#include "language/lexer/lexer.h"
#include "language/lexer/value-parser.h"
#include "language/lexer/variable-parser.h"
+#include "libpspp/assertion.h"
#include "libpspp/ll.h"
#include "libpspp/message.h"
#include "libpspp/misc.h"
const struct dictionary *dict;
+ int ss_type;
bool intercept;
double alpha;
+
+ bool dump_coding;
};
struct glm_workspace
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 && 3 < glm.ss_type )
{
- msg (ME, _("Only type 3 sum of squares are currently implemented"));
+ msg (ME, _("Only types 1, 2 & 3 sums of squares are currently implemented"));
goto error;
}
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
{
lex_error (lexer, NULL);
return CMD_FAILURE;
}
-static void get_ssq (struct covariance *, gsl_vector *,
- const struct glm_spec *);
-
static inline bool
not_dropped (size_t j, const bool *ff)
{
}
}
}
-
+
+
+/*
+ Type 1 sums of squares.
+ Populate SSQ with the Type 1 sums of squares according to COV
+ */
static void
-get_ssq (struct covariance *cov, gsl_vector *ssq, const struct glm_spec *cmd)
+ssq_type1 (struct covariance *cov, gsl_vector *ssq, const struct glm_spec *cmd)
+{
+ gsl_matrix *cm = covariance_calculate_unnormalized (cov);
+ size_t i;
+ size_t k;
+ 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);
+
+ size_t n_dropped_model = 0;
+ size_t n_dropped_submodel = 0;
+
+ for (i = cmd->n_dep_vars; i < covariance_dim (cov); i++)
+ {
+ n_dropped_model++;
+ n_dropped_submodel++;
+ model_dropped[i] = true;
+ submodel_dropped[i] = true;
+ }
+
+ for (k = 0; k < cmd->n_interactions; k++)
+ {
+ gsl_matrix *model_cov = NULL;
+ gsl_matrix *submodel_cov = NULL;
+
+ n_dropped_submodel = n_dropped_model;
+ for (i = cmd->n_dep_vars; i < covariance_dim (cov); i++)
+ {
+ submodel_dropped[i] = model_dropped[i];
+ }
+
+ for (i = cmd->n_dep_vars; i < covariance_dim (cov); i++)
+ {
+ const struct interaction * x =
+ categoricals_get_interaction_by_subscript (cats, i - cmd->n_dep_vars);
+
+ if ( x == cmd->interactions [k])
+ {
+ model_dropped[i] = false;
+ n_dropped_model--;
+ }
+ }
+
+ 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 (submodel_cov, 0, 0) - gsl_matrix_get (model_cov, 0, 0)
+ );
+
+ gsl_matrix_free (model_cov);
+ gsl_matrix_free (submodel_cov);
+ }
+
+ free (model_dropped);
+ free (submodel_dropped);
+ gsl_matrix_free (cm);
+}
+
+/*
+ Type 2 sums of squares.
+ Populate SSQ with the Type 2 sums of squares according to COV
+ */
+static void
+ssq_type2 (struct covariance *cov, gsl_vector *ssq, const struct glm_spec *cmd)
{
gsl_matrix *cm = covariance_calculate_unnormalized (cov);
size_t i;
}
model_cov = gsl_matrix_alloc (cm->size1 - n_dropped_model, cm->size2 - n_dropped_model);
- gsl_matrix_set (model_cov, 0, 0, gsl_matrix_get (cm, 0, 0));
- submodel_cov = gsl_matrix_calloc (cm->size1 - n_dropped_submodel, cm->size2 - n_dropped_submodel);
+ 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);
gsl_matrix_free (cm);
}
+/*
+ Type 3 sums of squares.
+ Populate SSQ with the Type 2 sums of squares according to COV
+ */
+static void
+ssq_type3 (struct covariance *cov, gsl_vector *ssq, const struct glm_spec *cmd)
+{
+ gsl_matrix *cm = covariance_calculate_unnormalized (cov);
+ size_t i;
+ size_t k;
+ 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);
+
+ double ss0;
+ gsl_matrix *submodel_cov = gsl_matrix_alloc (cm->size1, cm->size2);
+ fill_submatrix (cm, submodel_cov, submodel_dropped);
+ reg_sweep (submodel_cov, 0);
+ ss0 = gsl_matrix_get (submodel_cov, 0, 0);
+ gsl_matrix_free (submodel_cov);
+ free (submodel_dropped);
+
+ for (k = 0; k < cmd->n_interactions; k++)
+ {
+ gsl_matrix *model_cov = NULL;
+ size_t n_dropped_model = 0;
+
+ for (i = cmd->n_dep_vars; i < covariance_dim (cov); i++)
+ {
+ const struct interaction * x =
+ categoricals_get_interaction_by_subscript (cats, i - cmd->n_dep_vars);
+
+ model_dropped[i] = false;
+
+ if ( cmd->interactions [k] == x)
+ {
+ assert (n_dropped_model < covariance_dim (cov));
+ n_dropped_model++;
+ model_dropped[i] = true;
+ }
+ }
+
+ model_cov = gsl_matrix_alloc (cm->size1 - n_dropped_model, cm->size2 - n_dropped_model);
+
+ fill_submatrix (cm, model_cov, model_dropped);
+
+ reg_sweep (model_cov, 0);
+
+ gsl_vector_set (ssq, k + 1,
+ gsl_matrix_get (model_cov, 0, 0) - ss0);
+
+ gsl_matrix_free (model_cov);
+ }
+ free (model_dropped);
+
+ gsl_matrix_free (cm);
+}
+
+
+
//static void dump_matrix (const gsl_matrix *m);
static void
}
casereader_destroy (reader);
- for (reader = input;
+ if (cmd->dump_coding)
+ reader = casereader_clone (input);
+ else
+ 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);
*/
ws.ssq = gsl_vector_alloc (cm->size1);
gsl_vector_set (ws.ssq, 0, gsl_matrix_get (cm, 0, 0));
- get_ssq (cov, ws.ssq, cmd);
+ switch (cmd->ss_type)
+ {
+ case 1:
+ ssq_type1 (cov, ws.ssq, cmd);
+ break;
+ case 2:
+ ssq_type2 (cov, ws.ssq, cmd);
+ break;
+ case 3:
+ ssq_type3 (cov, ws.ssq, cmd);
+ break;
+ default:
+ NOT_REACHED ();
+ break;
+ }
// dump_matrix (cm);
gsl_matrix_free (cm);
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)
{
/* 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"));
projects / pspp-builds.git / blobdiff