/* PSPP - linear regression. Copyright (C) 2005 Free Software Foundation, Inc. Written by Jason H Stover . 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 2 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, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. */ #include #include #include #include #include #include #include "alloc.h" #include "case.h" #include "casefile.h" #include "category.h" #include "cat-routines.h" #include "command.h" #include "compiler.h" #include "design-matrix.h" #include "dictionary.h" #include "message.h" #include "file-handle-def.h" #include "gettext.h" #include "lexer.h" #include "linreg.h" #include "coefficient.h" #include "missing-values.h" #include "regression-export.h" #include "table.h" #include "value-labels.h" #include "variable.h" #include "procedure.h" #define REG_LARGE_DATA 1000 /* (headers) */ /* (specification) "REGRESSION" (regression_): *variables=varlist; statistics[st_]=r, coeff, anova, outs, zpp, label, sha, ci, bcov, ses, xtx, collin, tol, selection, f, defaults, all; export=custom; ^dependent=varlist; method=enter. */ /* (declarations) */ /* (functions) */ static struct cmd_regression cmd; /* Array holding the subscripts of the independent variables. */ size_t *indep_vars; /* File where the model will be saved if the EXPORT subcommand is given. */ struct file_handle *model_file; /* Return value for the procedure. */ int pspp_reg_rc = CMD_SUCCESS; static bool run_regression (const struct casefile *, void *); /* 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, 0, _("Model Summary")); tab_submit (t); } /* Table showing estimated regression coefficients. */ static void reg_stats_coeff (pspp_linreg_cache * c) { size_t i; size_t j; int n_cols = 7; int n_rows; double t_stat; double pval; double coeff; double std_err; double beta; const char *label; char *tmp; const struct variable *v; const union value *val; const char *val_s; struct tab_table *t; assert (c != NULL); tmp = xnmalloc (MAX_STRING, sizeof (*tmp)); n_rows = c->n_coeffs + 2; 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)")); coeff = c->coeff[0].estimate; tab_float (t, 2, 1, 0, coeff, 10, 2); std_err = sqrt (gsl_matrix_get (c->cov, 0, 0)); tab_float (t, 3, 1, 0, std_err, 10, 2); beta = coeff / c->depvar_std; tab_float (t, 4, 1, 0, beta, 10, 2); t_stat = coeff / 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 = 1; j <= c->n_indeps; j++) { i = indep_vars[j]; v = pspp_linreg_coeff_get_var (c->coeff + j, 0); label = var_to_string (v); /* Do not overwrite the variable's name. */ strncpy (tmp, label, MAX_STRING); if (v->type == ALPHA) { /* Append the value associated with this coefficient. This makes sense only if we us the usual binary encoding for that value. */ val = pspp_linreg_coeff_get_value (c->coeff + j, v); val_s = value_to_string (val, v); strncat (tmp, val_s, MAX_STRING); } tab_text (t, 1, j + 1, TAB_CENTER, tmp); /* Regression coefficients. */ coeff = c->coeff[j].estimate; tab_float (t, 2, j + 1, 0, coeff, 10, 2); /* Standard error of the coefficients. */ std_err = sqrt (gsl_matrix_get (c->cov, j, j)); tab_float (t, 3, j + 1, 0, std_err, 10, 2); /* 'Standardized' coefficient, i.e., regression coefficient if all variables had unit variance. */ beta = gsl_vector_get (c->indep_std, j); beta *= coeff / c->depvar_std; tab_float (t, 4, j + 1, 0, beta, 10, 2); /* Test statistic for H0: coefficient is 0. */ t_stat = coeff / std_err; tab_float (t, 5, j + 1, 0, t_stat, 10, 2); /* P values for the test statistic above. */ pval = 2 * gsl_cdf_tdist_Q (fabs (t_stat), 1.0); tab_float (t, 6, j + 1, 0, pval, 10, 2); } tab_title (t, 0, _("Coefficients")); tab_submit (t); free (tmp); } /* 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_float (t, 3, 1, 0, c->dfm, 4, 0); tab_float (t, 3, 2, 0, c->dfe, 4, 0); tab_float (t, 3, 3, 0, c->dft, 4, 0); /* 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, 0, _("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 j; 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 = 1; i < c->n_indeps + 1; i++) { j = indep_vars[(i - 1)]; struct variable *v = cmd.v_variables[j]; 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_indeps + 1; 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, 0, _("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); } static int reg_inserted (const struct variable *v, struct variable **varlist, int n_vars) { int i; for (i = 0; i < n_vars; i++) { if (v->index == varlist[i]->index) { return 1; } } return 0; } static void reg_print_categorical_encoding (FILE * fp, pspp_linreg_cache * c) { int i; size_t j; int n_vars = 0; struct variable **varlist; struct pspp_linreg_coeff *coeff; const struct variable *v; union value *val; fprintf (fp, "%s", reg_export_categorical_encode_1); varlist = xnmalloc (c->n_indeps, sizeof (*varlist)); for (i = 1; i < c->n_indeps; i++) /* c->coeff[0] is the intercept. */ { coeff = c->coeff + i; v = pspp_linreg_coeff_get_var (coeff, 0); if (v->type == ALPHA) { if (!reg_inserted (v, varlist, n_vars)) { fprintf (fp, "struct pspp_reg_categorical_variable %s;\n\t", v->name); varlist[n_vars] = (struct variable *) v; n_vars++; } } } fprintf (fp, "int n_vars = %d;\n\t", n_vars); fprintf (fp, "struct pspp_reg_categorical_variable *varlist[%d] = {", n_vars); for (i = 0; i < n_vars - 1; i++) { fprintf (fp, "&%s,\n\t\t", varlist[i]->name); } fprintf (fp, "&%s};\n\t", varlist[i]->name); for (i = 0; i < n_vars; i++) { coeff = c->coeff + i; fprintf (fp, "%s.name = \"%s\";\n\t", varlist[i]->name, varlist[i]->name); fprintf (fp, "%s.n_vals = %d;\n\t", varlist[i]->name, varlist[i]->obs_vals->n_categories); for (j = 0; j < varlist[i]->obs_vals->n_categories; j++) { val = cat_subscript_to_value ((const size_t) j, varlist[i]); fprintf (fp, "%s.values[%d] = \"%s\";\n\t", varlist[i]->name, j, value_to_string (val, varlist[i])); } } fprintf (fp, "%s", reg_export_categorical_encode_2); } static void reg_print_depvars (FILE * fp, pspp_linreg_cache * c) { int i; struct pspp_linreg_coeff *coeff; const struct variable *v; fprintf (fp, "char *model_depvars[%d] = {", c->n_indeps); for (i = 1; i < c->n_indeps; i++) { coeff = c->coeff + i; v = pspp_linreg_coeff_get_var (coeff, 0); fprintf (fp, "\"%s\",\n\t\t", v->name); } coeff = c->coeff + i; v = pspp_linreg_coeff_get_var (coeff, 0); fprintf (fp, "\"%s\"};\n\t", v->name); } static void reg_print_getvar (FILE * fp, pspp_linreg_cache * c) { fprintf (fp, "static int\npspp_reg_getvar (char *v_name)\n{\n\t"); fprintf (fp, "int i;\n\tint n_vars = %d;\n\t", c->n_indeps); reg_print_depvars (fp, c); fprintf (fp, "for (i = 0; i < n_vars; i++)\n\t{\n\t\t"); fprintf (fp, "if (strncmp (v_name, model_depvars[i], PSPP_REG_MAXLEN) == 0)\n\t\t{\n\t\t\t"); fprintf (fp, "return i;\n\t\t}\n\t}\n}\n"); } static void subcommand_export (int export, pspp_linreg_cache * c) { size_t i; size_t j; int n_quantiles = 100; double increment; double tmp; struct pspp_linreg_coeff coeff; if (export) { FILE *fp; assert (c != NULL); assert (model_file != NULL); assert (fp != NULL); fp = fopen (fh_get_filename (model_file), "w"); fprintf (fp, "%s", reg_preamble); reg_print_getvar (fp, c); reg_print_categorical_encoding (fp, c); fprintf (fp, "%s", reg_export_t_quantiles_1); increment = 0.5 / (double) increment; for (i = 0; i < n_quantiles - 1; i++) { tmp = 0.5 + 0.005 * (double) i; fprintf (fp, "%.15e,\n\t\t", gsl_cdf_tdist_Pinv (tmp, c->n_obs - c->n_indeps)); } fprintf (fp, "%.15e};\n\t", gsl_cdf_tdist_Pinv (.9995, c->n_obs - c->n_indeps)); fprintf (fp, "%s", reg_export_t_quantiles_2); fprintf (fp, "%s", reg_mean_cmt); fprintf (fp, "double\npspp_reg_estimate (const double *var_vals,"); fprintf (fp, "const char *var_names[])\n{\n\t"); fprintf (fp, "double model_coeffs[%d] = {", c->n_indeps); for (i = 1; i < c->n_indeps; i++) { coeff = c->coeff[i]; fprintf (fp, "%.15e,\n\t\t", coeff.estimate); } coeff = c->coeff[i]; fprintf (fp, "%.15e};\n\t", coeff.estimate); coeff = c->coeff[0]; fprintf (fp, "double estimate = %.15e;\n\t", coeff.estimate); fprintf (fp, "int i;\n\tint j;\n\n\t"); fprintf (fp, "for (i = 0; i < %d; i++)\n\t", c->n_indeps); fprintf (fp, "%s", reg_getvar); fprintf (fp, "const double cov[%d][%d] = {\n\t", c->n_coeffs, c->n_coeffs); for (i = 0; i < c->cov->size1 - 1; i++) { fprintf (fp, "{"); for (j = 0; j < c->cov->size2 - 1; j++) { fprintf (fp, "%.15e, ", gsl_matrix_get (c->cov, i, j)); } fprintf (fp, "%.15e},\n\t", gsl_matrix_get (c->cov, i, j)); } fprintf (fp, "{"); for (j = 0; j < c->cov->size2 - 1; j++) { fprintf (fp, "%.15e, ", gsl_matrix_get (c->cov, c->cov->size1 - 1, j)); } fprintf (fp, "%.15e}\n\t", gsl_matrix_get (c->cov, c->cov->size1 - 1, c->cov->size2 - 1)); fprintf (fp, "};\n\tint n_vars = %d;\n\tint i;\n\tint j;\n\t", c->n_indeps); fprintf (fp, "double unshuffled_vals[%d];\n\t", c->n_indeps); fprintf (fp, "%s", reg_variance); fprintf (fp, "%s", reg_export_confidence_interval); tmp = c->mse * c->mse; fprintf (fp, "%s %.15e", reg_export_prediction_interval_1, tmp); fprintf (fp, "%s %.15e", reg_export_prediction_interval_2, tmp); fprintf (fp, "%s", reg_export_prediction_interval_3); fclose (fp); fp = fopen ("pspp_model_reg.h", "w"); fprintf (fp, "%s", reg_header); fclose (fp); } } static int regression_custom_export (struct cmd_regression *cmd) { /* 0 on failure, 1 on success, 2 on failure that should result in syntax error */ if (!lex_force_match ('(')) return 0; if (lex_match ('*')) model_file = NULL; else { model_file = fh_parse (FH_REF_FILE); if (model_file == NULL) return 0; } if (!lex_force_match (')')) return 0; return 1; } int cmd_regression (void) { if (!parse_regression (&cmd)) return CMD_FAILURE; if (!multipass_procedure_with_splits (run_regression, &cmd)) return CMD_CASCADING_FAILURE; return pspp_reg_rc; } /* Is variable k one of the dependent variables? */ static int is_depvar (size_t k) { size_t j = 0; for (j = 0; j < cmd.n_dependent; j++) { /* compare_var_names returns 0 if the variable names match. */ if (!compare_var_names (cmd.v_dependent[j], cmd.v_variables[k], NULL)) return 1; } return 0; } /* Mark missing cases. Return the number of non-missing cases. */ static size_t mark_missing_cases (const struct casefile *cf, struct variable *v, int *is_missing_case, double n_data) { struct casereader *r; struct ccase c; size_t row; const union value *val; for (r = casefile_get_reader (cf); casereader_read (r, &c); case_destroy (&c)) { row = casereader_cnum (r) - 1; val = case_data (&c, v->fv); cat_value_update (v, val); if (mv_is_value_missing (&v->miss, val)) { if (!is_missing_case[row]) { /* Now it is missing. */ n_data--; is_missing_case[row] = 1; } } } casereader_destroy (r); return n_data; } static bool run_regression (const struct casefile *cf, void *cmd_ UNUSED) { size_t i; size_t n_data = 