/* PSPP - a program for statistical analysis.
- Copyright (C) 2009 Free Software Foundation, Inc.
+ Copyright (C) 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 <libpspp/assertion.h>
-#include <math/covariance.h>
-#include <math/correlation.h>
+#include <gsl/gsl_cdf.h>
#include <gsl/gsl_matrix.h>
-#include <data/casegrouper.h>
-#include <data/casereader.h>
-#include <data/dictionary.h>
-#include <data/procedure.h>
-#include <data/variable.h>
-#include <language/command.h>
-#include <language/dictionary/split-file.h>
-#include <language/lexer/lexer.h>
-#include <language/lexer/variable-parser.h>
-#include <output/tab.h>
-#include <libpspp/message.h>
-#include <data/format.h>
-#include <math/moments.h>
-
#include <math.h>
-#include "xalloc.h"
-#include "minmax.h"
-#include <libpspp/misc.h>
-#include <gsl/gsl_cdf.h>
+
+#include "data/casegrouper.h"
+#include "data/casereader.h"
+#include "data/dataset.h"
+#include "data/dictionary.h"
+#include "data/format.h"
+#include "data/variable.h"
+#include "language/command.h"
+#include "language/dictionary/split-file.h"
+#include "language/lexer/lexer.h"
+#include "language/lexer/variable-parser.h"
+#include "libpspp/assertion.h"
+#include "libpspp/message.h"
+#include "libpspp/misc.h"
+#include "math/correlation.h"
+#include "math/covariance.h"
+#include "math/moments.h"
+#include "output/tab.h"
+
+#include "gl/xalloc.h"
+#include "gl/minmax.h"
#include "gettext.h"
#define _(msgid) gettext (msgid)
case 3:
x = gsl_matrix_get (ns, r, 0);
break;
- default:
+ default:
NOT_REACHED ();
};
-
- tab_double (t, c, r + heading_rows, 0, x, NULL);
+
+ tab_double (t, c, r + heading_rows, 0, x, NULL, RC_OTHER);
}
}
nr += heading_rows;
t = tab_create (nc, nr);
+ tab_set_format (t, RC_WEIGHT, wfmt);
tab_title (t, _("Correlations"));
tab_headers (t, heading_columns, 0, heading_rows, 0);
nc - 1, nr - 1);
tab_vline (t, TAL_2, heading_columns, 0, nr - 1);
+
tab_vline (t, TAL_1, 1, heading_rows, nr - 1);
+ /* Row Headers */
for (r = 0 ; r < corr->n_vars1 ; ++r)
{
- tab_text (t, 0, 1 + r * rows_per_variable, TAB_LEFT | TAT_TITLE,
+ tab_text (t, 0, 1 + r * rows_per_variable, TAB_LEFT | TAT_TITLE,
var_to_string (corr->vars[r]));
tab_text (t, 1, 1 + r * rows_per_variable, TAB_LEFT | TAT_TITLE, _("Pearson Correlation"));
- tab_text (t, 1, 2 + r * rows_per_variable, TAB_LEFT | TAT_TITLE,
+ tab_text (t, 1, 2 + r * rows_per_variable, TAB_LEFT | TAT_TITLE,
(opts->tails == 2) ? _("Sig. (2-tailed)") : _("Sig. (1-tailed)"));
if (opts->statistics & STATS_XPROD)
tab_hline (t, TAL_1, 0, nc - 1, r * rows_per_variable + 1);
}
+ /* Column Headers */
for (c = 0 ; c < matrix_cols ; ++c)
{
- const struct variable *v = corr->n_vars_total > corr->n_vars1 ? corr->vars[corr->n_vars_total - corr->n_vars1 + c] : corr->vars[c];
- tab_text (t, heading_columns + c, 0, TAB_LEFT | TAT_TITLE, var_to_string (v));
+ const struct variable *v = corr->n_vars_total > corr->n_vars1 ?
+ corr->vars[corr->n_vars1 + c] : corr->vars[c];
+ tab_text (t, heading_columns + c, 0, TAB_LEFT | TAT_TITLE, var_to_string (v));
}
for (r = 0 ; r < corr->n_vars1 ; ++r)
const int row = r * rows_per_variable + heading_rows;
for (c = 0 ; c < matrix_cols ; ++c)
{
- unsigned char flags = 0;
- const int col_index = corr->n_vars_total - corr->n_vars1 + c;
+ unsigned char flags = 0;
+ const int col_index = corr->n_vars_total > corr->n_vars1 ?
