--- /dev/null
+/* PSPP - a program for statistical analysis.
+ Copyright (C) 2005, 2009, 2010, 2011, 2012 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 <http://www.gnu.org/licenses/>. */
+
+#include <config.h>
+
+#include <stdbool.h>
+
+#include <gsl/gsl_cdf.h>
+#include <gsl/gsl_matrix.h>
+
+#include <data/dataset.h>
+
+#include "language/command.h"
+#include "language/lexer/lexer.h"
+#include "language/lexer/value-parser.h"
+#include "language/lexer/variable-parser.h"
+
+
+#include "data/casegrouper.h"
+#include "data/casereader.h"
+#include "data/dictionary.h"
+
+#include "math/covariance.h"
+#include "math/linreg.h"
+#include "math/moments.h"
+
+#include "libpspp/message.h"
+#include "libpspp/taint.h"
+
+#include "output/tab.h"
+
+#include "gettext.h"
+#define _(msgid) gettext (msgid)
+#define N_(msgid) msgid
+
+
+#include <gl/intprops.h>
+
+#define REG_LARGE_DATA 1000
+
+struct regression
+{
+ struct dataset *ds;
+
+ const struct variable **vars;
+ size_t n_vars;
+
+ const struct variable **dep_vars;
+ size_t n_dep_vars;
+
+ bool r;
+ bool coeff;
+ bool anova;
+ bool bcov;
+
+
+ bool resid;
+ bool pred;
+
+ linreg **models;
+};
+
+
+static void run_regression (const struct regression *cmd, struct casereader *input);
+
+
+
+/*
+ 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? */
+ linreg *c; /* Linear model for this trns. */
+};
+
+/*
+ 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_;
+ linreg *model;
+ union value *output = NULL;
+ const union value *tmp;
+ double *vals;
+ const struct variable **vars = NULL;
+
+ assert (trns != NULL);
+ model = trns->c;
+ assert (model != NULL);
+ assert (model->depvar != NULL);
+ assert (model->pred != NULL);
+
+ vars = linreg_get_vars (model);
+ n_vals = linreg_n_coeffs (model);
+ vals = xnmalloc (n_vals, sizeof (*vals));
+ *c = case_unshare (*c);
+
+ output = case_data_rw (*c, model->pred);
+
+ for (i = 0; i < n_vals; i++)
+ {
+ tmp = case_data (*c, vars[i]);
+ vals[i] = tmp->f;
+ }
+ output->f = linreg_predict (model, vals, n_vals);
+ free (vals);
+ 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_;
+ linreg *model;
+ union value *output = NULL;
+ const union value *tmp;
+ double *vals = NULL;
+ double obs;
+ const struct variable **vars = NULL;
+
+ assert (trns != NULL);
+ model = trns->c;
+ assert (model != NULL);
+ assert (model->depvar != NULL);
+ assert (model->resid != NULL);
+
+ vars = linreg_get_vars (model);
+ n_vals = linreg_n_coeffs (model);
+
+ vals = xnmalloc (n_vals, sizeof (*vals));
+ *c = case_unshare (*c);
+ output = case_data_rw (*c, model->resid);
+ assert (output != NULL);
+
+ for (i = 0; i < n_vals; i++)
+ {
+ tmp = case_data (*c, vars[i]);
+ vals[i] = tmp->f;
+ }
+ tmp = case_data (*c, model->depvar);
+ obs = tmp->f;
+ output->f = linreg_residual (model, obs, vals, n_vals);
+ free (vals);
+
+ return TRNS_CONTINUE;
+}
+
+
+static char *
+reg_get_name (const struct dictionary *dict, const char *prefix)
+{
+ char *name;
+ int i;
+
+ /* XXX handle too-long prefixes */
+ name = xmalloc (strlen (prefix) + INT_BUFSIZE_BOUND (i) + 1);
+ for (i = 1; ; i++)
+ {
+ sprintf (name, "%s%d", prefix, i);
+ if (dict_lookup_var (dict, name) == NULL)
+ return name;
+ }
+}
+
+/*
+ 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 = linreg_free (t->c);
+ }
+ free (t);
+
+ return result;
+}
+
+static void
+reg_save_var (struct dataset *ds, const char *prefix, trns_proc_func * f,
+ linreg * c, struct variable **v, int n_trns)
+{
+ struct dictionary *dict = dataset_dict (ds);
+ static int trns_index = 1;
+ char *name;
