static int pspp_reg_rc = CMD_SUCCESS;
static bool run_regression (const struct ccase *,
- const struct casefile *, void *,
+ const struct casefile *, void *,
const struct dataset *);
/*
/*
P values for the test statistic above.
*/
- pval = 2 * gsl_cdf_tdist_Q (fabs (t_stat), (double) (c->n_obs - c->n_coeffs));
+ pval =
+ 2 * gsl_cdf_tdist_Q (fabs (t_stat),
+ (double) (c->n_obs - c->n_coeffs));
tab_float (t, 6, j + 1, 0, pval, 10, 2);
}
tab_title (t, _("Coefficients"));
}
static void
-reg_get_name (const struct dictionary *dict, char name[LONG_NAME_LEN], const char prefix[LONG_NAME_LEN])
+reg_get_name (const struct dictionary *dict, char name[LONG_NAME_LEN],
+ const char prefix[LONG_NAME_LEN])
{
int i = 1;
{
for (lc = models; lc < models + cmd.n_dependent; lc++)
{
- assert (*lc != NULL);
- pspp_linreg_cache_free (*lc);
+ if (*lc != NULL)
+ {
+ pspp_linreg_cache_free (*lc);
+ }
}
}
}
{
int n_categories = cat_get_n_categories (varlist[i]);
int j;
-
+
fprintf (fp, "%s.name = \"%s\";\n\t",
- var_get_name (varlist[i]),
- var_get_name (varlist[i]));
+ var_get_name (varlist[i]), var_get_name (varlist[i]));
fprintf (fp, "%s.n_vals = %d;\n\t",
- var_get_name (varlist[i]),
- n_categories);
+ var_get_name (varlist[i]), n_categories);
for (j = 0; j < n_categories; j++)
{
- union value *val = cat_subscript_to_value (j, varlist[i]);
+ union value *val = cat_subscript_to_value (j, varlist[i]);
fprintf (fp, "%s.values[%d] = \"%s\";\n\t",
- var_get_name (varlist[i]), j,
+ var_get_name (varlist[i]), j,
var_get_value_name (varlist[i], val));
}
}
{
v = pspp_coeff_get_var (c->coeff[i], 0);
if (var_is_alpha (v))
- return 1;
+ return 1;
}
return 0;
}
}
static int
-regression_custom_export (struct lexer *lexer, struct dataset *ds UNUSED, struct cmd_regression *cmd UNUSED, void *aux UNUSED)
+regression_custom_export (struct lexer *lexer, struct dataset *ds UNUSED,
+ struct cmd_regression *cmd UNUSED, void *aux UNUSED)
{
/* 0 on failure, 1 on success, 2 on failure that should result in syntax error */
if (!lex_force_match (lexer, '('))
int
cmd_regression (struct lexer *lexer, struct dataset *ds)
{
+ 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;
+ }
if (!multipass_procedure_with_splits (ds, run_regression, &cmd))
return CMD_CASCADING_FAILURE;
subcommand_save (ds, cmd.sbc_save, models);
static size_t
mark_missing_cases (const struct casefile *cf, const struct variable *v,
int *is_missing_case, double n_data,
- struct moments_var *mom)
+ struct moments_var *mom)
{
struct casereader *r;
struct ccase c;
/* Parser for the variables sub command */
static int
-regression_custom_variables (struct lexer *lexer, struct dataset *ds,
+regression_custom_variables (struct lexer *lexer, struct dataset *ds,
struct cmd_regression *cmd UNUSED,
void *aux UNUSED)
{
lex_match (lexer, '=');
- if ((lex_token (lexer) != T_ID || dict_lookup_var (dict, lex_tokid (lexer)) == NULL)
+ if ((lex_token (lexer) != T_ID
+ || dict_lookup_var (dict, lex_tokid (lexer)) == NULL)
&& lex_token (lexer) != T_ALL)
return 2;
- if (!parse_variables_const (lexer, dict, &v_variables, &n_variables, PV_NONE))
+ if (!parse_variables_const
+ (lexer, dict, &v_variables, &n_variables, PV_NONE))
{
free (v_variables);
return 0;
prepare_data (int n_data, int is_missing_case[],
const struct variable **indep_vars,
const struct variable *depvar, const struct casefile *cf,
- struct moments_var *mom)
+ struct moments_var *mom)
{
int i;
int j;
cat_stored_values_create (v_variables[i]);
}
n_data =
- mark_missing_cases (cf, v_variables[i], is_missing_case, n_data, mom + i);
+ mark_missing_cases (cf, v_variables[i], is_missing_case, n_data,
+ mom + i);
}
}
/*
Put the moments in the linreg cache.
