-/* PSPP - linear regression.
- Copyright (C) 2005 Free Software Foundation, Inc.
- Written by Jason H Stover <jason@sakla.net>.
+/* PSPP - a program for statistical analysis.
+ Copyright (C) 2005, 2009 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>
#include <math.h>
#include <stdlib.h>
-#include "regression-export.h"
#include <data/case.h>
-#include <data/casefile.h>
-#include <data/cat-routines.h>
+#include <data/casegrouper.h>
+#include <data/casereader.h>
#include <data/category.h>
#include <data/dictionary.h>
#include <data/missing-values.h>
+#include <data/procedure.h>
#include <data/transformations.h>
#include <data/value-labels.h>
#include <data/variable.h>
#include <language/command.h>
+#include <language/dictionary/split-file.h>
#include <language/data-io/file-handle.h>
#include <language/lexer/lexer.h>
-#include <libpspp/alloc.h>
#include <libpspp/compiler.h>
#include <libpspp/message.h>
+#include <libpspp/taint.h>
#include <math/design-matrix.h>
-#include <math/linreg/coefficient.h>
-#include <math/linreg/linreg.h>
+#include <math/coefficient.h>
+#include <math/linreg.h>
+#include <math/moments.h>
#include <output/table.h>
-#include <procedure.h>
+
+#include "xalloc.h"
#include "gettext.h"
+#define _(msgid) gettext (msgid)
#define REG_LARGE_DATA 1000
/* (specification)
"REGRESSION" (regression_):
*variables=custom;
- statistics[st_]=r,
- coeff,
- anova,
- outs,
- zpp,
- label,
- sha,
- ci,
- bcov,
- ses,
- xtx,
- collin,
- tol,
- selection,
- f,
- defaults,
- all;
- export=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.
+ +save[sv_]=resid,pred;
+ +method=enter.
*/
/* (declarations) */
/* (functions) */
static struct cmd_regression cmd;
-/* Linear regression models. */
-pspp_linreg_cache **models = NULL;
+/*
+ Moments for each of the variables used.
+ */
+struct moments_var
+{
+ struct moments1 *m;
+ const struct variable *v;
+};
/*
Transformations for saving predicted values
*/
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. */
+ 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 struct variable **v_variables;
+static const struct variable **v_variables;
/*
Number of variables.
*/
static size_t n_variables;
-/*
- File where the model will be saved if the EXPORT subcommand
- is given.
- */
-struct file_handle *model_file;
+static bool run_regression (struct casereader *, struct cmd_regression *,
+ struct dataset *, pspp_linreg_cache **);
/*
- 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 *);
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));
+ std_error = sqrt (pspp_linreg_mse (c));
t = tab_create (n_cols, n_rows, 0);
- tab_dim (t, tab_natural_dimensions);
+ tab_dim (t, tab_natural_dimensions, NULL);
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_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_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);
}
size_t j;
int n_cols = 7;
int n_rows;
+ int this_row;
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;
+ 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_dim (t, tab_natural_dimensions, NULL);
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_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);
+ tab_double (t, 2, 1, 0, c->intercept, NULL);
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);
+ tab_double (t, 3, 1, 0, std_err, NULL);
+ tab_double (t, 4, 1, 0, 0.0, NULL);
+ t_stat = c->intercept / std_err;
+ tab_double (t, 5, 1, 0, t_stat, NULL);
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++)
+ tab_double (t, 6, 1, 0, pval, NULL);
+ for (j = 0; j < c->n_coeffs; j++)
{
- v = pspp_linreg_coeff_get_var (c->coeff + j, 0);
+ 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. */
- strncpy (tmp, label, MAX_STRING);
- if (v->type == ALPHA)
+ ds_put_cstr (&tstr, label);
+ if (var_is_alpha (v))
{
/*
Append the value associated with this coefficient.
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);
+ val = pspp_coeff_get_value (c->coeff[j], v);
+
+ var_append_value_name (v, val, &tstr);
}
- tab_text (t, 1, j + 1, TAB_CENTER, tmp);
+ tab_text (t, 1, this_row, TAB_CENTER, ds_cstr (&tstr));
/*
Regression coefficients.