+ 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;
+
+ name = reg_get_name (dict, prefix);
+ new_var = dict_create_var_assert (dict, name, 0);
+ free (name);
+
+ *v = new_var;
+ add_transformation (ds, f, regression_trns_free, t);
+ trns_index++;
+}
+
+static void
+subcommand_save (const struct regression *cmd)
+{
+ linreg **lc;
+ int n_trns = 0;
+
+ if ( cmd->resid ) n_trns++;
+ if ( cmd->pred ) n_trns++;
+
+ n_trns *= cmd->n_dep_vars;
+
+ for (lc = cmd->models; lc < cmd->models + cmd->n_dep_vars; lc++)
+ {
+ if (*lc != NULL)
+ {
+ if ((*lc)->depvar != NULL)
+ {
+ if (cmd->resid)
+ {
+ 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);
+ }
+ }
+ }
+ }
+}
+
+int
+cmd_regression (struct lexer *lexer, struct dataset *ds)
+{
+ struct regression regression;
+ const struct dictionary *dict = dataset_dict (ds);
+
+ memset (®ression, 0, sizeof (struct regression));
+
+ regression.anova = true;
+ regression.coeff = true;
+ regression.r = true;
+
+ regression.pred = false;
+ regression.resid = false;
+
+ regression.ds = ds;
+
+ /* Accept an optional, completely pointless "/VARIABLES=" */
+ lex_match (lexer, T_SLASH);
+ if (lex_match_id (lexer, "VARIABLES"))
+ {
+ 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))
+ goto error;
+
+
+ while (lex_token (lexer) != T_ENDCMD)
+ {
+ lex_match (lexer, T_SLASH);
+
+ if (lex_match_id (lexer, "DEPENDENT"))
+ {
+ if (! lex_force_match (lexer, T_EQUALS) )
+ goto error;
+
+ if (!parse_variables_const (lexer, dict,
+ ®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);
+
+ if (!lex_force_match_id (lexer, "ENTER"))
+ {
+ goto error;
+ }
+ }
+ else if (lex_match_id (lexer, "STATISTICS"))
+ {
+ lex_match (lexer, T_EQUALS);
+
+ while (lex_token (lexer) != T_ENDCMD
+ && lex_token (lexer) != T_SLASH)
+ {
+ if (lex_match (lexer, T_ALL))
+ {
+ }
+ else if (lex_match_id (lexer, "DEFAULTS"))
+ {
+ }
+ else if (lex_match_id (lexer, "R"))
+ {
+ }
+ else if (lex_match_id (lexer, "COEFF"))
+ {
+ }
+ else if (lex_match_id (lexer, "ANOVA"))
+ {
+ }
+ else if (lex_match_id (lexer, "BCOV"))
+ {
+ }
+ else
+ {
+ lex_error (lexer, NULL);
+ goto error;
+ }
+ }
+ }
+ else if (lex_match_id (lexer, "SAVE"))
+ {
+ lex_match (lexer, T_EQUALS);
+
+ while (lex_token (lexer) != T_ENDCMD
+ && lex_token (lexer) != T_SLASH)
+ {
+ if (lex_match_id (lexer, "PRED"))
+ {
+ regression.pred = true;
+ }
+ else if (lex_match_id (lexer, "RESID"))
+ {
+ regression.resid = true;
+ }
+ else
+ {
+ lex_error (lexer, NULL);
+ goto error;
+ }
+ }
+ }
+ else
+ {
+ lex_error (lexer, NULL);
+ goto error;
+ }
+ }
+
+ if (!regression.vars)
+ {
+ dict_get_vars (dict, ®ression.vars, ®ression.n_vars, 0);
+ }
+
+
+ regression.models = xcalloc (regression.n_dep_vars, sizeof *regression.models);
+
+ {
+ struct casegrouper *grouper;
+ struct casereader *group;
+ bool ok;
+
+ grouper = casegrouper_create_splits (proc_open (ds), 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);
+
+
+ return CMD_SUCCESS;
+
+ error:
+ return CMD_FAILURE;
+}
+
+
+static size_t
+get_n_all_vars (const struct regression *cmd)
+{
+ size_t result = cmd->n_vars;
+ size_t i;
+ size_t j;
+
+ result += cmd->n_dep_vars;
+ 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--;
+ }
+ }
+ }
+ return result;
+}
+
+static void
+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];
+ }
+ for (i = 0; i < cmd->n_dep_vars; i++)
+ {
+ absent = true;
+ for (j = 0; j < cmd->n_vars; j++)
+ {
+ if (cmd->dep_vars[i] == cmd->vars[j])
+ {
+ absent = false;
+ break;
+ }
+ }
+ if (absent)
+ {
+ vars[i + cmd->n_vars] = cmd->dep_vars[i];
+ }
+ }
+}
+
+/*
+ Is variable k the dependent variable?
