/* PSPP - a program for statistical analysis. Copyright (C) 2005 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 . */ #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include "xalloc.h" #include "gettext.h" #define _(msgid) gettext (msgid) #define REG_LARGE_DATA 1000 /* (headers) */ /* (specification) "REGRESSION" (regression_): *variables=custom; +statistics[st_]=r, coeff, anova, outs, zpp, label, sha, ci, bcov, ses, xtx, collin, tol, selection, f, defaults, all; ^dependent=varlist; +save[sv_]=resid,pred; +method=enter. */ /* (declarations) */ /* (functions) */ static struct cmd_regression cmd; /* Moments for each of the variables used. */ struct moments_var { struct moments1 *m; const struct variable *v; }; /* Transformations for saving predicted values and residuals, etc. */ struct reg_trns { int n_trns; /* Number of transformations. */ int trns_id; /* Which trns is this one? */ pspp_linreg_cache *c; /* Linear model for this trns. */ }; /* Variables used (both explanatory and response). */ static const struct variable **v_variables; /* Number of variables. */ static size_t n_variables; static bool run_regression (struct casereader *, struct cmd_regression *, struct dataset *, pspp_linreg_cache **); /* STATISTICS subcommand output functions. */ static void reg_stats_r (pspp_linreg_cache *); static void reg_stats_coeff (pspp_linreg_cache *); static void reg_stats_anova (pspp_linreg_cache *); static void reg_stats_outs (pspp_linreg_cache *); static void reg_stats_zpp (pspp_linreg_cache *); static void reg_stats_label (pspp_linreg_cache *); static void reg_stats_sha (pspp_linreg_cache *); static void reg_stats_ci (pspp_linreg_cache *); static void reg_stats_f (pspp_linreg_cache *); static void reg_stats_bcov (pspp_linreg_cache *); static void reg_stats_ses (pspp_linreg_cache *); static void reg_stats_xtx (pspp_linreg_cache *); static void reg_stats_collin (pspp_linreg_cache *); static void reg_stats_tol (pspp_linreg_cache *); static void reg_stats_selection (pspp_linreg_cache *); static void statistics_keyword_output (void (*)(pspp_linreg_cache *), int, pspp_linreg_cache *); static void reg_stats_r (pspp_linreg_cache * c) { struct tab_table *t; int n_rows = 2; int n_cols = 5; double rsq; double adjrsq; double std_error; assert (c != NULL); rsq = c->ssm / c->sst; adjrsq = 1.0 - (1.0 - rsq) * (c->n_obs - 1.0) / (c->n_obs - c->n_indeps); std_error = sqrt ((c->n_indeps - 1.0) / (c->n_obs - 1.0)); t = tab_create (n_cols, n_rows, 0); tab_dim (t, tab_natural_dimensions); tab_box (t, TAL_2, TAL_2, -1, TAL_1, 0, 0, n_cols - 1, n_rows - 1); tab_hline (t, TAL_2, 0, n_cols - 1, 1); tab_vline (t, TAL_2, 2, 0, n_rows - 1); tab_vline (t, TAL_0, 1, 0, 0); tab_text (t, 1, 0, TAB_CENTER | TAT_TITLE, _("R")); tab_text (t, 2, 0, TAB_CENTER | TAT_TITLE, _("R Square")); tab_text (t, 3, 0, TAB_CENTER | TAT_TITLE, _("Adjusted R Square")); tab_text (t, 4, 0, TAB_CENTER | TAT_TITLE, _("Std. Error of the Estimate")); tab_float (t, 1, 1, TAB_RIGHT, sqrt (rsq), 10, 2); tab_float (t, 2, 1, TAB_RIGHT, rsq, 10, 2); tab_float (t, 3, 1, TAB_RIGHT, adjrsq, 10, 2); tab_float (t, 4, 1, TAB_RIGHT, std_error, 10, 2); tab_title (t, _("Model Summary")); tab_submit (t); } /* Table showing estimated regression coefficients. */ static void reg_stats_coeff (pspp_linreg_cache * c) { size_t j; int n_cols = 7; int n_rows; int this_row; double t_stat; double pval; double std_err; double beta; const char *label; const struct variable *v; const union value *val; struct tab_table *t; assert (c != NULL); n_rows = c->n_coeffs + 3; t = tab_create (n_cols, n_rows, 0); tab_headers (t, 2, 0, 1, 0); tab_dim (t, tab_natural_dimensions); tab_box (t, TAL_2, TAL_2, -1, TAL_1, 0, 0, n_cols - 1, n_rows - 1); tab_hline (t, TAL_2, 0, n_cols - 1, 1); tab_vline (t, TAL_2, 2, 0, n_rows - 1); tab_vline (t, TAL_0, 1, 0, 0); tab_text (t, 2, 0, TAB_CENTER | TAT_TITLE, _("B")); tab_text (t, 3, 0, TAB_CENTER | TAT_TITLE, _("Std. Error")); tab_text (t, 4, 0, TAB_CENTER | TAT_TITLE, _("Beta")); tab_text (t, 5, 0, TAB_CENTER | TAT_TITLE, _("t")); tab_text (t, 6, 0, TAB_CENTER | TAT_TITLE, _("Significance")); tab_text (t, 1, 1, TAB_LEFT | TAT_TITLE, _("(Constant)")); tab_float (t, 2, 1, 0, c->intercept, 10, 2); std_err = sqrt (gsl_matrix_get (c->cov, 0, 0)); tab_float (t, 3, 1, 0, std_err, 10, 2); tab_float (t, 4, 1, 0, 0.0, 10, 2); t_stat = c->intercept / std_err; tab_float (t, 5, 1, 0, t_stat, 10, 2); pval = 2 * gsl_cdf_tdist_Q (fabs (t_stat), 1.0); tab_float (t, 6, 1, 0, pval, 10, 2); for (j = 0; j < c->n_coeffs; j++) { struct string tstr; ds_init_empty (&tstr); this_row = j + 2; v = pspp_coeff_get_var (c->coeff[j], 0); label = var_to_string (v); /* Do not overwrite the variable's name. */ ds_put_cstr (&tstr, label); if (var_is_alpha (v)) { /* Append the value associated with this coefficient. This makes sense only if we us the usual binary encoding for that value. */ val = pspp_coeff_get_value (c->coeff[j], v); var_append_value_name (v, val, &tstr); } tab_text (t, 1, this_row, TAB_CENTER, ds_cstr (&tstr)); /* Regression coefficients. */ tab_float (t, 2, this_row, 0, c->coeff[j]->estimate, 10, 2); /* Standard error of the coefficients. */ std_err = sqrt (gsl_matrix_get (c->cov, j + 1, j + 1)); tab_float (t, 3, this_row, 0, std_err, 10, 2); /* Standardized coefficient, i.e., regression coefficient if all variables had unit variance. */ beta = pspp_coeff_get_sd (c->coeff[j]); beta *= c->coeff[j]->estimate / c->depvar_std; tab_float (t, 4, this_row, 0, beta, 10, 2); /* Test statistic for H0: coefficient is 0. */ t_stat = c->coeff[j]->estimate / std_err; tab_float (t, 5, this_row, 0, t_stat, 10, 2); /* P values for the test statistic above. */ pval = 2 * gsl_cdf_tdist_Q (fabs (t_stat), (double) (c->n_obs - c->n_coeffs)); tab_float (t, 6, this_row, 0, pval, 10, 2); ds_destroy (&tstr); } tab_title (t, _("Coefficients")); tab_submit (t); } /* Display the ANOVA table. */ static void reg_stats_anova (pspp_linreg_cache * c) { int n_cols = 7; int n_rows = 4; const double msm = c->ssm / c->dfm; const double mse = c->sse / c->dfe; const double F = msm / mse; const double pval = gsl_cdf_fdist_Q (F, c->dfm, c->dfe); struct tab_table *t; assert (c != NULL); t = tab_create (n_cols, n_rows, 0); tab_headers (t, 2, 0, 1, 0); tab_dim (t, tab_natural_dimensions); tab_box (t, TAL_2, TAL_2, -1, TAL_1, 0, 0, n_cols - 1, n_rows - 1); tab_hline (t, TAL_2, 0, n_cols - 1, 1); tab_vline (t, TAL_2, 2, 0, n_rows - 1); tab_vline (t, TAL_0, 1, 0, 0); tab_text (t, 2, 0, TAB_CENTER | TAT_TITLE, _("Sum of Squares")); tab_text (t, 3, 0, TAB_CENTER | TAT_TITLE, _("df")); tab_text (t, 4, 0, TAB_CENTER | TAT_TITLE, _("Mean Square")); tab_text (t, 5, 0, TAB_CENTER | TAT_TITLE, _("F")); tab_text (t, 6, 0, TAB_CENTER | TAT_TITLE, _("Significance")); tab_text (t, 1, 1, TAB_LEFT | TAT_TITLE, _("Regression")); tab_text (t, 1, 2, TAB_LEFT | TAT_TITLE, _("Residual")); tab_text (t, 1, 3, TAB_LEFT | TAT_TITLE, _("Total")); /* Sums of Squares */ tab_float (t, 2, 1, 0, c->ssm, 10, 2); tab_float (t, 2, 3, 0, c->sst, 10, 2); tab_float (t, 2, 2, 0, c->sse, 10, 2); /* Degrees of freedom */ tab_text (t, 3, 1, TAB_RIGHT | TAT_PRINTF, "%g", c->dfm); tab_text (t, 3, 2, TAB_RIGHT | TAT_PRINTF, "%g", c->dfe); tab_text (t, 3, 3, TAB_RIGHT | TAT_PRINTF, "%g", c->dft); /* Mean Squares */ tab_float (t, 4, 1, TAB_RIGHT, msm, 8, 3); tab_float (t, 4, 2, TAB_RIGHT, mse, 8, 3); tab_float (t, 5, 1, 0, F, 8, 3); tab_float (t, 6, 1, 0, pval, 8, 3); tab_title (t, _("ANOVA")); tab_submit (t); } static void reg_stats_outs (pspp_linreg_cache * c) { assert (c != NULL); } static void reg_stats_zpp (pspp_linreg_cache * c) { assert (c != NULL); } static void reg_stats_label (pspp_linreg_cache * c) { assert (c != NULL); } static void reg_stats_sha (pspp_linreg_cache * c) { assert (c != NULL); } static void reg_stats_ci (pspp_linreg_cache * c) { assert (c != NULL); } static void reg_stats_f (pspp_linreg_cache * c) { assert (c != NULL); } static void reg_stats_bcov (pspp_linreg_cache * c) { int n_cols; int n_rows; int i; int k; int row; int col; const char *label; struct tab_table *t; assert (c != NULL); n_cols = c->n_indeps + 1 + 2; n_rows = 2 * (c->n_indeps + 1); t = tab_create (n_cols, n_rows, 0); tab_headers (t, 2, 0, 1, 0); tab_dim (t, tab_natural_dimensions); tab_box (t, TAL_2, TAL_2, -1, TAL_1, 0, 0, n_cols - 1, n_rows - 1); tab_hline (t, TAL_2, 0, n_cols - 1, 1); tab_vline (t, TAL_2, 2, 0, n_rows - 1); tab_vline (t, TAL_0, 1, 0, 0); tab_text (t, 0, 0, TAB_CENTER | TAT_TITLE, _("Model")); tab_text (t, 1, 1, TAB_CENTER | TAT_TITLE, _("Covariances")); for (i = 0; i < c->n_coeffs; i++) { const struct variable *v = pspp_coeff_get_var (c->coeff[i], 0); label = var_to_string (v); tab_text (t, 2, i, TAB_CENTER, label); tab_text (t, i + 2, 0, TAB_CENTER, label); for (k = 1; k < c->n_coeffs; k++) { col = (i <= k) ? k : i; row = (i <= k) ? i : k; tab_float (t, k + 2, i, TAB_CENTER, gsl_matrix_get (c->cov, row, col), 8, 3); } } tab_title (t, _("Coefficient Correlations")); tab_submit (t); } static void reg_stats_ses (pspp_linreg_cache * c) { assert (c != NULL); } static void reg_stats_xtx (pspp_linreg_cache * c) { assert (c != NULL); } static void reg_stats_collin (pspp_linreg_cache * c) { assert (c != NULL); } static void reg_stats_tol (pspp_linreg_cache * c) { assert (c != NULL); } static void reg_stats_selection (pspp_linreg_cache * c) { assert (c != NULL); } static void statistics_keyword_output (void (*function) (pspp_linreg_cache *), int keyword, pspp_linreg_cache * c) { if (keyword) { (*function) (c); } } static void subcommand_statistics (int *keywords, pspp_linreg_cache * c) { /* The order here must match the order in which the STATISTICS keywords appear in the specification section above. */ enum { r, coeff, anova, outs, zpp, label, sha, ci, bcov, ses, xtx, collin, tol, selection, f, defaults, all }; int i; int d = 1; if (keywords[all]) { /* Set everything but F. */ for (i = 0; i < f; i++) { keywords[i] = 1; } } else { for (i = 0; i < all; i++) { if (keywords[i]) { d = 0; } } /* Default output: ANOVA table, parameter estimates, and statistics for variables not entered into model, if appropriate. */ if (keywords[defaults] | d) { keywords[anova] = 1; keywords[outs] = 1; keywords[coeff] = 1; keywords[r] = 1; } } statistics_keyword_output (reg_stats_r, keywords[r], c); statistics_keyword_output (reg_stats_anova, keywords[anova], c); statistics_keyword_output (reg_stats_coeff, keywords[coeff], c); statistics_keyword_output (reg_stats_outs, keywords[outs], c); statistics_keyword_output (reg_stats_zpp, keywords[zpp], c); statistics_keyword_output (reg_stats_label, keywords[label], c); statistics_keyword_output (reg_stats_sha, keywords[sha], c); statistics_keyword_output (reg_stats_ci, keywords[ci], c); statistics_keyword_output (reg_stats_f, keywords[f], c); statistics_keyword_output (reg_stats_bcov, keywords[bcov], c); statistics_keyword_output (reg_stats_ses, keywords[ses], c); statistics_keyword_output (reg_stats_xtx, keywords[xtx], c); statistics_keyword_output (reg_stats_collin, keywords[collin], c); statistics_keyword_output (reg_stats_tol, keywords[tol], c); statistics_keyword_output (reg_stats_selection, keywords[selection], c); } /* Free the transformation. Free its linear model if this transformation is the last one. */ static bool regression_trns_free (void *t_) { bool result = true; struct reg_trns *t = t_; if (t->trns_id == t->n_trns) { result = pspp_linreg_cache_free (t->c); } free (t); return result; } /* Gets the predicted values. */ static int regression_trns_pred_proc (void *t_, struct ccase *c, casenumber case_idx UNUSED) { size_t i; size_t n_vals; struct reg_trns *trns = t_; pspp_linreg_cache *model; union value *output = NULL; const union value **vals = NULL; const struct variable **vars = NULL; assert (trns != NULL); model = trns->c; assert (model != NULL); assert (model->depvar != NULL); assert (model->pred != NULL); vars = xnmalloc (model->n_coeffs, sizeof (*vars)); n_vals = (*model->get_vars) (model, vars); vals = xnmalloc (n_vals, sizeof (*vals)); output = case_data_rw (c, model->pred); assert (output != NULL); for (i = 0; i < n_vals; i++) { vals[i] = case_data (c, vars[i]); } output->f = (*model->predict) ((const struct variable **) vars, vals, model, n_vals); free (vals); free (vars); return TRNS_CONTINUE; } /* Gets the residuals. */ static int regression_trns_resid_proc (void *t_, struct ccase *c, casenumber case_idx UNUSED) { size_t i; size_t n_vals; struct reg_trns *trns = t_; pspp_linreg_cache *model; union value *output = NULL; const union value **vals = NULL; const union value *obs = NULL; const struct variable **vars = NULL; assert (trns != NULL); model = trns->c; assert (model != NULL); assert (model->depvar != NULL); assert (model->resid != NULL); vars = xnmalloc (model->n_coeffs, sizeof (*vars)); n_vals = (*model->get_vars) (model, vars); vals = xnmalloc (n_vals, sizeof (*vals)); output = case_data_rw (c, model->resid); assert (output != NULL); for (i = 0; i < n_vals; i++) { vals[i] = case_data (c, vars[i]); } obs = case_data (c, model->depvar); output->f = (*model->residual) ((const struct variable **) vars, vals, obs, model, n_vals); free (vals); free (vars); return TRNS_CONTINUE; } /* Returns false if NAME is a duplicate of any existing variable name. */ static bool try_name (const struct dictionary *dict, const char *name) { if (dict_lookup_var (dict, name) != NULL) return false; return true; } static void reg_get_name (const struct dictionary *dict, char name[VAR_NAME_LEN], const char prefix[VAR_NAME_LEN]) { int i = 1; snprintf (name, VAR_NAME_LEN, "%s%d", prefix, i); while (!try_name (dict, name)) { i++; snprintf (name, VAR_NAME_LEN, "%s%d", prefix, i); } } static void reg_save_var (struct dataset *ds, const char *prefix, trns_proc_func * f, pspp_linreg_cache * c, struct variable **v, int n_trns) { struct dictionary *dict = dataset_dict (ds); static int trns_index = 1; char name[VAR_NAME_LEN]; struct variable *new_var; struct reg_trns *t = NULL; t = xmalloc (sizeof (*t)); t->trns_id = trns_index; t->n_trns = n_trns; t->c = c; reg_get_name (dict, name, prefix); new_var = dict_create_var (dict, name, 0); assert (new_var != NULL); *v = new_var; add_transformation (ds, f, regression_trns_free, t); trns_index++; } static void subcommand_save (struct dataset *ds, int save, pspp_linreg_cache ** models) { pspp_linreg_cache **lc; int n_trns = 0; int i; assert (models != NULL); if (save) { /* Count the number of transformations we will need. */ for (i = 0; i < REGRESSION_SV_count; i++) { if (cmd.a_save[i]) { n_trns++; } } n_trns *= cmd.n_dependent; for (lc = models; lc < models + cmd.n_dependent; lc++) { assert (*lc != NULL); assert ((*lc)->depvar != NULL); if (cmd.a_save[REGRESSION_SV_RESID]) { reg_save_var (ds, "RES", regression_trns_resid_proc, *lc, &(*lc)->resid, n_trns); } if (cmd.a_save[REGRESSION_SV_PRED]) { reg_save_var (ds, "PRED", regression_trns_pred_proc, *lc, &(*lc)->pred, n_trns); } } } else { for (lc = models; lc < models + cmd.n_dependent; lc++) { if (*lc != NULL) { pspp_linreg_cache_free (*lc); } } } } int cmd_regression (struct lexer *lexer, struct dataset *ds) { struct casegrouper *grouper; struct casereader *group; pspp_linreg_cache **models; bool ok; size_t i; if (!parse_regression (lexer, ds, &cmd, NULL)) { return CMD_FAILURE; } models = xnmalloc (cmd.n_dependent, sizeof *models); for (i = 0; i < cmd.n_dependent; i++) { models[i] = NULL; } /* Data pass. */ grouper = casegrouper_create_splits (proc_open (ds), dataset_dict (ds)); while (casegrouper_get_next_group (grouper, &group)) run_regression (group, &cmd, ds, models); ok = casegrouper_destroy (grouper); ok = proc_commit (ds) && ok; subcommand_save (ds, cmd.sbc_save, models); free (v_variables); free (models); free_regression (&cmd); return ok ? CMD_SUCCESS : CMD_FAILURE; } /* Is variable k the dependent variable? */ static bool is_depvar (size_t k, const struct variable *v) { return v == v_variables[k]; } /* Parser for the variables sub command */ static int regression_custom_variables (struct lexer *lexer, struct dataset *ds, struct cmd_regression *cmd UNUSED, void *aux UNUSED) { const struct dictionary *dict = dataset_dict (ds); lex_match (lexer, '='); if ((lex_token (lexer) != T_ID || dict_lookup_var (dict, lex_tokid (lexer)) == NULL) && lex_token (lexer) != T_ALL) return 2; if (!parse_variables_const (lexer, dict, &v_variables, &n_variables, PV_NONE)) { free (v_variables); return 0; } assert (n_variables); return 1; } /* Identify the explanatory variables in v_variables. Returns the number of independent variables. */ static int identify_indep_vars (const struct variable **indep_vars, const struct variable *depvar) { int n_indep_vars = 0; int i; for (i = 0; i < n_variables; i++) if (!is_depvar (i, depvar)) indep_vars[n_indep_vars++] = v_variables[i]; if ((n_indep_vars < 1) && is_depvar (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.")); n_indep_vars = 1; indep_vars[0] = v_variables[0]; } return n_indep_vars; } /* Encode categorical variables. Returns number of valid cases. */ static int prepare_categories (struct casereader *input, const struct variable **vars, size_t n_vars, struct moments_var *mom) { int n_data; struct ccase c; size_t i; assert (vars != NULL); assert (mom != NULL); for (i = 0; i < n_vars; i++) if (var_is_alpha (vars[i])) cat_stored_values_create (vars[i]); n_data = 0; for (; casereader_read (input, &c); case_destroy (&c)) { /* The second condition ensures the program will run even if there is only one variable to act as both explanatory and response. */ for (i = 0; i < n_vars; i++) { const union value *val = case_data (&c, vars[i]); if (var_is_alpha (vars[i])) cat_value_update (vars[i], val); else moments1_add (mom[i].m, val->f, 