#include "math/correlation.h"
#include "math/covariance.h"
#include "math/moments.h"
-#include "output/tab.h"
+#include "output/pivot-table.h"
#include "gl/xalloc.h"
#include "gl/minmax.h"
static void
-output_descriptives (const struct corr *corr, const gsl_matrix *means,
+output_descriptives (const struct corr *corr, const struct corr_opts *opts,
+ const gsl_matrix *means,
const gsl_matrix *vars, const gsl_matrix *ns)
{
- const int nr = corr->n_vars_total + 1;
- const int nc = 4;
- int c, r;
+ struct pivot_table *table = pivot_table_create (
+ N_("Descriptive Statistics"));
+ pivot_table_set_weight_var (table, opts->wv);
- const int heading_columns = 1;
- 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);
- struct tab_table *t = tab_create (nc, nr);
- tab_title (t, _("Descriptive Statistics"));
+ struct pivot_dimension *variables = pivot_dimension_create (
+ table, PIVOT_AXIS_ROW, N_("Variable"));
- tab_headers (t, heading_columns, 0, heading_rows, 0);
-
- /* Outline the box */
- tab_box (t,
- TAL_2, TAL_2,
- -1, -1,
- 0, 0,
- nc - 1, nr - 1);
-
- /* Vertical lines */
- tab_box (t,
- -1, -1,
- -1, TAL_1,
- heading_columns, 0,
- nc - 1, nr - 1);
-
- tab_vline (t, TAL_2, heading_columns, 0, nr - 1);
- tab_hline (t, TAL_1, 0, nc - 1, heading_rows);
-
- tab_text (t, 1, 0, TAB_CENTER | TAT_TITLE, _("Mean"));
- tab_text (t, 2, 0, TAB_CENTER | TAT_TITLE, _("Std. Deviation"));
- tab_text (t, 3, 0, TAB_CENTER | TAT_TITLE, _("N"));
-
- for (r = 0 ; r < corr->n_vars_total ; ++r)
+ for (size_t r = 0 ; r < corr->n_vars_total ; ++r)
{
const struct variable *v = corr->vars[r];
- tab_text (t, 0, r + heading_rows, TAB_LEFT | TAT_TITLE, var_to_string (v));
-
- for (c = 1 ; c < nc ; ++c)
- {
- double x ;
- double n;
- switch (c)
- {
- case 1:
- x = gsl_matrix_get (means, r, 0);
- break;
- case 2:
- x = gsl_matrix_get (vars, r, 0);
- /* Here we want to display the non-biased estimator */
- n = gsl_matrix_get (ns, r, 0);
- x *= n / (n -1);
+ int row = pivot_category_create_leaf (variables->root,
+ pivot_value_new_variable (v));
- x = sqrt (x);
- break;
- case 3:
- x = gsl_matrix_get (ns, r, 0);
- break;
- default:
- NOT_REACHED ();
- };
-
- tab_double (t, c, r + heading_rows, 0, x, NULL);
- }
+ 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]));
}
- tab_submit (t);
+ pivot_table_submit (table);
}
static void
const gsl_matrix *cm, const gsl_matrix *samples,
const gsl_matrix *cv)
{
- 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;
-
- const int heading_columns = 2;
- const int heading_rows = 1;
+ struct pivot_table *table = pivot_table_create (N_("Correlations"));
+ pivot_table_set_weight_var (table, opts->wv);
- int rows_per_variable = opts->missing_type == CORR_LISTWISE ? 2 : 3;
+ /* Column variable dimension. */
+ struct pivot_dimension *columns = pivot_dimension_create (
+ table, PIVOT_AXIS_COLUMN, N_("Variables"));
- if (opts->statistics & STATS_XPROD)
- rows_per_variable += 2;
-
- /* Two header columns */
- nc += heading_columns;
-
- /* Three data per variable */
- nr *= rows_per_variable;
-
- /* One header row */
- nr += heading_rows;
-
- t = tab_create (nc, nr);
- tab_title (t, _("Correlations"));
-
- tab_headers (t, heading_columns, 0, heading_rows, 0);
-
- /* Outline the box */
- tab_box (t,
- TAL_2, TAL_2,
- -1, -1,
- 0, 0,
- nc - 1, nr - 1);
-
- /* Vertical lines */
- tab_box (t,
- -1, -1,
- -1, TAL_1,
- heading_columns, 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);
-
- for (r = 0 ; r < corr->n_vars1 ; ++r)
+ 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++)
{
- tab_text (t, 0, 1 + r * rows_per_variable, TAB_LEFT | TAT_TITLE,
- var_to_string (corr->vars[r]));
+ 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));
+ }
- 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)"));
+ /* 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->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, rows_per_variable + r * rows_per_variable, TAB_LEFT | TAT_TITLE, _("N"));
+ if (opts->statistics & STATS_XPROD)
+ pivot_category_create_leaves (statistics->root, N_("Cross-products"),
+ N_("Covariance"));
- tab_hline (t, TAL_1, 0, nc - 1, r * rows_per_variable + 1);
- }
+ if (opts->missing_type != CORR_LISTWISE)
+ pivot_category_create_leaves (statistics->root, N_("N"), PIVOT_RC_COUNT);
- 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 - 1 + c] : corr->vars[c];
- tab_text (t, heading_columns + c, 0, TAB_LEFT | TAT_TITLE, var_to_string (v));
- }
+ /* 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 (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 ?
- corr->n_vars_total - corr->n_vars1 - 1 + 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);
-
- 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);
-
- 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);
+ struct pivot_footnote *sig_footnote = pivot_table_create_footnote (
+ table, pivot_value_new_text (N_("Significant at .05 level")));
- tab_double (t, c + heading_columns, row + 2, 0, xprod_dev, NULL);
- tab_double (t, c + heading_columns, row + 3, 0, cov, 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);
}
{
struct ccase *c;
const gsl_matrix *var_matrix, *samples_matrix, *mean_matrix;
- 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);
+ opts->wv, opts->exclude,
+ true);
struct casereader *rc = casereader_clone (r);
for ( ; (c = casereader_read (r) ); case_unref (c))
{
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)
- output_descriptives (corr, mean_matrix, var_matrix, samples_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);
opts.statistics = STATS_ALL;
lex_get (lexer);
}
- else
+ else
{
lex_error (lexer, NULL);
goto error;
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