+*/
+static bool
+is_depvar (const struct regression *cmd, size_t k, const struct variable *v)
+{
+ return v == cmd->vars[k];
+}
+
+
+/* Identify the explanatory variables in v_variables. Returns
+ the number of independent variables. */
+static int
+identify_indep_vars (const struct regression *cmd,
+ const struct variable **indep_vars,
+ const struct variable *depvar)
+{
+ int n_indep_vars = 0;
+ int i;
+
+ for (i = 0; i < cmd->n_vars; i++)
+ if (!is_depvar (cmd, i, depvar))
+ indep_vars[n_indep_vars++] = cmd->vars[i];
+ 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."));
+ n_indep_vars = 1;
+ indep_vars[0] = cmd->vars[0];
+ }
+ return n_indep_vars;
+}
+
+
+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)
+{
+ size_t i;
+ size_t j;
+ size_t dep_subscript;
+ size_t *rows;
+ const gsl_matrix *ssizes;
+ 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)
+ 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 (all_vars[i] == dep_var)
+ {
+ 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);
+ 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]));
+ }
+ }
+ means[cov->size1 - 1] = gsl_matrix_get (mean_matrix, dep_subscript, 0)
+ / gsl_matrix_get (ssize_matrix, dep_subscript, 0);
+ ssizes = covariance_moments (all_cov, MOMENT_NONE);
+ 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));
+ if (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));
+ free (rows);
+ gsl_matrix_free (cm);
+ return result;
+}
+
+
+/*
+ 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 statistics_keyword_output (void (*)(linreg *, void *),
+ bool, linreg *, void *);
+
+
+
+static void
+subcommand_statistics (const struct regression *cmd , linreg * c, void *aux)
+{
+ 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);
+}
+
+
+static void
+run_regression (const struct regression *cmd, struct casereader *input)
+{
+ size_t i;
+ int n_indep = 0;
+ int k;
+ double *means;
+ struct ccase *c;
+ 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;
+
+ linreg **models = cmd->models;
+
+ n_all_vars = get_n_all_vars (cmd);
+ 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));
+ cov = covariance_1pass_create (n_all_vars, all_vars,
+ 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);
+
+
+ for (; (c = casereader_read (reader)) != NULL; case_unref (c))
+ {
+ covariance_accumulate (cov, c);
+ }
+
+ for (k = 0; k < cmd->n_dep_vars; k++)
+ {
+ double n_data;
+
+ 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);
+ models[k] = linreg_alloc (dep_var, (const struct variable **) vars,
+ 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_depvar_mean (models[k], means[i]);
+ /*
+ For large data sets, use QR decomposition.
+ */
+ if (n_data > sqrt (n_indep) && n_data > REG_LARGE_DATA)
+ {
+ 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);
+ }
+ }
+ else
+ {
+ msg (SE,
+ _("No valid data found. This command was skipped."));
+ linreg_free (models[k]);
+ models[k] = NULL;
+ }
+ gsl_matrix_free (this_cm);
+ }
+
+ casereader_destroy (reader);
+ free (vars);
+ free (all_vars);
+ free (means);
+ casereader_destroy (input);
+ covariance_destroy (cov);
+}
+
+
+\f
+
+
+static void
+reg_stats_r (linreg *c, void *aux UNUSED)
+{
+ struct tab_table *t;
+ int n_rows = 2;
+ int n_cols = 5;
+ double rsq;
+ double adjrsq;
+ double std_error;
+
+ 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));
+ 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);
+ 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_double (t, 1, 1, TAB_RIGHT, sqrt (rsq), NULL);
+ 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_submit (t);
+}
+
+/*
+ Table showing estimated regression coefficients.
