*/
size_t *indep_vars;
-static void run_regression( const struct casefile * );
+static void run_regression (const struct casefile *, void *);
/*
STATISTICS subcommand output functions.
*/
static void reg_stats_r (pspp_linreg_cache *);
static void reg_stats_coeff (pspp_linreg_cache *);
static void reg_stats_anova (pspp_linreg_cache *);
-static void reg_stats_outs(pspp_linreg_cache *);
+static void reg_stats_outs (pspp_linreg_cache *);
static void reg_stats_zpp (pspp_linreg_cache *);
static void reg_stats_label (pspp_linreg_cache *);
static void reg_stats_sha (pspp_linreg_cache *);
static void reg_stats_ci (pspp_linreg_cache *);
static void reg_stats_f (pspp_linreg_cache *);
-static void reg_stats_bcov(pspp_linreg_cache *);
+static void reg_stats_bcov (pspp_linreg_cache *);
static void reg_stats_ses (pspp_linreg_cache *);
static void reg_stats_xtx (pspp_linreg_cache *);
-static void reg_stats_collin(pspp_linreg_cache *);
+static void reg_stats_collin (pspp_linreg_cache *);
static void reg_stats_tol (pspp_linreg_cache *);
-static void reg_stats_selection(pspp_linreg_cache *);
-static void statistics_keyword_output ( void (*) (pspp_linreg_cache *),
- int, pspp_linreg_cache *);
+static void reg_stats_selection (pspp_linreg_cache *);
+static void statistics_keyword_output (void (*)(pspp_linreg_cache *),
+ int, pspp_linreg_cache *);
static void
-reg_stats_r (pspp_linreg_cache *c)
+reg_stats_r (pspp_linreg_cache * c)
{
- return 0;
+ assert (c != NULL);
}
+
/*
Table showing estimated regression coefficients.
*/
static void
-reg_stats_coeff (pspp_linreg_cache *c)
+reg_stats_coeff (pspp_linreg_cache * c)
{
size_t i;
size_t j;
double beta;
const char *label;
struct tab_table *t;
-
+
+ assert (c != NULL);
n_rows = 2 + c->param_estimates->size;
t = tab_create (n_cols, n_rows, 0);
tab_headers (t, 2, 0, 1, 0);
- tab_dim( t, tab_natural_dimensions);
- tab_box ( t, TAL_2, TAL_2, -1, TAL_1, 0, 0,
- n_cols - 1, n_rows - 1);
- tab_hline (t, TAL_2, 0, n_cols - 1, 1 );
+ tab_dim (t, tab_natural_dimensions);
+ tab_box (t, TAL_2, TAL_2, -1, TAL_1, 0, 0, n_cols - 1, n_rows - 1);
+ tab_hline (t, TAL_2, 0, n_cols - 1, 1);
tab_vline (t, TAL_2, 2, 0, n_rows - 1);
tab_vline (t, TAL_0, 1, 0, 0);
tab_text (t, 5, 0, TAB_CENTER | TAT_TITLE, _("t"));
tab_text (t, 6, 0, TAB_CENTER | TAT_TITLE, _("Significance"));
tab_text (t, 1, 1, TAB_LEFT | TAT_TITLE, _("(Constant)"));
- coeff = gsl_vector_get ( c->param_estimates, 0);
- tab_float ( t, 2, 1, 0, coeff, 10, 2 );
- std_err = sqrt(gsl_matrix_get ( c->cov, 0, 0));
- tab_float ( t, 3, 1, 0, std_err, 10, 2);
+ coeff = gsl_vector_get (c->param_estimates, 0);
+ tab_float (t, 2, 1, 0, coeff, 10, 2);
+ std_err = sqrt (gsl_matrix_get (c->cov, 0, 0));
+ tab_float (t, 3, 1, 0, std_err, 10, 2);
beta = coeff / c->depvar_std;
- tab_float ( t, 4, 1, 0, beta, 10, 2);
+ tab_float (t, 4, 1, 0, beta, 10, 2);
t_stat = coeff / std_err;
- tab_float ( t, 5, 1, 0, t_stat, 10, 2);
- pval = 2 * gsl_cdf_tdist_Q ( fabs(t_stat), 1.0);
- tab_float ( t, 6, 1, 0, pval, 10, 2);
- for( j = 0; j < c->n_indeps; j++ )
+ tab_float (t, 5, 1, 0, t_stat, 10, 2);
+ pval = 2 * gsl_cdf_tdist_Q (fabs (t_stat), 1.0);
+ tab_float (t, 6, 1, 0, pval, 10, 2);
+ for (j = 0; j < c->n_indeps; j++)
{
i = indep_vars[j];
struct variable *v = cmd.v_variables[i];
- label = var_to_string(v);
- tab_text ( t, 1, j + 2, TAB_CENTER, label);
+ label = var_to_string (v);
+ tab_text (t, 1, j + 2, TAB_CENTER, label);
/*
- Regression coefficients.
