X-Git-Url: https://pintos-os.org/cgi-bin/gitweb.cgi?a=blobdiff_plain;f=src%2Flanguage%2Fstats%2Fregression.c;h=e5cb96ce340051fffc4a65c73259de95a37fa558;hb=ecaed7ca4c27defc6fddc74214e5a5e48e87a100;hp=9e1aba8815e22f173fab6042db2acf5b293e1cef;hpb=4578c8923824a14086313d6946a48cd94551afbe;p=pspp diff --git a/src/language/stats/regression.c b/src/language/stats/regression.c index 9e1aba8815..e5cb96ce34 100644 --- a/src/language/stats/regression.c +++ b/src/language/stats/regression.c @@ -1,5 +1,5 @@ /* PSPP - a program for statistical analysis. - Copyright (C) 2005, 2009, 2010, 2011, 2012 Free Software Foundation, Inc. + Copyright (C) 2005, 2009, 2010, 2011, 2012, 2013 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 @@ -74,7 +74,8 @@ struct regression }; -static void run_regression (const struct regression *cmd, struct casereader *input); +static void run_regression (const struct regression *cmd, + struct casereader *input); @@ -84,9 +85,9 @@ static void run_regression (const struct regression *cmd, struct casereader *inp */ struct reg_trns { - int n_trns; /* Number of transformations. */ - int trns_id; /* Which trns is this one? */ - linreg *c; /* Linear model for this trns. */ + int n_trns; /* Number of transformations. */ + int trns_id; /* Which trns is this one? */ + linreg *c; /* Linear model for this trns. */ }; /* @@ -94,7 +95,7 @@ struct reg_trns */ static int regression_trns_pred_proc (void *t_, struct ccase **c, - casenumber case_idx UNUSED) + casenumber case_idx UNUSED) { size_t i; size_t n_vals; @@ -133,7 +134,7 @@ regression_trns_pred_proc (void *t_, struct ccase **c, */ static int regression_trns_resid_proc (void *t_, struct ccase **c, - casenumber case_idx UNUSED) + casenumber case_idx UNUSED) { size_t i; size_t n_vals; @@ -181,7 +182,7 @@ reg_get_name (const struct dictionary *dict, const char *prefix) /* XXX handle too-long prefixes */ name = xmalloc (strlen (prefix) + INT_BUFSIZE_BOUND (i) + 1); - for (i = 1; ; i++) + for (i = 1;; i++) { sprintf (name, "%s%d", prefix, i); if (dict_lookup_var (dict, name) == NULL) @@ -196,21 +197,20 @@ reg_get_name (const struct dictionary *dict, const char *prefix) static bool regression_trns_free (void *t_) { - bool result = true; struct reg_trns *t = t_; if (t->trns_id == t->n_trns) { - result = linreg_free (t->c); + linreg_unref (t->c); } free (t); - return result; + return true; } static void reg_save_var (struct dataset *ds, const char *prefix, trns_proc_func * f, - linreg * c, struct variable **v, int n_trns) + linreg * c, struct variable **v, int n_trns) { struct dictionary *dict = dataset_dict (ds); static int trns_index = 1; @@ -238,8 +238,10 @@ subcommand_save (const struct regression *cmd) linreg **lc; int n_trns = 0; - if ( cmd->resid ) n_trns++; - if ( cmd->pred ) n_trns++; + if (cmd->resid) + n_trns++; + if (cmd->pred) + n_trns++; n_trns *= cmd->n_dep_vars; @@ -249,15 +251,16 @@ subcommand_save (const struct regression *cmd) { if ((*lc)->depvar != NULL) { + (*lc)->refcnt++; if (cmd->resid) { - reg_save_var (cmd->ds, "RES", regression_trns_resid_proc, *lc, - &(*lc)->resid, n_trns); + reg_save_var (cmd->ds, "RES", regression_trns_resid_proc, + *lc, &(*lc)->resid, n_trns); } if (cmd->pred) { - reg_save_var (cmd->ds, "PRED", regression_trns_pred_proc, *lc, - &(*lc)->pred, n_trns); + reg_save_var (cmd->ds, "PRED", regression_trns_pred_proc, + *lc, &(*lc)->pred, n_trns); } } } @@ -270,6 +273,7 @@ cmd_regression (struct lexer *lexer, struct dataset *ds) int k; struct regression regression; const struct dictionary *dict = dataset_dict (ds); + bool save; memset (®ression, 0, sizeof (struct regression)); @@ -284,15 +288,15 @@ cmd_regression (struct lexer *lexer, struct dataset *ds) /* Accept an optional, completely pointless "/VARIABLES=" */ lex_match (lexer, T_SLASH); - if (lex_match_id (lexer, "VARIABLES")) + if (lex_match_id (lexer, "VARIABLES")) { - if (! lex_force_match (lexer, T_EQUALS) ) + if (!lex_force_match (lexer, T_EQUALS)) goto error; } if (!parse_variables_const (lexer, dict, - ®ression.vars, ®ression.n_vars, - PV_NO_DUPLICATE | PV_NUMERIC)) + ®ression.vars, ®ression.n_vars, + PV_NO_DUPLICATE | PV_NUMERIC)) goto error; @@ -300,19 +304,20 @@ cmd_regression (struct lexer *lexer, struct dataset *ds) { lex_match (lexer, T_SLASH); - if (lex_match_id (lexer, "DEPENDENT")) + if (lex_match_id (lexer, "DEPENDENT")) { - if (! lex_force_match (lexer, T_EQUALS) ) + if (!lex_force_match (lexer, T_EQUALS)) goto error; if (!parse_variables_const (lexer, dict, - ®ression.dep_vars, ®ression.n_dep_vars, + ®ression.dep_vars, + ®ression.n_dep_vars, PV_NO_DUPLICATE | PV_NUMERIC)) goto error; } else if (lex_match_id (lexer, "METHOD")) - { - lex_match (lexer, T_EQUALS); + { + lex_match (lexer, T_EQUALS); if (!lex_force_match_id (lexer, "ENTER")) { @@ -320,12 +325,12 @@ cmd_regression (struct lexer *lexer, struct dataset *ds) } } else if (lex_match_id (lexer, "STATISTICS")) - { - lex_match (lexer, T_EQUALS); + { + lex_match (lexer, T_EQUALS); - while (lex_token (lexer) != T_ENDCMD - && lex_token (lexer) != T_SLASH) - { + while (lex_token (lexer) != T_ENDCMD + && lex_token (lexer) != T_SLASH) + { if (lex_match (lexer, T_ALL)) { } @@ -352,12 +357,12 @@ cmd_regression (struct lexer *lexer, struct dataset *ds) } } else if (lex_match_id (lexer, "SAVE")) - { - lex_match (lexer, T_EQUALS); + { + lex_match (lexer, T_EQUALS); - while (lex_token (lexer) != T_ENDCMD - && lex_token (lexer) != T_SLASH) - { + while (lex_token (lexer) != T_ENDCMD + && lex_token (lexer) != T_SLASH) + { if (lex_match_id (lexer, "PRED")) { regression.pred = true; @@ -386,35 +391,50 @@ cmd_regression (struct lexer *lexer, struct dataset *ds) } - regression.models = xcalloc (regression.n_dep_vars, sizeof *regression.models); + regression.models = + xcalloc (regression.n_dep_vars, sizeof *regression.models); + + save = regression.pred || regression.resid; + if (save) + { + if (proc_make_temporary_transformations_permanent (ds)) + msg (SW, _("REGRESSION with SAVE ignores TEMPORARY. " + "Temporary transformations will be made permanent.")); + } { struct casegrouper *grouper; struct casereader *group; bool ok; - - grouper = casegrouper_create_splits (proc_open (ds), dict); + + grouper = casegrouper_create_splits (proc_open_filtering (ds, !save), + dict); while (casegrouper_get_next_group (grouper, &group)) run_regression (®ression, group); ok = casegrouper_destroy (grouper); ok = proc_commit (ds) && ok; } - if (regression.pred || regression.resid ) - subcommand_save (®ression); - + if (save) + { + subcommand_save (®ression); + } + for (k = 0; k < regression.n_dep_vars; k++) - linreg_free (regression.models[k]); + linreg_unref (regression.models[k]); free (regression.models); free (regression.vars); free (regression.dep_vars); return CMD_SUCCESS; - - error: - for (k = 0; k < regression.n_dep_vars; k++) - linreg_free (regression.models[k]); - free (regression.models); + +error: + if (regression.models) + { + for (k = 0; k < regression.n_dep_vars; k++) + linreg_unref (regression.models[k]); + free (regression.models); + } free (regression.vars); free (regression.dep_vars); return CMD_FAILURE; @@ -432,12 +452,12 @@ get_n_all_vars (const struct regression *cmd) for (i = 0; i < cmd->n_dep_vars; i++) { for (j = 0; j < cmd->n_vars; j++) - { - if (cmd->vars[j] == cmd->dep_vars[i]) - { - result--; - } - } + { + if (cmd->vars[j] == cmd->dep_vars[i]) + { + result--; + } + } } return result; } @@ -448,7 +468,7 @@ fill_all_vars (const struct variable **vars, const struct regression *cmd) size_t i; size_t j; bool absent; - + for (i = 0; i < cmd->n_vars; i++) { vars[i] = cmd->vars[i]; @@ -457,17 +477,17 @@ fill_all_vars (const struct variable **vars, const struct regression *cmd) { absent = true; for (j = 0; j < cmd->n_vars; j++) - { - if (cmd->dep_vars[i] == cmd->vars[j]) - { - absent = false; - break; - } - } + { + if (cmd->dep_vars[i] == cmd->vars[j]) + { + absent = false; + break; + } + } if (absent) - { - vars[i + cmd->n_vars] = cmd->dep_vars[i]; - } + { + vars[i + cmd->n_vars] = cmd->dep_vars[i]; + } } } @@ -484,9 +504,9 @@ is_depvar (const struct regression *cmd, size_t k, const struct variable *v) /* Identify the explanatory variables in v_variables. Returns the number of independent variables. */ static int -identify_indep_vars (const struct regression *cmd, +identify_indep_vars (const struct regression *cmd, const struct variable **indep_vars, - const struct variable *depvar) + const struct variable *depvar) { int n_indep_vars = 0; int i; @@ -497,13 +517,14 @@ identify_indep_vars (const struct regression *cmd, if ((n_indep_vars < 1) && is_depvar (cmd, 0, depvar)) { /* - There is only one independent variable, and it is the same - as the dependent variable. Print a warning and continue. - */ - msg (SE, - gettext ("The dependent variable is equal to the independent variable." - "The least squares line is therefore Y=X." - "Standard errors and related statistics may be meaningless.")); + There is only one independent variable, and it is the same + as the dependent variable. Print a warning and continue. + */ + msg (SW, + gettext + ("The dependent variable is equal to the independent variable." + "The least squares line is therefore Y=X." + "Standard errors and related statistics may be meaningless.")); n_indep_vars = 1; indep_vars[0] = cmd->vars[0]; } @@ -512,11 +533,11 @@ identify_indep_vars (const struct regression *cmd, static double -fill_covariance (gsl_matrix *cov, struct covariance *all_cov, - const struct variable **vars, - size_t n_vars, const struct variable *dep_var, - const struct variable **all_vars, size_t n_all_vars, - double *means) +fill_covariance (gsl_matrix * cov, struct covariance *all_cov, + const struct variable **vars, + size_t n_vars, const struct variable *dep_var, + const struct variable **all_vars, size_t n_all_vars, + double *means) { size_t i; size_t j; @@ -526,39 +547,39 @@ fill_covariance (gsl_matrix *cov, struct covariance *all_cov, const gsl_matrix *mean_matrix; const gsl_matrix *ssize_matrix; double result = 0.0; - + gsl_matrix *cm = covariance_calculate_unnormalized (all_cov); - if ( cm == NULL) + if (cm == NULL) return 0; rows = xnmalloc (cov->size1 - 1, sizeof (*rows)); - + for (i = 0; i < n_all_vars; i++) { for (j = 0; j < n_vars; j++) - { - if (vars[j] == all_vars[i]) - { - rows[j] = i; - } - } + { + if (vars[j] == all_vars[i]) + { + rows[j] = i; + } + } if (all_vars[i] == dep_var) - { - dep_subscript = i; - } + { + dep_subscript = i; + } } mean_matrix = covariance_moments (all_cov, MOMENT_MEAN); ssize_matrix = covariance_moments (all_cov, MOMENT_NONE); for (i = 0; i < cov->size1 - 1; i++) { means[i] = gsl_matrix_get (mean_matrix, rows[i], 0) - / gsl_matrix_get (ssize_matrix, rows[i], 0); + / gsl_matrix_get (ssize_matrix, rows[i], 0); for (j = 0; j < cov->size2 - 1; j++) - { - gsl_matrix_set (cov, i, j, gsl_matrix_get (cm, rows[i], rows[j])); - gsl_matrix_set (cov, j, i, gsl_matrix_get (cm, rows[j], rows[i])); - } + { + gsl_matrix_set (cov, i, j, gsl_matrix_get (cm, rows[i], rows[j])); + gsl_matrix_set (cov, j, i, gsl_matrix_get (cm, rows[j], rows[i])); + } } means[cov->size1 - 1] = gsl_matrix_get (mean_matrix, dep_subscript, 0) / gsl_matrix_get (ssize_matrix, dep_subscript, 0); @@ -566,17 +587,17 @@ fill_covariance (gsl_matrix *cov, struct covariance *all_cov, result = gsl_matrix_get (ssizes, dep_subscript, rows[0]); for (i = 0; i < cov->size1 - 1; i++) { - gsl_matrix_set (cov, i, cov->size1 - 1, - gsl_matrix_get (cm, rows[i], dep_subscript)); - gsl_matrix_set (cov, cov->size1 - 1, i, - gsl_matrix_get (cm, rows[i], dep_subscript)); + gsl_matrix_set (cov, i, cov->size1 - 1, + gsl_matrix_get (cm, rows[i], dep_subscript)); + gsl_matrix_set (cov, cov->size1 - 1, i, + gsl_matrix_get (cm, rows[i], dep_subscript)); if (result > gsl_matrix_get (ssizes, rows[i], dep_subscript)) - { - result = gsl_matrix_get (ssizes, rows[i], dep_subscript); - } + { + result = gsl_matrix_get (ssizes, rows[i], dep_subscript); + } } - gsl_matrix_set (cov, cov->size1 - 1, cov->size1 - 1, - gsl_matrix_get (cm, dep_subscript, dep_subscript)); + gsl_matrix_set (cov, cov->size1 - 1, cov->size1 - 1, + gsl_matrix_get (cm, dep_subscript, dep_subscript)); free (rows); gsl_matrix_free (cm); return result; @@ -586,23 +607,26 @@ fill_covariance (gsl_matrix *cov, struct covariance *all_cov, /* STATISTICS subcommand output functions. */ -static void reg_stats_r (linreg *, void *); -static void reg_stats_coeff (linreg *, void *); -static void reg_stats_anova (linreg *, void *); -static void reg_stats_bcov (linreg *, void *); +static void reg_stats_r (linreg *, void *, const struct variable *); +static void reg_stats_coeff (linreg *, void *, const struct variable *); +static void reg_stats_anova (linreg *, void *, const struct variable *); +static void reg_stats_bcov (linreg *, void *, const struct variable *); -static void statistics_keyword_output (void (*)(linreg *, void *), - bool, linreg *, void *); +static void +statistics_keyword_output (void (*) + (linreg *, void *, const struct variable *), bool, + linreg *, void *, const struct variable *); static void -subcommand_statistics (const struct regression *cmd , linreg * c, void *aux) +subcommand_statistics (const struct regression *cmd, linreg * c, void *aux, + const struct variable *var) { - statistics_keyword_output (reg_stats_r, cmd->r, c, aux); - statistics_keyword_output (reg_stats_anova, cmd->anova, c, aux); - statistics_keyword_output (reg_stats_coeff, cmd->coeff, c, aux); - statistics_keyword_output (reg_stats_bcov, cmd->bcov, c, aux); + statistics_keyword_output (reg_stats_r, cmd->r, c, aux, var); + statistics_keyword_output (reg_stats_anova, cmd->anova, c, aux, var); + statistics_keyword_output (reg_stats_coeff, cmd->coeff, c, aux, var); + statistics_keyword_output (reg_stats_bcov, cmd->bcov, c, aux, var); } @@ -617,7 +641,6 @@ run_regression (const struct regression *cmd, struct casereader *input) struct covariance *cov; const struct variable **vars; const struct variable **all_vars; - const struct variable *dep_var; struct casereader *reader; size_t n_all_vars; @@ -627,13 +650,14 @@ run_regression (const struct regression *cmd, struct casereader *input) all_vars = xnmalloc (n_all_vars, sizeof (*all_vars)); fill_all_vars (all_vars, cmd); vars = xnmalloc (cmd->n_vars, sizeof (*vars)); - means = xnmalloc (n_all_vars, sizeof (*means)); + means = xnmalloc (n_all_vars, sizeof (*means)); cov = covariance_1pass_create (n_all_vars, all_vars, - dict_get_weight (dataset_dict (cmd->ds)), MV_ANY); + dict_get_weight (dataset_dict (cmd->ds)), + MV_ANY); reader = casereader_clone (input); reader = casereader_create_filter_missing (reader, all_vars, n_all_vars, - MV_ANY, NULL, NULL); + MV_ANY, NULL, NULL); for (; (c = casereader_read (reader)) != NULL; case_unref (c)) @@ -644,52 +668,51 @@ run_regression (const struct regression *cmd, struct casereader *input) for (k = 0; k < cmd->n_dep_vars; k++) { double n_data; - + const struct variable *dep_var = cmd->dep_vars[k]; gsl_matrix *this_cm; - dep_var = cmd->dep_vars[k]; + n_indep = identify_indep_vars (cmd, vars, dep_var); - + this_cm = gsl_matrix_alloc (n_indep + 1, n_indep + 1); - n_data = fill_covariance (this_cm, cov, vars, n_indep, - dep_var, all_vars, n_all_vars, means); + n_data = fill_covariance (this_cm, cov, vars, n_indep, + dep_var, all_vars, n_all_vars, means); models[k] = linreg_alloc (dep_var, (const struct variable **) vars, - n_data, n_indep); + n_data, n_indep); models[k]->depvar = dep_var; for (i = 0; i < n_indep; i++) - { - linreg_set_indep_variable_mean (models[k], i, means[i]); - } + { + linreg_set_indep_variable_mean (models[k], i, means[i]); + } linreg_set_depvar_mean (models[k], means[i]); /* - For large data sets, use QR decomposition. - */ + For large data sets, use QR decomposition. + */ if (n_data > sqrt (n_indep) && n_data > REG_LARGE_DATA) - { - models[k]->method = LINREG_QR; - } + { + models[k]->method = LINREG_QR; + } if (n_data > 0) - { - /* - Find the least-squares estimates and other statistics. - */ - linreg_fit (this_cm, models[k]); - - if (!taint_has_tainted_successor (casereader_get_taint (input))) - { - subcommand_statistics (cmd, models[k], this_cm); - } - } + { + /* + Find the least-squares estimates and other statistics. + */ + linreg_fit (this_cm, models[k]); + + if (!taint_has_tainted_successor (casereader_get_taint (input))) + { + subcommand_statistics (cmd, models[k], this_cm, dep_var); + } + } else - { - msg (SE, - _("No valid data found. This command was skipped.")); - linreg_free (models[k]); + { + msg (SE, _("No valid data found. This command was skipped.")); + linreg_unref (models[k]); models[k] = NULL; - } + } gsl_matrix_free (this_cm); } - + casereader_destroy (reader); free (vars); free (all_vars); @@ -697,13 +720,13 @@ run_regression (const struct regression *cmd, struct casereader *input) casereader_destroy (input); covariance_destroy (cov); } + - static void -reg_stats_r (linreg *c, void *aux UNUSED) +reg_stats_r (linreg * c, void *aux UNUSED, const struct variable *var) { struct tab_table *t; int n_rows = 2; @@ -714,7 +737,9 @@ reg_stats_r (linreg *c, void *aux UNUSED) assert (c != NULL); rsq = linreg_ssreg (c) / linreg_sst (c); - adjrsq = 1.0 - (1.0 - rsq) * (linreg_n_obs (c) - 1.0) / (linreg_n_obs (c) - linreg_n_coeffs (c)); + adjrsq = rsq - + (1.0 - rsq) * linreg_n_coeffs (c) / (linreg_n_obs (c) - + linreg_n_coeffs (c) - 1); std_error = sqrt (linreg_mse (c)); t = tab_create (n_cols, n_rows); tab_box (t, TAL_2, TAL_2, -1, TAL_1, 0, 0, n_cols - 1, n_rows - 1); @@ -730,7 +755,7 @@ reg_stats_r (linreg *c, void *aux UNUSED) tab_double (t, 2, 1, TAB_RIGHT, rsq, NULL); tab_double (t, 3, 1, TAB_RIGHT, adjrsq, NULL); tab_double (t, 4, 1, TAB_RIGHT, std_error, NULL); - tab_title (t, _("Model Summary")); + tab_title (t, _("Model Summary (%s)"), var_to_string (var)); tab_submit (t); } @@ -738,7 +763,7 @@ reg_stats_r (linreg *c, void *aux UNUSED) Table showing estimated regression coefficients. */ static void -reg_stats_coeff (linreg * c, void *aux_) +reg_stats_coeff (linreg * c, void *aux_, const struct variable *var) { size_t j; int n_cols = 7; @@ -776,7 +801,9 @@ reg_stats_coeff (linreg * c, void *aux_) tab_double (t, 4, 1, 0, 0.0, NULL); t_stat = linreg_intercept (c) / std_err; tab_double (t, 5, 1, 0, t_stat, NULL); - pval = 2 * gsl_cdf_tdist_Q (fabs (t_stat), (double) (linreg_n_obs (c) - linreg_n_coeffs (c))); + pval = + 2 * gsl_cdf_tdist_Q (fabs (t_stat), + (double) (linreg_n_obs (c) - linreg_n_coeffs (c))); tab_double (t, 6, 1, 0, pval, NULL); for (j = 0; j < linreg_n_coeffs (c); j++) { @@ -790,38 +817,39 @@ reg_stats_coeff (linreg * c, void *aux_) ds_put_cstr (&tstr, label); tab_text (t, 1, this_row, TAB_CENTER, ds_cstr (&tstr)); /* - Regression coefficients. - */ + Regression coefficients. + */ tab_double (t, 2, this_row, 0, linreg_coeff (c, j), NULL); /* - Standard error of the coefficients. - */ + Standard error of the coefficients. + */ std_err = sqrt (gsl_matrix_get (linreg_cov (c), j + 1, j + 1)); tab_double (t, 3, this_row, 0, std_err, NULL); /* - Standardized coefficient, i.e., regression coefficient - if all variables had unit variance. - */ + Standardized coefficient, i.e., regression coefficient + if all variables had unit variance. + */ beta = sqrt (gsl_matrix_get (cov, j, j)); - beta *= linreg_coeff (c, j) / - sqrt (gsl_matrix_get (cov, cov->size1 - 1, cov->size2 - 1)); + beta *= linreg_coeff (c, j) / + sqrt (gsl_matrix_get (cov, cov->size1 - 1, cov->size2 - 1)); tab_double (t, 4, this_row, 0, beta, NULL); /* - Test statistic for H0: coefficient is 0. - */ + Test statistic for H0: coefficient is 0. + */ t_stat = linreg_coeff (c, j) / std_err; tab_double (t, 5, this_row, 0, t_stat, NULL); /* - P values for the test statistic above. - */ + P values for the test statistic above. + */ pval = - 2 * gsl_cdf_tdist_Q (fabs (t_stat), - (double) (linreg_n_obs (c) - linreg_n_coeffs (c))); + 2 * gsl_cdf_tdist_Q (fabs (t_stat), + (double) (linreg_n_obs (c) - + linreg_n_coeffs (c) - 1)); tab_double (t, 6, this_row, 0, pval, NULL); ds_destroy (&tstr); } - tab_title (t, _("Coefficients")); + tab_title (t, _("Coefficients (%s)"), var_to_string (var)); tab_submit (t); } @@ -829,7 +857,7 @@ reg_stats_coeff (linreg * c, void *aux_) Display the ANOVA table. */ static void -reg_stats_anova (linreg * c, void *aux UNUSED) +reg_stats_anova (linreg * c, void *aux UNUSED, const struct variable *var) { int n_cols = 7; int n_rows = 4; @@ -879,13 +907,13 @@ reg_stats_anova (linreg * c, void *aux UNUSED) tab_double (t, 6, 1, 0, pval, NULL); - tab_title (t, _("ANOVA")); + tab_title (t, _("ANOVA (%s)"), var_to_string (var)); tab_submit (t); } static void -reg_stats_bcov (linreg * c, void *aux UNUSED) +reg_stats_bcov (linreg * c, void *aux UNUSED, const struct variable *var) { int n_cols; int n_rows; @@ -914,23 +942,25 @@ reg_stats_bcov (linreg * c, void *aux UNUSED) tab_text (t, 2, i, TAB_CENTER, label); tab_text (t, i + 2, 0, TAB_CENTER, label); for (k = 1; k < linreg_n_coeffs (c); k++) - { - col = (i <= k) ? k : i; - row = (i <= k) ? i : k; - tab_double (t, k + 2, i, TAB_CENTER, + { + col = (i <= k) ? k : i; + row = (i <= k) ? i : k; + tab_double (t, k + 2, i, TAB_CENTER, gsl_matrix_get (c->cov, row, col), NULL); - } + } } - tab_title (t, _("Coefficient Correlations")); + tab_title (t, _("Coefficient Correlations (%s)"), var_to_string (var)); tab_submit (t); } static void -statistics_keyword_output (void (*function) (linreg *, void *), - bool keyword, linreg * c, void *aux) +statistics_keyword_output (void (*function) + (linreg *, void *, const struct variable * var), + bool keyword, linreg * c, void *aux, + const struct variable *var) { if (keyword) { - (*function) (c, aux); + (*function) (c, aux, var); } }