struct categoricals *cats;
- /*
+ /*
Sums of squares due to different variables. Element 0 is the SSE
for the entire model. For i > 0, element i is the SS due to
variable i.
PV_NO_DUPLICATE | PV_NUMERIC))
goto error;
- lex_force_match (lexer, T_BY);
+ if (! lex_force_match (lexer, T_BY))
+ goto error;
if (!parse_variables_const (lexer, glm.dict,
&glm.factor_vars, &glm.n_factor_vars,
lex_error (lexer, NULL);
goto error;
}
-
+
glm.alpha = lex_number (lexer);
lex_get (lexer);
if ( ! lex_force_match (lexer, T_RPAREN))
}
glm.ss_type = lex_integer (lexer);
- if (1 > glm.ss_type && 3 < glm.ss_type )
+ if (1 > glm.ss_type || 3 < glm.ss_type )
{
msg (ME, _("Only types 1, 2 & 3 sums of squares are currently implemented"));
goto error;
const_var_set_destroy (factors);
free (glm.factor_vars);
+ for (i = 0 ; i < glm.n_interactions; ++i)
+ interaction_destroy (glm.interactions[i]);
+
free (glm.interactions);
free (glm.dep_vars);
size_t j;
size_t n = 0;
size_t m = 0;
-
+
for (i = 0; i < cov->size1; i++)
{
if (not_dropped (i, dropped_f))
- {
+ {
m = 0;
for (j = 0; j < cov->size2; j++)
{
gsl_matrix_set (submatrix, n, m,
gsl_matrix_get (cov, i, j));
m++;
- }
+ }
}
n++;
}
}
-/*
+/*
Type 1 sums of squares.
Populate SSQ with the Type 1 sums of squares according to COV
*/
static void
ssq_type1 (struct covariance *cov, gsl_vector *ssq, const struct glm_spec *cmd)
{
- gsl_matrix *cm = covariance_calculate_unnormalized (cov);
+ const gsl_matrix *cm = covariance_calculate_unnormalized (cov);
size_t i;
size_t k;
bool *model_dropped = xcalloc (covariance_dim (cov), sizeof (*model_dropped));
{
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++)
{
for (i = cmd->n_dep_vars; i < covariance_dim (cov); i++)
{
- const struct interaction * x =
+ const struct interaction * x =
categoricals_get_interaction_by_subscript (cats, i - cmd->n_dep_vars);
if ( x == cmd->interactions [k])
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);
+ const gsl_matrix *cm = covariance_calculate_unnormalized (cov);
size_t i;
size_t k;
bool *model_dropped = xcalloc (covariance_dim (cov), sizeof (*model_dropped));
size_t n_dropped_submodel = 0;
for (i = cmd->n_dep_vars; i < covariance_dim (cov); i++)
{
- const struct interaction * x =
+ const struct interaction * x =
categoricals_get_interaction_by_subscript (cats, i - cmd->n_dep_vars);
model_dropped[i] = false;
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);
+ const gsl_matrix *cm = covariance_calculate_unnormalized (cov);
size_t i;
size_t k;
bool *model_dropped = xcalloc (covariance_dim (cov), sizeof (*model_dropped));
for (i = cmd->n_dep_vars; i < covariance_dim (cov); i++)
{
- const struct interaction * x =
+ const struct interaction * x =
categoricals_get_interaction_by_subscript (cats, i - cmd->n_dep_vars);
model_dropped[i] = false;
gsl_matrix_free (model_cov);
}
free (model_dropped);
-
- gsl_matrix_free (cm);
}
struct glm_workspace ws;
struct covariance *cov;
+ input = casereader_create_filter_missing (input,
+ cmd->dep_vars, cmd->n_dep_vars,
+ cmd->exclude,
+ NULL, NULL);
+
+ input = casereader_create_filter_missing (input,
+ cmd->factor_vars, cmd->n_factor_vars,
+ cmd->exclude,
+ NULL, NULL);
+
ws.cats = categoricals_create (cmd->interactions, cmd->n_interactions,
cmd->wv, cmd->exclude, MV_ANY);
cov = covariance_2pass_create (cmd->n_dep_vars, cmd->dep_vars,
- ws.cats, cmd->wv, cmd->exclude);
+ ws.cats, cmd->wv, cmd->exclude, true);
c = casereader_peek (input, 0);
}
{
- gsl_matrix *cm = covariance_calculate_unnormalized (cov);
+ const gsl_matrix *ucm = covariance_calculate_unnormalized (cov);
+ gsl_matrix *cm = gsl_matrix_alloc (ucm->size1, ucm->size2);
+ gsl_matrix_memcpy (cm, ucm);
// dump_matrix (cm);
break;
}
// dump_matrix (cm);
-
gsl_matrix_free (cm);
}
taint_destroy (taint);
}
-static const char *roman[] =
+static const char *roman[] =
{
"", /* The Romans had no concept of zero */
"I",
if (cmd->intercept)
nr += 2;
- msg (MW, "GLM is experimental. Do not rely on these results.");
t = tab_create (nc, nr);
+ tab_set_format (t, RC_WEIGHT, wfmt);
tab_title (t, _("Tests of Between-Subjects Effects"));
tab_headers (t, heading_columns, 0, heading_rows, 0);
/* TRANSLATORS: The parameter is a roman numeral */
tab_text_format (t, 1, 0, TAB_CENTER | TAT_TITLE,
- _("Type %s Sum of Squares"),