+ Regression coefficients.
*/
- coeff = gsl_vector_get ( c->param_estimates, j+1 );
- tab_float ( t, 2, j + 2, 0, coeff, 10, 2 );
+ coeff = gsl_vector_get (c->param_estimates, j + 1);
+ tab_float (t, 2, j + 2, 0, coeff, 10, 2);
/*
- Standard error of the coefficients.
+ Standard error of the coefficients.
*/
- std_err = sqrt ( gsl_matrix_get ( c->cov, j+1, j+1 ));
- tab_float ( t, 3, j + 2, 0, std_err, 10, 2 );
+ std_err = sqrt (gsl_matrix_get (c->cov, j + 1, j + 1));
+ tab_float (t, 3, j + 2, 0, std_err, 10, 2);
/*
- 'Standardized' coefficient, i.e., regression coefficient
- if all variables had unit variance.
+ 'Standardized' coefficient, i.e., regression coefficient
+ if all variables had unit variance.
*/
- beta = gsl_vector_get(c->indep_std, j+1);
+ beta = gsl_vector_get (c->indep_std, j + 1);
beta *= coeff / c->depvar_std;
- tab_float ( t, 4, j + 2, 0, beta, 10, 2);
+ tab_float (t, 4, j + 2, 0, beta, 10, 2);
/*
- Test statistic for H0: coefficient is 0.
+ Test statistic for H0: coefficient is 0.
*/
t_stat = coeff / std_err;
- tab_float ( t, 5, j + 2, 0, t_stat, 10, 2);
+ tab_float (t, 5, j + 2, 0, t_stat, 10, 2);
/*
- P values for the test statistic above.
+ P values for the test statistic above.
*/
- pval = 2 * gsl_cdf_tdist_Q ( fabs(t_stat), 1.0 );
- tab_float ( t, 6, j + 2, 0, pval, 10, 2);
+ pval = 2 * gsl_cdf_tdist_Q (fabs (t_stat), 1.0);
+ tab_float (t, 6, j + 2, 0, pval, 10, 2);
}
tab_title (t, 0, _("Coefficients"));
tab_submit (t);
}
+
/*
Display the ANOVA table.
*/
static void
-reg_stats_anova (pspp_linreg_cache *c)
+reg_stats_anova (pspp_linreg_cache * c)
{
- int n_cols =7;
+ int n_cols = 7;
int n_rows = 4;
const double msm = c->ssm / c->dfm;
const double mse = c->sse / c->dfe;
- const double F = msm / mse ;
- const double pval = gsl_cdf_fdist_Q(F, c->dfm, c->dfe);
+ const double F = msm / mse;
+ const double pval = gsl_cdf_fdist_Q (F, c->dfm, c->dfe);
struct tab_table *t;
- t = tab_create (n_cols,n_rows,0);
+ assert (c != NULL);
+ t = tab_create (n_cols, n_rows, 0);
tab_headers (t, 2, 0, 1, 0);
tab_dim (t, tab_natural_dimensions);
- tab_box (t,
- TAL_2, TAL_2,
- -1, TAL_1,
- 0, 0,
- n_cols - 1, n_rows - 1);
+ tab_box (t, TAL_2, TAL_2, -1, TAL_1, 0, 0, n_cols - 1, n_rows - 1);
- tab_hline (t, TAL_2, 0, n_cols - 1, 1 );
+ tab_hline (t, TAL_2, 0, n_cols - 1, 1);
tab_vline (t, TAL_2, 2, 0, n_rows - 1);
tab_vline (t, TAL_0, 1, 0, 0);
-
+
tab_text (t, 2, 0, TAB_CENTER | TAT_TITLE, _("Sum of Squares"));
tab_text (t, 3, 0, TAB_CENTER | TAT_TITLE, _("df"));
tab_text (t, 4, 0, TAB_CENTER | TAT_TITLE, _("Mean Square"));
tab_float (t, 3, 3, 0, c->dft, 4, 0);
/* Mean Squares */
-
+
tab_float (t, 4, 1, TAB_RIGHT, msm, 8, 3);
tab_float (t, 4, 2, TAB_RIGHT, mse, 8, 3);
- tab_float (t, 5, 1, 0, F, 8, 3);
-
+ tab_float (t, 5, 1, 0, F, 8, 3);
+
tab_float (t, 6, 1, 0, pval, 8, 3);
tab_title (t, 0, _("ANOVA"));
tab_submit (t);
}
static void
-reg_stats_outs(pspp_linreg_cache *c)