0; size_t row; size_t case_num; int n_indep; int j = 0; int k; /* Keep track of the missing cases. */ int *is_missing_case; const union value *val; struct casereader *r; struct ccase c; struct variable *v; struct variable *depvar; struct variable **indep_vars; struct design_matrix *X; gsl_vector *Y; pspp_linreg_cache *lcache; pspp_linreg_opts lopts; n_data = casefile_get_case_cnt (cf); for (i = 0; i < cmd.n_dependent; i++) { if (cmd.v_dependent[i]->type != NUMERIC) { msg (SE, gettext ("Dependent variable must be numeric.")); pspp_reg_rc = CMD_FAILURE; return true; } } is_missing_case = xnmalloc (n_data, sizeof (*is_missing_case)); for (i = 0; i < n_data; i++) is_missing_case[i] = 0; n_indep = cmd.n_variables - cmd.n_dependent; indep_vars = xnmalloc (n_indep, sizeof *indep_vars); lopts.get_depvar_mean_std = 1; lopts.get_indep_mean_std = xnmalloc (n_indep, sizeof (int)); /* Read from the active file. The first pass encodes categorical variables and drops cases with missing values. */ j = 0; for (i = 0; i < cmd.n_variables; i++) { if (!is_depvar (i)) { v = cmd.v_variables[i]; indep_vars[j] = v; j++; if (v->type == ALPHA) { /* Make a place to hold the binary vectors corresponding to this variable's values. */ cat_stored_values_create (v); } n_data = mark_missing_cases (cf, v, is_missing_case, n_data); } } /* Drop cases with missing values for any dependent variable. */ j = 0; for (i = 0; i < cmd.n_dependent; i++) { v = cmd.v_dependent[i]; j++; n_data = mark_missing_cases (cf, v, is_missing_case, n_data); } for (k = 0; k < cmd.n_dependent; k++) { depvar = cmd.v_dependent[k]; 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; } lcache = pspp_linreg_cache_alloc (X->m->size1, X->m->size2); lcache->indep_means = gsl_vector_alloc (X->m->size2); lcache->indep_std = gsl_vector_alloc (X->m->size2); lcache->depvar = (const struct variable *) depvar; /* For large data sets, use QR decomposition. */ if (n_data > sqrt (n_indep) && n_data > REG_LARGE_DATA) { lcache->method = PSPP_LINREG_SVD; } /* The second pass creates the design matrix. */ row = 0; for (r = casefile_get_reader (cf); casereader_read (r, &c); case_destroy (&c)) /* Iterate over the cases. */ { case_num = casereader_cnum (r) - 1; if (!is_missing_case[case_num]) { for (i = 0; i < cmd.n_variables; ++i) /* Iterate over the variables for the current case. */ { v = cmd.v_variables[i]; val = case_data (&c, v->fv); /* Independent/dependent variable separation. The 'variables' subcommand specifies a varlist which contains both dependent and independent variables. The dependent variables are specified with the 'dependent' subcommand, and maybe also in the 'variables' subcommand. We need to separate the two. */ if (!is_depvar (i)) { if (v->type == ALPHA) { design_matrix_set_categorical (X, row, v, val); } else if (v->type == NUMERIC) { design_matrix_set_numeric (X, row, v, val); } } } val = case_data (&c, depvar->fv); gsl_vector_set (Y, row, val->f); row++; } } /* Now that we know the number of coefficients, allocate space and store pointers to the variables that correspond to the coefficients. */ pspp_linreg_coeff_init (lcache, X); /* Find the least-squares estimates and other statistics. */ pspp_linreg ((const gsl_vector *) Y, X->m, &lopts, lcache); subcommand_statistics (cmd.a_statistics, lcache); subcommand_export (cmd.sbc_export, lcache); gsl_vector_free (Y); design_matrix_destroy (X); pspp_linreg_cache_free (lcache); free (lopts.get_indep_mean_std); casereader_destroy (r); } free (indep_vars); free (is_missing_case); return true; } /* Local Variables: mode: c End: */