#include <gsl/gsl_cdf.h>
#include <gsl/gsl_matrix.h>
+#include <gsl/gsl_combination.h>
#include <math.h>
#include "data/case.h"
const struct dictionary *dict;
+ int ss_type;
bool intercept;
double alpha;
+
+ bool dump_coding;
};
struct glm_workspace
gsl_vector *ssq;
};
+
+/* Default design: all possible interactions */
+static void
+design_full (struct glm_spec *glm)
+{
+ int sz;
+ int i = 0;
+ glm->n_interactions = (1 << glm->n_factor_vars) - 1;
+
+ glm->interactions = xcalloc (glm->n_interactions, sizeof *glm->interactions);
+
+ /* All subsets, with exception of the empty set, of [0, glm->n_factor_vars) */
+ for (sz = 1; sz <= glm->n_factor_vars; ++sz)
+ {
+ gsl_combination *c = gsl_combination_calloc (glm->n_factor_vars, sz);
+
+ do
+ {
+ struct interaction *iact = interaction_create (NULL);
+ int e;
+ for (e = 0 ; e < gsl_combination_k (c); ++e)
+ interaction_add_variable (iact, glm->factor_vars [gsl_combination_get (c, e)]);
+
+ glm->interactions[i++] = iact;
+ }
+ while (gsl_combination_next (c) == GSL_SUCCESS);
+
+ gsl_combination_free (c);
+ }
+}
+
static void output_glm (const struct glm_spec *,
const struct glm_workspace *ws);
static void run_glm (struct glm_spec *cmd, struct casereader *input,
glm.intercept = true;
glm.wv = dict_get_weight (glm.dict);
glm.alpha = 0.05;
+ glm.dump_coding = false;
+ glm.ss_type = 3;
if (!parse_variables_const (lexer, glm.dict,
&glm.dep_vars, &glm.n_dep_vars,
goto error;
}
- if (3 != lex_integer (lexer))
+ glm.ss_type = lex_integer (lexer);
+ if (1 != glm.ss_type && 2 != glm.ss_type )
{
- msg (ME, _("Only type 3 sum of squares are currently implemented"));
+ msg (ME, _("Only types 1 & 2 sum of squares are currently implemented"));
goto error;
}
if (! parse_design_spec (lexer, &glm))
goto error;
-
- if ( glm.n_interactions == 0)
- {
- msg (ME, _("One or more design variables must be given"));
- goto error;
- }
-
- design = true;
+
+ if (glm.n_interactions > 0)
+ design = true;
+ }
+ else if (lex_match_id (lexer, "SHOWCODES"))
+ /* Undocumented debug option */
+ {
+ lex_match (lexer, T_EQUALS);
+
+ glm.dump_coding = true;
}
else
{
if ( ! design )
{
- lex_error (lexer, _("/DESIGN is mandatory in GLM"));
- goto error;
+ design_full (&glm);
}
{
static void get_ssq (struct covariance *, gsl_vector *,
const struct glm_spec *);
-static bool
-not_dropped (size_t j, size_t * dropped, size_t n_dropped)
+static inline bool
+not_dropped (size_t j, const bool *ff)
{
- size_t i;
+ return ! ff[j];
+}
- for (i = 0; i < n_dropped; i++)
+static void
+fill_submatrix (const gsl_matrix * cov, gsl_matrix * submatrix, bool *dropped_f)
+{
+ size_t i;
+ size_t j;
+ size_t n = 0;
+ size_t m = 0;
+
+ for (i = 0; i < cov->size1; i++)
{
- if (j == dropped[i])
- return false;
+ if (not_dropped (i, dropped_f))
+ {
+ m = 0;
+ for (j = 0; j < cov->size2; j++)
+ {
+ if (not_dropped (j, dropped_f))
+ {
+ gsl_matrix_set (submatrix, n, m,
+ gsl_matrix_get (cov, i, j));
+ m++;
+ }
+ }
+ n++;
+ }
}
- return true;
}
-
+
static void
-get_ssq (struct covariance *cov, gsl_vector * ssq, const struct glm_spec *cmd)
+get_ssq (struct covariance *cov, gsl_vector *ssq, const struct glm_spec *cmd)
{
- const struct variable **vars;
- gsl_matrix *small_cov = NULL;
gsl_matrix *cm = covariance_calculate_unnormalized (cov);
size_t i;
- size_t j;
size_t k;
- size_t n;
- size_t m;
- size_t *dropped;
- size_t n_dropped;
-
- dropped = xcalloc (covariance_dim (cov), sizeof (*dropped));
- vars = xcalloc (covariance_dim (cov), sizeof (*vars));
- covariance_get_var_indices (cov, vars);
+ bool *model_dropped = xcalloc (covariance_dim (cov), sizeof (*model_dropped));
+ bool *submodel_dropped = xcalloc (covariance_dim (cov), sizeof (*submodel_dropped));
+ const struct categoricals *cats = covariance_get_categoricals (cov);
for (k = 0; k < cmd->n_interactions; k++)
{
- n_dropped = 0;
- for (i = 1; i < covariance_dim (cov); i++)
+ gsl_matrix *model_cov = NULL;
+ gsl_matrix *submodel_cov = NULL;
+ size_t n_dropped_model = 0;
+ size_t n_dropped_submodel = 0;
+ for (i = cmd->n_dep_vars; i < covariance_dim (cov); i++)