+reg_stats_outs (pspp_linreg_cache * c)
{
- return 0;
+ assert (c != NULL);
}
static void
-reg_stats_zpp (pspp_linreg_cache *c)
+reg_stats_zpp (pspp_linreg_cache * c)
{
- return 0;
+ assert (c != NULL);
}
static void
-reg_stats_label (pspp_linreg_cache *c)
+reg_stats_label (pspp_linreg_cache * c)
{
- return 0;
+ assert (c != NULL);
}
static void
-reg_stats_sha (pspp_linreg_cache *c)
+reg_stats_sha (pspp_linreg_cache * c)
{
- return 0;
+ assert (c != NULL);
}
static void
-reg_stats_ci (pspp_linreg_cache *c)
+reg_stats_ci (pspp_linreg_cache * c)
{
- return 0;
+ assert (c != NULL);
}
static void
-reg_stats_f (pspp_linreg_cache *c)
+reg_stats_f (pspp_linreg_cache * c)
{
- return 0;
+ assert (c != NULL);
}
static void
-reg_stats_bcov(pspp_linreg_cache *c)
+reg_stats_bcov (pspp_linreg_cache * c)
{
- return 0;
+ assert (c != NULL);
}
static void
-reg_stats_ses (pspp_linreg_cache *c)
+reg_stats_ses (pspp_linreg_cache * c)
{
- return 0;
+ assert (c != NULL);
}
static void
-reg_stats_xtx (pspp_linreg_cache *c)
+reg_stats_xtx (pspp_linreg_cache * c)
{
- return 0;
+ assert (c != NULL);
}
static void
-reg_stats_collin(pspp_linreg_cache *c)
+reg_stats_collin (pspp_linreg_cache * c)
{
- return 0;
+ assert (c != NULL);
}
static void
-reg_stats_tol (pspp_linreg_cache *c)
+reg_stats_tol (pspp_linreg_cache * c)
{
- return 0;
+ assert (c != NULL);
}
static void
-reg_stats_selection(pspp_linreg_cache *c)
+reg_stats_selection (pspp_linreg_cache * c)
{
- return 0;
+ assert (c != NULL);
}
static void
-statistics_keyword_output ( void (*function) (pspp_linreg_cache *),
- int keyword,
- pspp_linreg_cache *c)
+statistics_keyword_output (void (*function) (pspp_linreg_cache *),
+ int keyword, pspp_linreg_cache * c)
{
- if(keyword)
+ if (keyword)
{
- (*function)(c);
+ (*function) (c);
}
}
static void
-subcommand_statistics ( int *keywords,
- pspp_linreg_cache *c)
+subcommand_statistics (int *keywords, pspp_linreg_cache * c)
{
/*
The order here must match the order in which the STATISTICS
keywords appear in the specification section above.
*/
- enum {r,
- coeff,
- anova,
- outs,
- zpp,
- label,
- sha,
- ci,
- bcov,
- ses,
- xtx,
- collin,
- tol,
- selection,
- f,
- defaults,
- all};
+ enum
+ { r,
+ coeff,
+ anova,
+ outs,
+ zpp,
+ label,
+ sha,
+ ci,
+ bcov,
+ ses,
+ xtx,
+ collin,
+ tol,
+ selection,
+ f,
+ defaults,
+ all
+ };
int i;
int d = 1;
-
- if(keywords[all])
+
+ if (keywords[all])
{
/*
- Set everything but F.
+ Set everything but F.
*/
- for ( i = 0; i < f; i++)
+ for (i = 0; i < f; i++)
{
*(keywords + i) = 1;
}
}
- else
+ else
{
- for ( i = 0; i < all; i++)
+ for (i = 0; i < all; i++)
{
- if(keywords[i])
+ if (keywords[i])
{
d = 0;
}
}
/*
- Default output: ANOVA table, parameter estimates,
- and statistics for variables not entered into model,
- if appropriate.
- */
- if(keywords[defaults] | d)
+ Default output: ANOVA table, parameter estimates,
+ and statistics for variables not entered into model,
+ if appropriate.
