/* PSPP - a program for statistical analysis. -*-c-*-
- Copyright (C) 2010, 2011 Free Software Foundation, Inc.
+ Copyright (C) 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
#include "language/stats/runs.h"
+#include <float.h>
#include <gsl/gsl_cdf.h>
#include <math.h>
#include "libpspp/misc.h"
#include "math/percentiles.h"
#include "math/sort.h"
-#include "output/tab.h"
+#include "output/pivot-table.h"
#include "gettext.h"
+#define N_(msgid) msgid
#define _(msgid) gettext (msgid)
/* Return the Z statistic representing the assympototic
- distribution of the the number of runs */
+ distribution of the number of runs */
static double
runs_statistic (const struct run_state *rs)
{
z = rs->runs - mu;
- if ( rs->n < 50)
+ if (rs->n < 50)
{
if (z <= -0.5)
z += 0.5;
static void show_runs_result (const struct runs_test *, const struct run_state *, const struct dictionary *);
-void
+void
runs_execute (const struct dataset *ds,
struct casereader *input,
enum mv_class exclude,
struct one_sample_test *otp = UP_CAST (test, struct one_sample_test, parent);
struct runs_test *rt = UP_CAST (otp, struct runs_test, parent);
- struct run_state *rs = xcalloc (otp->n_vars, sizeof (*rs));
+ struct run_state *rs = XCALLOC (otp->n_vars, struct run_state);
- switch ( rt->cp_mode)
+ switch (rt->cp_mode)
{
case CP_MODE:
{
const struct variable *var = otp->vars[v];
reader = sort_execute_1var (reader, var);
-
+
grouper = casegrouper_create_vars (reader, &var, 1);
last_cc = SYSMIS;
while (casegrouper_get_next_group (grouper, &group))
struct ccase *c;
for (; (c = casereader_read (group)); case_unref (c))
{
- const double w = weight ? case_data (c, weight)->f: 1.0;
+ const double w = weight ? case_num (c, weight) : 1.0;
const union value *val = case_data (c, var);
- if ( var_is_value_missing (var, val, exclude))
+ if (var_is_value_missing (var, val) & exclude)
continue;
x = val->f;
cc += w;
}
- if ( cc > last_cc)
+ if (cc > last_cc)
{
run->cutpoint = x;
}
- else if ( cc == last_cc)
+ else if (cc == last_cc)
{
multimodal = true;
- if ( x > run->cutpoint)
+ if (x > run->cutpoint)
run->cutpoint = x;
}
last_cc = cc;
}
casegrouper_destroy (grouper);
if (multimodal)
- msg (MW, _("Multiple modes exist for varible `%s'. Using %g as the threshold value."),
- var_get_name (var), run->cutpoint);
+ msg (MW, _("Multiple modes exist for variable `%s'. "
+ "Using %.*g as the threshold value."),
+ var_get_name (var), DBL_DIG + 1, run->cutpoint);
}
}
break;
subcase_init_var (&sc, var, SC_ASCEND);
writer = sort_create_writer (&sc, casereader_get_proto (reader));
- for (; (c = casereader_read (reader)); )
+ for (; (c = casereader_read (reader));)
{
const union value *val = case_data (c, var);
- const double w = weight ? case_data (c, weight)->f: 1.0;
- if ( var_is_value_missing (var, val, exclude))
+ const double w = weight ? case_num (c, weight) : 1.0;
+ if (var_is_value_missing (var, val) & exclude)
{
case_unref (c);
continue;
cc += w;
casewriter_write (writer, c);
}
- subcase_destroy (&sc);
+ subcase_uninit (&sc);
casereader_destroy (reader);
reader = casewriter_make_reader (writer);
median = percentile_create (0.5, cc);
os = &median->parent;
-
+
order_stats_accumulate (&os, 1,
reader,
weight,
struct casereader *reader = casereader_clone (input);
for (; (c = casereader_read (reader)); case_unref (c))
{
- const double w = weight ? case_data (c, weight)->f: 1.0;
+ const double w = weight ? case_num (c, weight) : 1.0;
for (v = 0; v < otp->n_vars; ++v)
{
const struct variable *var = otp->vars[v];
const double x = val->f;
struct run_state *run = &rs[v];
- if ( var_is_value_missing (var, val, exclude))
+ if (var_is_value_missing (var, val) & exclude)
continue;
run->cutpoint += x * w;
for (; (c = casereader_read (input)); case_unref (c))
{
- const double w = weight ? case_data (c, weight)->f: 1.0;
+ const double w = weight ? case_num (c, weight) : 1.0;
for (v = 0; v < otp->n_vars; ++v)
{
double d = x - run->cutpoint;
short sign = 0;
