/* PSPP - a program for statistical analysis.
- Copyright (C) 1997-9, 2000, 2007, 2009, 2010 Free Software Foundation, Inc.
+ Copyright (C) 1997-9, 2000, 2007, 2009, 2010, 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
#include <config.h>
-#include <data/case.h>
-#include <data/casegrouper.h>
-#include <data/casereader.h>
-
-#include <math/covariance.h>
-#include <math/categoricals.h>
-#include <math/moments.h>
-#include <gsl/gsl_matrix.h>
-#include <linreg/sweep.h>
-
-#include <libpspp/ll.h>
-
-#include <language/lexer/lexer.h>
-#include <language/lexer/variable-parser.h>
-#include <language/lexer/value-parser.h>
-#include <language/command.h>
-
-#include <data/procedure.h>
-#include <data/value.h>
-#include <data/dictionary.h>
-
-#include <language/dictionary/split-file.h>
-#include <libpspp/hash.h>
-#include <libpspp/taint.h>
-#include <math/group-proc.h>
-#include <math/levene.h>
-#include <libpspp/misc.h>
-
-#include <output/tab.h>
-
#include <gsl/gsl_cdf.h>
+#include <gsl/gsl_matrix.h>
#include <math.h>
-#include <data/format.h>
-#include <libpspp/message.h>
+#include "data/case.h"
+#include "data/casegrouper.h"
+#include "data/casereader.h"
+#include "data/dataset.h"
+#include "data/dictionary.h"
+#include "data/format.h"
+#include "data/value.h"
+#include "language/command.h"
+#include "language/dictionary/split-file.h"
+#include "language/lexer/lexer.h"
+#include "language/lexer/value-parser.h"
+#include "language/lexer/variable-parser.h"
+#include "libpspp/ll.h"
+#include "libpspp/message.h"
+#include "libpspp/misc.h"
+#include "libpspp/taint.h"
+#include "linreg/sweep.h"
+#include "math/categoricals.h"
+#include "math/covariance.h"
+#include "math/levene.h"
+#include "math/moments.h"
+#include "output/tab.h"
#include "gettext.h"
#define _(msgid) gettext (msgid)
+
enum missing_type
{
MISS_LISTWISE,
/* Workspace variable for each dependent variable */
struct per_var_ws
{
+ struct categoricals *cat;
struct covariance *cov;
double sst;
int n_groups;
double mse;
+ double levene_w;
};
struct oneway_workspace
missing values are disregarded */
int actual_number_of_groups;
- /* A hash table containing all the distinct values of the independent
- variable */
- struct hsh_table *group_hash;
-
struct per_var_ws *vws;
/* An array of descriptive data. One for each dependent variable */
ll_init (&oneway.contrast_list);
- if ( lex_match (lexer, '/'))
+ if ( lex_match (lexer, T_SLASH))
{
if (!lex_force_match_id (lexer, "VARIABLES"))
{
goto error;
}
- lex_match (lexer, '=');
+ lex_match (lexer, T_EQUALS);
}
if (!parse_variables_const (lexer, dict,
oneway.indep_var = parse_variable_const (lexer, dict);
- while (lex_token (lexer) != '.')
+ while (lex_token (lexer) != T_ENDCMD)
{
- lex_match (lexer, '/');
+ lex_match (lexer, T_SLASH);
if (lex_match_id (lexer, "STATISTICS"))
{
- lex_match (lexer, '=');
- while (lex_token (lexer) != '.' && lex_token (lexer) != '/')
+ lex_match (lexer, T_EQUALS);
+ while (lex_token (lexer) != T_ENDCMD && lex_token (lexer) != T_SLASH)
{
if (lex_match_id (lexer, "DESCRIPTIVES"))
{
struct contrasts_node *cl = xzalloc (sizeof *cl);
struct ll_list *coefficient_list = &cl->coefficient_list;
- lex_match (lexer, '=');
+ lex_match (lexer, T_EQUALS);
ll_init (coefficient_list);
- while (lex_token (lexer) != '.' && lex_token (lexer) != '/')
+ while (lex_token (lexer) != T_ENDCMD && lex_token (lexer) != T_SLASH)
{
if ( lex_is_number (lexer))
{
}