+ corr->n_vars1 + c :
+ c;
double pearson = gsl_matrix_get (cm, r, col_index);
double w = gsl_matrix_get (samples, r, col_index);
double sig = opts->tails * significance_of_correlation (pearson, w);
if ( opts->missing_type != CORR_LISTWISE )
- tab_double (t, c + heading_columns, row + rows_per_variable - 1, 0, w, wfmt);
+ tab_double (t, c + heading_columns, row + rows_per_variable - 1, 0, w, NULL, RC_WEIGHT);
- if ( c != r)
- tab_double (t, c + heading_columns, row + 1, 0, sig, NULL);
+ if ( col_index != r)
+ tab_double (t, c + heading_columns, row + 1, 0, sig, NULL, RC_PVALUE);
- if ( opts->sig && c != r && sig < 0.05)
+ if ( opts->sig && col_index != r && sig < 0.05)
flags = TAB_EMPH;
-
- tab_double (t, c + heading_columns, row, flags, pearson, NULL);
+
+ tab_double (t, c + heading_columns, row, flags, pearson, NULL, RC_OTHER);
if (opts->statistics & STATS_XPROD)
{
const double xprod_dev = cov * w;
cov *= w / (w - 1.0);
- tab_double (t, c + heading_columns, row + 2, 0, xprod_dev, NULL);
- tab_double (t, c + heading_columns, row + 3, 0, cov, NULL);
+ tab_double (t, c + heading_columns, row + 2, 0, xprod_dev, NULL, RC_OTHER);
+ tab_double (t, c + heading_columns, row + 3, 0, cov, NULL, RC_OTHER);
}
}
}
{
struct ccase *c;
const gsl_matrix *var_matrix, *samples_matrix, *mean_matrix;
- const gsl_matrix *cov_matrix;
- gsl_matrix *corr_matrix;
+ gsl_matrix *cov_matrix = NULL;
+ gsl_matrix *corr_matrix = NULL;
struct covariance *cov = covariance_2pass_create (corr->n_vars_total, corr->vars,
NULL,
opts->wv, opts->exclude);
{
covariance_accumulate_pass2 (cov, c);
}
+ casereader_destroy (rc);
cov_matrix = covariance_calculate (cov);
-
- casereader_destroy (rc);
+ if (! cov_matrix)
+ {
+ msg (SE, _("The data for the chosen variables are all missing or empty."));
+ goto error;
+ }
samples_matrix = covariance_moments (cov, MOMENT_NONE);
var_matrix = covariance_moments (cov, MOMENT_VARIANCE);
corr_matrix = correlation_from_covariance (cov_matrix, var_matrix);
- if ( opts->statistics & STATS_DESCRIPTIVES)
+ if ( opts->statistics & STATS_DESCRIPTIVES)
output_descriptives (corr, mean_matrix, var_matrix, samples_matrix);
- output_correlation (corr, opts,
- corr_matrix,
- samples_matrix,
- cov_matrix);
+ output_correlation (corr, opts, corr_matrix,
+ samples_matrix, cov_matrix);
+ error:
covariance_destroy (cov);
gsl_matrix_free (corr_matrix);
+ gsl_matrix_free (cov_matrix);
}
int
opts.statistics = 0;
/* Parse CORRELATIONS. */
- while (lex_token (lexer) != '.')
+ while (lex_token (lexer) != T_ENDCMD)
{
- lex_match (lexer, '/');
+ lex_match (lexer, T_SLASH);
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, "PAIRWISE"))
opts.missing_type = CORR_PAIRWISE;
lex_error (lexer, NULL);
goto error;
}
- lex_match (lexer, ',');
+ lex_match (lexer, T_COMMA);
}
}
else if (lex_match_id (lexer, "PRINT"))
{
- 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, "TWOTAIL"))
opts.tails = 2;
goto error;
}
- lex_match (lexer, ',');
+ lex_match (lexer, T_COMMA);
}
}
else 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"))
opts.statistics = STATS_DESCRIPTIVES;
opts.statistics = STATS_ALL;
lex_get (lexer);
}
- else
+ else
{
lex_error (lexer, NULL);
goto error;
}
- lex_match (lexer, ',');
+ lex_match (lexer, T_COMMA);
}
}
else
{
if (lex_match_id (lexer, "VARIABLES"))
{
- lex_match (lexer, '=');
+ lex_match (lexer, T_EQUALS);
}
corr = xrealloc (corr, sizeof (*corr) * (n_corrs + 1));
corr[n_corrs].n_vars_total = corr[n_corrs].n_vars1 = 0;
-
- if ( ! parse_variables_const (lexer, dict, &corr[n_corrs].vars,
+
+ if ( ! parse_variables_const (lexer, dict, &corr[n_corrs].vars,
&corr[n_corrs].n_vars_total,
PV_NUMERIC))
{
return ok ? CMD_SUCCESS : CMD_CASCADING_FAILURE;
error:
- free (corr->vars);
+ if (corr)
+ free (corr->vars);
free (corr);
return CMD_FAILURE;
}
projects / pspp / blobdiff