/* 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 "alloc.h" #include "case.h" #include "dictionary.h" #include "file-handle.h" #include "command.h" #include "lexer.h" #include "tab.h" #include "var.h" #include "vfm.h" #include "casefile.h" #include #include "cat.h" /* (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; ^dependent=varlist; ^method=enter. */ /* (declarations) */ /* (functions) */ static struct cmd_regression cmd; /* Array holding the subscripts of the independent variables. */ size_t *indep_vars; static void run_regression( const struct casefile * ); /* 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) { return 0; } /* 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; struct tab_table *t; n_rows = 2 + c->param_estimates->size; 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 = gsl_vector_get ( c->param_estimates, 0); 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 = 0; j < c->n_indeps; j++ ) { i = indep_vars[j]; struct variable *v = cmd.v_variables[i]; label = var_to_string(v); tab_text ( t, 1, j + 2, TAB_CENTER, label); /* Regression coefficients. */ coeff = gsl_vector_get ( c->param_estimates, j+1 ); tab_float ( t, 2, j + 2, 0, coeff, 10, 2 ); /* Standard error of the coefficients. */ std_err = sqrt ( gsl_matrix_get ( c->cov, j+1, j+1 )); tab_float ( t, 3, j + 2, 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+1); beta *= coeff / c->depvar_std; tab_float ( t, 4, j + 2, 0, beta, 10, 2); /* Test statistic for H0: coefficient is 0. */ t_stat = coeff / std_err; tab_float ( t, 5, j + 2, 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 + 2, 0, pval, 10, 2); } tab_title (t, 0, _("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; 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) { return 0; } static void reg_stats_zpp (pspp_linreg_cache *c) { return 0; } static void reg_stats_label (pspp_linreg_cache *c) { return 0; } static void reg_stats_sha (pspp_linreg_cache *c) { return 0; } static void reg_stats_ci (pspp_linreg_cache *c) { return 0; } static void reg_stats_f (pspp_linreg_cache *c) { return 0; } static void reg_stats_bcov(pspp_linreg_cache *c) { return 0; } static void reg_stats_ses (pspp_linreg_cache *c) { return 0; } static void reg_stats_xtx (pspp_linreg_cache *c) { return 0; } static void reg_stats_collin(pspp_linreg_cache *c) { return 0; } static void reg_stats_tol (pspp_linreg_cache *c) { return 0; } static void reg_stats_selection(pspp_linreg_cache *c) { return 0; } 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 ); } int cmd_regression(void) { if(!parse_regression(&cmd)) { return CMD_FAILURE; } multipass_procedure_with_splits (run_regression, &cmd); return CMD_SUCCESS; } /* 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; } static void run_regression ( const struct casefile *cf ) { size_t i; size_t k; size_t n_data = 0; size_t row; int n_indep; const union value *val; struct casereader *r; struct casereader *r2; struct ccase c; const struct variable *v; struct recoded_categorical_array *ca; struct recoded_categorical *rc; struct design_matrix *X; gsl_vector *Y; pspp_linreg_cache *lcache; pspp_linreg_opts lopts; n_data = casefile_get_case_cnt (cf); n_indep = cmd.n_variables - cmd.n_dependent; indep_vars = (size_t *) malloc ( n_indep * sizeof (*indep_vars)); Y = gsl_vector_alloc (n_data); lopts.get_depvar_mean_std = 1; lopts.get_indep_mean_std = (int *) malloc ( n_indep * sizeof (int)); lcache = pspp_linreg_cache_alloc(n_data, n_indep); lcache->indep_means = gsl_vector_alloc(n_indep); lcache->indep_std = gsl_vector_alloc(n_indep); /* Read from the active file. The first pass encodes categorical variables. */ ca = cr_recoded_cat_ar_create ( cmd.n_variables, cmd.v_variables ); for (r = casefile_get_reader (cf); casereader_read (r, &c ); case_destroy (&c)) { for (i = 0; i < ca->n_vars; i++) { v = (*(ca->a + i))->v; val = case_data ( &c, v->fv ); cr_value_update ( *(ca->a + i), val); } n_data++; } cr_create_value_matrices ( ca ); X = design_matrix_create ( n_indep, cmd.v_variables, ca, n_data ); /* The second pass creates the design matrix. */ for(r2 = casefile_get_reader (cf); casereader_read (r2, &c) ; case_destroy (&c)) /* Iterate over the cases. */ { k = 0; row = casereader_cnum(r2) - 1; 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. We need to separate the two. */ if(is_depvar(i)) { if ( v->type == NUMERIC ) { gsl_vector_set(Y, row, val->f); } else { errno = EINVAL; fprintf( stderr, "%s:%d: Dependent variable should be numeric: %s\n", __FILE__,__LINE__,strerror(errno)); err_cond_fail(); } } else { if ( v->type == ALPHA ) { rc = cr_var_to_recoded_categorical ( v, ca ); design_matrix_set_categorical ( X, row, v, val, rc); } else if (v->type == NUMERIC) { design_matrix_set_numeric ( X, row, v, val); } indep_vars[k] = i; k++; lopts.get_indep_mean_std[i] = 1; } } } /* Find the least-squares estimates and other statistics. */ pspp_linreg ( Y, X->m, &lopts, lcache ); subcommand_statistics ( &cmd.a_statistics, lcache ); gsl_vector_free(Y); design_matrix_destroy(X); pspp_linreg_cache_free(lcache); free( lopts.get_indep_mean_std ); free (indep_vars); casereader_destroy(r); } /* Local Variables: mode: c End: */