+*/
+static void
+reg_stats_coeff (linreg * c, void *aux_)
+{
+ 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;
+ struct tab_table *t;
+ gsl_matrix *cov = aux_;
+
+ assert (c != NULL);
+ n_rows = linreg_n_coeffs (c) + 3;
+
+ t = tab_create (n_cols, n_rows);
+ tab_headers (t, 2, 0, 1, 0);
+ 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_double (t, 2, 1, 0, linreg_intercept (c), NULL);
+ std_err = sqrt (gsl_matrix_get (linreg_cov (c), 0, 0));
+ tab_double (t, 3, 1, 0, std_err, NULL);
+ 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)));
+ tab_double (t, 6, 1, 0, pval, NULL);
+ for (j = 0; j < linreg_n_coeffs (c); j++)
+ {
+ struct string tstr;
+ ds_init_empty (&tstr);
+ this_row = j + 2;
+
+ v = linreg_indep_var (c, j);
+ label = var_to_string (v);
+ /* Do not overwrite the variable's name. */
+ ds_put_cstr (&tstr, label);
+ tab_text (t, 1, this_row, TAB_CENTER, ds_cstr (&tstr));
+ /*
+ Regression coefficients.
+ */
+ tab_double (t, 2, this_row, 0, linreg_coeff (c, j), NULL);
+ /*
+ 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.
+ */
+ beta = sqrt (gsl_matrix_get (cov, j, j));
+ 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.
+ */
+ 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.
+ */
+ pval =
+ 2 * gsl_cdf_tdist_Q (fabs (t_stat),
+ (double) (linreg_n_obs (c) - linreg_n_coeffs (c)));
+ tab_double (t, 6, this_row, 0, pval, NULL);
+ ds_destroy (&tstr);
+ }
+ tab_title (t, _("Coefficients"));
+ tab_submit (t);
+}
+
+/*
+ Display the ANOVA table.
+*/
+static void
+reg_stats_anova (linreg * c, void *aux UNUSED)
+{
+ int n_cols = 7;
+ int n_rows = 4;
+ const double msm = linreg_ssreg (c) / linreg_dfmodel (c);
+ const double mse = linreg_mse (c);
+ 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);
+ tab_headers (t, 2, 0, 1, 0);
+
+ 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_double (t, 2, 1, 0, linreg_ssreg (c), NULL);
+ tab_double (t, 2, 3, 0, linreg_sst (c), NULL);
+ tab_double (t, 2, 2, 0, linreg_sse (c), NULL);
+
+
+ /* Degrees of freedom */
+ tab_text_format (t, 3, 1, TAB_RIGHT, "%g", c->dfm);
+ tab_text_format (t, 3, 2, TAB_RIGHT, "%g", c->dfe);
+ tab_text_format (t, 3, 3, TAB_RIGHT, "%g", c->dft);
+
+ /* Mean Squares */
+ tab_double (t, 4, 1, TAB_RIGHT, msm, NULL);
+ tab_double (t, 4, 2, TAB_RIGHT, mse, NULL);
+
+ tab_double (t, 5, 1, 0, F, NULL);
+
+ tab_double (t, 6, 1, 0, pval, NULL);
+
+ tab_title (t, _("ANOVA"));
+ tab_submit (t);
+}
+
+
+static void
+reg_stats_bcov (linreg * c, void *aux UNUSED)
+{
+ 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);
+ tab_headers (t, 2, 0, 1, 0);
+ 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 < linreg_n_coeffs (c); i++)
+ {
+ const struct variable *v = linreg_indep_var (c, i);
+ 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 < linreg_n_coeffs (c); k++)
+ {
+ 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_submit (t);
+}
+
+static void
+statistics_keyword_output (void (*function) (linreg *, void *),
+ bool keyword, linreg * c, void *aux)
+{
+ if (keyword)
+ {
+ (*function) (c, aux);
+ }
+}
+++ /dev/null
-/* PSPP - a program for statistical analysis.
