/* 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 <math/design-matrix.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/manager.h>
-#include <output/table.h>
-#include <libpspp/message.h>
-#include <data/format.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/pivot-table.h"
+
+#include "gl/xalloc.h"
+#include "gl/minmax.h"
#include "gettext.h"
#define _(msgid) gettext (msgid)
#define N_(msgid) msgid
-/* Returns the correlation matrix corresponding to the covariance
-matrix COV. The return value must be freed with gsl_matrix_free
-when no longer required.
-*/
-static gsl_matrix *
-covariance_to_correlation (const gsl_matrix *cov)
-{
- size_t r, c;
- gsl_matrix *corr = gsl_matrix_alloc (cov->size1, cov->size2);
-
- for (r = 0 ; r < cov->size1; ++r)
- {
- for (c = 0 ; c < cov->size2 ; ++c)
- {
- double x = gsl_matrix_get (cov, r, c);
- x /= sqrt (gsl_matrix_get (cov, r, r)
- * gsl_matrix_get (cov, c, c) );
- gsl_matrix_set (corr, r, c, x);
- }
- }
-
- return corr;
-}
-
-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
{
CORR_LISTWISE /* Discard entire case if any variable is missing. */
};
+enum stats_opts
+ {
+ STATS_DESCRIPTIVES = 0x01,
+ STATS_XPROD = 0x02,
+ STATS_ALL = STATS_XPROD | STATS_DESCRIPTIVES
+ };
+
struct corr_opts
{
enum corr_missing_type missing_type;
bool sig; /* Flag significant values or not */
int tails; /* Report significance with how many tails ? */
+ enum stats_opts statistics;
const struct variable *wv; /* The weight variable (if any) */
};
static void
-output_correlation (const struct corr *corr, const struct corr_opts *opts,
- const gsl_matrix *cm, const gsl_matrix *samples)
+output_descriptives (const struct corr *corr, const struct corr_opts *opts,
+ const gsl_matrix *means,
+ const gsl_matrix *vars, const gsl_matrix *ns)
{
- int r, c;
- struct tab_table *t;
- int matrix_cols;
- int nr = corr->n_vars1;
- int nc = matrix_cols = corr->n_vars_total > corr->n_vars1 ?
- corr->n_vars_total - corr->n_vars1 : corr->n_vars1;
-
- const struct fmt_spec *wfmt = opts->wv ? var_get_print_format (opts->wv) : & F_8_0;
+ struct pivot_table *table = pivot_table_create (
+ N_("Descriptive Statistics"));
+ pivot_table_set_weight_var (table, opts->wv);
- const int heading_columns = 2;
- const int heading_rows = 1;
+ pivot_dimension_create (table, PIVOT_AXIS_COLUMN, N_("Statistics"),
+ N_("Mean"), PIVOT_RC_OTHER,
+ N_("Std. Deviation"), PIVOT_RC_OTHER,
+ N_("N"), PIVOT_RC_COUNT);
- const int rows_per_variable = opts->missing_type == CORR_LISTWISE ? 2 : 3;
+ struct pivot_dimension *variables = pivot_dimension_create (
+ table, PIVOT_AXIS_ROW, N_("Variable"));
- /* Two header columns */
- nc += heading_columns;
+ for (size_t r = 0 ; r < corr->n_vars_total ; ++r)
+ {
+ const struct variable *v = corr->vars[r];
+
+ int row = pivot_category_create_leaf (variables->root,
+ pivot_value_new_variable (v));
+
+ double mean = gsl_matrix_get (means, r, 0);
+ /* Here we want to display the non-biased estimator */
+ double n = gsl_matrix_get (ns, r, 0);
+ double stddev = sqrt (gsl_matrix_get (vars, r, 0) * n / (n - 1));
+ double entries[] = { mean, stddev, n };
+ for (size_t i = 0; i < sizeof entries / sizeof *entries; i++)
