along with this program. If not, see <http://www.gnu.org/licenses/>. */
-/*
- References:
+/*
+ References:
1. "Coding Logistic Regression with Newton-Raphson", James McCaffrey
http://msdn.microsoft.com/en-us/magazine/jj618304.aspx
2. "SPSS Statistical Algorithms" Chapter LOGISTIC REGRESSION Algorithms
- The Newton Raphson method finds successive approximations to $\bf b$ where
+ The Newton Raphson method finds successive approximations to $\bf b$ where
approximation ${\bf b}_t$ is (hopefully) better than the previous ${\bf b}_{t-1}$.
$ {\bf b}_t = {\bf b}_{t -1} + ({\bf X}^T{\bf W}_{t-1}{\bf X})^{-1}{\bf X}^T({\bf y} - {\bf \pi}_{t-1})$
where:
- $\bf X$ is the $n \times p$ design matrix, $n$ being the number of cases,
+ $\bf X$ is the $n \times p$ design matrix, $n$ being the number of cases,
$p$ the number of parameters, \par
$\bf W$ is the diagonal matrix whose diagonal elements are
$\hat{\pi}_0(1 - \hat{\pi}_0), \, \hat{\pi}_1(1 - \hat{\pi}_2)\dots \hat{\pi}_{n-1}(1 - \hat{\pi}_{n-1})$
#include <config.h>
-#include <gsl/gsl_blas.h>
+#include <gsl/gsl_blas.h>
#include <gsl/gsl_linalg.h>
#include <gsl/gsl_cdf.h>
#include "language/lexer/value-parser.h"
#include "language/lexer/variable-parser.h"
#include "libpspp/assertion.h"
+#include "libpspp/hash-functions.h"
+#include "libpspp/hmap.h"
#include "libpspp/ll.h"
#include "libpspp/message.h"
#include "libpspp/misc.h"
#include "math/categoricals.h"
#include "math/interaction.h"
-#include "libpspp/hmap.h"
-#include "libpspp/hash-functions.h"
-
-#include "output/tab.h"
+#include "output/pivot-table.h"
#include "gettext.h"
+#define N_(msgid) msgid
#define _(msgid) gettext (msgid)
*/
struct lr_result
{
- /* Used to indicate if a pass should flag a warning when
+ /* Used to indicate if a pass should flag a warning when
invalid (ie negative or missing) weight values are encountered */
bool warn_bad_weight;
/* The estimates of the predictor coefficients */
gsl_vector *beta_hat;
- /* The predicted classifications:
+ /* The predicted classifications:
True Negative, True Positive, False Negative, False Positive */
double tn, tp, fn, fp;
};
static void output_depvarmap (const struct lr_spec *cmd, const struct lr_result *);
-static void output_variables (const struct lr_spec *cmd,
+static void output_variables (const struct lr_spec *cmd,
const struct lr_result *);
static void output_model_summary (const struct lr_result *,
/* Return the value of case C corresponding to the INDEX'th entry in the
model */
static double
-predictor_value (const struct ccase *c,
- const struct variable **x, size_t n_x,
+predictor_value (const struct ccase *c,
+ const struct variable **x, size_t n_x,
const struct categoricals *cats,
size_t index)
{
/* Values of the scalar predictor variables */
- if (index < n_x)
- return case_data (c, x[index])->f;
+ if (index < n_x)
+ return case_num (c, x[index]);
/* Coded values of categorical predictor variables (or interactions) */
if (cats && index - n_x < categoricals_df_total (cats))
/*
- Return the probability beta_hat (that is the estimator logit(y) )
+ Return the probability beta_hat (that is the estimator logit(y))
corresponding to the coefficient estimator for case C
*/
-static double
-pi_hat (const struct lr_spec *cmd,
+static double
+pi_hat (const struct lr_spec *cmd,
const struct lr_result *res,
const struct variable **x, size_t n_x,
const struct ccase *c)
pi += gsl_vector_get (res->beta_hat, res->beta_hat->size - 1);
n_coeffs--;
}
-
+
for (v0 = 0; v0 < n_coeffs; ++v0)
{
- pi += gsl_vector_get (res->beta_hat, v0) *
+ pi += gsl_vector_get (res->beta_hat, v0) *
predictor_value (c, x, n_x, res->cats, v0);
}
/*
Calculates the Hessian matrix X' V X,
where: X is the n by N_X matrix comprising the n cases in INPUT
- V is a diagonal matrix { (pi_hat_0)(1 - pi_hat_0), (pi_hat_1)(1 - pi_hat_1), ... (pi_hat_{N-1})(1 - pi_hat_{N-1})}
+ V is a diagonal matrix { (pi_hat_0)(1 - pi_hat_0), (pi_hat_1)(1 - pi_hat_1), ... (pi_hat_{N-1})(1 - pi_hat_{N-1})}
(the partial derivative of the predicted values)
If ALL predicted values derivatives are close to zero or one, then CONVERGED
will be set to true.
