struct corr
-{
- size_t n_vars_total;
- size_t n_vars1;
+ {
+ size_t n_vars_total;
+ size_t n_vars1;
- const struct variable **vars;
-};
+ const struct variable **vars;
+ };
/* Handling of missing values. */
enum corr_missing_type
{
- CORR_PAIRWISE, /* Handle missing values on a per-variable-pair basis. */
- CORR_LISTWISE /* Discard entire case if any variable is missing. */
- };
-
-enum stats_opts
- {
- STATS_DESCRIPTIVES = 0x01,
- STATS_XPROD = 0x02,
- STATS_ALL = STATS_XPROD | STATS_DESCRIPTIVES
+ CORR_PAIRWISE, /* Handle missing values on a per-variable-pair basis. */
+ CORR_LISTWISE /* Discard entire case if any variable is missing. */
};
struct corr_opts
bool sig; /* Flag significant values or not */
int tails; /* Report significance with how many tails ? */
- enum stats_opts statistics;
+ bool descriptive_stats;
+ bool xprod_stats;
const struct variable *wv; /* The weight variable (if any) */
};
struct pivot_dimension *variables = pivot_dimension_create (
table, PIVOT_AXIS_ROW, N_("Variable"));
- for (size_t r = 0 ; r < corr->n_vars_total ; ++r)
+ for (size_t r = 0; r < corr->n_vars_total; ++r)
{
const struct variable *v = corr->vars[r];
struct pivot_dimension *columns = pivot_dimension_create (
table, PIVOT_AXIS_COLUMN, N_("Variables"));
- int matrix_cols = (corr->n_vars_total > corr->n_vars1
- ? corr->n_vars_total - corr->n_vars1
- : corr->n_vars1);
- for (int c = 0; c < matrix_cols; c++)
+ size_t matrix_cols = (corr->n_vars_total > corr->n_vars1
+ ? corr->n_vars_total - corr->n_vars1
+ : corr->n_vars1);
+ for (size_t c = 0; c < matrix_cols; c++)
{
const struct variable *v = corr->n_vars_total > corr->n_vars1 ?
corr->vars[corr->n_vars1 + c] : corr->vars[c];
opts->tails == 2 ? N_("Sig. (2-tailed)") : N_("Sig. (1-tailed)"),
PIVOT_RC_SIGNIFICANCE);
- if (opts->statistics & STATS_XPROD)
+ if (opts->xprod_stats)
pivot_category_create_leaves (statistics->root, N_("Cross-products"),
N_("Covariance"));
struct pivot_footnote *sig_footnote = pivot_table_create_footnote (
table, pivot_value_new_text (N_("Significant at .05 level")));
- for (int r = 0; r < corr->n_vars1; r++)
- for (int c = 0; c < matrix_cols; c++)
+ for (size_t r = 0; r < corr->n_vars1; r++)
+ for (size_t c = 0; c < matrix_cols; c++)
{
const int col_index = (corr->n_vars_total > corr->n_vars1
? corr->n_vars1 + c
int n = 0;
entries[n++] = pearson;
entries[n++] = col_index != r ? sig : SYSMIS;
- if (opts->statistics & STATS_XPROD)
+ if (opts->xprod_stats)
{
double cov = gsl_matrix_get (cv, r, col_index);
const double xprod_dev = cov * w;
static void
run_corr (struct casereader *r, const struct corr_opts *opts, const struct corr *corr)
{
- struct ccase *c;
- const gsl_matrix *var_matrix, *samples_matrix, *mean_matrix;
- gsl_matrix *cov_matrix = NULL;
- gsl_matrix *corr_matrix = NULL;
- struct covariance *cov = covariance_2pass_create (corr->n_vars_total, corr->vars,
- NULL,
- opts->wv, opts->exclude,
- true);
+ struct covariance *cov = covariance_2pass_create (
+ corr->n_vars_total, corr->vars, NULL,opts->wv, opts->exclude, true);
struct casereader *rc = casereader_clone (r);
+ struct ccase *c;
for (; (c = casereader_read (r)); case_unref (c))
- {
- covariance_accumulate_pass1 (cov, c);
- }
-
