REGRESSION: New option /STATISTICS=COLLIN

diff --git a/doc/regression.texi b/doc/regression.texi
index 9f236b8ea0e60fdc09eb8586ebf54f9578961234..2396e812a7ebdf28e3fd2a7077affa1c5aaea0c2 100644 (file)
--- a/doc/regression.texi
+++ b/doc/regression.texi
@@ -54,7 +54,7 @@ linear model.
 REGRESSION
         /VARIABLES=@var{var_list}
         /DEPENDENT=@var{var_list}
 REGRESSION
         /VARIABLES=@var{var_list}
         /DEPENDENT=@var{var_list}

-        /STATISTICS=@{ALL, DEFAULTS, R, COEFF, ANOVA, BCOV, CI[@var{conf}]@}
+        /STATISTICS=@{ALL, DEFAULTS, R, COEFF, ANOVA, BCOV, CI[@var{conf}, COLLIN]@}

         @{ /ORIGIN | /NOORIGIN @}
         /SAVE=@{PRED, RESID@}
 @end display
         @{ /ORIGIN | /NOORIGIN @}
         /SAVE=@{PRED, RESID@}
 @end display


@@ -90,6 +90,8 @@ which must be in parentheses, is the desired confidence level expressed as a per
 Analysis of variance table for the model.
 @item BCOV
 The covariance matrix for the estimated model coefficients.
 Analysis of variance table for the model.
 @item BCOV
 The covariance matrix for the estimated model coefficients.

+@item COLLIN
+The variance inflation factor.  This has no effect unless COEFF is also given.

 @item DEFAULT
 The same as if R, COEFF, and ANOVA had been selected.
 This is what you get if the /STATISTICS command is not specified,
 @item DEFAULT
 The same as if R, COEFF, and ANOVA had been selected.
 This is what you get if the /STATISTICS command is not specified,

diff --git a/src/language/stats/regression.c b/src/language/stats/regression.c
index 264a7c1ade150652131ad2b0fe2c0d96a7fb3bf7..3e0871df9eb176f819ccfad6ae2917f33aaa2f6b 100644 (file)
--- a/src/language/stats/regression.c
+++ b/src/language/stats/regression.c
@@ -60,6 +60,7 @@
 #define STATS_OUTS   8
 #define STATS_CI    16
 #define STATS_BCOV  32
 #define STATS_OUTS   8
 #define STATS_CI    16
 #define STATS_BCOV  32

+#define STATS_COLLIN 64

 
 #define STATS_DEFAULT  (STATS_R | STATS_COEFF | STATS_ANOVA | STATS_OUTS)
 
 
 #define STATS_DEFAULT  (STATS_R | STATS_COEFF | STATS_ANOVA | STATS_OUTS)
 


@@ -308,6 +309,10 @@ cmd_regression (struct lexer *lexer, struct dataset *ds)
                 {
                  statistics |= STATS_BCOV;
                 }
                 {
                  statistics |= STATS_BCOV;
                 }

+              else if (lex_match_id (lexer, "COLLIN"))
+                {
+                 statistics |= STATS_COLLIN;
+                }

               else if (lex_match_id (lexer, "CI"))
                 {
                  statistics |= STATS_CI;
               else if (lex_match_id (lexer, "CI"))
                 {
                  statistics |= STATS_CI;


@@ -512,6 +517,24 @@ fill_all_vars (const struct variable **vars, const struct regression *cmd)
     }
 }
 
     }
 }
 

+
+/* Fill the array VARS, with all the predictor variables from CMD, except
+   variable X */
+static void
+fill_predictor_x (const struct variable **vars, const struct variable *x, const struct regression *cmd)
+{
+  size_t i;
+  size_t n = 0;
+
+  for (i = 0; i < cmd->n_vars; i++)
+    {
+      if (cmd->vars[i] == x)
+       continue;
+
+      vars[n++] = cmd->vars[i];
+    }
+}
+

