
-	      gsl_vector_set (c->indep_means, i, mean);
-	      gsl_vector_set (c->indep_std, i, sqrt (variance));
-	    }
-	}
-    }
-}
-
 static bool
 run_regression (struct casereader *input, struct cmd_regression *cmd,
 		struct dataset *ds, pspp_linreg_cache **models)
@@ -956,9 +923,9 @@ run_regression (struct casereader *input, struct cmd_regression *cmd,
       n_indep = identify_indep_vars (indep_vars, dep_var);
       reader = casereader_clone (input);
       reader = casereader_create_filter_missing (reader, indep_vars, n_indep,
-						 MV_ANY, NULL);
+						 MV_ANY, NULL, NULL);
       reader = casereader_create_filter_missing (reader, &dep_var, 1,
-						 MV_ANY, NULL);
+						 MV_ANY, NULL, NULL);
       n_data = prepare_categories (casereader_clone (reader),
 				   indep_vars, n_indep, mom);
 
@@ -973,7 +940,8 @@ run_regression (struct casereader *input, struct cmd_regression *cmd,
 	    {
 	      lopts.get_indep_mean_std[i] = 1;
 	    }
-	  models[k] = pspp_linreg_cache_alloc (X->m->size1, X->m->size2);
+	  models[k] = pspp_linreg_cache_alloc (dep_var, (const struct variable **) indep_vars,
+					       X->m->size1, X->m->size2);
 	  models[k]->depvar = dep_var;
 	  /*
 	     For large data sets, use QR decomposition.
@@ -1010,8 +978,7 @@ run_regression (struct casereader *input, struct cmd_regression *cmd,
 	  /*
 	     Find the least-squares estimates and other statistics.
 	   */
-	  pspp_linreg ((const gsl_vector *) Y, X->m, &lopts, models[k]);
-	  compute_moments (models[k], mom, X, n_variables);
+	  pspp_linreg ((const gsl_vector *) Y, X, &lopts, models[k]);
 
 	  if (!taint_has_tainted_successor (casereader_get_taint (input)))
 	    {