*/
static void
-hessian (const struct lr_spec *cmd,
+hessian (const struct lr_spec *cmd,
struct lr_result *res,
struct casereader *input,
const struct variable **x, size_t n_x,
}
casereader_destroy (reader);
- if ( max_w < cmd->min_epsilon)
+ if (max_w < cmd->min_epsilon)
{
*converged = true;
msg (MN, _("All predicted values are either 1 or 0"));
/* Calculates the value X' (y - pi)
- where X is the design model,
+ where X is the design model,
y is the vector of observed independent variables
pi is the vector of estimates for y
*freq += weight;
}
-static void
-frq_destroy (const void *aux1 UNUSED, void *aux2 UNUSED, void *user_data UNUSED)
+static void
+frq_destroy (const void *aux1 UNUSED, void *aux2 UNUSED, void *user_data)
{
free (user_data);
}
\f
-/*
+/*
Makes an initial pass though the data, doing the following:
* Checks that the dependent variable is dichotomous,
res->cp.destroy = frq_destroy;
res->cats = categoricals_create (cmd->cat_predictors, cmd->n_cat_predictors,
- cmd->wv, cmd->exclude, MV_ANY);
+ cmd->wv, MV_ANY);
categoricals_set_payload (res->cats, &res->cp, cmd, res);
}
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))
+ if (var_is_value_missing (cmd->dep_var, depval) & cmd->exclude)
{
missing = true;
}
- else
+ else
for (v = 0; v < cmd->n_indep_vars; ++v)
{
const union value *val = case_data (c, cmd->indep_vars[v]);
- if (var_is_value_missing (cmd->indep_vars[v], val, cmd->exclude))
+ if (var_is_value_missing (cmd->indep_vars[v], val) & cmd->exclude)
{
missing = true;
break;
}
else if (!v1set)
{
- if ( !value_equal (&res->y0, depval, width))
+ if (!value_equal (&res->y0, depval, width))
{
value_clone (&res->y1, depval, width);
v1set = true;
if (! value_equal (&res->y0, depval, width)
&&
! value_equal (&res->y1, depval, width)
- )
+ )
{
msg (ME, _("Dependent variable's values are not dichotomous."));
case_unref (c);
work.hessian = NULL;
/* Get the initial estimates of \beta and their standard errors.
- And perform other auxilliary initialisation. */
+ And perform other auxiliary initialisation. */
if (! initial_pass (cmd, &work, input))
goto error;
-
+
for (i = 0; i < cmd->n_cat_predictors; ++i)
{
if (1 >= categoricals_n_count (work.cats, i))
{
struct string str;
ds_init_empty (&str);
-
+
interaction_to_string (cmd->cat_predictors[i], &str);
msg (ME, _("Category %s does not have at least two distinct values. Logistic regression will not be run."),
work.hessian = gsl_matrix_calloc (work.beta_hat->size, work.beta_hat->size);
/* Start the Newton Raphson iteration process... */
- for( i = 0 ; i < cmd->max_iter ; ++i)
+ for(i = 0 ; i < cmd->max_iter ; ++i)
{
double min, max;
gsl_vector *v ;
-
+
hessian (cmd, &work, input,
cmd->predictor_vars, cmd->n_predictor_vars,
&converged);
gsl_vector_minmax (delta, &min, &max);
- if ( fabs (min) < cmd->bcon && fabs (max) < cmd->bcon)
+ if (fabs (min) < cmd->bcon && fabs (max) < cmd->bcon)
{
msg (MN, _("Estimation terminated at iteration number %d because parameter estimates changed by less than %g"),
i + 1, cmd->bcon);
- if ( ! converged)
- msg (MW, _("Estimation terminated at iteration number %d because maximum iterations has been reached"), i );
+ if (! converged)
+ msg (MW, _("Estimation terminated at iteration number %d because maximum iterations has been reached"), i);
output_model_summary (&work, initial_log_likelihood, log_likelihood);
casereader_destroy (input);
