/* The estimates of the predictor coefficients */
gsl_vector *beta_hat;
+
+ /* The predicted classifications:
+ True Negative, True Positive, False Negative, False Positive */
+ double tn, tp, fn, fp;
};
return SYSMIS;
}
+static void output_classification_table (const struct lr_spec *cmd, const struct lr_result *res);
static void output_categories (const struct lr_spec *cmd, const struct lr_result *res);
y is the vector of observed independent variables
pi is the vector of estimates for y
- As a side effect, the likelihood is stored in LIKELIHOOD
+ Side effects:
+ the likelihood is stored in LIKELIHOOD;
+ the predicted values are placed in the respective tn, fn, tp fp values in RES
*/
static gsl_vector *
xt_times_y_pi (const struct lr_spec *cmd,
gsl_vector *output = gsl_vector_calloc (res->beta_hat->size);
*likelihood = 1.0;
+ res->tn = res->tp = res->fn = res->fp = 0;
for (reader = casereader_clone (input);
(c = casereader_read (reader)) != NULL; case_unref (c))
{
+ double pred_y = 0;
int v0;
double pi = pi_hat (cmd, res, x, n_x, c);
double weight = dict_get_case_weight (cmd->dict, c, &res->warn_bad_weight);
double in0 = predictor_value (c, x, n_x, res->cats, v0);
double *o = gsl_vector_ptr (output, v0);
*o += in0 * (y - pi) * weight;
+ pred_y += gsl_vector_get (res->beta_hat, v0) * in0;
+ }
+
+ pred_y = 1 / (1.0 + exp(-pred_y));
+ assert (pred_y >= 0);
+ assert (pred_y <= 1);
+
+ if (pred_y <= cmd->cut_point)
+ {
+ if (y == 0)
+ res->tn += weight;
+ else
+ res->fn += weight;
+ }
+ else
+ {
+ if (y == 0)
+ res->fp += weight;
+ else
+ res->tp += weight;
}
}
if (work.cats)
output_categories (cmd, &work);
+ output_classification_table (cmd, &work);
output_variables (cmd, &work);
gsl_matrix_free (work.hessian);
}
}
}
+ else if (lex_match_id (lexer, "CUT"))
+ {
+ if (lex_force_match (lexer, T_LPAREN))
+ {
+ if (! lex_force_num (lexer))
+ {
+ lex_error (lexer, NULL);
+ goto error;
+ }
+ lr.cut_point = lex_number (lexer);
+ if (lr.cut_point < 0 || lr.cut_point > 1.0)
+ {
+ msg (ME, _("Cut point value must be in the range [0,1]"));
+ goto error;
+ }
+ lex_get (lexer);
+ if ( ! lex_force_match (lexer, T_RPAREN))
+ {
+ lex_error (lexer, NULL);
+ goto error;
+ }
+ }
+ }
else
{
lex_error (lexer, NULL);
tab_submit (t);
}
+
+
+static void
+output_classification_table (const struct lr_spec *cmd, const struct lr_result *res)
+{
+ const struct fmt_spec *wfmt =
+ cmd->wv ? var_get_print_format (cmd->wv) : &F_8_0;
+
+ const int heading_columns = 3;
+ const int heading_rows = 3;
+
+ struct string sv0, sv1;
+
+ const int nc = heading_columns + 3;
+ const int nr = heading_rows + 3;
+
+ struct tab_table *t = tab_create (nc, nr);
+
+ ds_init_empty (&sv0);
+ ds_init_empty (&sv1);
+
+ tab_title (t, _("Classification Table"));
+
+ tab_headers (t, heading_columns, 0, heading_rows, 0);
+
+ tab_box (t, TAL_2, TAL_2, -1, -1, 0, 0, nc - 1, nr - 1);
+ tab_box (t, -1, -1, -1, TAL_1, heading_columns, 0, nc - 1, nr - 1);
+
+ tab_hline (t, TAL_2, 0, nc - 1, heading_rows);
+ tab_vline (t, TAL_2, heading_columns, 0, nr - 1);
+
+ tab_text (t, 0, heading_rows, TAB_CENTER | TAT_TITLE, _("Step 1"));
+
+
+ tab_joint_text (t, heading_columns, 0, nc - 1, 0,
+ TAB_CENTER | TAT_TITLE, _("Predicted"));
+
+ tab_joint_text (t, heading_columns, 1, heading_columns + 1, 1,
+ 0, var_to_string (cmd->dep_var) );
+
+ tab_joint_text (t, 1, 2, 2, 2,
+ TAB_LEFT | TAT_TITLE, _("Observed"));
+
+ tab_text (t, 1, 3, TAB_LEFT, var_to_string (cmd->dep_var) );
+
+
+ tab_joint_text (t, nc - 1, 1, nc - 1, 2,
+ TAB_CENTER | TAT_TITLE, _("Percentage\nCorrect"));
+
+
+ tab_joint_text (t, 1, nr - 1, 2, nr - 1,
+ TAB_LEFT | TAT_TITLE, _("Overall Percentage"));
+
+
+ tab_hline (t, TAL_1, 1, nc - 1, nr - 1);
+
+ var_append_value_name (cmd->dep_var, &res->y0, &sv0);
+ var_append_value_name (cmd->dep_var, &res->y1, &sv1);
+
+ tab_text (t, 2, heading_rows, TAB_LEFT, ds_cstr (&sv0));
+ tab_text (t, 2, heading_rows + 1, TAB_LEFT, ds_cstr (&sv1));
+
+ tab_text (t, heading_columns, 2, 0, ds_cstr (&sv0));
+ tab_text (t, heading_columns + 1, 2, 0, ds_cstr (&sv1));
+
+ ds_destroy (&sv0);
+ ds_destroy (&sv1);
+
+ tab_double (t, heading_columns, 3, 0, res->tn, wfmt);
+ tab_double (t, heading_columns + 1, 4, 0, res->tp, wfmt);
+
+ tab_double (t, heading_columns + 1, 3, 0, res->fp, wfmt);
+ tab_double (t, heading_columns, 4, 0, res->fn, wfmt);
+
+ tab_double (t, heading_columns + 2, 3, 0, 100 * res->tn / (res->tn + res->fp), 0);
+ tab_double (t, heading_columns + 2, 4, 0, 100 * res->tp / (res->tp + res->fn), 0);
+
+ tab_double (t, heading_columns + 2, 5, 0,
+ 100 * (res->tp + res->tn) / (res->tp + res->tn + res->fp + res->fn), 0);
+
+
+ tab_submit (t);
+}
projects / pspp / blobdiff