 /*
   Is variable k the dependent variable?
 */
 /*
   Is variable k the dependent variable?
 */


@@ -630,41 +653,58 @@ fill_covariance (gsl_matrix * cov, struct covariance *all_cov,
   STATISTICS subcommand output functions.
 */
 static void reg_stats_r (const struct linreg *,     const struct variable *);
   STATISTICS subcommand output functions.
 */
 static void reg_stats_r (const struct linreg *,     const struct variable *);

-static void reg_stats_coeff (const struct linreg *, const gsl_matrix *, const struct variable *, const struct regression *);
+static void reg_stats_coeff (const struct regression *, const struct regression_workspace *,
+                            const struct linreg *, const struct linreg *,
+                            const gsl_matrix *, const struct variable *);

 static void reg_stats_anova (const struct linreg *, const struct variable *);
 static void reg_stats_bcov (const struct linreg *,  const struct variable *);
 
 
 static void reg_stats_anova (const struct linreg *, const struct variable *);
 static void reg_stats_bcov (const struct linreg *,  const struct variable *);
 
 

-static void
-subcommand_statistics (const struct regression *cmd, const struct linreg * c, const gsl_matrix * cm,
-                       const struct variable *var)
-{
-  if (cmd->stats & STATS_R)
-    reg_stats_r     (c, var);
-
-  if (cmd->stats & STATS_ANOVA)
-    reg_stats_anova (c, var);
-
-  if (cmd->stats & STATS_COEFF)
-    reg_stats_coeff (c, cm, var, cmd);
-
-  if (cmd->stats & STATS_BCOV)
-    reg_stats_bcov  (c, var);
-}
-
-
-static void
-run_regression (const struct regression *cmd,
-                struct regression_workspace *ws,
-                struct casereader *input)
+static struct linreg **
+run_regression_get_models (const struct regression *cmd,
+                          struct regression_workspace *ws,
+                          struct casereader *input,
+                          bool output)

 {
   size_t i;
 {
   size_t i;

-  struct linreg **models;
+  struct linreg **models = NULL;
+  struct linreg **models_x = NULL;

 
 

-  int k;

   struct ccase *c;
   struct covariance *cov;
   struct casereader *reader;
   struct ccase *c;
   struct covariance *cov;
   struct casereader *reader;

+
+  if (cmd->stats & STATS_COLLIN)
+    {
+      for (i = 0; i < cmd->n_vars; i++)
+       {
+         struct regression subreg;
+         subreg.origin = cmd->origin;
+         subreg.ds = cmd->ds;
+         subreg.n_vars = cmd->n_vars - 1;
+         subreg.n_dep_vars = 1;
+         subreg.vars = xmalloc (sizeof (*subreg.vars) * cmd->n_vars - 1);
+         subreg.dep_vars = xmalloc (sizeof (*subreg.dep_vars));
+         fill_predictor_x (subreg.vars, cmd->vars[i], cmd);
+         subreg.dep_vars[0] = cmd->vars[i];
+         subreg.stats = STATS_R;
+         subreg.ci = 0;
+         subreg.resid = false;
+         subreg.pred = false;
+
+         struct regression_workspace subws;
+         subws.extras = 0;
+         subws.res_idx = -1;
+         subws.pred_idx = -1;
+         subws.writer = NULL;
+         subws.reader = NULL;
+         subws.residvars = NULL;
+         subws.predvars = NULL;
+
+         models_x = run_regression_get_models (&subreg, &subws, input, false);
+       }
+    }
+

   size_t n_all_vars = get_n_all_vars (cmd);
   const struct variable **all_vars = xnmalloc (n_all_vars, sizeof (*all_vars));
 
   size_t n_all_vars = get_n_all_vars (cmd);
   const struct variable **all_vars = xnmalloc (n_all_vars, sizeof (*all_vars));
 