*/
static void
-compute_moments (pspp_linreg_cache *c, struct moments_var *mom, struct design_matrix *dm, size_t n)
+compute_moments (pspp_linreg_cache * c, struct moments_var *mom,
+ struct design_matrix *dm, size_t n)
{
size_t i;
size_t j;
double skewness;
double kurtosis;
/*
- Scan the variable names in the columns of the design matrix.
- When we find the variable we need, insert its mean in the cache.
+ Scan the variable names in the columns of the design matrix.
+ When we find the variable we need, insert its mean in the cache.
*/
for (i = 0; i < dm->m->size2; i++)
{
}
static bool
run_regression (const struct ccase *first,
- const struct casefile *cf, void *cmd_ UNUSED, const struct dataset *ds)
+ const struct casefile *cf, void *cmd_ UNUSED,
+ const struct dataset *ds)
{
size_t i;
size_t n_data = 0; /* Number of valide cases. */
n_data = prepare_data (n_cases, is_missing_case, indep_vars,
cmd.v_dependent[k],
(const struct casefile *) cf, mom);
- 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)
- {
- models[k]->method = PSPP_LINREG_SVD;
- }
-
- /*
- The second pass fills the design matrix.
- */
- row = 0;
- for (r = casefile_get_reader (cf, NULL); casereader_read (r, &c);
- case_destroy (&c))
- /* Iterate over the cases. */
+ if (n_data > 0)
{
- case_num = casereader_cnum (r) - 1;
- if (!is_missing_case[case_num])
+ 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)
+ {
+ models[k]->method = PSPP_LINREG_SVD;
+ }
+
+ /*
+ The second pass fills the design matrix.
+ */
+ row = 0;
+ for (r = casefile_get_reader (cf, NULL); casereader_read (r, &c);
+ case_destroy (&c))
+ /* Iterate over the cases. */
{
- for (i = 0; i < n_variables; ++i) /* Iterate over the
- variables for the
- current case.
- */
+ case_num = casereader_cnum (r) - 1;
+ if (!is_missing_case[case_num])
{
- val = case_data (&c, v_variables[i]);
- /*
- 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]))
+ for (i = 0; i < n_variables; ++i) /* Iterate over the
+ variables for the
+ current case.
+ */
{
- if (var_is_alpha (v_variables[i]))
- {
- design_matrix_set_categorical (X, row,
- v_variables[i], val);
- }
- else
+ val = case_data (&c, v_variables[i]);
+ /*
+ 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]))
{
- design_matrix_set_numeric (X, row, v_variables[i],
- val);
+ if (var_is_alpha (v_variables[i]))
+ {
+ design_matrix_set_categorical (X, row,
+ v_variables[i], val);
+ }
+ else
+ {
+ design_matrix_set_numeric (X, row, v_variables[i],
+ val);
+ }
}
}
+ val = case_data (&c, cmd.v_dependent[k]);
+ gsl_vector_set (Y, row, val->f);
+ row++;
}
- val = case_data (&c, cmd.v_dependent[k]);
- gsl_vector_set (Y, row, val->f);
- row++;
}
+ /*
+ Now that we know the number of coefficients, allocate space
+ and store pointers to the variables that correspond to the
+ coefficients.
+ */
+ coeff_init (models[k], X);
+
+ /*
+ Find the least-squares estimates and other statistics.
+ */
+ pspp_linreg ((const gsl_vector *) Y, X->m, &lopts, models[k]);
+ compute_moments (models[k], mom, X, n_variables);
+ 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);
}
- /*
- 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]);
- compute_moments (models[k], mom, X, n_variables);
- 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);
}
for (i = 0; i < n_variables; i++)
{
projects / pspp-builds.git / commitdiff