+ covariance_accumulate_pass1 (cov, c);
for (; (c = casereader_read (rc)); case_unref (c))
- {
- covariance_accumulate_pass2 (cov, c);
- }
+ covariance_accumulate_pass2 (cov, c);
casereader_destroy (rc);
- cov_matrix = covariance_calculate (cov);
- if (! cov_matrix)
+ gsl_matrix *cov_matrix = covariance_calculate (cov);
+ if (!cov_matrix)
{
msg (SE, _("The data for the chosen variables are all missing or empty."));
- goto error;
+ covariance_destroy (cov);
+ return;
}
- samples_matrix = covariance_moments (cov, MOMENT_NONE);
- var_matrix = covariance_moments (cov, MOMENT_VARIANCE);
- mean_matrix = covariance_moments (cov, MOMENT_MEAN);
+ const gsl_matrix *samples_matrix = covariance_moments (cov, MOMENT_NONE);
+ const gsl_matrix *var_matrix = covariance_moments (cov, MOMENT_VARIANCE);
+ const gsl_matrix *mean_matrix = covariance_moments (cov, MOMENT_MEAN);
- corr_matrix = correlation_from_covariance (cov_matrix, var_matrix);
+ gsl_matrix *corr_matrix = correlation_from_covariance (cov_matrix, var_matrix);
- if (opts->statistics & STATS_DESCRIPTIVES)
+ if (opts->descriptive_stats)
output_descriptives (corr, opts, mean_matrix, var_matrix, samples_matrix);
- output_correlation (corr, opts, corr_matrix,
- samples_matrix, cov_matrix);
+ output_correlation (corr, opts, corr_matrix, samples_matrix, cov_matrix);
- error:
covariance_destroy (cov);
gsl_matrix_free (corr_matrix);
gsl_matrix_free (cov_matrix);
int
cmd_correlations (struct lexer *lexer, struct dataset *ds)
{
- int i;
- int n_all_vars = 0; /* Total number of variables involved in this command */
- const struct variable **all_vars ;
+ size_t n_all_vars = 0; /* Total number of variables involved in this command */
const struct dictionary *dict = dataset_dict (ds);
- bool ok = true;
-
- struct casegrouper *grouper;
- struct casereader *group;
- struct corr *corr = NULL;
+ struct corr *corrs = NULL;
size_t n_corrs = 0;
+ size_t allocated_corrs = 0;
- struct corr_opts opts;
- opts.missing_type = CORR_PAIRWISE;
- opts.wv = dict_get_weight (dict);
- opts.tails = 2;
- opts.sig = false;
- opts.exclude = MV_ANY;
- opts.statistics = 0;
+ struct corr_opts opts = {
+ .missing_type = CORR_PAIRWISE,
+ .wv = dict_get_weight (dict),
+ .tails = 2,
+ .exclude = MV_ANY,
+ };
/* Parse CORRELATIONS. */
while (lex_token (lexer) != T_ENDCMD)
opts.missing_type = CORR_PAIRWISE;
else if (lex_match_id (lexer, "LISTWISE"))
opts.missing_type = CORR_LISTWISE;
-
else if (lex_match_id (lexer, "INCLUDE"))
opts.exclude = MV_SYSTEM;
else if (lex_match_id (lexer, "EXCLUDE"))
opts.exclude = MV_ANY;
else
{
- lex_error (lexer, NULL);
+ lex_error_expecting (lexer, "PAIRWISE", "LISTWISE",
+ "INCLUDE", "EXCLUDE");
goto error;
}
lex_match (lexer, T_COMMA);
opts.sig = true;
else
{
- lex_error (lexer, NULL);
+ lex_error_expecting (lexer, "TWOTAIL", "ONETAIL",
+ "SIG", "NOSIG");
goto error;
}
while (lex_token (lexer) != T_ENDCMD && lex_token (lexer) != T_SLASH)
{
if (lex_match_id (lexer, "DESCRIPTIVES"))
- opts.statistics = STATS_DESCRIPTIVES;
+ opts.descriptive_stats = true;
else if (lex_match_id (lexer, "XPROD"))
- opts.statistics = STATS_XPROD;
+ opts.xprod_stats = true;
else if (lex_token (lexer) == T_ALL)
{
- opts.statistics = STATS_ALL;
