--- /dev/null
+/* PSPP - a program for statistical analysis.
+ 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
+ the Free Software Foundation, either version 3 of the License, or
+ (at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+ GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+ along with this program. If not, see <http://www.gnu.org/licenses/>. */
+
+#include <config.h>
+
+#include <gsl/gsl_cdf.h>
+#include <gsl/gsl_matrix.h>
+#include <math.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/commands/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
+
+
+struct corr
+ {
+ size_t n_vars_total;
+ size_t n_vars1;
+
+ const struct variable **vars;
+ };
+
+
+/* Handling of missing values. */
+enum corr_missing_type
+ {
+ CORR_PAIRWISE, /* Handle missing values on a per-variable-pair basis. */
+ CORR_LISTWISE /* Discard entire case if any variable is missing. */
+ };
+
+struct corr_opts
+{
+ enum corr_missing_type missing_type;
+ enum mv_class exclude; /* Classes of missing values to exclude. */
+
+ bool sig; /* Flag significant values or not */
+ int tails; /* Report significance with how many tails ? */
+ bool descriptive_stats;
+ bool xprod_stats;
+
+ const struct variable *wv; /* The weight variable (if any) */
+};
+
+
+static void
+output_descriptives (const struct corr *corr, const struct corr_opts *opts,
+ const gsl_matrix *means,
+ const gsl_matrix *vars, const gsl_matrix *ns)
+{
+ struct pivot_table *table = pivot_table_create (
+ N_("Descriptive Statistics"));
+ pivot_table_set_weight_var (table, opts->wv);
+
+ 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);
+
+ struct pivot_dimension *variables = pivot_dimension_create (
+ table, PIVOT_AXIS_ROW, N_("Variable"));
+
+ 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]));
+ }
+
+ pivot_table_submit (table);
+}
+
+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);
+
+ /* Column variable dimension. */
+ struct pivot_dimension *columns = pivot_dimension_create (
+ table, PIVOT_AXIS_COLUMN, N_("Variables"));
+
+ size_t matrix_cols = (corr->n_vars_total > corr->n_vars1
+ ? corr->n_vars_total - corr->n_vars1
+ : corr->n_vars1);
+ for (size_t 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));
+ }
+
+ /* 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);
+
+ if (opts->xprod_stats)
+ pivot_category_create_leaves (statistics->root, N_("Cross-products"),
+ N_("Covariance"));
+
+ if (opts->missing_type != CORR_LISTWISE)
+ pivot_category_create_leaves (statistics->root, N_("N"), PIVOT_RC_COUNT);
+
+ /* 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]));
+
+ struct pivot_footnote *sig_footnote = pivot_table_create_footnote (
+ table, pivot_value_new_text (N_("Significant at .05 level")));
+
+ for (size_t r = 0; r < corr->n_vars1; r++)
+ for (size_t 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->xprod_stats)
+ {
+ 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);
+ }
+ }
+
+ pivot_table_submit (table);
+}
+
+
+static void
+run_corr (struct casereader *r, const struct corr_opts *opts, const struct corr *corr)
+{
+ struct covariance *cov = covariance_2pass_create (
+ corr->n_vars_total, corr->vars, NULL,opts->wv, opts->exclude, true);
+
+ struct casereader *rc = casereader_clone (r);
+ struct ccase *c;
+ 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);
+
+ gsl_matrix *cov_matrix = covariance_calculate (cov);
+ if (!cov_matrix)
+ {
+ msg (SE, _("The data for the chosen variables are all missing or empty."));
+ covariance_destroy (cov);
+ return;
+ }
+
+ const gsl_matrix *samples_matrix = covariance_moments (cov, MOMENT_NONE);
+ const gsl_matrix *var_matrix = covariance_moments (cov, MOMENT_VARIANCE);
+ const gsl_matrix *mean_matrix = covariance_moments (cov, MOMENT_MEAN);
+
+ gsl_matrix *corr_matrix = correlation_from_covariance (cov_matrix, var_matrix);
+
+ if (opts->descriptive_stats)
+ output_descriptives (corr, opts, mean_matrix, var_matrix, samples_matrix);