gsl_matrix_free (work.hessian);
- gsl_vector_free (work.beta_hat);
+ 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);
+ gsl_vector_free (work.beta_hat);
categoricals_destroy (work.cats);
return false;
{
if (vn->var == var)
break;
-
- fprintf (stderr, "Warning: Hash table collision\n");
}
-
+
return vn;
}
if (! (lr.dep_var = parse_variable_const (lexer, lr.dict)))
goto error;
- lex_force_match (lexer, T_WITH);
+ if (! lex_force_match (lexer, T_WITH))
+ goto error;
if (!parse_variables_const (lexer, lr.dict,
&pred_vars, &n_pred_vars,
sizeof (*lr.cat_predictors) * ++lr.n_cat_predictors);
lr.cat_predictors[lr.n_cat_predictors - 1] = 0;
}
- while (parse_design_interaction (lexer, lr.dict,
+ while (parse_design_interaction (lexer, lr.dict,
lr.cat_predictors + lr.n_cat_predictors - 1));
lr.n_cat_predictors--;
}
}
lr.confidence = lex_number (lexer);
lex_get (lexer);
- if ( ! lex_force_match (lexer, T_RPAREN))
+ if (! lex_force_match (lexer, T_RPAREN))
{
lex_error (lexer, NULL);
goto error;
}
lr.bcon = lex_number (lexer);
lex_get (lexer);
- if ( ! lex_force_match (lexer, T_RPAREN))
+ if (! lex_force_match (lexer, T_RPAREN))
{
lex_error (lexer, NULL);
goto error;
{
if (lex_force_match (lexer, T_LPAREN))
{
- if (! lex_force_int (lexer))
+ if (! lex_force_int_range (lexer, "ITERATE", 0, INT_MAX))
{
lex_error (lexer, NULL);
goto error;
}
lr.max_iter = lex_integer (lexer);
lex_get (lexer);
- if ( ! lex_force_match (lexer, T_RPAREN))
+ if (! lex_force_match (lexer, T_RPAREN))
{
lex_error (lexer, NULL);
goto error;
}
lr.lcon = lex_number (lexer);
lex_get (lexer);
- if ( ! lex_force_match (lexer, T_RPAREN))
+ if (! lex_force_match (lexer, T_RPAREN))
{
lex_error (lexer, NULL);
goto error;
}
lr.min_epsilon = lex_number (lexer);
lex_get (lexer);
- if ( ! lex_force_match (lexer, T_RPAREN))
+ if (! lex_force_match (lexer, T_RPAREN))
{
lex_error (lexer, NULL);
goto error;
goto error;
}
cp = lex_number (lexer);
-
+
if (cp < 0 || cp > 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))
+ if (! lex_force_match (lexer, T_RPAREN))
{
lex_error (lexer, NULL);
goto error;
}
lr.ilogit_cut_point = - log (1/cp - 1);
-
- /* Copy the predictor variables from the temporary location into the
+
+ /* Copy the predictor variables from the temporary location into the
final one, dropping any categorical variables which appear there.
FIXME: This is O(NxM).
*/
{
vn = xmalloc (sizeof *vn);
vn->var = ivar;
-
+
hmap_insert (&allvars, &vn->node, hash);
}
free (vn);
}
hmap_destroy (&allvars);
- }
+ }
/* logistical regression for each split group */
static void
output_depvarmap (const struct lr_spec *cmd, const struct lr_result *res)
{
- const int heading_columns = 0;
- const int heading_rows = 1;
- struct tab_table *t;
- struct string str;
-
- const int nc = 2;
- int nr = heading_rows + 2;
+ struct pivot_table *table = pivot_table_create (
+ N_("Dependent Variable Encoding"));
- t = tab_create (nc, nr);
- tab_title (t, _("Dependent Variable Encoding"));
+ pivot_dimension_create (table, PIVOT_AXIS_COLUMN, N_("Mapping"),
+ N_("Internal Value"));
- tab_headers (t, heading_columns, 0, heading_rows, 0);
+ struct pivot_dimension *original = pivot_dimension_create (
+ table, PIVOT_AXIS_ROW, N_("Original Value"));
+ original->root->show_label = true;
- tab_box (t, TAL_2, TAL_2, -1, TAL_1, 0, 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, 0, TAB_CENTER | TAT_TITLE, _("Original Value"));