+ _("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"));
const double df = 1.0;
const double F = intercept_ssq / df / mse;
tab_text (t, 0, r, TAB_LEFT | TAT_TITLE, _("Intercept"));
- 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_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);
+ /* The intercept for unbalanced models is of limited use and
+ nobody knows how to calculate it properly */
+ if (categoricals_isbalanced (ws->cats))
+ {
+ tab_double (t, 1, r, 0, intercept_ssq, NULL, RC_OTHER);
+ tab_double (t, 2, r, 0, 1.00, NULL, RC_WEIGHT);
+ tab_double (t, 3, r, 0, intercept_ssq / df, NULL, RC_OTHER);
+ tab_double (t, 4, r, 0, F, NULL, RC_OTHER);
+ tab_double (t, 5, r, 0, gsl_cdf_fdist_Q (F, df, n_total - df_corr),
+ NULL, RC_PVALUE);
+ }
r++;
}
ssq_effects += ssq;
- if (! cmd->intercept)
+ if (! cmd->intercept)
{
df++;
ssq += intercept_ssq;
tab_text (t, 0, r, TAB_LEFT | TAT_TITLE, ds_cstr (&str));
ds_destroy (&str);
- tab_double (t, 1, r, 0, ssq, NULL);
- tab_double (t, 2, r, 0, df, wfmt);
- tab_double (t, 3, r, 0, ssq / df, NULL);
- tab_double (t, 4, r, 0, F, NULL);
+ tab_double (t, 1, r, 0, ssq, NULL, RC_OTHER);
+ tab_double (t, 2, r, 0, df, NULL, RC_WEIGHT);
+ tab_double (t, 3, r, 0, ssq / df, NULL, RC_OTHER);
+ tab_double (t, 4, r, 0, F, NULL, RC_OTHER);
tab_double (t, 5, r, 0, gsl_cdf_fdist_Q (F, df, n_total - df_corr),
- NULL);
+ NULL, RC_PVALUE);
r++;
}
ssq += intercept_ssq;
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, heading_rows, 0, ssq / df, NULL);
- tab_double (t, 4, heading_rows, 0, F, NULL);
+ tab_double (t, 1, heading_rows, 0, ssq, NULL, RC_OTHER);
+ tab_double (t, 2, heading_rows, 0, df, NULL, RC_WEIGHT);
+ tab_double (t, 3, heading_rows, 0, ssq / df, NULL, RC_OTHER);
+ tab_double (t, 4, heading_rows, 0, F, NULL, RC_OTHER);
tab_double (t, 5, heading_rows, 0,
- gsl_cdf_fdist_Q (F, df, n_total - df_corr), NULL);
+ gsl_cdf_fdist_Q (F, df, n_total - df_corr), NULL, RC_PVALUE);
}
{
const double ssq = gsl_vector_get (ws->ssq, 0);
const double mse = ssq / df;
tab_text (t, 0, r, TAB_LEFT | TAT_TITLE, _("Error"));
- tab_double (t, 1, r, 0, ssq, NULL);
- tab_double (t, 2, r, 0, df, wfmt);
- tab_double (t, 3, r++, 0, mse, NULL);
+ tab_double (t, 1, r, 0, ssq, NULL, RC_OTHER);
+ tab_double (t, 2, r, 0, df, NULL, RC_WEIGHT);
+ tab_double (t, 3, r++, 0, mse, NULL, RC_OTHER);
}
{
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);
-
+ tab_double (t, 1, r, 0, ws->total_ssq + intercept_ssq, NULL, RC_OTHER);
+ tab_double (t, 2, r, 0, n_total, NULL, RC_WEIGHT);
+
r++;
}
if (cmd->intercept)
{
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_double (t, 1, r, 0, ws->total_ssq, NULL, RC_OTHER);
+ tab_double (t, 2, r, 0, n_total - 1.0, NULL, RC_WEIGHT);
}
tab_submit (t);
\f
-
-/* Match a variable.
- If the match succeeds, the variable will be placed in VAR.
- Returns true if successful */
-static bool
-lex_match_variable (struct lexer *lexer, const struct dictionary *dict, const struct variable **var)
-{
- if (lex_token (lexer) != T_ID)
- return false;
-
- *var = parse_variable_const (lexer, dict);
-
- if ( *var == NULL)
- return false;
- return true;
-}
-
-/* An interaction is a variable followed by {*, BY} followed by an interaction */
-static bool
-parse_design_interaction (struct lexer *lexer, const struct dictionary *dict, struct interaction **iact)
-{
- const struct variable *v = NULL;
- assert (iact);
-
- switch (lex_next_token (lexer, 1))
- {
- case T_ENDCMD:
- case T_SLASH:
- case T_COMMA:
- case T_ID:
- case T_BY:
- case T_ASTERISK:
- break;
- default:
- return false;
- break;
- }
-
- if (! lex_match_variable (lexer, dict, &v))
- {
- interaction_destroy (*iact);
- *iact = NULL;
- return false;
- }
-
- assert (v);
-
- if ( *iact == NULL)
- *iact = interaction_create (v);
- else
- interaction_add_variable (*iact, v);
-
- if ( lex_match (lexer, T_ASTERISK) || lex_match (lexer, T_BY))
- {
- return parse_design_interaction (lexer, dict, iact);
- }
-
- return true;
-}
-
static bool
parse_nested_variable (struct lexer *lexer, struct glm_spec *glm)
{
return false;
}
- lex_error (lexer, "Nested variables are not yet implemented"); return false;
+ lex_error (lexer, "Nested variables are not yet implemented"); return false;
return true;
}
projects / pspp / blobdiff