- Copyright (C) 2005, 2009, 2010, 2011 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 <http://www.gnu.org/licenses/>. */
-
-#include <config.h>
-
-#include <gsl/gsl_cdf.h>
-#include <gsl/gsl_matrix.h>
-#include <gsl/gsl_vector.h>
-#include <math.h>
-#include <stdlib.h>
-
-#include "data/case.h"
-#include "data/casegrouper.h"
-#include "data/casereader.h"
-#include "data/dataset.h"
-#include "data/dictionary.h"
-#include "data/missing-values.h"
-#include "data/transformations.h"
-#include "data/value-labels.h"
-#include "data/variable.h"
-#include "language/command.h"
-#include "language/data-io/file-handle.h"
-#include "language/dictionary/split-file.h"
-#include "language/lexer/lexer.h"
-#include "libpspp/compiler.h"
-#include "libpspp/message.h"
-#include "libpspp/taint.h"
-#include "math/covariance.h"
-#include "math/linreg.h"
-#include "math/moments.h"
-#include "output/tab.h"
-
-#include "gl/intprops.h"
-#include "gl/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? */
- linreg *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 *, linreg **);
-
-/*
- 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_outs (linreg *, void *);
-static void reg_stats_zpp (linreg *, void *);
-static void reg_stats_label (linreg *, void *);
-static void reg_stats_sha (linreg *, void *);
-static void reg_stats_ci (linreg *, void *);
-static void reg_stats_f (linreg *, void *);
-static void reg_stats_bcov (linreg *, void *);
-static void reg_stats_ses (linreg *, void *);
-static void reg_stats_xtx (linreg *, void *);
-static void reg_stats_collin (linreg *, void *);
-static void reg_stats_tol (linreg *, void *);
-static void reg_stats_selection (linreg *, void *);
-static void statistics_keyword_output (void (*)(linreg *, void *),
- int, linreg *, void *);
-
-static void
-reg_stats_r (linreg *c, void *aux UNUSED)
-{
- struct tab_table *t;
- int n_rows = 2;
- int n_cols = 5;
- double rsq;
- double adjrsq;
- double std_error;
-
- 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));
- 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);
- 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_double (t, 1, 1, TAB_RIGHT, sqrt (rsq), NULL);
- 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_submit (t);
-}
-
-/*
- Table showing estimated regression coefficients.
- */
-static void
-reg_stats_coeff (linreg * c, void *aux_)
-{
- 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;
- struct tab_table *t;
- gsl_matrix *cov = aux_;
-
- assert (c != NULL);
- n_rows = linreg_n_coeffs (c) + 3;
-
- t = tab_create (n_cols, n_rows);
- tab_headers (t, 2, 0, 1, 0);
- 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_double (t, 2, 1, 0, linreg_intercept (c), NULL);
- std_err = sqrt (gsl_matrix_get (linreg_cov (c), 0, 0));
- tab_double (t, 3, 1, 0, std_err, NULL);
- 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)));
- tab_double (t, 6, 1, 0, pval, NULL);
- for (j = 0; j < linreg_n_coeffs (c); j++)
- {
- struct string tstr;
- ds_init_empty (&tstr);
- this_row = j + 2;
-
- v = linreg_indep_var (c, j);
- label = var_to_string (v);
- /* Do not overwrite the variable's name. */
- ds_put_cstr (&tstr, label);
- tab_text (t, 1, this_row, TAB_CENTER, ds_cstr (&tstr));
- /*
- Regression coefficients.
- */
- tab_double (t, 2, this_row, 0, linreg_coeff (c, j), NULL);
- /*
- 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.
- */
- beta = sqrt (gsl_matrix_get (cov, j, j));
- 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.
- */
- 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.
- */
- pval =
- 2 * gsl_cdf_tdist_Q (fabs (t_stat),
- (double) (linreg_n_obs (c) - linreg_n_coeffs (c)));
- tab_double (t, 6, this_row, 0, pval, NULL);
- ds_destroy (&tstr);
- }
- tab_title (t, _("Coefficients"));
- tab_submit (t);
-}
-
-/*
- Display the ANOVA table.
