#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
static bool parse_design_spec (struct lexer *lexer, struct glm_spec *glm);
-/* Define to 1 if the /DESIGN subcommand should not be optional */
-#define DESIGN_MANDATORY 1
int
cmd_glm (struct lexer *lexer, struct dataset *ds)
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 (! parse_design_spec (lexer, &glm))
goto error;
-#if DESIGN_MANDATORY
- if ( glm.n_interactions == 0)
- {
- msg (ME, _("One or more design variables must be given"));
- goto error;
- }
-
- design = true;
-#else
if (glm.n_interactions > 0)
design = true;
-#endif
+ }
+ else if (lex_match_id (lexer, "SHOWCODES"))
+ /* Undocumented debug option */
+ {
+ lex_match (lexer, T_EQUALS);
+
+ glm.dump_coding = true;
}
else
{
if ( ! design )
{
-#if DESIGN_MANDATORY
- lex_error (lexer, _("/DESIGN is mandatory in GLM"));
- goto error;
-#endif
-
design_full (&glm);
}
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)
+{
+ return ! ff[j];
+}
-static bool
-not_dropped (size_t j, const size_t *dropped, size_t n_dropped)
+static void
+fill_submatrix (const gsl_matrix * cov, gsl_matrix * submatrix, bool *dropped_f)
{
size_t i;
-
- for (i = 0; i < n_dropped; 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;
}
+
+/*
+ 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 j;
size_t k;
- size_t *dropped = xcalloc (covariance_dim (cov), sizeof (*dropped));
+ 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++)
{
- size_t n = 0;
- size_t m = 0;
- gsl_matrix *small_cov = NULL;
- size_t n_dropped = 0;
+ 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++)
{
- if (categoricals_get_interaction_by_subscript (cats, i - cmd->n_dep_vars)
- == cmd->interactions[k])
+ const struct interaction * x =
+ categoricals_get_interaction_by_subscript (cats, i - cmd->n_dep_vars);
+
+ if ( x == cmd->interactions [k])
{
- assert (n_dropped < covariance_dim (cov));
- dropped[n_dropped++] = i;
+ model_dropped[i] = false;
+ n_dropped_model--;
}
}
- 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));
- for (i = 0; i < cm->size1; i++)
+
+ 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;
+ 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);
+
+ for (k = 0; k < cmd->n_interactions; k++)
+ {
+ 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 (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 (model_dropped);
+ free (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)
{
cmd->wv ? var_get_print_format (cmd->wv) : &F_8_0;
double n_total, mean;
- double df_corr = 0.0;
+ double df_corr = 1.0;
double mse = 0;
int f;
struct tab_table *t;
const int nc = 6;
- int nr = heading_rows + 4 + cmd->n_interactions;
+ int nr = heading_rows + 3 + cmd->n_interactions;
if (cmd->intercept)
- nr++;
+ nr += 2;
+ 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"));
moments_calculate (ws->totals, &n_total, &mean, NULL, NULL, NULL);
- if (cmd->intercept)
- df_corr += 1.0;
-
df_corr += categoricals_df_total (ws->cats);
- mse = gsl_vector_get (ws->ssq, 0) / (n_total - df_corr);
-
r = heading_rows;
- tab_text (t, 0, r, TAB_LEFT | TAT_TITLE, _("Corrected Model"));
+ if (cmd->intercept)
+ tab_text (t, 0, r, TAB_LEFT | TAT_TITLE, _("Corrected Model"));
+ else
+ tab_text (t, 0, r, TAB_LEFT | TAT_TITLE, _("Model"));
r++;
+ mse = gsl_vector_get (ws->ssq, 0) / (n_total - df_corr);
+
+ const double intercept_ssq = pow2 (mean * n_total) / n_total;
+
+ double ssq_effects = 0.0;
if (cmd->intercept)
{
- const double intercept = pow2 (mean * n_total) / n_total;
const double df = 1.0;
- const double F = intercept / df / mse;
+ const double F = intercept_ssq / df / mse;
tab_text (t, 0, r, TAB_LEFT | TAT_TITLE, _("Intercept"));
- tab_double (t, 1, r, 0, intercept, NULL);
+ tab_double (t, 1, r, 0, intercept_ssq, NULL);
tab_double (t, 2, r, 0, 1.00, wfmt);
- tab_double (t, 3, r, 0, intercept / df, NULL);
+ tab_double (t, 3, r, 0, intercept_ssq / df, NULL);
tab_double (t, 4, r, 0, F, NULL);
tab_double (t, 5, r, 0, gsl_cdf_fdist_Q (F, df, n_total - df_corr),
NULL);
for (f = 0; f < cmd->n_interactions; ++f)
{
struct string str = DS_EMPTY_INITIALIZER;
- const double df = categoricals_df (ws->cats, f);
- const double ssq = gsl_vector_get (ws->ssq, f + 1);
+ double df = categoricals_df (ws->cats, f);
+
+ double ssq = gsl_vector_get (ws->ssq, f + 1);
+ ssq_effects += ssq;
+
+ if (! cmd->intercept)
+ {
+ df++;
+ ssq += intercept_ssq;
+ }
+
const double F = ssq / df / mse;
interaction_to_string (cmd->interactions[f], &str);
tab_text (t, 0, r, TAB_LEFT | TAT_TITLE, ds_cstr (&str));
}
{
- /* Corrected Model */
- const double df = df_corr - 1.0;
- const double ssq = ws->total_ssq - gsl_vector_get (ws->ssq, 0);
+ /* Model / Corrected Model */
+ double df = df_corr;
+ double ssq = ws->total_ssq - gsl_vector_get (ws->ssq, 0);
+ if ( cmd->intercept )
+ df --;
+ else
+ ssq += intercept_ssq;
+
const double F = ssq / df / mse;
tab_double (t, 1, heading_rows, 0, ssq, NULL);
tab_double (t, 2, heading_rows, 0, df, wfmt);
tab_double (t, 3, r++, 0, mse, NULL);
}
+ {
+ tab_text (t, 0, r, TAB_LEFT | TAT_TITLE, _("Total"));
+ tab_double (t, 1, r, 0, ws->total_ssq + intercept_ssq, NULL);
+ tab_double (t, 2, r, 0, n_total, wfmt);
+
+ r++;
+ }
+
if (cmd->intercept)
{
- const double intercept = pow2 (mean * n_total) / n_total;
- const double ssq = intercept + ws->total_ssq;
-
- tab_text (t, 0, r, TAB_LEFT | TAT_TITLE, _("Total"));
- tab_double (t, 1, r, 0, ssq, NULL);
- tab_double (t, 2, r, 0, n_total, wfmt);
-
- r++;
+ tab_text (t, 0, r, TAB_LEFT | TAT_TITLE, _("Corrected Total"));
+ tab_double (t, 1, r, 0, ws->total_ssq, NULL);
+ tab_double (t, 2, r, 0, n_total - 1.0, wfmt);
}
- tab_text (t, 0, r, TAB_LEFT | TAT_TITLE, _("Corrected Total"));
-
-
- tab_double (t, 1, r, 0, ws->total_ssq, NULL);
- tab_double (t, 2, r, 0, n_total - 1.0, wfmt);
-
tab_submit (t);
}
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);
}
projects / pspp-builds.git / blobdiff