+ */
+ if (keywords[defaults] | d)
{
*(keywords + anova) = 1;
*(keywords + outs) = 1;
*(keywords + r) = 1;
}
}
- statistics_keyword_output ( reg_stats_r,
- keywords[r], c );
- statistics_keyword_output ( reg_stats_anova,
- keywords[anova], c );
- statistics_keyword_output ( reg_stats_coeff,
- keywords[coeff], c );
- statistics_keyword_output ( reg_stats_outs,
- keywords[outs], c );
- statistics_keyword_output ( reg_stats_zpp,
- keywords[zpp], c );
- statistics_keyword_output ( reg_stats_label,
- keywords[label], c );
- statistics_keyword_output ( reg_stats_sha,
- keywords[sha], c );
- statistics_keyword_output ( reg_stats_ci,
- keywords[ci], c );
- statistics_keyword_output ( reg_stats_f,
- keywords[f], c );
- statistics_keyword_output ( reg_stats_bcov,
- keywords[bcov], c );
- statistics_keyword_output ( reg_stats_ses,
- keywords[ses], c );
- statistics_keyword_output ( reg_stats_xtx,
- keywords[xtx], c );
- statistics_keyword_output ( reg_stats_collin,
- keywords[collin], c );
- statistics_keyword_output ( reg_stats_tol,
- keywords[tol], c );
- statistics_keyword_output ( reg_stats_selection,
- keywords[selection], c );
+ statistics_keyword_output (reg_stats_r, keywords[r], c);
+ statistics_keyword_output (reg_stats_anova, keywords[anova], c);
+ statistics_keyword_output (reg_stats_coeff, keywords[coeff], c);
+ statistics_keyword_output (reg_stats_outs, keywords[outs], c);
+ statistics_keyword_output (reg_stats_zpp, keywords[zpp], c);
+ statistics_keyword_output (reg_stats_label, keywords[label], c);
+ statistics_keyword_output (reg_stats_sha, keywords[sha], c);
+ statistics_keyword_output (reg_stats_ci, keywords[ci], c);
+ statistics_keyword_output (reg_stats_f, keywords[f], c);
+ statistics_keyword_output (reg_stats_bcov, keywords[bcov], c);
+ statistics_keyword_output (reg_stats_ses, keywords[ses], c);
+ statistics_keyword_output (reg_stats_xtx, keywords[xtx], c);
+ statistics_keyword_output (reg_stats_collin, keywords[collin], c);
+ statistics_keyword_output (reg_stats_tol, keywords[tol], c);
+ statistics_keyword_output (reg_stats_selection, keywords[selection], c);
}
int
-cmd_regression(void)
+cmd_regression (void)
{
- if(!parse_regression(&cmd))
+ if (!parse_regression (&cmd))
{
return CMD_FAILURE;
}
return CMD_SUCCESS;
}
+
/*
Is variable k one of the dependent variables?
*/
static int
-is_depvar ( size_t k)
+is_depvar (size_t k)
{
size_t j = 0;
- for ( j = 0; j < cmd.n_dependent; j++)
+ for (j = 0; j < cmd.n_dependent; j++)
{
/*
- compare_var_names returns 0 if the variable
- names match.
+ compare_var_names returns 0 if the variable
+ names match.
*/
- if (!compare_var_names( cmd.v_dependent[j],
- cmd.v_variables[k], NULL))
+ if (!compare_var_names (cmd.v_dependent[j], cmd.v_variables[k], NULL))
return 1;
}
return 0;
}
static void
-run_regression ( const struct casefile *cf )
+run_regression (const struct casefile *cf, void *cmd_)
{
size_t i;
size_t k;
n_data = casefile_get_case_cnt (cf);
n_indep = cmd.n_variables - cmd.n_dependent;
- indep_vars = (size_t *) malloc ( n_indep * sizeof (*indep_vars));
+ indep_vars = (size_t *) malloc (n_indep * sizeof (*indep_vars));
Y = gsl_vector_alloc (n_data);
lopts.get_depvar_mean_std = 1;
- lopts.get_indep_mean_std = (int *) malloc ( n_indep * sizeof (int));
-
- lcache = pspp_linreg_cache_alloc(n_data, n_indep);
- lcache->indep_means = gsl_vector_alloc(n_indep);
- lcache->indep_std = gsl_vector_alloc(n_indep);
+ lopts.get_indep_mean_std = (int *) malloc (n_indep * sizeof (int));
+
+ lcache = pspp_linreg_cache_alloc (n_data, n_indep);
+ lcache->indep_means = gsl_vector_alloc (n_indep);
+ lcache->indep_std = gsl_vector_alloc (n_indep);
/*
- Read from the active file. The first pass encodes categorical
- variables.