- if ( var_is_value_missing (var, val, exclude))
+ if (var_is_value_missing (var, val) & exclude)
continue;
if (d >= 0)
static void
show_runs_result (const struct runs_test *rt, const struct run_state *rs, const struct dictionary *dict)
{
- const struct variable *weight = dict_get_weight (dict);
- const struct fmt_spec *wfmt = weight ? var_get_print_format (weight) : &F_8_0;
-
const struct one_sample_test *otp = &rt->parent;
- int i;
- const int row_headers = 1;
- const int column_headers = 1;
- struct tab_table *table =
- tab_create (row_headers + otp->n_vars, column_headers + 7);
-
- tab_headers (table, row_headers, 0, column_headers, 0);
-
- tab_title (table, _("Runs Test"));
-
- /* Box around the table and vertical lines inside*/
- tab_box (table, TAL_2, TAL_2, -1, TAL_1,
- 0, 0, tab_nc (table) - 1, tab_nr (table) - 1 );
-
- tab_hline (table, TAL_2, 0, tab_nc (table) -1, column_headers);
- tab_vline (table, TAL_2, row_headers, 0, tab_nr (table) - 1);
-
- for (i = 0 ; i < otp->n_vars; ++i)
+ struct pivot_table *table = pivot_table_create (N_("Runs Test"));
+ pivot_table_set_weight_var (table, dict_get_weight (dict));
+
+ pivot_dimension_create (
+ table, PIVOT_AXIS_ROW, N_("Statistics"),
+ (rt->cp_mode == CP_CUSTOM ? N_("Test Value")
+ : rt->cp_mode == CP_MODE ? N_("Test Value (mode)")
+ : rt->cp_mode == CP_MEAN ? N_("Test Value (mean)")
+ : N_("Test Value (median)")), PIVOT_RC_OTHER,
+ N_("Cases < Test Value"), PIVOT_RC_COUNT,
+ N_("Cases ≥ Test Value"), PIVOT_RC_COUNT,
+ N_("Total Cases"), PIVOT_RC_COUNT,
+ N_("Number of Runs"), PIVOT_RC_INTEGER,
+ N_("Z"), PIVOT_RC_OTHER,
+ N_("Asymp. Sig. (2-tailed)"), PIVOT_RC_SIGNIFICANCE);
+
+ struct pivot_dimension *variables = pivot_dimension_create (
+ table, PIVOT_AXIS_COLUMN, N_("Variable"));
+
+ for (size_t i = 0 ; i < otp->n_vars; ++i)
{
const struct run_state *run = &rs[i];
- double z = runs_statistic (run);
-
- tab_text (table, row_headers + i, 0,
- TAT_TITLE | TAB_CENTER ,
- var_to_string (otp->vars[i]));
-
- tab_double (table, row_headers +i, 1, 0,
- run->cutpoint, 0);
-
- tab_double (table, row_headers +i, 2, 0,
- run->nn, wfmt);
-
- tab_double (table, row_headers +i, 3, 0,
- run->np, wfmt);
-
- tab_double (table, row_headers +i, 4, 0,
- run->n, wfmt);
+ int col = pivot_category_create_leaf (
+ variables->root, pivot_value_new_variable (otp->vars[i]));
- tab_double (table, row_headers +i, 5, 0,
- run->runs, &F_8_0);
-
- tab_double (table, row_headers +i, 6, 0,
- z, 0);
+ double z = runs_statistic (run);
- tab_double (table, row_headers +i, 7, 0,
- 2.0 * gsl_cdf_ugaussian_P (z), 0);
+ double rows[] = {
+ run->cutpoint,
+ run->nn,
+ run->np,
+ run->n,
+ run->runs,
+ z,
+ 2.0 * (1.0 - gsl_cdf_ugaussian_P (fabs (z))),
+ };
+
+ for (int row = 0; row < sizeof rows / sizeof *rows; row++)
+ pivot_table_put2 (table, row, col, pivot_value_new_number (rows[row]));
}
- switch ( rt->cp_mode)
- {
- case CP_CUSTOM:
- tab_text (table, 0, column_headers ,
- TAT_TITLE | TAB_LEFT , _("Test Value"));
- break;
- case CP_MODE:
- tab_text (table, 0, column_headers ,
- TAT_TITLE | TAB_LEFT , _("Test Value (mode)"));
- break;
- case CP_MEAN:
- tab_text (table, 0, column_headers ,
- TAT_TITLE | TAB_LEFT , _("Test Value (mean)"));
- break;
- case CP_MEDIAN:
- tab_text (table, 0, column_headers ,
- TAT_TITLE | TAB_LEFT , _("Test Value (median)"));
- break;
- }
-
- tab_text (table, 0, column_headers + 1,
- TAT_TITLE | TAB_LEFT , _("Cases < Test Value"));
-
- tab_text (table, 0, column_headers + 2,
- TAT_TITLE | TAB_LEFT , _("Cases ≥ Test Value"));
-
- tab_text (table, 0, column_headers + 3,
- TAT_TITLE | TAB_LEFT , _("Total Cases"));
-
- tab_text (table, 0, column_headers + 4,
- TAT_TITLE | TAB_LEFT , _("Number of Runs"));
-
- tab_text (table, 0, column_headers + 5,
- TAT_TITLE | TAB_LEFT , _("Z"));
-
- tab_text (table, 0, column_headers + 6,
- TAT_TITLE | TAB_LEFT , _("Asymp. Sig. (2-tailed)"));
-
- tab_submit (table);
+ pivot_table_submit (table);
}
-
-
projects / pspp / blobdiff