-/* PSPP - linear regression.
+/* 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 2 of the
- License, or (at your option) any later version.
+ 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.
+ 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. */
+ along with this program. If not, see <http://www.gnu.org/licenses/>. */
#include <config.h>
static struct file_handle *model_file;
static bool run_regression (struct casereader *, struct cmd_regression *,
- struct dataset *);
+ struct dataset *);
/*
STATISTICS subcommand output functions.
/* 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)
+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 < 2) && 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;
}
Returns number of valid cases. */
static int
prepare_categories (struct casereader *input,
- const struct variable **vars, size_t n_vars,
- struct moments_var *mom)
+ 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]);
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.
+ 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);
- }
+ {
+ 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 bool
run_regression (struct casereader *input, struct cmd_regression *cmd,
- struct dataset *ds)
+ struct dataset *ds)
{
size_t i;
int n_indep = 0;
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);
+ MV_ANY, NULL);
reader = casereader_create_filter_missing (reader, &dep_var, 1,
- MV_ANY, NULL);
- n_data = prepare_categories (casereader_clone (reader),
- indep_vars, n_indep, mom);
+ MV_ANY, NULL);
+ n_data = prepare_categories (casereader_clone (reader),
+ indep_vars, n_indep, mom);
if ((n_data > 0) && (n_indep > 0))
{
models[k] = pspp_linreg_cache_alloc (X->m->size1, X->m->size2);
models[k]->indep_means = gsl_vector_alloc (X->m->size2);
models[k]->indep_std = gsl_vector_alloc (X->m->size2);
- models[k]->depvar = dep_var;
- /*
+ 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_SVD;
+ 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));
- }
- casereader_destroy (reader);
+ 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
pspp_linreg ((const gsl_vector *) Y, X->m, &lopts, models[k]);
compute_moments (models[k], mom, X, n_variables);
- if (!taint_has_tainted_successor (casereader_get_taint (input)))
- {
- subcommand_statistics (cmd->a_statistics, models[k]);
- subcommand_export (cmd->sbc_export, models[k]);
- }
+ if (!taint_has_tainted_successor (casereader_get_taint (input)))
+ {
+ subcommand_statistics (cmd->a_statistics, models[k]);
+ subcommand_export (cmd->sbc_export, models[k]);
+ }
gsl_vector_free (Y);
design_matrix_destroy (X);
}
else
{
- msg (SE, gettext ("No valid data found. This command was skipped."));
+ msg (SE,
+ gettext ("No valid data found. This command was skipped."));
}
+ casereader_destroy (reader);
}
free (indep_vars);
free (lopts.get_indep_mean_std);
projects / pspp-builds.git / blobdiff