+ pivot_table_put2 (table, i, row, pivot_value_new_number (entries[i]));
+ }
- /* Three data per variable */
- nr *= rows_per_variable;
+ pivot_table_submit (table);
+}
- /* One header row */
- nr += heading_rows;
+static void
+output_correlation (const struct corr *corr, const struct corr_opts *opts,
+ const gsl_matrix *cm, const gsl_matrix *samples,
+ const gsl_matrix *cv)
+{
+ struct pivot_table *table = pivot_table_create (N_("Correlations"));
+ pivot_table_set_weight_var (table, opts->wv);
- t = tab_create (nc, nr, 0);
- tab_title (t, _("Correlations"));
- tab_dim (t, tab_natural_dimensions, NULL);
+ /* Column variable dimension. */
+ struct pivot_dimension *columns = pivot_dimension_create (
+ table, PIVOT_AXIS_COLUMN, N_("Variables"));
- tab_headers (t, heading_columns, 0, heading_rows, 0);
+ int matrix_cols = (corr->n_vars_total > corr->n_vars1
+ ? corr->n_vars_total - corr->n_vars1
+ : corr->n_vars1);
+ for (int c = 0; c < matrix_cols; c++)
+ {
+ const struct variable *v = corr->n_vars_total > corr->n_vars1 ?
+ corr->vars[corr->n_vars1 + c] : corr->vars[c];
+ pivot_category_create_leaf (columns->root, pivot_value_new_variable (v));
+ }
- /* Outline the box */
- tab_box (t,
- TAL_2, TAL_2,
- -1, -1,
- 0, 0,
- nc - 1, nr - 1);
+ /* Statistics dimension. */
+ struct pivot_dimension *statistics = pivot_dimension_create (
+ table, PIVOT_AXIS_ROW, N_("Statistics"),
+ N_("Pearson Correlation"), PIVOT_RC_CORRELATION,
+ opts->tails == 2 ? N_("Sig. (2-tailed)") : N_("Sig. (1-tailed)"),
+ PIVOT_RC_SIGNIFICANCE);
- /* Vertical lines */
- tab_box (t,
- -1, -1,
- -1, TAL_1,
- heading_columns, 0,
- nc - 1, nr - 1);
+ if (opts->statistics & STATS_XPROD)
+ pivot_category_create_leaves (statistics->root, N_("Cross-products"),
+ N_("Covariance"));
- tab_vline (t, TAL_2, heading_columns, 0, nr - 1);
- tab_vline (t, TAL_1, 1, heading_rows, nr - 1);
+ if (opts->missing_type != CORR_LISTWISE)
+ pivot_category_create_leaves (statistics->root, N_("N"), PIVOT_RC_COUNT);
- for (r = 0 ; r < corr->n_vars1 ; ++r)
- {
- 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,
- (opts->tails == 2) ? _("Sig. (2-tailed)") : _("Sig. (1-tailed)"));
- if ( opts->missing_type != CORR_LISTWISE )
- tab_text (t, 1, 3 + r * rows_per_variable, TAB_LEFT | TAT_TITLE, _("N"));
- tab_hline (t, TAL_1, 0, nc - 1, r * rows_per_variable + 1);
- }
+ /* Row variable dimension. */
+ struct pivot_dimension *rows = pivot_dimension_create (
+ table, PIVOT_AXIS_ROW, N_("Variables"));
+ for (size_t r = 0; r < corr->n_vars1; r++)
+ pivot_category_create_leaf (rows->root,
+ pivot_value_new_variable (corr->vars[r]));
- 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));
- }
+ struct pivot_footnote *sig_footnote = pivot_table_create_footnote (
+ table, pivot_value_new_text (N_("Significant at .05 level")));
- 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;
- 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);
-
- if ( c != r)
- tab_double (t, c + heading_columns, row + 1, 0, sig, NULL);
-
- if ( opts->sig && c != r && sig < 0.05)
- flags = TAB_EMPH;
-
- tab_double (t, c + heading_columns, row, flags, pearson, NULL);
- }
- }
+ for (int r = 0; r < corr->n_vars1; r++)
