/* What results should be presented */
unsigned int print;
- double cut_point;
+ /* Inverse logit of the cut point */
+ double ilogit_cut_point;
};
struct casereader *input,
const struct variable **x, size_t n_x,
const struct variable *y_var,
- double *likelihood)
+ double *llikelihood)
{
struct casereader *reader;
struct ccase *c;
gsl_vector *output = gsl_vector_calloc (res->beta_hat->size);
- *likelihood = 1.0;
+ *llikelihood = 0.0;
res->tn = res->tp = res->fn = res->fp = 0;
for (reader = casereader_clone (input);
(c = casereader_read (reader)) != NULL; case_unref (c))
double y = map_dependent_var (cmd, res, case_data (c, y_var));
- *likelihood *= pow (pi, weight * y) * pow (1 - pi, weight * (1 - y));
+ *llikelihood += (weight * y) * log (pi) + log (1 - pi) * weight * (1 - y);
for (v0 = 0; v0 < res->beta_hat->size; ++v0)
{
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)
+ /* Count the number of cases which would be correctly/incorrectly classified by this
+ estimated model */
+ if (pred_y <= cmd->ilogit_cut_point)
{
if (y == 0)
res->tn += weight;
double weight = dict_get_case_weight (cmd->dict, c, &res->warn_bad_weight);
const union value *depval = case_data (c, cmd->dep_var);
+ if (var_is_value_missing (cmd->dep_var, depval, cmd->exclude))
+ {
+ missing = true;
+ }
+ else
for (v = 0; v < cmd->n_indep_vars; ++v)
{
const union value *val = case_data (c, cmd->indep_vars[v]);
bool converged = false;
- /* Set the likelihoods to a negative sentinel value */
- double likelihood = -1;
- double prev_likelihood = -1;
- double initial_likelihood = -1;
+ /* Set the log likelihoods to a sentinel value */
+ double log_likelihood = SYSMIS;
+ double prev_log_likelihood = SYSMIS;
+ double initial_log_likelihood = SYSMIS;
struct lr_result work;
work.n_missing = 0;
work.warn_bad_weight = true;
work.cats = NULL;
work.beta_hat = NULL;
+ work.hessian = NULL;
/* Get the initial estimates of \beta and their standard errors.
And perform other auxilliary initialisation. */
if (! initial_pass (cmd, &work, input))
- return false;
+ goto error;
for (i = 0; i < cmd->n_cat_predictors; ++i)
{
msg (ME, _("Category %s does not have at least two distinct values. Logistic regression will not be run."),
ds_cstr(&str));
ds_destroy (&str);
- return false;
+ goto error;
}
}
NULL,
NULL);
+ input = casereader_create_filter_missing (input,
+ &cmd->dep_var,
+ 1,
+ cmd->exclude,
+ NULL,
+ NULL);
work.hessian = gsl_matrix_calloc (work.beta_hat->size, work.beta_hat->size);
v = xt_times_y_pi (cmd, &work, input,
cmd->predictor_vars, cmd->n_predictor_vars,
cmd->dep_var,
- &likelihood);
+ &log_likelihood);
{
/* delta = M.v */
gsl_vector_free (delta);
}
- if ( prev_likelihood >= 0)
+ if (i > 0)
{
- if (-log (likelihood) > -(1.0 - cmd->lcon) * log (prev_likelihood))
+ if (-log_likelihood > -(1.0 - cmd->lcon) * prev_log_likelihood)
{
msg (MN, _("Estimation terminated at iteration number %d because Log Likelihood decreased by less than %g%%"), i + 1, 100 * cmd->lcon);
converged = true;
}
}
if (i == 0)
- initial_likelihood = likelihood;
- prev_likelihood = likelihood;
+ initial_log_likelihood = log_likelihood;
+ prev_log_likelihood = log_likelihood;
if (converged)
break;
}
- casereader_destroy (input);
- assert (initial_likelihood >= 0);
+
+
if ( ! converged)
msg (MW, _("Estimation terminated at iteration number %d because maximum iterations has been reached"), i );
- output_model_summary (&work, initial_likelihood, likelihood);
+ output_model_summary (&work, initial_log_likelihood, log_likelihood);
if (work.cats)
output_categories (cmd, &work);
output_classification_table (cmd, &work);
output_variables (cmd, &work);
+ casereader_destroy (input);
gsl_matrix_free (work.hessian);
gsl_vector_free (work.beta_hat);
-
categoricals_destroy (work.cats);
return true;
+
+ error:
+ casereader_destroy (input);
+ gsl_matrix_free (work.hessian);
+ gsl_vector_free (work.beta_hat);
+ categoricals_destroy (work.cats);
+
+ return false;
}
struct variable_node
int
cmd_logistic (struct lexer *lexer, struct dataset *ds)
{
+ int i;
/* Temporary location for the predictor variables.