else if (lex_match_id (lexer, "MISSING"))
{
- lex_match (lexer, '=');
- while (lex_token (lexer) != '.' && lex_token (lexer) != '/')
+ lex_match (lexer, T_EQUALS);
+ while (lex_token (lexer) != T_ENDCMD && lex_token (lexer) != T_SLASH)
{
if (lex_match_id (lexer, "INCLUDE"))
{
\f
-static int
-compare_double_3way (const void *a_, const void *b_, const void *aux UNUSED)
-{
- const double *a = a_;
- const double *b = b_;
- return *a < *b ? -1 : *a > *b;
-}
-
-static unsigned
-do_hash_double (const void *value_, const void *aux UNUSED)
-{
- const double *value = value_;
- return hash_double (*value, 0);
-}
-
-static void
-free_double (void *value_, const void *aux UNUSED)
-{
- double *value = value_;
- free (value);
-}
-
-
-
-static void postcalc (const struct oneway_spec *cmd);
-static void precalc (const struct oneway_spec *cmd);
static struct descriptive_data *
dd_create (const struct variable *var)
return dd;
}
+static void
+dd_destroy (struct descriptive_data *dd)
+{
+ moments1_destroy (dd->mom);
+ free (dd);
+}
static void *
makeit (void *aux1, void *aux2 UNUSED)
const union value *valx = case_data (c, varp);
+ struct descriptive_data *dd_total = aux2;
+
+ double weight;
+
if ( var_is_value_missing (varp, valx, exclude))
return;
- struct descriptive_data *dd_total = aux2;
-
- double weight = 1.0;
- if (wv)
- weight = case_data (c, wv)->f;
+ weight = wv != NULL ? case_data (c, wv)->f : 1.0;
moments1_add (dd->mom, valx->f, weight);
- if (valx->f * weight < dd->minimum)
- dd->minimum = valx->f * weight;
+ if (valx->f < dd->minimum)
+ dd->minimum = valx->f;
- if (valx->f * weight > dd->maximum)
- dd->maximum = valx->f * weight;
+ if (valx->f > dd->maximum)
+ dd->maximum = valx->f;
{
const struct variable *var = dd_total->var;
val->f,
weight);
- if (val->f * weight < dd_total->minimum)
- dd_total->minimum = val->f * weight;
+ if (val->f < dd_total->minimum)
+ dd_total->minimum = val->f;
- if (val->f * weight > dd_total->maximum)
- dd_total->maximum = val->f * weight;
+ if (val->f > dd_total->maximum)
+ dd_total->maximum = val->f;
}
}
struct oneway_workspace ws;
ws.actual_number_of_groups = 0;
- ws.vws = xmalloc (cmd->n_vars * sizeof (*ws.vws));
+ ws.vws = xzalloc (cmd->n_vars * sizeof (*ws.vws));
ws.dd_total = xmalloc (sizeof (struct descriptive_data) * cmd->n_vars);
for (v = 0 ; v < cmd->n_vars; ++v)
for (v = 0; v < cmd->n_vars; ++v)
{
- struct categoricals *cats = categoricals_create (&cmd->indep_var, 1,
- cmd->wv, cmd->exclude,
- makeit,
- updateit,
- cmd->vars[v], ws.dd_total[v]);
+ ws.vws[v].cat = categoricals_create (&cmd->indep_var, 1, cmd->wv,
+ cmd->exclude, makeit, updateit,
+ CONST_CAST (struct variable *,
+ cmd->vars[v]),
+ ws.dd_total[v]);
ws.vws[v].cov = covariance_2pass_create (1, &cmd->vars[v],
- cats,
+ ws.vws[v].cat,
cmd->wv, cmd->exclude);
}
if (c == NULL)
{
casereader_destroy (input);
- return;
+ goto finish;
}
output_split_file_values (ds, c);
case_unref (c);
taint = taint_clone (casereader_get_taint (input));
- ws.group_hash = hsh_create (4,
- compare_double_3way,
- do_hash_double,
- free_double,
- cmd->indep_var);
-
- precalc (cmd);
-
input = casereader_create_filter_missing (input, &cmd->indep_var, 1,
cmd->exclude, NULL, NULL);
if (cmd->missing_type == MISS_LISTWISE)
cmd->exclude, NULL, NULL);
input = casereader_create_filter_weight (input, dict, NULL, NULL);
+
+ if (cmd->stats & STATS_HOMOGENEITY)
+ for (v = 0; v < cmd->n_vars; ++v)
+ {
