#include <libpspp/assertion.h>
#include <math/covariance.h>
-#include <math/design-matrix.h>
+#include <math/correlation.h>
#include <gsl/gsl_matrix.h>
#include <data/casegrouper.h>
#include <data/casereader.h>
#include <language/dictionary/split-file.h>
#include <language/lexer/lexer.h>
#include <language/lexer/variable-parser.h>
-#include <output/manager.h>
-#include <output/table.h>
+#include <output/tab.h>
#include <libpspp/message.h>
#include <data/format.h>
#include <math/moments.h>
#define N_(msgid) msgid
-static double
-significance_of_correlation (double rho, double w)
-{
- double t = w - 2;
- t /= 1 - MIN (1, pow2 (rho));
- t = sqrt (t);
- t *= rho;
-
- if (t > 0)
- return gsl_cdf_tdist_Q (t, w - 2);
- else
- return gsl_cdf_tdist_P (t, w - 2);
-}
-
-
struct corr
{
size_t n_vars_total;
const int heading_columns = 1;
const int heading_rows = 1;
- struct tab_table *t = tab_create (nc, nr, 0);
+ struct tab_table *t = tab_create (nc, nr);
tab_title (t, _("Descriptive Statistics"));
- tab_dim (t, tab_natural_dimensions, NULL);
tab_headers (t, heading_columns, 0, heading_rows, 0);
static void
output_correlation (const struct corr *corr, const struct corr_opts *opts,
- const gsl_matrix *cm, const gsl_matrix *samples)
+ const gsl_matrix *cm, const gsl_matrix *samples,
+ const gsl_matrix *cv)
{
int r, c;
struct tab_table *t;
const int heading_columns = 2;
const int heading_rows = 1;
- const int rows_per_variable = opts->missing_type == CORR_LISTWISE ? 2 : 3;
+ int rows_per_variable = opts->missing_type == CORR_LISTWISE ? 2 : 3;
+
+ if (opts->statistics & STATS_XPROD)
+ rows_per_variable += 2;
/* Two header columns */
nc += heading_columns;
/* One header row */
nr += heading_rows;
- t = tab_create (nc, nr, 0);
+ t = tab_create (nc, nr);
tab_title (t, _("Correlations"));
- tab_dim (t, tab_natural_dimensions, NULL);
tab_headers (t, heading_columns, 0, heading_rows, 0);
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,
(opts->tails == 2) ? _("Sig. (2-tailed)") : _("Sig. (1-tailed)"));
+
+ if (opts->statistics & STATS_XPROD)
+ {
+ tab_text (t, 1, 3 + r * rows_per_variable, TAB_LEFT | TAT_TITLE, _("Cross-products"));
+ tab_text (t, 1, 4 + r * rows_per_variable, TAB_LEFT | TAT_TITLE, _("Covariance"));
+ }
+
if ( opts->missing_type != CORR_LISTWISE )
- tab_text (t, 1, 3 + r * rows_per_variable, TAB_LEFT | TAT_TITLE, _("N"));
+ tab_text (t, 1, rows_per_variable + r * rows_per_variable, TAB_LEFT | TAT_TITLE, _("N"));
+
tab_hline (t, TAL_1, 0, nc - 1, r * rows_per_variable + 1);
}
for (c = 0 ; c < matrix_cols ; ++c)
{
unsigned char flags = 0;
- int col_index = corr->n_vars_total - corr->n_vars1 + c;
+ const int col_index = corr->n_vars_total - corr->n_vars1 + 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 + 2, 0, w, wfmt);
+ tab_double (t, c + heading_columns, row + rows_per_variable - 1, 0, w, wfmt);
if ( c != r)
tab_double (t, c + heading_columns, row + 1, 0, sig, NULL);
flags = TAB_EMPH;
tab_double (t, c + heading_columns, row, flags, pearson, NULL);
- }
- }
-
- tab_submit (t);
-}
+ if (opts->statistics & STATS_XPROD)
+ {
+ double cov = gsl_matrix_get (cv, r, col_index);
+ const double xprod_dev = cov * w;
+ cov *= w / (w - 1.0);
-static gsl_matrix *
-correlation_from_covariance (const gsl_matrix *cv, const gsl_matrix *v)
-{
- size_t i, j;
- gsl_matrix *corr = gsl_matrix_calloc (cv->size1, cv->size2);
-
- for (i = 0 ; i < cv->size1; ++i)
- {
- for (j = 0 ; j < cv->size2; ++j)
- {
- double rho = gsl_matrix_get (cv, i, j);
-
- rho /= sqrt (gsl_matrix_get (v, i, j))
- *
- sqrt (gsl_matrix_get (v, j, i));
-
- gsl_matrix_set (corr, i, j, rho);
+ tab_double (t, c + heading_columns, row + 2, 0, xprod_dev, NULL);
+ tab_double (t, c + heading_columns, row + 3, 0, cov, NULL);
+ }
}
}
-
- return corr;
-}
-
+ tab_submit (t);
+}
static void
const gsl_matrix *var_matrix, *samples_matrix, *mean_matrix;
const gsl_matrix *cov_matrix;
gsl_matrix *corr_matrix;
- struct covariance *cov = covariance_create (corr->n_vars_total, corr->vars,
- opts->wv, opts->exclude);
+ struct covariance *cov = covariance_2pass_create (corr->n_vars_total, corr->vars,
+ NULL,
+ opts->wv, opts->exclude);
+ struct casereader *rc = casereader_clone (r);
for ( ; (c = casereader_read (r) ); case_unref (c))
{
- covariance_accumulate (cov, c);
+ covariance_accumulate_pass1 (cov, c);
+ }
+
+ for ( ; (c = casereader_read (rc) ); case_unref (c))
+ {
+ covariance_accumulate_pass2 (cov, c);
}
cov_matrix = covariance_calculate (cov);
+ casereader_destroy (rc);
+
samples_matrix = covariance_moments (cov, MOMENT_NONE);
var_matrix = covariance_moments (cov, MOMENT_VARIANCE);
mean_matrix = covariance_moments (cov, MOMENT_MEAN);
output_correlation (corr, opts,
corr_matrix,
- samples_matrix );
+ samples_matrix,
+ cov_matrix);
covariance_destroy (cov);
gsl_matrix_free (corr_matrix);
opts.statistics = STATS_DESCRIPTIVES;
else if (lex_match_id (lexer, "XPROD"))
opts.statistics = STATS_XPROD;
- else
- opts.statistics = STATS_ALL;
+ else if (lex_token (lexer) == T_ALL)
+ {
+ opts.statistics = STATS_ALL;
+ lex_get (lexer);
+ }
+ else
+ {
+ lex_error (lexer, NULL);
+ goto error;
+ }
lex_match (lexer, ',');
}
/* Done. */
+ free (corr->vars);
free (corr);
+
return ok ? CMD_SUCCESS : CMD_CASCADING_FAILURE;
error:
+ free (corr->vars);
free (corr);
return CMD_FAILURE;
}
projects / pspp / blobdiff