-/* PSPP - computes sample statistics.
- Copyright (C) 2006 Free Software Foundation, Inc.
+/* PSPP - a program for statistical analysis.
+ Copyright (C) 2006, 2009, 2010, 2011, 2014 Free Software Foundation, Inc.
- This program is free software; you can redistribute it and/or
- modify it under the terms of the GNU General Public License as
- published by the Free Software Foundation; either version 2 of the
- License, or (at your option) any later version.
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+ (at your option) any later version.
- This program is distributed in the hope that it will be useful, but
- WITHOUT ANY WARRANTY; without even the implied warranty of
- MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
- General Public License for more details.
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+ GNU General Public License for more details.
You should have received a copy of the GNU General Public License
- along with this program; if not, write to the Free Software
- Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA
- 02110-1301, USA. */
+ along with this program. If not, see <http://www.gnu.org/licenses/>. */
#include <config.h>
-#include <libpspp/compiler.h>
-#include <output/table.h>
-#include <libpspp/alloc.h>
-#include <data/case.h>
-#include <data/casefile.h>
-#include <data/dictionary.h>
-#include <data/procedure.h>
-#include <data/variable.h>
-#include <data/value.h>
-#include <data/value-labels.h>
-#include <data/casefilter.h>
-
-#include <libpspp/message.h>
-#include <libpspp/assertion.h>
-
-#include "binomial.h"
-#include "freq.h"
-
-#include "gettext.h"
-#define _(msgid) gettext (msgid)
-
-#include <libpspp/misc.h>
+#include "language/stats/binomial.h"
+#include <float.h>
#include <gsl/gsl_cdf.h>
#include <gsl/gsl_randist.h>
-#include <gsl-extras/gsl-extras.h>
-#include <minmax.h>
+#include "data/case.h"
+#include "data/casereader.h"
+#include "data/dataset.h"
+#include "data/dictionary.h"
+#include "data/format.h"
+#include "data/value-labels.h"
+#include "data/value.h"
+#include "data/variable.h"
+#include "language/stats/freq.h"
+#include "libpspp/assertion.h"
+#include "libpspp/compiler.h"
+#include "libpspp/message.h"
+#include "libpspp/misc.h"
+#include "output/pivot-table.h"
+
+#include "gl/xalloc.h"
+#include "gl/minmax.h"
-#include <libpspp/hash.h>
+#include "gettext.h"
+#define N_(msgid) msgid
+#define _(msgid) gettext (msgid)
static double calculate_binomial_internal (double n1, double n2,
double p);
calculate_binomial (double n1, double n2, double p)
{
const double n = n1 + n2;
- const bool test_reversed = (n1 / n > p ) ;
- if ( test_reversed )
+ const bool test_reversed = (n1 / n > p) ;
+ if (test_reversed)
{
p = 1 - p ;
swap (&n1, &n2);
static double
calculate_binomial_internal (double n1, double n2, double p)
{
- /* SPSS Statistical Algorithms has completely different and WRONG
+ /* SPSS Statistical Algorithms has completely different and WRONG
advice here. */
- double sig1tailed = gslextras_cdf_binomial_P (n1, n1 + n2, p);
+ double sig1tailed = gsl_cdf_binomial_P (n1, p, n1 + n2);
- if ( p == 0.5 )
+ if (p == 0.5)
return sig1tailed > 0.5 ? 1.0 :sig1tailed * 2.0;
return sig1tailed ;
}
-static void
+static bool
do_binomial (const struct dictionary *dict,
- const struct casefile *cf,
- const struct binomial_test *bst,
+ struct casereader *input,
+ const struct one_sample_test *ost,
struct freq *cat1,
struct freq *cat2,
- const struct casefilter *filter
- )
+ enum mv_class exclude
+ )
{
+ const struct binomial_test *bst = UP_CAST (ost, const struct binomial_test, parent);
bool warn = true;
- const struct one_sample_test *ost = (const struct one_sample_test *) bst;
- struct ccase c;
- struct casereader *r = casefile_get_reader (cf, NULL);
+ struct ccase *c;