1.0); } n_data++; } casereader_destroy (input); return n_data; } static void coeff_init (pspp_linreg_cache * c, struct design_matrix *dm) { c->coeff = xnmalloc (dm->m->size2, sizeof (*c->coeff)); pspp_coeff_init (c->coeff, dm); } static bool run_regression (struct casereader *input, struct cmd_regression *cmd, struct dataset *ds, pspp_linreg_cache **models) { size_t i; int n_indep = 0; int k; struct ccase c; const struct variable **indep_vars; struct design_matrix *X; struct moments_var *mom; gsl_vector *Y; pspp_linreg_opts lopts; assert (models != NULL); if (!casereader_peek (input, 0, &c)) { casereader_destroy (input); return true; } output_split_file_values (ds, &c); case_destroy (&c); if (!v_variables) { dict_get_vars (dataset_dict (ds), &v_variables, &n_variables, 0); } for (i = 0; i < cmd->n_dependent; i++) { if (!var_is_numeric (cmd->v_dependent[i])) { msg (SE, _("Dependent variable must be numeric.")); return false; } } mom = xnmalloc (n_variables, sizeof (*mom)); for (i = 0; i < n_variables; i++) { (mom + i)->m = moments1_create (MOMENT_VARIANCE); (mom + i)->v = v_variables[i]; } lopts.get_depvar_mean_std = 1; lopts.get_indep_mean_std = xnmalloc (n_variables, sizeof (int)); indep_vars = xnmalloc (n_variables, sizeof *indep_vars); for (k = 0; k < cmd->n_dependent; k++) { const struct variable *dep_var; struct casereader *reader; casenumber row; struct ccase c; size_t n_data; /* Number of valid cases. */ dep_var = cmd->v_dependent[k]; n_indep = identify_indep_vars (indep_vars, dep_var); reader = casereader_clone (input); reader = casereader_create_filter_missing (reader, indep_vars, n_indep, MV_ANY, NULL, NULL); reader = casereader_create_filter_missing (reader, &dep_var, 1, MV_ANY, NULL, NULL); n_data = prepare_categories (casereader_clone (reader), indep_vars, n_indep, mom); if ((n_data > 0) && (n_indep > 0)) { Y = gsl_vector_alloc (n_data); X = design_matrix_create (n_indep, (const struct variable **) indep_vars, n_data); for (i = 0; i < X->m->size2; i++) { lopts.get_indep_mean_std[i] = 1; } models[k] = pspp_linreg_cache_alloc (dep_var, (const struct variable **) indep_vars, X->m->size1, X->m->size2); models[k]->depvar = dep_var; /* For large data sets, use QR decomposition. */ if (n_data > sqrt (n_indep) && n_data > REG_LARGE_DATA) { models[k]->method = PSPP_LINREG_QR; } /* The second pass fills the design matrix. */ reader = casereader_create_counter (reader, &row, -1); for (; casereader_read (reader, &c); case_destroy (&c)) { for (i = 0; i < n_indep; ++i) { const struct variable *v = indep_vars[i]; const union value *val = case_data (&c, v); if (var_is_alpha (v)) design_matrix_set_categorical (X, row, v, val); else design_matrix_set_numeric (X, row, v, val); } gsl_vector_set (Y, row, case_num (&c, dep_var)); } /* Now that we know the number of coefficients, allocate space and store pointers to the variables that correspond to the coefficients. */ coeff_init (models[k], X); /* Find the least-squares estimates and other statistics. */ pspp_linreg ((const gsl_vector *) Y, X, &lopts, models[k]); if (!taint_has_tainted_successor (casereader_get_taint (input))) { subcommand_statistics (cmd->a_statistics, models[k]); } gsl_vector_free (Y); design_matrix_destroy (X); } else { msg (SE, gettext ("No valid data found. This command was skipped.")); } casereader_destroy (reader); } for (i = 0; i < n_variables; i++) { moments1_destroy ((mom + i)->m); } free (mom); free (indep_vars); free (lopts.get_indep_mean_std); casereader_destroy (input); return true; } /* Local Variables: mode: c End: */