@@ -691,13 +731,14 @@ run_regression (const struct regression *cmd,
   }
 
   models = xcalloc (cmd->n_dep_vars, sizeof (*models));
   }
 
   models = xcalloc (cmd->n_dep_vars, sizeof (*models));

-  for (k = 0; k < cmd->n_dep_vars; k++)
+
+  for (int k = 0; k < cmd->n_dep_vars; k++)

     {
       const struct variable **vars = xnmalloc (cmd->n_vars, sizeof (*vars));
       const struct variable *dep_var = cmd->dep_vars[k];
       int n_indep = identify_indep_vars (cmd, vars, dep_var);
     {
       const struct variable **vars = xnmalloc (cmd->n_vars, sizeof (*vars));
       const struct variable *dep_var = cmd->dep_vars[k];
       int n_indep = identify_indep_vars (cmd, vars, dep_var);

-      gsl_matrix *this_cm = gsl_matrix_alloc (n_indep + 1, n_indep + 1);
-      double n_data = fill_covariance (this_cm, cov, vars, n_indep,
+      gsl_matrix *cov_matrix = gsl_matrix_alloc (n_indep + 1, n_indep + 1);
+      double n_data = fill_covariance (cov_matrix, cov, vars, n_indep,

                                 dep_var, all_vars, n_all_vars, means);
       models[k] = linreg_alloc (dep_var, vars,  n_data, n_indep, cmd->origin);
       for (i = 0; i < n_indep; i++)
                                 dep_var, all_vars, n_all_vars, means);
       models[k] = linreg_alloc (dep_var, vars,  n_data, n_indep, cmd->origin);
       for (i = 0; i < n_indep; i++)


@@ -707,39 +748,75 @@ run_regression (const struct regression *cmd,
       linreg_set_depvar_mean (models[k], means[i]);
       if (n_data > 0)
         {
       linreg_set_depvar_mean (models[k], means[i]);
       if (n_data > 0)
         {

-          /*
-             Find the least-squares estimates and other statistics.
-           */
-          linreg_fit (this_cm, models[k]);
+         linreg_fit (cov_matrix, models[k]);

 
 

-          if (!taint_has_tainted_successor (casereader_get_taint (input)))
+          if (output && !taint_has_tainted_successor (casereader_get_taint (input)))

             {
             {

-              subcommand_statistics (cmd, models[k], this_cm, dep_var);
+             /*
+               Find the least-squares estimates and other statistics.
+             */
+             if (cmd->stats & STATS_R)
+               reg_stats_r (models[k], dep_var);
+
+             if (cmd->stats & STATS_ANOVA)
+               reg_stats_anova (models[k], dep_var);
+
+             if (cmd->stats & STATS_COEFF)
+               reg_stats_coeff (cmd, ws, models[k],
+                                models_x ? models_x[k] : NULL,
+                                cov_matrix, dep_var);
+
+             if (cmd->stats & STATS_BCOV)
+               reg_stats_bcov  (models[k], dep_var);

             }
         }
       else
         {
           msg (SE, _("No valid data found. This command was skipped."));
         }
             }
         }
       else
         {
           msg (SE, _("No valid data found. This command was skipped."));
         }

-      gsl_matrix_free (this_cm);

       free (vars);
     }
 
 
       free (vars);
     }
 
 

+  casereader_destroy (reader);
+
+
+  if (models_x)
+    {
+      for (int k = 0; k < cmd->n_dep_vars; k++)
+       linreg_unref (models_x[k]);
+
+      free (models_x);
+    }
+
+  free (all_vars);
+  free (means);
+  covariance_destroy (cov);
+  return models;
+}
+
+static void
+run_regression (const struct regression *cmd,
+                struct regression_workspace *ws,
+                struct casereader *input)
+{
+  struct linreg **models = run_regression_get_models (cmd, ws, input, true);
+

   if (ws->extras > 0)
    {
   if (ws->extras > 0)
    {

-      struct casereader *r = casereader_clone (reader);
+     struct ccase *c;
+      struct casereader *r = casereader_clone (input);

 
       for (; (c = casereader_read (r)) != NULL; case_unref (c))
         {
           struct ccase *outc = case_create (casewriter_get_proto (ws->writer));
 
       for (; (c = casereader_read (r)) != NULL; case_unref (c))
         {
           struct ccase *outc = case_create (casewriter_get_proto (ws->writer));