{
- if (vars[i] == cmd->interactions[k]->vars[0])
- {
- assert (n_dropped < covariance_dim (cov));
- dropped[n_dropped++] = i;
- }
- }
- small_cov =
- gsl_matrix_alloc (cm->size1 - n_dropped, cm->size2 - n_dropped);
- gsl_matrix_set (small_cov, 0, 0, gsl_matrix_get (cm, 0, 0));
- n = 0;
- m = 0;
- for (i = 0; i < cm->size1; i++)
- {
- if (not_dropped (i, dropped, n_dropped))
+ const struct interaction * x =
+ categoricals_get_interaction_by_subscript (cats, i - cmd->n_dep_vars);
+
+ model_dropped[i] = false;
+ submodel_dropped[i] = false;
+ if (interaction_is_subset (cmd->interactions [k], x))
{
- m = 0;
- for (j = 0; j < cm->size2; j++)
+ assert (n_dropped_submodel < covariance_dim (cov));
+ n_dropped_submodel++;
+ submodel_dropped[i] = true;
+
+ if ( cmd->interactions [k]->n_vars < x->n_vars)
{
- if (not_dropped (j, dropped, n_dropped))
- {
- gsl_matrix_set (small_cov, n, m,
- gsl_matrix_get (cm, i, j));
- m++;
- }
+ assert (n_dropped_model < covariance_dim (cov));
+ n_dropped_model++;
+ model_dropped[i] = true;
}
- n++;
}
}
- reg_sweep (small_cov, 0);
+
+ model_cov = gsl_matrix_alloc (cm->size1 - n_dropped_model, cm->size2 - n_dropped_model);
+ submodel_cov = gsl_matrix_alloc (cm->size1 - n_dropped_submodel, cm->size2 - n_dropped_submodel);
+
+ fill_submatrix (cm, model_cov, model_dropped);
+ fill_submatrix (cm, submodel_cov, submodel_dropped);
+
+ reg_sweep (model_cov, 0);
+ reg_sweep (submodel_cov, 0);
+
gsl_vector_set (ssq, k + 1,
- gsl_matrix_get (small_cov, 0, 0)
- - gsl_vector_get (ssq, 0));
- gsl_matrix_free (small_cov);
+ gsl_matrix_get (submodel_cov, 0, 0) - gsl_matrix_get (model_cov, 0, 0)
+ );
+
+ gsl_matrix_free (model_cov);
+ gsl_matrix_free (submodel_cov);
}
- free (dropped);
- free (vars);
+ free (model_dropped);
+ free (submodel_dropped);
gsl_matrix_free (cm);
}
}
casereader_destroy (reader);
- categoricals_done (ws.cats);
+ if (cmd->dump_coding)
+ reader = casereader_clone (input);
+ else
+ reader = input;
- for (reader = input;
+ for (;
(c = casereader_read (reader)) != NULL; case_unref (c))
{
double weight = dict_get_case_weight (dict, c, &warn_bad_weight);
}
casereader_destroy (reader);
+
+ if (cmd->dump_coding)
+ {
+ struct tab_table *t =
+ covariance_dump_enc_header (cov,
+ 1 + casereader_count_cases (input));
+ for (reader = input;
+ (c = casereader_read (reader)) != NULL; case_unref (c))
+ {
+ covariance_dump_enc (cov, c, t);
+ }
+ casereader_destroy (reader);
+ tab_submit (t);
+ }
+
{
gsl_matrix *cm = covariance_calculate_unnormalized (cov);
taint_destroy (taint);
}
+static const char *roman[] =
+ {
+ "", /* The Romans had no concept of zero */
+ "I",
+ "II",
+ "III",
+ "IV"
+ };
+
static void
output_glm (const struct glm_spec *cmd, const struct glm_workspace *ws)
{
if (cmd->intercept)
nr++;
+ msg (MW, "GLM is experimental. Do not rely on these results.");
t = tab_create (nc, nr);
tab_title (t, _("Tests of Between-Subjects Effects"));
/* TRANSLATORS: The parameter is a roman numeral */
tab_text_format (t, 1, 0, TAB_CENTER | TAT_TITLE,
- _("Type %s Sum of Squares"), "III");
+ _("Type %s Sum of Squares"),
+ roman[cmd->ss_type]);
tab_text (t, 2, 0, TAB_CENTER | TAT_TITLE, _("df"));
tab_text (t, 3, 0, TAB_CENTER | TAT_TITLE, _("Mean Square"));
tab_text (t, 4, 0, TAB_CENTER | TAT_TITLE, _("F"));
if (cmd->intercept)
df_corr += 1.0;
- for (f = 0; f < cmd->n_interactions; ++f)
- df_corr += categoricals_n_count (ws->cats, f) - 1.0;
+ df_corr += categoricals_df_total (ws->cats);
mse = gsl_vector_get (ws->ssq, 0) / (n_total - df_corr);
for (f = 0; f < cmd->n_interactions; ++f)
{
struct string str = DS_EMPTY_INITIALIZER;
- const double df = categoricals_n_count (ws->cats, f) - 1.0;
+ const double df = categoricals_df (ws->cats, f);
const double ssq = gsl_vector_get (ws->ssq, f + 1);
const double F = ssq / df / mse;
interaction_to_string (cmd->interactions[f], &str);
if ( lex_match (lexer, T_ASTERISK) || lex_match (lexer, T_BY))
{
- // lex_error (lexer, "Interactions are not yet implemented"); return false;
return parse_design_interaction (lexer, glm, iact);
}