- */
-static void
-reg_stats_anova (linreg * c, void *aux UNUSED)
-{
- int n_cols = 7;
- int n_rows = 4;
- const double msm = linreg_ssreg (c) / linreg_dfmodel (c);
- const double mse = linreg_mse (c);
- 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);
- tab_headers (t, 2, 0, 1, 0);
-
- 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_double (t, 2, 1, 0, linreg_ssreg (c), NULL);
- tab_double (t, 2, 3, 0, linreg_sst (c), NULL);
- tab_double (t, 2, 2, 0, linreg_sse (c), NULL);
-
-
- /* Degrees of freedom */
- tab_text_format (t, 3, 1, TAB_RIGHT, "%g", c->dfm);
- tab_text_format (t, 3, 2, TAB_RIGHT, "%g", c->dfe);
- tab_text_format (t, 3, 3, TAB_RIGHT, "%g", c->dft);
-
- /* Mean Squares */
- tab_double (t, 4, 1, TAB_RIGHT, msm, NULL);
- tab_double (t, 4, 2, TAB_RIGHT, mse, NULL);
-
- tab_double (t, 5, 1, 0, F, NULL);
-
- tab_double (t, 6, 1, 0, pval, NULL);
-
- tab_title (t, _("ANOVA"));
- tab_submit (t);
-}
-
-static void
-reg_stats_outs (linreg * c, void *aux UNUSED)
-{
- assert (c != NULL);
-}
-
-static void
-reg_stats_zpp (linreg * c, void *aux UNUSED)
-{
- assert (c != NULL);
-}
-
-static void
-reg_stats_label (linreg * c, void *aux UNUSED)
-{
- assert (c != NULL);
-}
-
-static void
-reg_stats_sha (linreg * c, void *aux UNUSED)
-{
- assert (c != NULL);
-}
-static void
-reg_stats_ci (linreg * c, void *aux UNUSED)
-{
- assert (c != NULL);
-}
-static void
-reg_stats_f (linreg * c, void *aux UNUSED)
-{
- assert (c != NULL);
-}
-static void
-reg_stats_bcov (linreg * c, void *aux UNUSED)
-{
- 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);
- tab_headers (t, 2, 0, 1, 0);
- 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 < linreg_n_coeffs (c); i++)
- {
- const struct variable *v = linreg_indep_var (c, i);
- 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 < linreg_n_coeffs (c); k++)
- {
- 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_submit (t);
-}
-static void
-reg_stats_ses (linreg * c, void *aux UNUSED)
-{
- assert (c != NULL);
-}
-static void
-reg_stats_xtx (linreg * c, void *aux UNUSED)
-{
- assert (c != NULL);
-}
-static void
-reg_stats_collin (linreg * c, void *aux UNUSED)
-{
- assert (c != NULL);
-}
-static void
-reg_stats_tol (linreg * c, void *aux UNUSED)
-{
- assert (c != NULL);
-}
-static void
-reg_stats_selection (linreg * c, void *aux UNUSED)
-{
- assert (c != NULL);
-}
-
-static void
-statistics_keyword_output (void (*function) (linreg *, void *),
- int keyword, linreg * c, void *aux)
-{
- if (keyword)
- {
- (*function) (c, aux);
- }
-}
-
-static void
-subcommand_statistics (int *keywords, linreg * c, void *aux)
-{
- /*
- 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, aux);
- statistics_keyword_output (reg_stats_anova, keywords[anova], c, aux);
- statistics_keyword_output (reg_stats_coeff, keywords[coeff], c, aux);
- statistics_keyword_output (reg_stats_outs, keywords[outs], c, aux);
- statistics_keyword_output (reg_stats_zpp, keywords[zpp], c, aux);
- statistics_keyword_output (reg_stats_label, keywords[label], c, aux);
- statistics_keyword_output (reg_stats_sha, keywords[sha], c, aux);
- statistics_keyword_output (reg_stats_ci, keywords[ci], c, aux);
- statistics_keyword_output (reg_stats_f, keywords[f], c, aux);
- statistics_keyword_output (reg_stats_bcov, keywords[bcov], c, aux);
- statistics_keyword_output (reg_stats_ses, keywords[ses], c, aux);
- statistics_keyword_output (reg_stats_xtx, keywords[xtx], c, aux);
- statistics_keyword_output (reg_stats_collin, keywords[collin], c, aux);
- statistics_keyword_output (reg_stats_tol, keywords[tol], c, aux);
