--- /dev/null
+/* PSPP - a program for statistical analysis.
+ Copyright (C) 2011 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 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.
+
+ You should have received a copy of the GNU General Public License
+ along with this program. If not, see <http://www.gnu.org/licenses/>.
+*/
+
+#include <config.h>
+
+
+#include "t-test.h"
+
+#include <math.h>
+#include <gsl/gsl_cdf.h>
+
+#include "data/variable.h"
+#include "data/format.h"
+#include "data/casereader.h"
+#include "data/dictionary.h"
+#include "libpspp/hash-functions.h"
+#include "libpspp/hmapx.h"
+#include "math/moments.h"
+
+#include "output/pivot-table.h"
+
+#include "gettext.h"
+#define N_(msgid) msgid
+#define _(msgid) gettext (msgid)
+
+
+struct per_var_stats
+{
+ const struct variable *var;
+
+ /* N, Mean, Variance */
+ struct moments *mom;
+
+ /* Sum of the differences */
+ double sum_diff;
+};
+
+
+struct one_samp
+{
+ struct per_var_stats *stats;
+ size_t n_stats;
+ double testval;
+};
+
+
+static void
+one_sample_test (const struct tt *tt, const struct one_samp *os)
+{
+ struct pivot_table *table = pivot_table_create (N_("One-Sample Test"));
+ pivot_table_set_weight_var (table, tt->wv);
+
+ struct pivot_dimension *statistics = pivot_dimension_create (
+ table, PIVOT_AXIS_COLUMN, N_("Statistics"));
+ struct pivot_category *group = pivot_category_create_group__ (
+ statistics->root, pivot_value_new_user_text_nocopy (
+ xasprintf (_("Test Value = %.*g"), DBL_DIG + 1, os->testval)));
+ pivot_category_create_leaves (
+ group,
+ N_("t"), PIVOT_RC_OTHER,
+ N_("df"), PIVOT_RC_COUNT,
+ N_("Sig. (2-tailed)"), PIVOT_RC_SIGNIFICANCE,
+ N_("Mean Difference"), PIVOT_RC_OTHER);
+ struct pivot_category *subgroup = pivot_category_create_group__ (
+ group, pivot_value_new_user_text_nocopy (
+ xasprintf (_("%g%% Confidence Interval of the Difference"),
+ tt->confidence * 100.0)));
+ pivot_category_create_leaves (subgroup,
+ N_("Lower"), PIVOT_RC_OTHER,
+ N_("Upper"), PIVOT_RC_OTHER);
+
+ struct pivot_dimension *dep_vars = pivot_dimension_create (
+ table, PIVOT_AXIS_ROW, N_("Dependent Variables"));
+
+ for (size_t i = 0; i < os->n_stats; i++)
+ {
+ const struct per_var_stats *per_var_stats = &os->stats[i];
+ const struct moments *m = per_var_stats->mom;
+
+ int dep_var_idx = pivot_category_create_leaf (
+ dep_vars->root, pivot_value_new_variable (per_var_stats->var));
+
+ double cc, mean, sigma;
+ moments_calculate (m, &cc, &mean, &sigma, NULL, NULL);
+ double tval = (mean - os->testval) * sqrt (cc / sigma);
+ double mean_diff = per_var_stats->sum_diff / cc;
+ double se_mean = sqrt (sigma / cc);
+ double df = cc - 1.0;
+ double p = gsl_cdf_tdist_P (tval, df);
+ double q = gsl_cdf_tdist_Q (tval, df);
+ double sig = 2.0 * (tval > 0 ? q : p);
+ double tval_qinv = gsl_cdf_tdist_Qinv ((1.0 - tt->confidence) / 2.0, df);
+ double lower = mean_diff - tval_qinv * se_mean;
+ double upper = mean_diff + tval_qinv * se_mean;
+
+ double entries[] = { tval, df, sig, mean_diff, lower, upper };