- tab_text (t, 1, 0, TAB_CENTER | TAT_TITLE, _("Internal Value"));
-
-
-
- ds_init_empty (&str);
- var_append_value_name (cmd->dep_var, &res->y0, &str);
- tab_text (t, 0, 0 + heading_rows, 0, ds_cstr (&str));
-
- ds_clear (&str);
- var_append_value_name (cmd->dep_var, &res->y1, &str);
- tab_text (t, 0, 1 + heading_rows, 0, ds_cstr (&str));
-
-
- tab_double (t, 1, 0 + heading_rows, 0, map_dependent_var (cmd, res, &res->y0), &F_8_0);
- tab_double (t, 1, 1 + heading_rows, 0, map_dependent_var (cmd, res, &res->y1), &F_8_0);
- ds_destroy (&str);
+ for (int i = 0; i < 2; i++)
+ {
+ const union value *v = i ? &res->y1 : &res->y0;
+ int orig_idx = pivot_category_create_leaf (
+ original->root, pivot_value_new_var_value (cmd->dep_var, v));
+ pivot_table_put2 (table, 0, orig_idx, pivot_value_new_number (
+ map_dependent_var (cmd, res, v)));
+ }
- tab_submit (t);
+ pivot_table_submit (table);
}
/* Show the Variables in the Equation box */
static void
-output_variables (const struct lr_spec *cmd,
+output_variables (const struct lr_spec *cmd,
const struct lr_result *res)
{
- int row = 0;
- const int heading_columns = 1;
- int heading_rows = 1;
- struct tab_table *t;
+ struct pivot_table *table = pivot_table_create (
+ N_("Variables in the Equation"));
+
+ struct pivot_dimension *statistics = pivot_dimension_create (
+ table, PIVOT_AXIS_COLUMN, N_("Statistics"),
+ N_("B"), PIVOT_RC_OTHER,
+ N_("S.E."), PIVOT_RC_OTHER,
+ N_("Wald"), PIVOT_RC_OTHER,
+ N_("df"), PIVOT_RC_INTEGER,
+ N_("Sig."), PIVOT_RC_SIGNIFICANCE,
+ N_("Exp(B)"), PIVOT_RC_OTHER);
+ if (cmd->print & PRINT_CI)
+ {
+ struct pivot_category *group = pivot_category_create_group__ (
+ statistics->root,
+ pivot_value_new_text_format (N_("%d%% CI for Exp(B)"),
+ cmd->confidence));
+ pivot_category_create_leaves (group, N_("Lower"), N_("Upper"));
+ }
+
+ struct pivot_dimension *variables = pivot_dimension_create (
+ table, PIVOT_AXIS_ROW, N_("Variables"));
+ struct pivot_category *step1 = pivot_category_create_group (
+ variables->root, N_("Step 1"));
- int nc = 8;
- int nr ;
- int i = 0;
int ivar = 0;
int idx_correction = 0;
+ int i = 0;
- if (cmd->print & PRINT_CI)
- {
- nc += 2;
- heading_rows += 1;
- row++;
- }
- nr = heading_rows + cmd->n_predictor_vars;
+ int nr = cmd->n_predictor_vars;
if (cmd->constant)
nr++;
-
if (res->cats)
nr += categoricals_df_total (res->cats) + cmd->n_cat_predictors;
- t = tab_create (nc, nr);
- tab_title (t, _("Variables in the Equation"));
-
- tab_headers (t, heading_columns, 0, heading_rows, 0);
-
- tab_box (t, TAL_2, TAL_2, -1, TAL_1, 0, 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, row + 1, TAB_CENTER | TAT_TITLE, _("Step 1"));
-
- tab_text (t, 2, row, TAB_CENTER | TAT_TITLE, _("B"));
- tab_text (t, 3, row, TAB_CENTER | TAT_TITLE, _("S.E."));
- tab_text (t, 4, row, TAB_CENTER | TAT_TITLE, _("Wald"));
- tab_text (t, 5, row, TAB_CENTER | TAT_TITLE, _("df"));
- tab_text (t, 6, row, TAB_CENTER | TAT_TITLE, _("Sig."));
- tab_text (t, 7, row, TAB_CENTER | TAT_TITLE, _("Exp(B)"));
-
- if (cmd->print & PRINT_CI)
- {
- tab_joint_text_format (t, 8, 0, 9, 0,
- TAB_CENTER | TAT_TITLE, _("%d%% CI for Exp(B)"), cmd->confidence);
-
- tab_text (t, 8, row, TAB_CENTER | TAT_TITLE, _("Lower"));
- tab_text (t, 9, row, TAB_CENTER | TAT_TITLE, _("Upper"));
- }
-
- for (row = heading_rows ; row < nr; ++row)
+ for (int row = 0; row < nr; row++)