- while (casereader_read(r, &c))
+ for (; (c = casereader_read (input)) != NULL; case_unref (c))
{
int v;
- double w =
- dict_get_case_weight (dict, &c, &warn);
+ double w = dict_get_case_weight (dict, c, &warn);
- for (v = 0 ; v < ost->n_vars ; ++v )
+ for (v = 0 ; v < ost->n_vars ; ++v)
{
const struct variable *var = ost->vars[v];
- const union value *value = case_data (&c, var);
+ double value = case_num (c, var);
- if ( casefilter_variable_missing (filter, &c, var))
- break;
+ if (var_is_num_missing (var, value) & exclude)
+ continue;
- if ( NULL == cat1[v].value )
+ if (bst->cutpoint != SYSMIS)
{
- cat1[v].value = value_dup (value, var_get_width (var));
- cat1[v].count = w;
+ if (cat1[v].values[0].f >= value)
+ cat1[v].count += w;
+ else
+ cat2[v].count += w;
}
- else if ( 0 == compare_values (cat1[v].value, value,
- var_get_width (var)))
- cat1[v].count += w;
- else if ( NULL == cat2[v].value )
+ else
{
- cat2[v].value = value_dup (value, var_get_width (var));
- cat2[v].count = w;
+ if (SYSMIS == cat1[v].values[0].f)
+ {
+ cat1[v].values[0].f = value;
+ cat1[v].count = w;
+ }
+ else if (cat1[v].values[0].f == value)
+ cat1[v].count += w;
+ else if (SYSMIS == cat2[v].values[0].f)
+ {
+ cat2[v].values[0].f = value;
+ cat2[v].count = w;
+ }
+ else if (cat2[v].values[0].f == value)
+ cat2[v].count += w;
+ else if (bst->category1 == SYSMIS)
+ msg (ME, _("Variable %s is not dichotomous"), var_get_name (var));
}
- else if ( 0 == compare_values (cat2[v].value, value,
- var_get_width (var)))
- cat2[v].count += w;
- else if ( bst->category1 == SYSMIS)
- msg (ME, _("Variable %s is not dichotomous"), var_get_name (var));
}
-
- case_destroy (&c);
}
- casereader_destroy (r);
+ return casereader_destroy (input);
}
void
binomial_execute (const struct dataset *ds,
- const struct casefile *cf,
- struct casefilter *filter,
- const struct npar_test *test)
+ struct casereader *input,
+ enum mv_class exclude,
+ const struct npar_test *test,
+ bool exact UNUSED,
+ double timer UNUSED)
{
- int v;
- const struct binomial_test *bst = (const struct binomial_test *) test;
- const struct one_sample_test *ost = (const struct one_sample_test*) test;
+ const struct dictionary *dict = dataset_dict (ds);
+ const struct one_sample_test *ost = UP_CAST (test, const struct one_sample_test, parent);
+ const struct binomial_test *bst = UP_CAST (ost, const struct binomial_test, parent);
- struct freq *cat1 = xzalloc (sizeof (*cat1) * ost->n_vars);
- struct freq *cat2 = xzalloc (sizeof (*cat1) * ost->n_vars);
- struct tab_table *table ;
+ struct freq *cat[2];
+ int i;
- assert ((bst->category1 == SYSMIS) == (bst->category2 == SYSMIS) );
+ assert ((bst->category1 == SYSMIS) == (bst->category2 == SYSMIS) || bst->cutpoint != SYSMIS);
- if ( bst->category1 != SYSMIS )
+ for (i = 0; i < 2; i++)
{
- union value v;
- v.f = bst->category1;
- cat1->value = value_dup (&v, 0);
+ double value;
+ if (i == 0)
+ value = bst->cutpoint != SYSMIS ? bst->cutpoint : bst->category1;
+ else
+ value = bst->category2;
+
+ cat[i] = xnmalloc (ost->n_vars, sizeof *cat[i]);
+ for (size_t v = 0; v < ost->n_vars; v++)
+ {
+ cat[i][v].values[0].f = value;
+ cat[i][v].count = 0;
+ }
}
- if ( bst->category2 != SYSMIS )
+ if (do_binomial (dataset_dict (ds), input, ost, cat[0], cat[1], exclude))
{
- union value v;
- v.f = bst->category2;
- cat2->value = value_dup (&v, 0);
+ struct pivot_table *table = pivot_table_create (N_("Binomial Test"));
+ pivot_table_set_weight_var (table, dict_get_weight (dict));
+
+ pivot_dimension_create (
+ table, PIVOT_AXIS_COLUMN, N_("Statistics"),
+ N_("Category"),
+ N_("N"), PIVOT_RC_COUNT,
+ N_("Observed Prop."), PIVOT_RC_OTHER,
+ N_("Test Prop."), PIVOT_RC_OTHER,
+ (bst->p == 0.5
+ ? N_("Exact Sig. (2-tailed)")
+ : N_("Exact Sig. (1-tailed)")), PIVOT_RC_SIGNIFICANCE);