-          for (k = 0; k < cmd->n_dep_vars; k++)
+          for (int k = 0; k < cmd->n_dep_vars; k++)

             {
               const struct variable **vars = xnmalloc (cmd->n_vars, sizeof (*vars));
               const struct variable *dep_var = cmd->dep_vars[k];
               int n_indep = identify_indep_vars (cmd, vars, dep_var);
               double *vals = xnmalloc (n_indep, sizeof (*vals));
             {
               const struct variable **vars = xnmalloc (cmd->n_vars, sizeof (*vars));
               const struct variable *dep_var = cmd->dep_vars[k];
               int n_indep = identify_indep_vars (cmd, vars, dep_var);
               double *vals = xnmalloc (n_indep, sizeof (*vals));

-              for (i = 0; i < n_indep; i++)
+              for (int i = 0; i < n_indep; i++)

                 {
                   const union value *tmp = case_data (c, vars[i]);
                   vals[i] = tmp->f;
                 {
                   const union value *tmp = case_data (c, vars[i]);
                   vals[i] = tmp->f;


@@ -765,18 +842,13 @@ run_regression (const struct regression *cmd,
       casereader_destroy (r);
     }
 
       casereader_destroy (r);
     }
 

-  casereader_destroy (reader);
-
-  for (k = 0; k < cmd->n_dep_vars; k++)
+  for (int k = 0; k < cmd->n_dep_vars; k++)

     {
       linreg_unref (models[k]);
     }
     {
       linreg_unref (models[k]);
     }

-  free (models);

 
 

-  free (all_vars);
-  free (means);
+  free (models);

   casereader_destroy (input);
   casereader_destroy (input);

-  covariance_destroy (cov);

 }
 
 \f
 }
 
 \f


@@ -812,7 +884,9 @@ reg_stats_r (const struct linreg * c, const struct variable *var)
   Table showing estimated regression coefficients.
 */
 static void
   Table showing estimated regression coefficients.
 */
 static void

-reg_stats_coeff (const struct linreg * c, const gsl_matrix *cov, const struct variable *var, const struct regression *cmd)
+reg_stats_coeff (const struct regression *cmd, const struct regression_workspace *ws,
+                const struct linreg *c, const struct linreg *c_x,
+                const gsl_matrix *cov, const struct variable *var)

 {
   struct pivot_table *table = pivot_table_create__ (
     pivot_value_new_text_format (N_("Coefficients (%s)"),
 {
   struct pivot_table *table = pivot_table_create__ (
     pivot_value_new_text_format (N_("Coefficients (%s)"),


@@ -837,6 +911,12 @@ reg_stats_coeff (const struct linreg * c, const gsl_matrix *cov, const struct va
                                     N_("Upper Bound"));
     }
 
                                     N_("Upper Bound"));
     }
 

+  if (cmd->stats & STATS_COLLIN)
+    pivot_category_create_group (statistics->root,
+                                N_("Collinearity Statistics"),
+                                N_("Tolerance"), N_("VIF"));
+
+

   struct pivot_dimension *variables = pivot_dimension_create (
     table, PIVOT_AXIS_ROW, N_("Variables"));
 
   struct pivot_dimension *variables = pivot_dimension_create (
     table, PIVOT_AXIS_ROW, N_("Variables"));
 


@@ -851,19 +931,31 @@ reg_stats_coeff (const struct linreg * c, const gsl_matrix *cov, const struct va
 
       double std_err = sqrt (gsl_matrix_get (linreg_cov (c), 0, 0));
       double t_stat = linreg_intercept (c) / std_err;
 
       double std_err = sqrt (gsl_matrix_get (linreg_cov (c), 0, 0));
       double t_stat = linreg_intercept (c) / std_err;