{
- const int idx = row - heading_rows - idx_correction;
-
- const double b = gsl_vector_get (res->beta_hat, idx);
- const double sigma2 = gsl_matrix_get (res->hessian, idx, idx);
- const double wald = pow2 (b) / sigma2;
- const double df = 1;
+ const int idx = row - idx_correction;
+ int var_idx;
if (idx < cmd->n_predictor_vars)
- {
- tab_text (t, 1, row, TAB_LEFT | TAT_TITLE,
- var_to_string (cmd->predictor_vars[idx]));
- }
+ var_idx = pivot_category_create_leaf (
+ step1, pivot_value_new_variable (cmd->predictor_vars[idx]));
else if (i < cmd->n_cat_predictors)
{
- double wald;
- bool summary = false;
- struct string str;
const struct interaction *cat_predictors = cmd->cat_predictors[i];
- const int df = categoricals_df (res->cats, i);
-
- ds_init_empty (&str);
+ struct string str = DS_EMPTY_INITIALIZER;
interaction_to_string (cat_predictors, &str);
-
- if (ivar == 0)
+ if (ivar != 0)
+ ds_put_format (&str, "(%d)", ivar);
+ var_idx = pivot_category_create_leaf (
+ step1, pivot_value_new_user_text_nocopy (ds_steal_cstr (&str)));
+
+ int df = categoricals_df (res->cats, i);
+ bool summary = ivar == 0;
+ if (summary)
{
/* Calculate the Wald statistic,
which is \beta' C^-1 \beta .
where \beta is the vector of the coefficient estimates comprising this
- categorial variable. and C is the corresponding submatrix of the
+ categorial variable. and C is the corresponding submatrix of the
hessian matrix.
*/
gsl_matrix_const_view mv =
gsl_linalg_cholesky_invert (subhessian);
gsl_blas_dgemv (CblasTrans, 1.0, subhessian, &vv.vector, 0, temp);
+ double wald;
gsl_blas_ddot (temp, &vv.vector, &wald);
- tab_double (t, 4, row, 0, wald, 0);
- tab_double (t, 5, row, 0, df, &F_8_0);
- tab_double (t, 6, row, 0, gsl_cdf_chisq_Q (wald, df), 0);
+ double entries[] = { wald, df, gsl_cdf_chisq_Q (wald, df) };
+ for (size_t j = 0; j < sizeof entries / sizeof *entries; j++)
+ pivot_table_put2 (table, j + 2, var_idx,
+ pivot_value_new_number (entries[j]));
- idx_correction ++;
- summary = true;
+ idx_correction++;
gsl_matrix_free (subhessian);
gsl_vector_free (temp);
}
- else
- {
- ds_put_format (&str, "(%d)", ivar);
- }
- tab_text (t, 1, row, TAB_LEFT | TAT_TITLE, ds_cstr (&str));
if (ivar++ == df)
{
++i; /* next interaction */
ivar = 0;
}
- ds_destroy (&str);
-
if (summary)
continue;
}
else
- {
- tab_text (t, 1, row, TAB_LEFT | TAT_TITLE, _("Constant"));
- }
-
- tab_double (t, 2, row, 0, b, 0);
- tab_double (t, 3, row, 0, sqrt (sigma2), 0);
- tab_double (t, 4, row, 0, wald, 0);
- tab_double (t, 5, row, 0, df, &F_8_0);
- tab_double (t, 6, row, 0, gsl_cdf_chisq_Q (wald, df), 0);
- tab_double (t, 7, row, 0, exp (b), 0);
-
- 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 (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);
- }
- }
+ var_idx = pivot_category_create_leaves (step1, N_("Constant"));
+
+ double b = gsl_vector_get (res->beta_hat, idx);
+ double sigma2 = gsl_matrix_get (res->hessian, idx, idx);
+ double wald = pow2 (b) / sigma2;
+ double df = 1;
+ double wc = (gsl_cdf_ugaussian_Pinv (0.5 + cmd->confidence / 200.0)
+ * sqrt (sigma2));
+ bool show_ci = cmd->print & PRINT_CI && row < nr - cmd->constant;
+
+ double entries[] = {
+ b,
+ sqrt (sigma2),
+ wald,
+ df,
+ gsl_cdf_chisq_Q (wald, df),
+ exp (b),
+ show_ci ? exp (b - wc) : SYSMIS,
+ show_ci ? exp (b + wc) : SYSMIS,
+ };
+ for (size_t j = 0; j < sizeof entries / sizeof *entries; j++)