+
+ output_correlation (corr, opts, corr_matrix, samples_matrix, cov_matrix);
+
+ covariance_destroy (cov);
+ gsl_matrix_free (corr_matrix);
+ gsl_matrix_free (cov_matrix);
+}
+
+int
+cmd_correlations (struct lexer *lexer, struct dataset *ds)
+{
+ size_t n_all_vars = 0; /* Total number of variables involved in this command */
+ const struct dictionary *dict = dataset_dict (ds);
+
+ struct corr *corrs = NULL;
+ size_t n_corrs = 0;
+ size_t allocated_corrs = 0;
+
+ struct corr_opts opts = {
+ .missing_type = CORR_PAIRWISE,
+ .wv = dict_get_weight (dict),
+ .tails = 2,
+ .exclude = MV_ANY,
+ };
+
+ /* Parse CORRELATIONS. */
+ while (lex_token (lexer) != T_ENDCMD)
+ {
+ lex_match (lexer, T_SLASH);
+ if (lex_match_id (lexer, "MISSING"))
+ {
+ 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;
+ else if (lex_match_id (lexer, "LISTWISE"))
+ opts.missing_type = CORR_LISTWISE;
+ else if (lex_match_id (lexer, "INCLUDE"))
+ opts.exclude = MV_SYSTEM;
+ else if (lex_match_id (lexer, "EXCLUDE"))
+ opts.exclude = MV_ANY;
+ else
+ {
+ lex_error_expecting (lexer, "PAIRWISE", "LISTWISE",
+ "INCLUDE", "EXCLUDE");
+ goto error;
+ }
+ lex_match (lexer, T_COMMA);
+ }
+ }
+ else if (lex_match_id (lexer, "PRINT"))
+ {
+ 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;
+ else if (lex_match_id (lexer, "ONETAIL"))
+ opts.tails = 1;
+ else if (lex_match_id (lexer, "SIG"))
+ opts.sig = false;
+ else if (lex_match_id (lexer, "NOSIG"))
+ opts.sig = true;
+ else
+ {
+ lex_error_expecting (lexer, "TWOTAIL", "ONETAIL",
+ "SIG", "NOSIG");
+ goto error;
+ }
+
+ 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.descriptive_stats = true;
+ else if (lex_match_id (lexer, "XPROD"))
+ opts.xprod_stats = true;
+ else if (lex_token (lexer) == T_ALL)
+ {
+ opts.descriptive_stats = opts.xprod_stats = true;
+ lex_get (lexer);
+ }
+ else
+ {
+ lex_error_expecting (lexer, "DESCRIPTIVES", "XPROD", "ALL");
+ goto error;
+ }
+
+ lex_match (lexer, T_COMMA);
+ }
+ }
+ else
+ {
+ if (lex_match_id (lexer, "VARIABLES"))
+ lex_match (lexer, T_EQUALS);
+
+ const struct variable **vars;
+ size_t n_vars1;
+ if (!parse_variables_const (lexer, dict, &vars, &n_vars1, PV_NUMERIC))
+ goto error;
+
+ size_t n_vars_total = n_vars1;
+ if (lex_match (lexer, T_WITH)
+ && !parse_variables_const (lexer, dict, &vars, &n_vars_total,
+ PV_NUMERIC | PV_APPEND))
+ goto error;
+
+ if (n_corrs >= allocated_corrs)
+ corrs = x2nrealloc (corrs, &allocated_corrs, sizeof *corrs);
+ corrs[n_corrs++] = (struct corr) {
+ .n_vars1 = n_vars1,
+ .n_vars_total = n_vars_total,
+ .vars = vars,
+ };
+
+ n_all_vars += n_vars_total;
+ }
+ }
+ if (n_corrs == 0)
+ {
+ lex_ofs_error (lexer, 0, lex_ofs (lexer) - 1,
+ _("No variables specified."));
+ goto error;
+ }
+
+ const struct variable **all_vars = xmalloc (n_all_vars * sizeof *all_vars);
+ const struct variable **vv = all_vars;
+ for (size_t i = 0; i < n_corrs; ++i)
+ {
+ const struct corr *c = &corrs[i];
+ for (size_t v = 0; v < c->n_vars_total; ++v)
+ *vv++ = c->vars[v];
+ }
+
+ struct casegrouper *grouper = casegrouper_create_splits (proc_open (ds), dict);
+ struct casereader *group;
+ while (casegrouper_get_next_group (grouper, &group))
+ {
+ for (size_t i = 0; i < n_corrs; ++i)
+ {
+ /* FIXME: No need to iterate the data multiple times */
+ struct casereader *r = casereader_clone (group);
+
+ if (opts.missing_type == CORR_LISTWISE)
+ r = casereader_create_filter_missing (r, all_vars, n_all_vars,
+ opts.exclude, NULL, NULL);
+
+
+ run_corr (r, &opts, &corrs[i]);
+ casereader_destroy (r);
+ }
+ casereader_destroy (group);
+ }
+ bool ok = casegrouper_destroy (grouper);
+ ok = proc_commit (ds) && ok;
+
+ free (all_vars);
+
+ /* Done. */
+ for (size_t i = 0; i < n_corrs; i++)
+ free (corrs[i].vars);
+ free (corrs);
+
+ return ok ? CMD_SUCCESS : CMD_CASCADING_FAILURE;
+
+error:
+ for (size_t i = 0; i < n_corrs; i++)
+ free (corrs[i].vars);
+ free (corrs);
+ return CMD_FAILURE;
+}
projects / pspp / blobdiff