-      double entries[] = {
+      double base_entries[] = {

         linreg_intercept (c),
         std_err,
         0.0,
         t_stat,
         2.0 * gsl_cdf_tdist_Q (fabs (t_stat),
                                linreg_n_obs (c) - linreg_n_coeffs (c)),
         linreg_intercept (c),
         std_err,
         0.0,
         t_stat,
         2.0 * gsl_cdf_tdist_Q (fabs (t_stat),
                                linreg_n_obs (c) - linreg_n_coeffs (c)),

-        linreg_intercept (c) - tval * std_err,
-        linreg_intercept (c) + tval * std_err,

       };
       };

-      for (size_t i = 0; i < sizeof entries / sizeof *entries; i++)
-        pivot_table_put2 (table, i, var_idx,
-                          pivot_value_new_number (entries[i]));
+
+      size_t col = 0;
+      for (size_t i = 0; i < sizeof base_entries / sizeof *base_entries; i++)
+        pivot_table_put2 (table, col++, var_idx,
+                          pivot_value_new_number (base_entries[i]));
+
+      if (cmd->stats & STATS_CI)
+       {
+         double interval_entries[] = {
+           linreg_intercept (c) - tval * std_err,
+           linreg_intercept (c) + tval * std_err,
+         };
+
+         for (size_t i = 0; i < sizeof interval_entries / sizeof *interval_entries; i++)
+           pivot_table_put2 (table, col++, var_idx,
+                             pivot_value_new_number (interval_entries[i]));
+       }

     }
 
   for (size_t j = 0; j < linreg_n_coeffs (c); j++)
     }
 
   for (size_t j = 0; j < linreg_n_coeffs (c); j++)


@@ -874,19 +966,47 @@ reg_stats_coeff (const struct linreg * c, const gsl_matrix *cov, const struct va
 
       double std_err = sqrt (gsl_matrix_get (linreg_cov (c), j + 1, j + 1));
       double t_stat = linreg_coeff (c, j) / std_err;
 
       double std_err = sqrt (gsl_matrix_get (linreg_cov (c), j + 1, j + 1));
       double t_stat = linreg_coeff (c, j) / std_err;

-      double entries[] = {
+      double base_entries[] = {

         linreg_coeff (c, j),
         sqrt (gsl_matrix_get (linreg_cov (c), j + 1, j + 1)),
         (sqrt (gsl_matrix_get (cov, j, j)) * linreg_coeff (c, j) /
          sqrt (gsl_matrix_get (cov, cov->size1 - 1, cov->size2 - 1))),
         t_stat,
         linreg_coeff (c, j),
         sqrt (gsl_matrix_get (linreg_cov (c), j + 1, j + 1)),
         (sqrt (gsl_matrix_get (cov, j, j)) * linreg_coeff (c, j) /
          sqrt (gsl_matrix_get (cov, cov->size1 - 1, cov->size2 - 1))),
         t_stat,

-        2 * gsl_cdf_tdist_Q (fabs (t_stat), df),
-        linreg_coeff (c, j)  - tval * std_err,
-        linreg_coeff (c, j)  + tval * std_err,
+        2 * gsl_cdf_tdist_Q (fabs (t_stat), df)

       };
       };