+ if (entries[j] != SYSMIS)
+ pivot_table_put2 (table, j, var_idx,
+ pivot_value_new_number (entries[j]));
}
- tab_submit (t);
+ pivot_table_submit (table);
}
output_model_summary (const struct lr_result *res,
double initial_log_likelihood, double log_likelihood)
{
- const int heading_columns = 0;
- const int heading_rows = 1;
- struct tab_table *t;
-
- const int nc = 4;
- int nr = heading_rows + 1;
- double cox;
-
- t = tab_create (nc, nr);
- tab_title (t, _("Model Summary"));
-
- tab_headers (t, heading_columns, 0, heading_rows, 0);
-
- tab_box (t, TAL_2, TAL_2, -1, TAL_1, 0, 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, 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_text (t, 2, 0, TAB_CENTER | TAT_TITLE, _("Cox & Snell R Square"));
- 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 - exp(initial_log_likelihood * (2 / res->cc))), 0);
-
-
- tab_submit (t);
+ struct pivot_table *table = pivot_table_create (N_("Model Summary"));
+
+ pivot_dimension_create (table, PIVOT_AXIS_COLUMN, N_("Statistics"),
+ N_("-2 Log likelihood"), PIVOT_RC_OTHER,
+ N_("Cox & Snell R Square"), PIVOT_RC_OTHER,
+ N_("Nagelkerke R Square"), PIVOT_RC_OTHER);
+
+ struct pivot_dimension *step = pivot_dimension_create (
+ table, PIVOT_AXIS_ROW, N_("Step"));
+ step->root->show_label = true;
+ pivot_category_create_leaf (step->root, pivot_value_new_integer (1));
+
+ double cox = (1.0 - exp ((initial_log_likelihood - log_likelihood)
+ * (2 / res->cc)));
+ double entries[] = {
+ -2 * log_likelihood,
+ cox,
+ cox / (1.0 - exp(initial_log_likelihood * (2 / res->cc)))
+ };
+ for (size_t i = 0; i < sizeof entries / sizeof *entries; i++)
+ pivot_table_put2 (table, i, 0, pivot_value_new_number (entries[i]));
+
+ pivot_table_submit (table);
}
/* Show the case processing summary box */
static void
case_processing_summary (const struct lr_result *res)
{
- const int heading_columns = 1;
- const int heading_rows = 1;
- struct tab_table *t;
-
- const int nc = 3;
- const int nr = heading_rows + 3;
- casenumber total;
-
- t = tab_create (nc, nr);
- tab_title (t, _("Case Processing Summary"));
-
- tab_headers (t, heading_columns, 0, heading_rows, 0);
-
- tab_box (t, TAL_2, TAL_2, -1, TAL_1, 0, 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, 0, TAB_LEFT | TAT_TITLE, _("Unweighted Cases"));
- tab_text (t, 1, 0, TAB_CENTER | TAT_TITLE, _("N"));
- tab_text (t, 2, 0, TAB_CENTER | TAT_TITLE, _("Percent"));
-
+ struct pivot_table *table = pivot_table_create (
+ N_("Case Processing Summary"));
- tab_text (t, 0, 1, TAB_LEFT | TAT_TITLE, _("Included in Analysis"));
- tab_text (t, 0, 2, TAB_LEFT | TAT_TITLE, _("Missing Cases"));
- tab_text (t, 0, 3, TAB_LEFT | TAT_TITLE, _("Total"));
+ pivot_dimension_create (table, PIVOT_AXIS_COLUMN, N_("Statistics"),
+ N_("N"), PIVOT_RC_COUNT,
+ N_("Percent"), PIVOT_RC_PERCENT);
- tab_double (t, 1, 1, 0, res->n_nonmissing, &F_8_0);
- tab_double (t, 1, 2, 0, res->n_missing, &F_8_0);
+ struct pivot_dimension *cases = pivot_dimension_create (
+ table, PIVOT_AXIS_ROW, N_("Unweighted Cases"),
+ N_("Included in Analysis"), N_("Missing Cases"), N_("Total"));
+ cases->root->show_label = true;
- total = res->n_nonmissing + res->n_missing;
- tab_double (t, 1, 3, 0, total , &F_8_0);
-
- tab_double (t, 2, 1, 0, 100 * res->n_nonmissing / (double) total, 0);
- tab_double (t, 2, 2, 0, 100 * res->n_missing / (double) total, 0);