+
+ pivot_dimension_create (table, PIVOT_AXIS_ROW, N_("Groups"),
+ N_("Group 1"), N_("Group 2"), N_("Total"));
+
+ struct pivot_dimension *variables = pivot_dimension_create (
+ table, PIVOT_AXIS_ROW, N_("Variables"));
+
+ for (size_t v = 0; v < ost->n_vars; ++v)
+ {
+ const struct variable *var = ost->vars[v];
+
+ int var_idx = pivot_category_create_leaf (
+ variables->root, pivot_value_new_variable (var));
+
+ /* Category. */
+ if (bst->cutpoint != SYSMIS)
+ pivot_table_put3 (
+ table, 0, 0, var_idx,
+ pivot_value_new_user_text_nocopy (
+ xasprintf ("<= %.*g", DBL_DIG + 1, bst->cutpoint)));
+ else
+ for (int i = 0; i < 2; i++)
+ pivot_table_put3 (
+ table, 0, i, var_idx,
+ pivot_value_new_var_value (var, cat[i][v].values));
+
+ double n_total = cat[0][v].count + cat[1][v].count;
+ double sig = calculate_binomial (cat[0][v].count, cat[1][v].count,
+ bst->p);
+ struct entry
+ {
+ int stat_idx;
+ int group_idx;
+ double x;
+ }
+ entries[] = {
+ /* N. */
+ { 1, 0, cat[0][v].count },
+ { 1, 1, cat[1][v].count },
+ { 1, 2, n_total },
+ /* Observed Prop. */
+ { 2, 0, cat[0][v].count / n_total },
+ { 2, 1, cat[1][v].count / n_total },
+ { 2, 2, 1.0 },
+ /* Test Prop. */
+ { 3, 0, bst->p },
+ /* Significance. */
+ { 4, 0, sig }
+ };
+ for (size_t i = 0; i < sizeof entries / sizeof *entries; i++)
+ {
+ const struct entry *e = &entries[i];
+ pivot_table_put3 (table, e->stat_idx, e->group_idx,
+ var_idx, pivot_value_new_number (e->x));
+ }
+ }
+
+ pivot_table_submit (table);
}
- do_binomial (dataset_dict(ds), cf, bst, cat1, cat2, filter);
-
- table = tab_create (7, ost->n_vars * 3 + 1, 0);
-
- tab_dim (table, tab_natural_dimensions);
-
- tab_title (table, _("Binomial Test"));
-
- tab_headers (table, 2, 0, 1, 0);
-
- tab_box (table, TAL_1, TAL_1, -1, TAL_1,
- 0, 0, table->nc - 1, tab_nr(table) - 1 );
-
- for (v = 0 ; v < ost->n_vars; ++v)
- {
- double n_total, sig;
- const struct variable *var = ost->vars[v];
- tab_hline (table, TAL_1, 0, tab_nc (table) -1, 1 + v * 3);
-
- /* Titles */
- tab_text (table, 0, 1 + v * 3, TAB_LEFT,
- var_to_string (var));
-
- tab_text (table, 1, 1 + v * 3, TAB_LEFT,
- _("Group1"));
-
- tab_text (table, 1, 2 + v * 3, TAB_LEFT,
- _("Group2"));
-
- tab_text (table, 1, 3 + v * 3, TAB_LEFT,
- _("Total"));
-
- /* Test Prop */
- tab_float (table, 5, 1 + v * 3, TAB_NONE, bst->p, 8, 3);
-
- /* Category labels */
- tab_text (table, 2, 1 + v * 3, TAB_NONE,
- var_get_value_name (var, cat1[v].value));
-
- tab_text (table, 2, 2 + v * 3, TAB_NONE,
- var_get_value_name (var, cat2[v].value));
-
- /* Observed N */
- tab_float (table, 3, 1 + v * 3, TAB_NONE,
- cat1[v].count, 8, 0);
-
- tab_float (table, 3, 2 + v * 3, TAB_NONE,
- cat2[v].count, 8, 0);
-
- n_total = cat1[v].count + cat2[v].count;
-
-
- tab_float (table, 3, 3 + v * 3, TAB_NONE,
- n_total, 8, 0);
-
- /* Observed Proportions */
-
- tab_float (table, 4, 1 + v * 3, TAB_NONE,
- cat1[v].count / n_total, 8, 3);
-
- tab_float (table, 4, 2 + v * 3, TAB_NONE,
- cat2[v].count / n_total, 8, 3);
-
- tab_float (table, 4, 3 + v * 3, TAB_NONE,
- (cat1[v].count + cat2[v].count) / n_total, 8, 2);
-
-
- /* Significance */
- sig = calculate_binomial (cat1[v].count, cat2[v].count,
- bst->p);
-
- tab_float (table, 6, 1 + v * 3, TAB_NONE,
- sig, 8, 3);
- }
-
- tab_text (table, 2, 0, TAB_CENTER, _("Category"));
- tab_text (table, 3, 0, TAB_CENTER, _("N"));
- tab_text (table, 4, 0, TAB_CENTER, _("Observed Prop."));
- tab_text (table, 5, 0, TAB_CENTER, _("Test Prop."));
-
- tab_text (table, 6, 0, TAB_CENTER | TAT_PRINTF,
- _("Exact Sig. (%d-tailed)"),
- bst->p == 0.5 ? 2: 1);
-
- tab_vline (table, TAL_2, 2, 0, tab_nr (table) -1);
-
- free (cat1);
- free (cat2);
-
- tab_submit (table);
-
+ for (i = 0; i < 2; i++)
+ free (cat[i]);
}
projects / pspp / blobdiff