-      for (size_t i = 0; i < sizeof entries / sizeof *entries; i++)
-        pivot_table_put2 (table, i, var_idx,
-                          pivot_value_new_number (entries[i]));
+
+      size_t col = 0;
+      for (size_t i = 0; i < sizeof base_entries / sizeof *base_entries; i++)
+        pivot_table_put2 (table, col++, var_idx,
+                          pivot_value_new_number (base_entries[i]));
+
+      if (cmd->stats & STATS_CI)
+       {
+         double interval_entries[] = {
+           linreg_coeff (c, j)  - tval * std_err,
+           linreg_coeff (c, j)  + tval * std_err,
+         };
+
+
+         for (size_t i = 0; i < sizeof interval_entries / sizeof *interval_entries; i++)
+           pivot_table_put2 (table, col++, var_idx,
+                             pivot_value_new_number (interval_entries[i]));
+       }
+
+      if (cmd->stats & STATS_COLLIN)
+       {
+         assert (c_x);
+         double rsq = linreg_ssreg (c_x) / linreg_sst (c_x);
+
+         double collin_entries[] = {
+           1.0 - rsq,
+           1.0 / (1.0 - rsq),
+         };
+
+         for (size_t i = 0; i < sizeof collin_entries / sizeof *collin_entries; i++)
+           pivot_table_put2 (table, col++, var_idx,
+                             pivot_value_new_number (collin_entries[i]));
+       }

     }
 
   pivot_table_submit (table);
     }
 
   pivot_table_submit (table);


@@ -978,4 +1098,3 @@ reg_stats_bcov (const struct linreg * c, const struct variable *var)
 
   pivot_table_submit (table);
 }
 
   pivot_table_submit (table);
 }

-

diff --git a/tests/language/stats/regression.at b/tests/language/stats/regression.at
index 1fc5662da03e4c0e7af55a8c3623fc139aed469e..af49f875843ad48e01713057b19ce8df2d6e9de2 100644 (file)
--- a/tests/language/stats/regression.at
+++ b/tests/language/stats/regression.at
@@ -2380,3 +2380,80 @@ Table: Coefficients (Mean time to repair (hours) )
 Mean time between failures (months) ,3.11,.09,.99,33.39,.000
 ])
 AT_CLEANUP
 Mean time between failures (months) ,3.11,.09,.99,33.39,.000
 ])
 AT_CLEANUP

+
+
+AT_SETUP([LINEAR REGRESSION collinearity])
+AT_DATA([regression-collin.sps], [dnl
+SET FORMAT=F10.3.
+
+data list notable  list /competence_x1 motivation_x2 performance_y.
+begin data
+32   34   36
+35   37   39
+38   45   49
+31   41   41
+36   40   38
+32   38   36
+33   39   37
+31   40   41
+30   37   40
+35   37   43
+31   34   36
+34   32   35
+31   42   34
+25   36   40
+35   42   40
+36   41   44
+30   38   32
+34   41   41
+34   41   44
+22   27   26
+27   26   33
+30   30   35
+30   35   37
+37   39   44
+29   35   36
+31   35   29
+31   45   41
+29   30   32
+29   35   36
+31   37   37
+36   45   42
+32   44   39
+27   26   31
+33   39   35
+20   25   28
+30   36   39
+27   37   39
+25   39   36
+32   38   38
+32   38   35
+end data.
+
+regression /variables=competence_x1 motivation_x2
+       /statistics=defaults collin
+       /dependent=performance_y
+       .
+])
+
+
+AT_CHECK([pspp -O format=csv regression-collin.sps], [0], [dnl
+Table: Model Summary (performance_y)
+R,R Square,Adjusted R Square,Std. Error of the Estimate
+.785,.616,.595,2.980
+
+Table: ANOVA (performance_y)
+,Sum of Squares,df,Mean Square,F,Sig.
+Regression,526.494,2,263.247,29.641,.000
+Residual,328.606,37,8.881,,
+Total,855.100,39,,,
+
+Table: Coefficients (performance_y)
+,Unstandardized Coefficients,,Standardized Coefficients,t,Sig.,Collinearity Statistics,
+,B,Std. Error,Beta,,,Tolerance,VIF
+(Constant),7.220,4.020,.000,1.796,.080,,
+competence_x1,.432,.166,.358,2.609,.013,.552,1.812
+motivation_x2,.453,.125,.499,3.636,.001,.552,1.812
+])
+
+AT_CLEANUP