+ Read from the active file. The first pass encodes categorical
+ variables.
*/
- ca = cr_recoded_cat_ar_create ( cmd.n_variables, cmd.v_variables );
+ ca = cr_recoded_cat_ar_create (cmd.n_variables, cmd.v_variables);
for (r = casefile_get_reader (cf);
- casereader_read (r, &c ); case_destroy (&c))
+ casereader_read (r, &c); case_destroy (&c))
{
for (i = 0; i < ca->n_vars; i++)
{
v = (*(ca->a + i))->v;
- val = case_data ( &c, v->fv );
- cr_value_update ( *(ca->a + i), val);
+ val = case_data (&c, v->fv);
+ cr_value_update (*(ca->a + i), val);
}
n_data++;
}
- cr_create_value_matrices ( ca );
- X = design_matrix_create ( n_indep, cmd.v_variables,
- ca, n_data );
+ cr_create_value_matrices (ca);
+ X =
+ design_matrix_create (n_indep, (const struct variable **) cmd.v_variables,
+ ca, n_data);
/*
- The second pass creates the design matrix.
+ The second pass creates the design matrix.
*/
- for(r2 = casefile_get_reader (cf);
- casereader_read (r2, &c) ;
- case_destroy (&c)) /* Iterate over the cases. */
+ for (r2 = casefile_get_reader (cf); casereader_read (r2, &c);
+ case_destroy (&c))
+ /* Iterate over the cases. */
{
k = 0;
- row = casereader_cnum(r2) - 1;
- for(i = 0; i < cmd.n_variables ; ++i) /* Iterate over the variables
- for the current case.
- */
+ row = casereader_cnum (r2) - 1;
+ for (i = 0; i < cmd.n_variables; ++i) /* Iterate over the variables
+ for the current case.
+ */
{
v = cmd.v_variables[i];
- val = case_data ( &c, v->fv );
+ val = case_data (&c, v->fv);
/*
- Independent/dependent variable separation. The
- 'variables' subcommand specifies a varlist which contains
- both dependent and independent variables. The dependent
- variables are specified with the 'dependent'
- subcommand. We need to separate the two.
+ Independent/dependent variable separation. The
+ 'variables' subcommand specifies a varlist which contains
+ both dependent and independent variables. The dependent
+ variables are specified with the 'dependent'
+ subcommand. We need to separate the two.
*/
- if(is_depvar(i))
+ if (is_depvar (i))
{
- if ( v->type == NUMERIC )
+ if (v->type == NUMERIC)
{
- gsl_vector_set(Y, row, val->f);
+ gsl_vector_set (Y, row, val->f);
}
else
{
errno = EINVAL;
- fprintf( stderr, "%s:%d: Dependent variable should be numeric: %s\n",
- __FILE__,__LINE__,strerror(errno));
- err_cond_fail();
+ fprintf (stderr,
+ "%s:%d: Dependent variable should be numeric: %s\n",
+ __FILE__, __LINE__, strerror (errno));
+ err_cond_fail ();
}
}
else
{
- if ( v->type == ALPHA )
+ if (v->type == ALPHA)
{
- rc = cr_var_to_recoded_categorical ( v, ca );
- design_matrix_set_categorical ( X, row, v, val, rc);
+ rc = cr_var_to_recoded_categorical (v, ca);
+ design_matrix_set_categorical (X, row, v, val, rc);
}
else if (v->type == NUMERIC)
{
- design_matrix_set_numeric ( X, row, v, val);
+ design_matrix_set_numeric (X, row, v, val);
}
indep_vars[k] = i;
/*
Find the least-squares estimates and other statistics.
*/
- pspp_linreg ( Y, X->m, &lopts, lcache );
- subcommand_statistics ( &cmd.a_statistics, lcache );
- gsl_vector_free(Y);
- design_matrix_destroy(X);
- pspp_linreg_cache_free(lcache);
- free( lopts.get_indep_mean_std );
+ pspp_linreg ((const gsl_vector *) Y, X->m, &lopts, lcache);
+ subcommand_statistics (cmd.a_statistics, lcache);
+ gsl_vector_free (Y);
+ design_matrix_destroy (X);
+ pspp_linreg_cache_free (lcache);
+ free (lopts.get_indep_mean_std);
free (indep_vars);
- casereader_destroy(r);
+ casereader_destroy (r);
}
+
/*
Local Variables:
mode: c
End:
*/
-
projects / pspp-builds.git / commitdiff