- statistics_keyword_output (reg_stats_selection, keywords[selection], c, aux);
-}
-
-/*
- 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 = linreg_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_;
- linreg *model;
- union value *output = NULL;
- const union value *tmp;
- double *vals;
- const struct variable **vars = NULL;
-
- assert (trns != NULL);
- model = trns->c;
- assert (model != NULL);
- assert (model->depvar != NULL);
- assert (model->pred != NULL);
-
- vars = linreg_get_vars (model);
- n_vals = linreg_n_coeffs (model);
- vals = xnmalloc (n_vals, sizeof (*vals));
- *c = case_unshare (*c);
-
- output = case_data_rw (*c, model->pred);
-
- for (i = 0; i < n_vals; i++)
- {
- tmp = case_data (*c, vars[i]);
- vals[i] = tmp->f;
- }
- output->f = linreg_predict (model, vals, n_vals);
- free (vals);
- 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_;
- linreg *model;
- union value *output = NULL;
- const union value *tmp;
- double *vals = NULL;
- double obs;
- const struct variable **vars = NULL;
-
- assert (trns != NULL);
- model = trns->c;
- assert (model != NULL);
- assert (model->depvar != NULL);
- assert (model->resid != NULL);
-
- vars = linreg_get_vars (model);
- n_vals = linreg_n_coeffs (model);
-
- vals = xnmalloc (n_vals, sizeof (*vals));
- *c = case_unshare (*c);
- output = case_data_rw (*c, model->resid);
- assert (output != NULL);
-
- for (i = 0; i < n_vals; i++)
- {
- tmp = case_data (*c, vars[i]);
- vals[i] = tmp->f;
- }
- tmp = case_data (*c, model->depvar);
- obs = tmp->f;
- output->f = linreg_residual (model, obs, vals, n_vals);
- free (vals);
-
- return TRNS_CONTINUE;
-}
-
-static char *
-reg_get_name (const struct dictionary *dict, const char *prefix)
-{
- char *name;
- int i;
-
- /* XXX handle too-long prefixes */
- name = xmalloc (strlen (prefix) + INT_BUFSIZE_BOUND (i) + 1);
- for (i = 1; ; i++)
- {
- sprintf (name, "%s%d", prefix, i);
- if (dict_lookup_var (dict, name) == NULL)
- return name;
- }
-}
-
-static void
-reg_save_var (struct dataset *ds, const char *prefix, trns_proc_func * f,
- linreg * c, struct variable **v, int n_trns)
-{
- struct dictionary *dict = dataset_dict (ds);
- static int trns_index = 1;
- char *name;
- 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;
-
- name = reg_get_name (dict, prefix);
- new_var = dict_create_var_assert (dict, name, 0);
- free (name);
-
- *v = new_var;
- add_transformation (ds, f, regression_trns_free, t);
- trns_index++;
-}
-static void
-subcommand_save (struct dataset *ds, int save, linreg ** models)
-{
- linreg **lc;
- int n_trns = 0;
- int i;
-
- 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++)
- {
- if (*lc != NULL)
- {
- if ((*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)
- {
- linreg_free (*lc);
- }
- }
- }
-}
-
-int
-cmd_regression (struct lexer *lexer, struct dataset *ds)
-{
- struct casegrouper *grouper;
- struct casereader *group;
- linreg **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, T_EQUALS);
-
- if ((lex_token (lexer) != T_ID
- || dict_lookup_var (dict, lex_tokcstr (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;
-}
-
-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)
-{
- size_t i;
- size_t j;
- size_t dep_subscript;
- size_t *rows;
- const gsl_matrix *ssizes;
- 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)
- 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 (all_vars[i] == dep_var)
- {
- 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);
- 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]));
- }
- }