+ struct per_var_ws *pvw = &ws.vws[v];
+
+ pvw->levene_w = levene (input, cmd->indep_var, cmd->vars[v], cmd->wv, cmd->exclude);
+ }
+
reader = casereader_clone (input);
for (; (c = casereader_read (reader)) != NULL; case_unref (c))
{
- size_t i;
-
- const double weight = dict_get_case_weight (dict, c, NULL);
-
- const union value *indep_val = case_data (c, cmd->indep_var);
- void **p = hsh_probe (ws.group_hash, &indep_val->f);
- if (*p == NULL)
- {
- double *value = *p = xmalloc (sizeof *value);
- *value = indep_val->f;
- }
+ int i;
for (i = 0; i < cmd->n_vars; ++i)
{
- {
- struct per_var_ws *pvw = &ws.vws[i];
-
- covariance_accumulate_pass1 (pvw->cov, c);
- }
-
+ struct per_var_ws *pvw = &ws.vws[i];
const struct variable *v = cmd->vars[i];
-
const union value *val = case_data (c, v);
- struct group_proc *gp = group_proc_get (cmd->vars[i]);
- struct hsh_table *group_hash = gp->group_hash;
-
- struct group_statistics *gs;
-
- gs = hsh_find (group_hash, indep_val );
-
- if ( ! gs )
+ if ( MISS_ANALYSIS == cmd->missing_type)
{
- gs = xmalloc (sizeof *gs);
- gs->id = *indep_val;
- gs->sum = 0;
- gs->n = 0;
- gs->ssq = 0;
- gs->sum_diff = 0;
- gs->minimum = DBL_MAX;
- gs->maximum = -DBL_MAX;
-
- hsh_insert ( group_hash, gs );
+ if ( var_is_value_missing (v, val, cmd->exclude))
+ continue;
}
- if (!var_is_value_missing (v, val, cmd->exclude))
- {
- struct group_statistics *totals = &gp->ugs;
-
- totals->n += weight;
- totals->sum += weight * val->f;
- totals->ssq += weight * pow2 (val->f);
-
- if ( val->f * weight < totals->minimum )
- totals->minimum = val->f * weight;
-
- if ( val->f * weight > totals->maximum )
- totals->maximum = val->f * weight;
-
- gs->n += weight;
- gs->sum += weight * val->f;
- gs->ssq += weight * pow2 (val->f);
-
- if ( val->f * weight < gs->minimum )
- gs->minimum = val->f * weight;
-
- if ( val->f * weight > gs->maximum )
- gs->maximum = val->f * weight;
- }
-
- gp->n_groups = hsh_count (group_hash );
+ covariance_accumulate_pass1 (pvw->cov, c);
}
-
}
casereader_destroy (reader);
reader = casereader_clone (input);
for (i = 0; i < cmd->n_vars; ++i)
{
struct per_var_ws *pvw = &ws.vws[i];
+ const struct variable *v = cmd->vars[i];
+ const union value *val = case_data (c, v);
+
+ if ( MISS_ANALYSIS == cmd->missing_type)
+ {
+ if ( var_is_value_missing (v, val, cmd->exclude))
+ continue;
+ }
+
covariance_accumulate_pass2 (pvw->cov, c);
}
}
pvw->n_groups = categoricals_total (cats);
pvw->mse = (pvw->sst - pvw->ssa) / (n - pvw->n_groups);
- }
- postcalc (cmd);
+ gsl_matrix_free (cm);
+ }
-
for (v = 0; v < cmd->n_vars; ++v)
{
- struct categoricals *cats = covariance_get_categoricals (ws.vws[v].cov);
+ const struct categoricals *cats = covariance_get_categoricals (ws.vws[v].cov);
categoricals_done (cats);
ws.actual_number_of_groups = categoricals_total (cats);
}
- if ( cmd->stats & STATS_HOMOGENEITY )
- levene (dict, casereader_clone (input), cmd->indep_var,
- cmd->n_vars, cmd->vars, cmd->exclude);
-
casereader_destroy (input);
if (!taint_has_tainted_successor (taint))
output_oneway (cmd, &ws);
taint_destroy (taint);
-}
-/* Pre calculations */
-static void
-precalc (const struct oneway_spec *cmd)
-{
- size_t i = 0;
-
- for (i = 0; i < cmd->n_vars; ++i)
+ finish:
+ for (v = 0; v < cmd->n_vars; ++v)
{
- struct group_proc *gp = group_proc_get (cmd->vars[i]);
- struct group_statistics *totals = &gp->ugs;
-
- /* Create a hash for each of the dependent variables.