+ for (int c = 0; c < matrix_cols; c++)
+ {
+ 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);
+
+ double entries[5];
+ int n = 0;
+ entries[n++] = pearson;
+ entries[n++] = col_index != r ? sig : SYSMIS;
+ 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);
+
+ entries[n++] = xprod_dev;
+ entries[n++] = cov;
+ }
+ if (opts->missing_type != CORR_LISTWISE)
+ entries[n++] = w;
+
+ for (int i = 0; i < n; i++)
+ if (entries[i] != SYSMIS)
+ {
+ struct pivot_value *v = pivot_value_new_number (entries[i]);
+ if (!i && opts->sig && col_index != r && sig < 0.05)
+ pivot_value_add_footnote (v, sig_footnote);
+ pivot_table_put3 (table, c, i, r, v);
+ }
+ }
- tab_submit (t);
+ pivot_table_submit (table);
}
+
static void
run_corr (struct casereader *r, const struct corr_opts *opts, const struct corr *corr)
{
struct ccase *c;
- const struct design_matrix *cov_matrix;
- const gsl_matrix *samples_matrix;
-
+ const gsl_matrix *var_matrix, *samples_matrix, *mean_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,
+ true);
+
+ struct casereader *rc = casereader_clone (r);
for ( ; (c = casereader_read (r) ); case_unref (c))
{
+ covariance_accumulate_pass1 (cov, c);
+ }
+
+ for ( ; (c = casereader_read (rc) ); case_unref (c))
+ {
+ covariance_accumulate_pass2 (cov, c);
+ }
+ casereader_destroy (rc);
+ cov_matrix = covariance_calculate (cov);
+ 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);
+ mean_matrix = covariance_moments (cov, MOMENT_MEAN);
- output_correlation (corr, opts,
- covariance_to_correlation (cov_matrix->m),
- samples_matrix );
+ corr_matrix = correlation_from_covariance (cov_matrix, var_matrix);
+
+ if ( opts->statistics & STATS_DESCRIPTIVES)
+ output_descriptives (corr, opts, mean_matrix, var_matrix, samples_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
cmd_correlation (struct lexer *lexer, struct dataset *ds)
{
+ int i;
int n_all_vars = 0; /* Total number of variables involved in this command */
+ const struct variable **all_vars ;
const struct dictionary *dict = dataset_dict (ds);
bool ok = true;
opts.tails = 2;
opts.sig = false;
opts.exclude = MV_ANY;
+ 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, T_EQUALS);
+ while (lex_token (lexer) != T_ENDCMD && lex_token (lexer) != T_SLASH)
+ {
+ if ( lex_match_id (lexer, "DESCRIPTIVES"))
+ opts.statistics = STATS_DESCRIPTIVES;
+ else if (lex_match_id (lexer, "XPROD"))
+ opts.statistics = STATS_XPROD;
+ else if (lex_token (lexer) == T_ALL)
+ {
+ opts.statistics = STATS_ALL;
+ lex_get (lexer);
+ }
+ else
+ {
+ lex_error (lexer, NULL);
+ goto error;
+ }
+
+ 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))
{
}
- const struct variable **all_vars = xmalloc (sizeof (*all_vars) * n_all_vars);
- int i;
+ all_vars = xmalloc (sizeof (*all_vars) * n_all_vars);
{
/* FIXME: Using a hash here would make more sense */
r = casereader_create_filter_missing (r, all_vars, n_all_vars,
opts.exclude, NULL, NULL);
+
run_corr (r, &opts, &corr[i]);
casereader_destroy (r);
}
/* Done. */
+ free (corr->vars);
free (corr);
+
return ok ? CMD_SUCCESS : CMD_CASCADING_FAILURE;
error:
+ if (corr)
+ free (corr->vars);
free (corr);
return CMD_FAILURE;
}
projects / pspp / blobdiff