- tab_double (t, 2, 3, 0, 100 * total / (double) total, 0);
-
- tab_submit (t);
+ double total = res->n_nonmissing + res->n_missing;
+ struct entry
+ {
+ int stat_idx;
+ int case_idx;
+ double x;
+ }
+ entries[] = {
+ { 0, 0, res->n_nonmissing },
+ { 0, 1, res->n_missing },
+ { 0, 2, total },
+ { 1, 0, 100.0 * res->n_nonmissing / total },
+ { 1, 1, 100.0 * res->n_missing / total },
+ { 1, 2, 100.0 },
+ };
+ for (size_t i = 0; i < sizeof entries / sizeof *entries; i++)
+ pivot_table_put2 (table, entries[i].stat_idx, entries[i].case_idx,
+ pivot_value_new_number (entries[i].x));
+
+ pivot_table_submit (table);
}
static void
output_categories (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;
-
- int cumulative_df;
- int i = 0;
- const int heading_columns = 2;
- const int heading_rows = 2;
- struct tab_table *t;
-
- int nc ;
- int nr ;
-
- int v;
- int r = 0;
+ struct pivot_table *table = pivot_table_create (
+ N_("Categorical Variables' Codings"));
+ pivot_table_set_weight_var (table, dict_get_weight (cmd->dict));
int max_df = 0;
int total_cats = 0;
- for (i = 0; i < cmd->n_cat_predictors; ++i)
+ for (int i = 0; i < cmd->n_cat_predictors; ++i)
{
size_t n = categoricals_n_count (res->cats, i);
size_t df = categoricals_df (res->cats, i);
total_cats += n;
}
- nc = heading_columns + 1 + max_df;
- nr = heading_rows + total_cats;
-
- t = tab_create (nc, nr);
- tab_title (t, _("Categorical Variables' Codings"));
-
- tab_headers (t, heading_columns, 0, heading_rows, 0);
-
- tab_box (t, TAL_2, TAL_2, -1, TAL_1, 0, 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, heading_columns, 1, TAB_CENTER | TAT_TITLE, _("Frequency"));
-
- tab_joint_text_format (t, heading_columns + 1, 0, nc - 1, 0,
- TAB_CENTER | TAT_TITLE, _("Parameter coding"));
-
-
- for (i = 0; i < max_df; ++i)
- {
- int c = heading_columns + 1 + i;
- tab_text_format (t, c, 1, TAB_CENTER | TAT_TITLE, _("(%d)"), i + 1);
- }
-
- cumulative_df = 0;
- for (v = 0; v < cmd->n_cat_predictors; ++v)
+ struct pivot_dimension *codings = pivot_dimension_create (
+ table, PIVOT_AXIS_COLUMN, N_("Codings"),
+ N_("Frequency"), PIVOT_RC_COUNT);
+ struct pivot_category *coding_group = pivot_category_create_group (
+ codings->root, N_("Parameter coding"));
+ for (int i = 0; i < max_df; ++i)
+ pivot_category_create_leaf_rc (
+ coding_group,
+ pivot_value_new_user_text_nocopy (xasprintf ("(%d)", i + 1)),
+ PIVOT_RC_INTEGER);
+
+ struct pivot_dimension *categories = pivot_dimension_create (
+ table, PIVOT_AXIS_ROW, N_("Categories"));
+
+ int cumulative_df = 0;
+ for (int v = 0; v < cmd->n_cat_predictors; ++v)
{
int cat;
const struct interaction *cat_predictors = cmd->cat_predictors[v];
- int df = categoricals_df (res->cats, v);
- struct string str;
- ds_init_empty (&str);
+ int df = categoricals_df (res->cats, v);
+ struct string str = DS_EMPTY_INITIALIZER;
interaction_to_string (cat_predictors, &str);
-
- tab_text (t, 0, heading_rows + r, TAB_LEFT | TAT_TITLE, ds_cstr (&str) );
-
- ds_destroy (&str);
+ struct pivot_category *var_group = pivot_category_create_group__ (
+ categories->root,
+ pivot_value_new_user_text_nocopy (ds_steal_cstr (&str)));
for (cat = 0; cat < categoricals_n_count (res->cats, v) ; ++cat)
{
- struct string str;
- const struct ccase *c = categoricals_get_case_by_category_real (res->cats, v, cat);
- const double *freq = categoricals_get_user_data_by_category_real (res->cats, v, cat);