- The hash contains a group_statistics structure,
- and is keyed by value of the independent variable */
-
- gp->group_hash = hsh_create (4, compare_group, hash_group,
- (hsh_free_func *) free_group,
- cmd->indep_var);
-
- totals->sum = 0;
- totals->n = 0;
- totals->ssq = 0;
- totals->sum_diff = 0;
- totals->maximum = -DBL_MAX;
- totals->minimum = DBL_MAX;
+ covariance_destroy (ws.vws[v].cov);
+ dd_destroy (ws.dd_total[v]);
}
-}
-
-/* Post calculations for the ONEWAY command */
-static void
-postcalc (const struct oneway_spec *cmd)
-{
- size_t i = 0;
+ free (ws.vws);
+ free (ws.dd_total);
- for (i = 0; i < cmd->n_vars; ++i)
- {
- struct group_proc *gp = group_proc_get (cmd->vars[i]);
- struct hsh_table *group_hash = gp->group_hash;
- struct group_statistics *totals = &gp->ugs;
-
- struct hsh_iterator g;
- struct group_statistics *gs;
-
- for (gs = hsh_first (group_hash, &g);
- gs != 0;
- gs = hsh_next (group_hash, &g))
- {
- gs->mean = gs->sum / gs->n;
- gs->s_std_dev = sqrt (gs->ssq / gs->n - pow2 (gs->mean));
-
- gs->std_dev = sqrt (
- gs->n / (gs->n - 1) *
- ( gs->ssq / gs->n - pow2 (gs->mean))
- );
-
- gs->se_mean = gs->std_dev / sqrt (gs->n);
- gs->mean_diff = gs->sum_diff / gs->n;
- }
-
- totals->mean = totals->sum / totals->n;
- totals->std_dev = sqrt (
- totals->n / (totals->n - 1) *
- (totals->ssq / totals->n - pow2 (totals->mean))
- );
-
- totals->se_mean = totals->std_dev / sqrt (totals->n);
- }
}
static void show_contrast_coeffs (const struct oneway_spec *cmd, const struct oneway_workspace *ws);
show_contrast_coeffs (cmd, ws);
show_contrast_tests (cmd, ws);
}
-
- /* Clean up */
- for (i = 0; i < cmd->n_vars; ++i )
- {
- struct hsh_table *group_hash = group_proc_get (cmd->vars[i])->group_hash;
-
- hsh_destroy (group_hash);
- }
-
- hsh_destroy (ws->group_hash);
}
for (i = 0; i < cmd->n_vars; ++i)
{
double n;
- moments1_calculate (ws->dd_total[i]->mom, &n, NULL, NULL, NULL, NULL);
-
+ double df1, df2;
+ double msa;
+ const char *s = var_to_string (cmd->vars[i]);
const struct per_var_ws *pvw = &ws->vws[i];
- const double df1 = pvw->n_groups - 1;
- const double df2 = n - pvw->n_groups;
- const double msa = pvw->ssa / df1;
- const char *s = var_to_string (cmd->vars[i]);
+ moments1_calculate (ws->dd_total[i]->mom, &n, NULL, NULL, NULL, NULL);
+
+ df1 = pvw->n_groups - 1;
+ df2 = n - pvw->n_groups;
+ msa = pvw->ssa / df1;
tab_text (t, 0, i * 3 + 1, TAB_LEFT | TAT_TITLE, s);
tab_text (t, 1, i * 3 + 1, TAB_LEFT | TAT_TITLE, _("Between Groups"));
/* Now fill in the numbers ... */
- tab_fixed (t, 2, row + count, 0, n, 8, 0);
+ tab_double (t, 2, row + count, 0, n, wfmt);
tab_double (t, 3, row + count, 0, mean, NULL);
for (v = 0; v < cmd->n_vars; ++v)
{
double n;
- moments1_calculate (ws->dd_total[v]->mom, &n, NULL, NULL, NULL, NULL);
-
const struct per_var_ws *pvw = &ws->vws[v];
- const struct categoricals *cats = covariance_get_categoricals (pvw->cov);
+ double F = pvw->levene_w;
const struct variable *var = cmd->vars[v];
- const struct group_proc *gp = group_proc_get (cmd->vars[v]);
const char *s = var_to_string (var);
+ double df1, df2;
+
+ moments1_calculate (ws->dd_total[v]->mom, &n, NULL, NULL, NULL, NULL);
- const double df1 = pvw->n_groups - 1;
- const double df2 = n - pvw->n_groups;
- double F = gp->levene;
+ df1 = pvw->n_groups - 1;
+ df2 = n - pvw->n_groups;
tab_text (t, 0, v + 1, TAB_LEFT | TAT_TITLE, s);
-
tab_double (t, 1, v + 1, TAB_RIGHT, F, NULL);
tab_fixed (t, 2, v + 1, TAB_RIGHT, df1, 8, 0);
tab_fixed (t, 3, v + 1, TAB_RIGHT, df2, 8, 0);
{
double n, mean, variance;
const struct descriptive_data *dd = categoricals_get_user_data_by_subscript (cats, ci);
-
- moments1_calculate (dd->mom, &n, &mean, &variance, NULL, NULL);
-
struct coeff_node *cn = ll_data (coeffi, struct coeff_node, ll);
const double coef = cn->coeff;
+ double winv ;
+
+ moments1_calculate (dd->mom, &n, &mean, &variance, NULL, NULL);
- const double winv = variance / n;
+ winv = variance / n;
contrast_value += coef * mean;
projects / pspp-builds.git / blobdiff