*/
- coeff = c->coeff[j].estimate;
- tab_float (t, 2, j + 1, 0, coeff, 10, 2);
+ tab_double (t, 2, this_row, 0, c->coeff[j]->estimate, NULL);
/*
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);
+ std_err = sqrt (gsl_matrix_get (c->cov, j + 1, j + 1));
+ tab_double (t, 3, this_row, 0, std_err, NULL);
/*
- 'Standardized' coefficient, i.e., regression coefficient
+ 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);
+ beta = pspp_coeff_get_sd (c->coeff[j]);
+ beta *= c->coeff[j]->estimate / c->depvar_std;
+ tab_double (t, 4, this_row, 0, beta, NULL);
/*
Test statistic for H0: coefficient is 0.
*/
- t_stat = coeff / std_err;
- tab_float (t, 5, j + 1, 0, t_stat, 10, 2);
+ t_stat = c->coeff[j]->estimate / 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), 1.0);
- tab_float (t, 6, j + 1, 0, pval, 10, 2);
+ pval =
+ 2 * gsl_cdf_tdist_Q (fabs (t_stat),
+ (double) (c->n_obs - c->n_coeffs));
+ tab_double (t, 6, this_row, 0, pval, NULL);
+ ds_destroy (&tstr);
}
tab_title (t, _("Coefficients"));
tab_submit (t);
- free (tmp);
}
/*
int n_cols = 7;
int n_rows = 4;
const double msm = c->ssm / c->dfm;
- const double mse = c->sse / c->dfe;
+ const double mse = pspp_linreg_mse (c);
const double F = msm / mse;
const double pval = gsl_cdf_fdist_Q (F, c->dfm, c->dfe);
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_dim (t, tab_natural_dimensions, NULL);
tab_box (t, TAL_2, TAL_2, -1, TAL_1, 0, 0, n_cols - 1, n_rows - 1);
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);
+ tab_double (t, 2, 1, 0, c->ssm, NULL);
+ tab_double (t, 2, 3, 0, c->sst, NULL);
+ tab_double (t, 2, 2, 0, c->sse, NULL);
/* 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);
+ 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_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_double (t, 5, 1, 0, F, NULL);
- tab_float (t, 6, 1, 0, pval, 8, 3);
+ tab_double (t, 6, 1, 0, pval, NULL);
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)
{
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_dim (t, tab_natural_dimensions, NULL);
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_coeffs; i++)
+ for (i = 0; i < c->n_coeffs; i++)
{
- const struct variable *v = pspp_linreg_coeff_get_var (c->coeff + i, 0);
+ 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);
{
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_double (t, k + 2, i, TAB_CENTER,
+ gsl_matrix_get (c->cov, row, col), NULL);
}
}
tab_title (t, _("Coefficient Correlations"));
static void
subcommand_statistics (int *keywords, pspp_linreg_cache * c)
{
- /*
- The order here must match the order in which the STATISTICS
+ /*
+ The order here must match the order in which the STATISTICS
keywords appear in the specification section above.
*/
enum
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_)
+static bool
+regression_trns_free (void *t_)
{
bool result = true;
struct reg_trns *t = t_;
return result;
}
+
/*
Gets the predicted values.
*/
static int
-regression_trns_pred_proc (void *t_, struct ccase *c, int case_idx UNUSED)
+regression_trns_pred_proc (void *t_, struct ccase **c,
+ casenumber case_idx UNUSED)
{
size_t i;
size_t n_vals;
pspp_linreg_cache *model;
union value *output = NULL;
const union value **vals = NULL;
- struct variable **vars = NULL;
-
- assert (trns!= NULL);
+ const struct variable **vars = NULL;
+
+ assert (trns != NULL);
model = trns->c;
assert (model != NULL);
assert (model->depvar != NULL);
n_vals = (*model->get_vars) (model, vars);
vals = xnmalloc (n_vals, sizeof (*vals));
- output = case_data_rw (c, model->pred->fv);
- assert (output != NULL);
+ *c = case_unshare (*c);
+ output = case_data_rw (*c, model->pred);
for (i = 0; i < n_vals; i++)
{
- vals[i] = case_data (c, vars[i]->fv);
+ vals[i] = case_data (*c, vars[i]);
}
- output->f = (*model->predict) ((const struct variable **) vars,
- vals, model, n_vals);
+ 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, int case_idx UNUSED)
+regression_trns_resid_proc (void *t_, struct ccase **c,
+ casenumber case_idx UNUSED)
{
size_t i;
size_t n_vals;
union value *output = NULL;
const union value **vals = NULL;
const union value *obs = NULL;
- struct variable **vars = NULL;
-
- assert (trns!= NULL);
+ const struct variable **vars = NULL;
+
+ assert (trns != NULL);
model = trns->c;
assert (model != NULL);
assert (model->depvar != NULL);
n_vals = (*model->get_vars) (model, vars);
vals = xnmalloc (n_vals, sizeof (*vals));
- output = case_data_rw (c, model->resid->fv);
+ *c = case_unshare (*c);
+ output = case_data_rw (*c, model->resid);
assert (output != NULL);
for (i = 0; i < n_vals; i++)
{
- vals[i] = case_data (c, vars[i]->fv);
+ vals[i] = case_data (*c, vars[i]);
}
- obs = case_data (c, model->depvar->fv);
- output->f = (*model->residual) ((const struct variable **) vars,
+ 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 0 if NAME is a duplicate of any existing variable name.