+ for (size_t j = 0; j < sizeof entries / sizeof *entries; j++)
+ pivot_table_put2 (table, j, dep_var_idx,
+ pivot_value_new_number (entries[j]));
+ }
+
+ pivot_table_submit (table);
+}
+
+static void
+one_sample_summary (const struct tt *tt, const struct one_samp *os)
+{
+ struct pivot_table *table = pivot_table_create (N_("One-Sample Statistics"));
+ pivot_table_set_weight_var (table, tt->wv);
+
+ pivot_dimension_create (table, PIVOT_AXIS_COLUMN, N_("Statistics"),
+ N_("N"), PIVOT_RC_COUNT,
+ N_("Mean"), PIVOT_RC_OTHER,
+ N_("Std. Deviation"), PIVOT_RC_OTHER,
+ N_("S.E. Mean"), PIVOT_RC_OTHER);
+
+ struct pivot_dimension *variables = pivot_dimension_create (
+ table, PIVOT_AXIS_ROW, N_("Variables"));
+
+ for (size_t i = 0; i < os->n_stats; i++)
+ {
+ const struct per_var_stats *per_var_stats = &os->stats[i];
+ const struct moments *m = per_var_stats->mom;
+
+ int var_idx = pivot_category_create_leaf (
+ variables->root, pivot_value_new_variable (per_var_stats->var));
+
+ double cc, mean, sigma;
+ moments_calculate (m, &cc, &mean, &sigma, NULL, NULL);
+
+ double entries[] = { cc, mean, sqrt (sigma), sqrt (sigma / cc) };
+ for (size_t j = 0; j < sizeof entries / sizeof *entries; j++)
+ pivot_table_put2 (table, j, var_idx,
+ pivot_value_new_number (entries[j]));
+ }
+
+ pivot_table_submit (table);
+}
+
+void
+one_sample_run (const struct tt *tt, double testval, struct casereader *reader)
+{
+ struct one_samp os;
+ os.testval = testval;
+ os.stats = xcalloc (tt->n_vars, sizeof *os.stats);
+ os.n_stats = tt->n_vars;
+ for (size_t i = 0; i < tt->n_vars; ++i)
+ {
+ struct per_var_stats *per_var_stats = &os.stats[i];
+ per_var_stats->var = tt->vars[i];
+ per_var_stats->mom = moments_create (MOMENT_VARIANCE);
+ }
+
+ struct casereader *r = casereader_clone (reader);
+ struct ccase *c;
+ for (; (c = casereader_read (r)); case_unref (c))
+ {
+ double w = dict_get_case_weight (tt->dict, c, NULL);
+ for (size_t i = 0; i < os.n_stats; i++)
+ {
+ const struct per_var_stats *per_var_stats = &os.stats[i];
+ const struct variable *var = per_var_stats->var;
+ const union value *val = case_data (c, var);
+ if (var_is_value_missing (var, val) & tt->exclude)
+ continue;
+
+ moments_pass_one (per_var_stats->mom, val->f, w);
+ }
+ }
+ casereader_destroy (r);
+
+ r = reader;
+ for (; (c = casereader_read (r)); case_unref (c))
+ {
+ double w = dict_get_case_weight (tt->dict, c, NULL);
+ for (size_t i = 0; i < os.n_stats; i++)
+ {
+ struct per_var_stats *per_var_stats = &os.stats[i];
+ const struct variable *var = per_var_stats->var;
+ const union value *val = case_data (c, var);
+ if (var_is_value_missing (var, val) & tt->exclude)
+ continue;
+
+ moments_pass_two (per_var_stats->mom, val->f, w);
+ per_var_stats->sum_diff += w * (val->f - os.testval);
+ }
+ }
+ casereader_destroy (r);
+
+ one_sample_summary (tt, &os);
+ one_sample_test (tt, &os);
+
+ for (size_t i = 0; i < os.n_stats; i++)
+ {
+ const struct per_var_stats *per_var_stats = &os.stats[i];
+ moments_destroy (per_var_stats->mom);
+ }
+ free (os.stats);
+}
+
projects / pspp / blobdiff