-
- int x;
- ds_init_empty (&str);
-
- for (x = 0; x < cat_predictors->n_vars; ++x)
+ const struct ccase *c = categoricals_get_case_by_category_real (
+ res->cats, v, cat);
+ struct string label = DS_EMPTY_INITIALIZER;
+ for (int x = 0; x < cat_predictors->n_vars; ++x)
{
- const union value *val = case_data (c, cat_predictors->vars[x]);
- var_append_value_name (cat_predictors->vars[x], val, &str);
+ if (!ds_is_empty (&label))
+ ds_put_byte (&label, ' ');
- if (x < cat_predictors->n_vars - 1)
- ds_put_cstr (&str, " ");
+ const union value *val = case_data (c, cat_predictors->vars[x]);
+ var_append_value_name (cat_predictors->vars[x], val, &label);
}
-
- tab_text (t, 1, heading_rows + r, 0, ds_cstr (&str));
- ds_destroy (&str);
- tab_double (t, 2, heading_rows + r, 0, *freq, wfmt);
+ int cat_idx = pivot_category_create_leaf (
+ var_group,
+ pivot_value_new_user_text_nocopy (ds_steal_cstr (&label)));
- for (x = 0; x < df; ++x)
- {
- tab_double (t, heading_columns + 1 + x, heading_rows + r, 0, (cat == x), &F_8_0);
- }
- ++r;
+ double *freq = categoricals_get_user_data_by_category_real (
+ res->cats, v, cat);
+ pivot_table_put2 (table, 0, cat_idx, pivot_value_new_number (*freq));
+
+ for (int x = 0; x < df; ++x)
+ pivot_table_put2 (table, x + 1, cat_idx,
+ pivot_value_new_number (cat == x));
}
cumulative_df += df;
}
- tab_submit (t);
-
+ pivot_table_submit (table);
}
+static void
+create_classification_dimension (const struct lr_spec *cmd,
+ const struct lr_result *res,
+ struct pivot_table *table,
+ enum pivot_axis_type axis_type,
+ const char *label, const char *total)
+{
+ struct pivot_dimension *d = pivot_dimension_create (
+ table, axis_type, label);
+ d->root->show_label = true;
+ struct pivot_category *pred_group = pivot_category_create_group__ (
+ d->root, pivot_value_new_variable (cmd->dep_var));
+ for (int i = 0; i < 2; i++)
+ {
+ const union value *y = i ? &res->y1 : &res->y0;
+ pivot_category_create_leaf_rc (
+ pred_group, pivot_value_new_var_value (cmd->dep_var, y),
+ PIVOT_RC_COUNT);
+ }
+ pivot_category_create_leaves (d->root, total, PIVOT_RC_PERCENT);
+}
-static void
+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"));
-
+ struct pivot_table *table = pivot_table_create (N_("Classification Table"));
+ pivot_table_set_weight_var (table, cmd->wv);
- tab_joint_text (t, heading_columns, 0, nc - 1, 0,
- TAB_CENTER | TAT_TITLE, _("Predicted"));
+ create_classification_dimension (cmd, res, table, PIVOT_AXIS_COLUMN,
+ N_("Predicted"), N_("Percentage Correct"));
+ create_classification_dimension (cmd, res, table, PIVOT_AXIS_ROW,
+ N_("Observed"), N_("Overall Percentage"));
- 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);
+ pivot_dimension_create (table, PIVOT_AXIS_ROW, N_("Step"), N_("Step 1"));
+ struct entry
+ {
+ int pred_idx;
+ int obs_idx;
+ double x;
+ }
+ entries[] = {
+ { 0, 0, res->tn },
+ { 0, 1, res->fn },
+ { 1, 0, res->fp },
+ { 1, 1, res->tp },
+ { 2, 0, 100 * res->tn / (res->tn + res->fp) },
+ { 2, 1, 100 * res->tp / (res->tp + res->fn) },
+ { 2, 2,
+ 100 * (res->tp + res->tn) / (res->tp + res->tn + res->fp + res->fn)},
+ };
+ for (size_t i = 0; i < sizeof entries / sizeof *entries; i++)
+ {
+ const struct entry *e = &entries[i];
+ pivot_table_put3 (table, e->pred_idx, e->obs_idx, 0,
+ pivot_value_new_number (e->x));
+ }
- tab_submit (t);
+ pivot_table_submit (table);
}
projects / pspp / blobdiff