+
+/*
+ Returns false if NAME is a duplicate of any existing variable name.
*/
-static int
-try_name (char *name)
+static bool
+try_name (const struct dictionary *dict, const char *name)
{
- if (dict_lookup_var (default_dict, name) != NULL)
- return 0;
+ if (dict_lookup_var (dict, name) != NULL)
+ return false;
- return 1;
+ return true;
}
-static
-void reg_get_name (char name[LONG_NAME_LEN], const char prefix[LONG_NAME_LEN])
+
+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, LONG_NAME_LEN, "%s%d", prefix, i);
- while (!try_name(name))
+ snprintf (name, VAR_NAME_LEN, "%s%d", prefix, i);
+ while (!try_name (dict, name))
{
i++;
- snprintf (name, LONG_NAME_LEN, "%s%d", prefix, i);
+ snprintf (name, VAR_NAME_LEN, "%s%d", prefix, i);
}
}
-static void
-reg_save_var (const char *prefix, trns_proc_func *f,
- pspp_linreg_cache *c, struct variable **v,
- int n_trns)
+
+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[LONG_NAME_LEN];
+ char name[VAR_NAME_LEN];
struct variable *new_var;
struct reg_trns *t = NULL;
t->trns_id = trns_index;
t->n_trns = n_trns;
t->c = c;
- reg_get_name (name, prefix);
- new_var = dict_create_var (default_dict, name, 0);
+ reg_get_name (dict, name, prefix);
+ new_var = dict_create_var (dict, name, 0);
assert (new_var != NULL);
*v = new_var;
- add_transformation (f, regression_trns_free, t);
+ add_transformation (ds, f, regression_trns_free, t);
trns_index++;
}
static void
-subcommand_save (int save, pspp_linreg_cache **models)
+subcommand_save (struct dataset *ds, int save, pspp_linreg_cache ** models)
{
pspp_linreg_cache **lc;
int n_trns = 0;
for (lc = models; lc < models + cmd.n_dependent; lc++)
{
- assert (*lc != NULL);
- assert ((*lc)->depvar != NULL);
- if (cmd.a_save[REGRESSION_SV_RESID])
+ if (*lc != NULL)
{
- reg_save_var ("RES", regression_trns_resid_proc, *lc, &(*lc)->resid, n_trns);
- }
- if (cmd.a_save[REGRESSION_SV_PRED])
- {
- reg_save_var ("PRED", regression_trns_pred_proc, *lc, &(*lc)->pred, n_trns);
+ 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
+ else
{
for (lc = models; lc < models + cmd.n_dependent; lc++)
{
- assert (*lc != NULL);
- pspp_linreg_cache_free (*lc);
- }
- }
-}
-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))
+ if (*lc != NULL)
{
- fprintf (fp, "struct pspp_reg_categorical_variable %s;\n\t",
- v->name);
- varlist[n_vars] = (struct variable *) v;
- n_vars++;
+ pspp_linreg_cache_free (*lc);
}
}
}
- 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 int
-reg_has_categorical (pspp_linreg_cache * c)
-{
- int i;
- const struct variable *v;
-
- for (i = 1; i < c->n_coeffs; i++)
- {
- v = pspp_linreg_coeff_get_var (c->coeff + i, 0);
- if (v->type == ALPHA)
- {
- return 1;
- }
- }
- return 0;
-}
-
-static void
-subcommand_export (int export, pspp_linreg_cache * c)
+int
+cmd_regression (struct lexer *lexer, struct dataset *ds)
{
- FILE *fp;
+ struct casegrouper *grouper;
+ struct casereader *group;
+ pspp_linreg_cache **models;
+ bool ok;
size_t i;
- size_t j;
- int n_quantiles = 100;
- double increment;
- double tmp;
- struct pspp_linreg_coeff coeff;
- if (export)
+ if (!parse_regression (lexer, ds, &cmd, NULL))
{
- assert (c != NULL);
- assert (model_file != NULL);
- fp = fopen (fh_get_file_name (model_file), "w");
- assert (fp != NULL);
- fprintf (fp, "%s", reg_preamble);
- reg_print_getvar (fp, c);
- if (reg_has_categorical (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);
+ return CMD_FAILURE;
}
-}
-static int
-regression_custom_export (struct cmd_regression *cmd UNUSED)