+ opts.descriptive_stats = opts.xprod_stats = true;
lex_get (lexer);
}
else
{
- lex_error (lexer, NULL);
+ lex_error_expecting (lexer, "DESCRIPTIVES", "XPROD", "ALL");
goto error;
}
else
{
if (lex_match_id (lexer, "VARIABLES"))
- {
- lex_match (lexer, T_EQUALS);
- }
-
- corr = xrealloc (corr, sizeof (*corr) * (n_corrs + 1));
- corr[n_corrs].n_vars_total = corr[n_corrs].n_vars1 = 0;
-
- if (! parse_variables_const (lexer, dict, &corr[n_corrs].vars,
- &corr[n_corrs].n_vars_total,
- PV_NUMERIC))
- {
- ok = false;
- break;
- }
-
-
- corr[n_corrs].n_vars1 = corr[n_corrs].n_vars_total;
-
- if (lex_match (lexer, T_WITH))
- {
- if (! parse_variables_const (lexer, dict,
- &corr[n_corrs].vars, &corr[n_corrs].n_vars_total,
- PV_NUMERIC | PV_APPEND))
- {
- ok = false;
- break;
- }
- }
-
- n_all_vars += corr[n_corrs].n_vars_total;
-
- n_corrs++;
+ lex_match (lexer, T_EQUALS);
+
+ const struct variable **vars;
+ size_t n_vars1;
+ if (!parse_variables_const (lexer, dict, &vars, &n_vars1, PV_NUMERIC))
+ goto error;
+
+ size_t n_vars_total = n_vars1;
+ if (lex_match (lexer, T_WITH)
+ && !parse_variables_const (lexer, dict, &vars, &n_vars_total,
+ PV_NUMERIC | PV_APPEND))
+ goto error;
+
+ if (n_corrs >= allocated_corrs)
+ corrs = x2nrealloc (corrs, &allocated_corrs, sizeof *corrs);
+ corrs[n_corrs++] = (struct corr) {
+ .n_vars1 = n_vars1,
+ .n_vars_total = n_vars_total,
+ .vars = vars,
+ };
+
+ n_all_vars += n_vars_total;
}
}
-
if (n_corrs == 0)
{
- msg (SE, _("No variables specified."));
+ lex_ofs_error (lexer, 0, lex_ofs (lexer) - 1,
+ _("No variables specified."));
goto error;
}
+ const struct variable **all_vars = xmalloc (n_all_vars * sizeof *all_vars);
+ const struct variable **vv = all_vars;
+ for (size_t i = 0; i < n_corrs; ++i)
+ {
+ const struct corr *c = &corrs[i];
+ for (size_t v = 0; v < c->n_vars_total; ++v)
+ *vv++ = c->vars[v];
+ }
- all_vars = xmalloc (sizeof (*all_vars) * n_all_vars);
-
- {
- /* FIXME: Using a hash here would make more sense */
- const struct variable **vv = all_vars;
-
- for (i = 0 ; i < n_corrs; ++i)
- {
- int v;
- const struct corr *c = &corr[i];
- for (v = 0 ; v < c->n_vars_total; ++v)
- *vv++ = c->vars[v];
- }
- }
-
- grouper = casegrouper_create_splits (proc_open (ds), dict);
-
+ struct casegrouper *grouper = casegrouper_create_splits (proc_open (ds), dict);
+ struct casereader *group;
while (casegrouper_get_next_group (grouper, &group))
{
- for (i = 0 ; i < n_corrs; ++i)
+ for (size_t i = 0; i < n_corrs; ++i)
{
/* FIXME: No need to iterate the data multiple times */
struct casereader *r = casereader_clone (group);
opts.exclude, NULL, NULL);
- run_corr (r, &opts, &corr[i]);
+ run_corr (r, &opts, &corrs[i]);
casereader_destroy (r);
}
casereader_destroy (group);
}
-
- ok = casegrouper_destroy (grouper);
+ bool ok = casegrouper_destroy (grouper);
ok = proc_commit (ds) && ok;
free (all_vars);
-
/* Done. */
- free (corr->vars);
- free (corr);
+ for (size_t i = 0; i < n_corrs; i++)
+ free (corrs[i].vars);
+ free (corrs);
return ok ? CMD_SUCCESS : CMD_CASCADING_FAILURE;
- error:
- if (corr)
- free (corr->vars);
- free (corr);
+error:
+ for (size_t i = 0; i < n_corrs; i++)
+ free (corrs[i].vars);
+ free (corrs);
return CMD_FAILURE;
}
projects / pspp / commitdiff