- means[cov->size1 - 1] = gsl_matrix_get (mean_matrix, dep_subscript, 0)
- / gsl_matrix_get (ssize_matrix, dep_subscript, 0);
- ssizes = covariance_moments (all_cov, MOMENT_NONE);
- 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));
- if (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));
- free (rows);
- gsl_matrix_free (cm);
- return result;
-}
-static size_t
-get_n_all_vars (struct cmd_regression *cmd)
-{
- size_t result = n_variables;
- size_t i;
- size_t j;
-
- result += cmd->n_dependent;
- for (i = 0; i < cmd->n_dependent; i++)
- {
- for (j = 0; j < n_variables; j++)
- {
- if (v_variables[j] == cmd->v_dependent[i])
- {
- result--;
- }
- }
- }
- return result;
-}
-static void
-fill_all_vars (const struct variable **vars, struct cmd_regression *cmd)
-{
- size_t i;
- size_t j;
- bool absent;
-
- for (i = 0; i < n_variables; i++)
- {
- vars[i] = v_variables[i];
- }
- for (i = 0; i < cmd->n_dependent; i++)
- {
- absent = true;
- for (j = 0; j < n_variables; j++)
- {
- if (cmd->v_dependent[i] == v_variables[j])
- {
- absent = false;
- break;
- }
- }
- if (absent)
- {
- vars[i + n_variables] = cmd->v_dependent[i];
- }
- }
-}
-static bool
-run_regression (struct casereader *input, struct cmd_regression *cmd,
- struct dataset *ds, linreg **models)
-{
- size_t i;
- int n_indep = 0;
- int k;
- double n_data;
- double *means;
- struct ccase *c;
- struct covariance *cov;
- const struct variable **vars;
- const struct variable **all_vars;
- const struct variable *dep_var;
- struct casereader *reader;
- const struct dictionary *dict;
- size_t n_all_vars;
-
- assert (models != NULL);
-
- for (i = 0; i < n_variables; i++)
- {
- if (!var_is_numeric (v_variables[i]))
- {
- msg (SE, _("REGRESSION requires numeric variables."));
- return false;
- }
- }
-
- c = casereader_peek (input, 0);
- if (c == NULL)
- {
- casereader_destroy (input);
- return true;
- }
- output_split_file_values (ds, c);
- case_unref (c);
-
- dict = dataset_dict (ds);
- if (!v_variables)
- {
- dict_get_vars (dict, &v_variables, &n_variables, 0);
- }
- n_all_vars = get_n_all_vars (cmd);
- all_vars = xnmalloc (n_all_vars, sizeof (*all_vars));
- fill_all_vars (all_vars, cmd);
- vars = xnmalloc (n_variables, sizeof (*vars));
- means = xnmalloc (n_all_vars, sizeof (*means));
- cov = covariance_1pass_create (n_all_vars, all_vars,
- dict_get_weight (dict), MV_ANY);
-
- reader = casereader_clone (input);
- reader = casereader_create_filter_missing (reader, all_vars, n_all_vars,
- MV_ANY, NULL, NULL);
-
-
- for (; (c = casereader_read (reader)) != NULL; case_unref (c))
- {
- covariance_accumulate (cov, c);
- }
-
- for (k = 0; k < cmd->n_dependent; k++)
- {
- gsl_matrix *this_cm;
- dep_var = cmd->v_dependent[k];
- n_indep = identify_indep_vars (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);
- models[k] = linreg_alloc (dep_var, (const struct variable **) vars,
- 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_depvar_mean (models[k], means[i]);
- /*
- For large data sets, use QR decomposition.
- */
- if (n_data > sqrt (n_indep) && n_data > REG_LARGE_DATA)
- {
- 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->a_statistics, models[k], this_cm);
- }
- }
- else
- {
- msg (SE,
- gettext ("No valid data found. This command was skipped."));
- linreg_free (models[k]);
- models[k] = NULL;
- }
- gsl_matrix_free (this_cm);
- }
-
- casereader_destroy (reader);
- free (vars);
- free (all_vars);
- free (means);
- casereader_destroy (input);
- covariance_destroy (cov);
-
- return true;
-}
-
-/*
- Local Variables:
- mode: c
- End:
-*/
projects / pspp / commitdiff