-{
- /* 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
+ models = xnmalloc (cmd.n_dependent, sizeof *models);
+ for (i = 0; i < cmd.n_dependent; i++)
{
- model_file = fh_parse (FH_REF_FILE);
- if (model_file == NULL)
- return 0;
+ models[i] = NULL;
}
- if (!lex_force_match (')'))
- return 0;
-
- return 1;
-}
-
-int
-cmd_regression (void)
-{
- if (!parse_regression (&cmd))
- return CMD_FAILURE;
+ /* 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;
- models = xnmalloc (cmd.n_dependent, sizeof *models);
- if (!multipass_procedure_with_splits (run_regression, &cmd))
- return CMD_CASCADING_FAILURE;
- subcommand_save (cmd.sbc_save, models);
+ subcommand_save (ds, cmd.sbc_save, models);
free (v_variables);
free (models);
- return pspp_reg_rc;
+ free_regression (&cmd);
+
+ return ok ? CMD_SUCCESS : CMD_FAILURE;
}
/*
Is variable k the dependent variable?
*/
-static int
+static bool
is_depvar (size_t k, const struct variable *v)
{
- /*
- compare_var_names returns 0 if the variable
- names match.
- */
- if (!compare_var_names (v, 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;
+ return v == v_variables[k];
}
/* Parser for the variables sub command */
static int
-regression_custom_variables(struct cmd_regression *cmd UNUSED)
+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('=');
+ lex_match (lexer, '=');
- if ((token != T_ID || dict_lookup_var (default_dict, tokid) == NULL)
- && token != T_ALL)
+ if ((lex_token (lexer) != T_ID
+ || dict_lookup_var (dict, lex_tokid (lexer)) == NULL)
+ && lex_token (lexer) != T_ALL)
return 2;
-
- if (!parse_variables (default_dict, &v_variables, &n_variables,
- PV_NONE ))
+
+ if (!parse_variables_const
+ (lexer, dict, &v_variables, &n_variables, PV_NONE))
{
free (v_variables);
return 0;
}
- assert(n_variables);
+ assert (n_variables);
return 1;
}
-/*
- Count the explanatory variables. The user may or may
- not have specified a response variable in the syntax.
- */
-static
-int get_n_indep (const struct variable *v)
+
+/* 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 result;
- int i = 0;
+ int n_indep_vars = 0;
+ int i;
- result = n_variables;
- while (i < n_variables)
+ 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))
{
- if (is_depvar (i, v))
- {
- result--;
- i = n_variables;
- }
- i++;
+ /*
+ 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 result;
+ return n_indep_vars;
}
-/*
- Read from the active file. Identify the explanatory variables in
- v_variables. Encode categorical variables. Drop cases with missing
- values.
-*/
-static
-int prepare_data (int n_data, int is_missing_case[],
- struct variable **indep_vars,
- struct variable *depvar,
- const struct casefile *cf)
+
+/* 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 i;
- int j;
+ int n_data;
+ struct ccase *c;
+ size_t i;
- assert (indep_vars != NULL);
- j = 0;
- for (i = 0; i < n_variables; i++)
- {
- if (!is_depvar (i, depvar))
+ 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 (; (c = casereader_read (input)) != NULL; case_unref (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++)
{
- indep_vars[j] = v_variables[i];
- j++;
- if (v_variables[i]->type == ALPHA)
- {
- /* Make a place to hold the binary vectors
- corresponding to this variable's values. */
- cat_stored_values_create (v_variables[i]);
- }
- n_data = mark_missing_cases (cf, v_variables[i], is_missing_case, n_data);
+ 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++;
}
- /*
- Mark missing cases for the dependent variable.