These may or may not include the categorical predictors */
const struct variable **pred_vars;
size_t n_pred_vars;
+ double cp = 0.5;
int v, x;
struct lr_spec lr;
lr.lcon = 0.0000;
lr.bcon = 0.001;
lr.min_epsilon = 0.00000001;
- lr.cut_point = 0.5;
lr.constant = true;
lr.confidence = 95;
lr.print = PRINT_DEFAULT;
lex_error (lexer, NULL);
goto error;
}
- lr.cut_point = lex_number (lexer);
- if (lr.cut_point < 0 || lr.cut_point > 1.0)
+ cp = lex_number (lexer);
+
+ if (cp < 0 || cp > 1.0)
{
msg (ME, _("Cut point value must be in the range [0,1]"));
goto error;
}
}
+ lr.ilogit_cut_point = - log (1/cp - 1);
+
+
/* Copy the predictor variables from the temporary location into the
final one, dropping any categorical variables which appear there.
FIXME: This is O(NxM).
*/
-
{
struct variable_node *vn, *next;
struct hmap allvars;
ok = proc_commit (ds) && ok;
}
+ for (i = 0 ; i < lr.n_cat_predictors; ++i)
+ {
+ interaction_destroy (lr.cat_predictors[i]);
+ }
free (lr.predictor_vars);
free (lr.cat_predictors);
free (lr.indep_vars);
error:
+ for (i = 0 ; i < lr.n_cat_predictors; ++i)
+ {
+ interaction_destroy (lr.cat_predictors[i]);
+ }
free (lr.predictor_vars);
free (lr.cat_predictors);
free (lr.indep_vars);
if (cmd->print & PRINT_CI)
{
+ int last_ci = nr;
double wc = gsl_cdf_ugaussian_Pinv (0.5 + cmd->confidence / 200.0);
wc *= sqrt (sigma2);
- if (idx < cmd->n_predictor_vars)
+ if (cmd->constant)
+ last_ci--;
+
+ if (row < last_ci)
{
tab_double (t, 8, row, 0, exp (b - wc), 0);
tab_double (t, 9, row, 0, exp (b + wc), 0);
/* Show the model summary box */
static void
output_model_summary (const struct lr_result *res,
- double initial_likelihood, double likelihood)
+ double initial_log_likelihood, double log_likelihood)
{
const int heading_columns = 0;
const int heading_rows = 1;
tab_text (t, 0, 0, TAB_LEFT | TAT_TITLE, _("Step 1"));
tab_text (t, 1, 0, TAB_CENTER | TAT_TITLE, _("-2 Log likelihood"));
- tab_double (t, 1, 1, 0, -2 * log (likelihood), 0);
+ tab_double (t, 1, 1, 0, -2 * log_likelihood, 0);
tab_text (t, 2, 0, TAB_CENTER | TAT_TITLE, _("Cox & Snell R Square"));
- cox = 1.0 - pow (initial_likelihood /likelihood, 2 / res->cc);
+ cox = 1.0 - exp((initial_log_likelihood - log_likelihood) * (2 / res->cc));
tab_double (t, 2, 1, 0, cox, 0);
tab_text (t, 3, 0, TAB_CENTER | TAT_TITLE, _("Nagelkerke R Square"));
- tab_double (t, 3, 1, 0, cox / ( 1.0 - pow (initial_likelihood, 2 / res->cc)), 0);
+ tab_double (t, 3, 1, 0, cox / ( 1.0 - exp(initial_log_likelihood * (2 / res->cc))), 0);
tab_submit (t);
projects / pspp / blobdiff