X-Git-Url: https://pintos-os.org/cgi-bin/gitweb.cgi?a=blobdiff_plain;f=src%2Flanguage%2Fstats%2Fcorrelations.c;h=b33baf5758372a4a50d869cda1cecd9a4224a0a6;hb=4d777aeacfa602840718862c31c9059e3d289eed;hp=ebfea2583e71f7f11fc4ebe21bf9c42572c8a633;hpb=7741e39c8dd2ca7b6a5c68c7af74ca0507690644;p=pspp diff --git a/src/language/stats/correlations.c b/src/language/stats/correlations.c index ebfea2583e..b33baf5758 100644 --- a/src/language/stats/correlations.c +++ b/src/language/stats/correlations.c @@ -36,7 +36,7 @@ #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" @@ -83,77 +83,39 @@ struct corr_opts 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, RC_OTHER); - } + 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 @@ -161,123 +123,84 @@ output_correlation (const struct corr *corr, const struct corr_opts *opts, 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; + struct pivot_table *table = pivot_table_create (N_("Correlations")); + pivot_table_set_weight_var (table, opts->wv); - const int heading_columns = 2; - const int heading_rows = 1; + /* Column variable dimension. */ + struct pivot_dimension *columns = pivot_dimension_create ( + table, PIVOT_AXIS_COLUMN, N_("Variables")); - 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; - - /* Three data per variable */ - nr *= rows_per_variable; - - /* One header row */ - 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); - - /* 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); - - /* Row Headers */ - 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])); - - 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)")); + 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)); + } - 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")); - } + /* 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->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); - /* Column Headers */ - for (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]; - 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_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, NULL, RC_WEIGHT); - - if ( col_index != r) - tab_double (t, c + heading_columns, row + 1, 0, sig, NULL, RC_PVALUE); - - if ( opts->sig && col_index != r && sig < 0.05) - flags = TAB_EMPH; - - tab_double (t, c + heading_columns, row, flags, pearson, NULL, RC_OTHER); - - 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, RC_OTHER); - tab_double (t, c + heading_columns, row + 3, 0, cov, NULL, RC_OTHER); - } - } - } + 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); } @@ -290,35 +213,36 @@ run_corr (struct casereader *r, const struct corr_opts *opts, const struct corr 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)) + for (; (c = casereader_read (r)); case_unref (c)) { covariance_accumulate_pass1 (cov, c); } - for ( ; (c = casereader_read (rc) ); case_unref (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); 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); @@ -383,7 +307,7 @@ cmd_correlation (struct lexer *lexer, struct dataset *ds) lex_match (lexer, T_EQUALS); while (lex_token (lexer) != T_ENDCMD && lex_token (lexer) != T_SLASH) { - if ( lex_match_id (lexer, "TWOTAIL")) + if (lex_match_id (lexer, "TWOTAIL")) opts.tails = 2; else if (lex_match_id (lexer, "ONETAIL")) opts.tails = 1; @@ -405,7 +329,7 @@ cmd_correlation (struct lexer *lexer, struct dataset *ds) lex_match (lexer, T_EQUALS); while (lex_token (lexer) != T_ENDCMD && lex_token (lexer) != T_SLASH) { - if ( lex_match_id (lexer, "DESCRIPTIVES")) + if (lex_match_id (lexer, "DESCRIPTIVES")) opts.statistics = STATS_DESCRIPTIVES; else if (lex_match_id (lexer, "XPROD")) opts.statistics = STATS_XPROD; @@ -414,7 +338,7 @@ cmd_correlation (struct lexer *lexer, struct dataset *ds) opts.statistics = STATS_ALL; lex_get (lexer); } - else + else { lex_error (lexer, NULL); goto error; @@ -432,8 +356,8 @@ cmd_correlation (struct lexer *lexer, struct dataset *ds) 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)) { @@ -444,9 +368,9 @@ cmd_correlation (struct lexer *lexer, struct dataset *ds) corr[n_corrs].n_vars1 = corr[n_corrs].n_vars_total; - if ( lex_match (lexer, T_WITH)) + if (lex_match (lexer, T_WITH)) { - if ( ! parse_variables_const (lexer, dict, + if (! parse_variables_const (lexer, dict, &corr[n_corrs].vars, &corr[n_corrs].n_vars_total, PV_NUMERIC | PV_APPEND)) { @@ -492,7 +416,7 @@ cmd_correlation (struct lexer *lexer, struct dataset *ds) /* FIXME: No need to iterate the data multiple times */ struct casereader *r = casereader_clone (group); - if ( opts.missing_type == CORR_LISTWISE) + if (opts.missing_type == CORR_LISTWISE) r = casereader_create_filter_missing (r, all_vars, n_all_vars, opts.exclude, NULL, NULL);