- */
- n_data = mark_missing_cases (cf, depvar, is_missing_case, 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 (const struct casefile *cf, void *cmd_ UNUSED)
+run_regression (struct casereader *input, struct cmd_regression *cmd,
+ struct dataset *ds, pspp_linreg_cache **models)
{
size_t i;
- size_t n_data = 0; /* Number of valide cases. */
- size_t n_cases; /* Number of cases. */
- size_t row;
- size_t case_num;
int n_indep = 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 **indep_vars;
+ 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 (!v_variables)
+
+ c = casereader_peek (input, 0);
+ if (c == NULL)
{
- dict_get_vars (default_dict, &v_variables, &n_variables,
- 1u << DC_SYSTEM);
+ casereader_destroy (input);
+ return true;
}
+ output_split_file_values (ds, c);
+ case_unref (c);
- n_cases = casefile_get_case_cnt (cf);
+ if (!v_variables)
+ {
+ dict_get_vars (dataset_dict (ds), &v_variables, &n_variables, 0);
+ }
- for (i = 0; i < cmd.n_dependent; i++)
+ for (i = 0; i < cmd->n_dependent; i++)
{
- if (cmd.v_dependent[i]->type != NUMERIC)
+ if (!var_is_numeric (cmd->v_dependent[i]))
{
- msg (SE, gettext ("Dependent variable must be numeric."));
- pspp_reg_rc = CMD_FAILURE;
- return true;
+ msg (SE, _("Dependent variable must be numeric."));
+ return false;
}
}
- is_missing_case = xnmalloc (n_cases, sizeof (*is_missing_case));
-
+ 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;
- for (k = 0; k < cmd.n_dependent; k++)
- {
- n_indep = get_n_indep ((const struct variable *) cmd.v_dependent[k]);
- lopts.get_indep_mean_std = xnmalloc (n_indep, sizeof (int));
- indep_vars = xnmalloc (n_indep, sizeof *indep_vars);
- assert (indep_vars != NULL);
+ lopts.get_indep_mean_std = xnmalloc (n_variables, sizeof (int));
+ indep_vars = xnmalloc (n_variables, sizeof *indep_vars);
- for (i = 0; i < n_cases; i++)
- {
- is_missing_case[i] = 0;
- }
- n_data = prepare_data (n_cases, is_missing_case, indep_vars,
- cmd.v_dependent[k],
- (const struct casefile *) cf);
- 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 (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 = (const struct variable *) cmd.v_dependent[k];
- /*
- For large data sets, use QR decomposition.
- */
- if (n_data > sqrt (n_indep) && n_data > REG_LARGE_DATA)
+ 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))
{
- models[k]->method = PSPP_LINREG_SVD;
- }
+ 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, n_indep);
+ 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.
- */
- 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])
+ /*
+ The second pass fills the design matrix.
+ */
+ reader = casereader_create_counter (reader, &row, -1);
+ for (; (c = casereader_read (reader)) != NULL; case_unref (c))
{
- for (i = 0; i < n_variables; ++i) /* Iterate over the
- variables for the
- current case.
- */
+ for (i = 0; i < n_indep; ++i)
{
- val = case_data (&c, v_variables[i]->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, cmd.v_dependent[k]))
- {
- if (v_variables[i]->type == ALPHA)
- {
- design_matrix_set_categorical (X, row, v_variables[i], val);
- }
- else if (v_variables[i]->type == NUMERIC)
- {
- design_matrix_set_numeric (X, row, v_variables[i], val);
- }
- }
+ 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);
}
- val = case_data (&c, cmd.v_dependent[k]->fv);
- gsl_vector_set (Y, row, val->f);
- row++;
+ 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.
- */
- pspp_linreg_coeff_init (models[k], X);
+ /*
+ 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->m, &lopts, models[k]);
- subcommand_statistics (cmd.a_statistics, models[k]);
- subcommand_export (cmd.sbc_export, models[k]);
-
- gsl_vector_free (Y);
- design_matrix_destroy (X);
- free (indep_vars);
- free (lopts.get_indep_mean_std);
- casereader_destroy (r);
- }
+ /*
+ 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]);
+ }
- free (is_missing_case);
+ 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:
+ Local Variables:
mode: c
End:
*/
projects / pspp-builds.git / blobdiff