/* PSPP - a program for statistical analysis.
- Copyright (C) 2005 Free Software Foundation, Inc.
+ Copyright (C) 2005, 2009 Free Software Foundation, Inc.
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
tab_text (t, 2, 0, TAB_CENTER | TAT_TITLE, _("R Square"));
tab_text (t, 3, 0, TAB_CENTER | TAT_TITLE, _("Adjusted R Square"));
tab_text (t, 4, 0, TAB_CENTER | TAT_TITLE, _("Std. Error of the Estimate"));
- tab_float (t, 1, 1, TAB_RIGHT, sqrt (rsq), 10, 2);
- tab_float (t, 2, 1, TAB_RIGHT, rsq, 10, 2);
- tab_float (t, 3, 1, TAB_RIGHT, adjrsq, 10, 2);
- tab_float (t, 4, 1, TAB_RIGHT, std_error, 10, 2);
+ tab_double (t, 1, 1, TAB_RIGHT, sqrt (rsq), NULL);
+ tab_double (t, 2, 1, TAB_RIGHT, rsq, NULL);
+ tab_double (t, 3, 1, TAB_RIGHT, adjrsq, NULL);
+ tab_double (t, 4, 1, TAB_RIGHT, std_error, NULL);
tab_title (t, _("Model Summary"));
tab_submit (t);
}
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)"));
- tab_float (t, 2, 1, 0, c->intercept, 10, 2);
+ tab_double (t, 2, 1, 0, c->intercept, NULL);
std_err = sqrt (gsl_matrix_get (c->cov, 0, 0));
- tab_float (t, 3, 1, 0, std_err, 10, 2);
- tab_float (t, 4, 1, 0, 0.0, 10, 2);
+ tab_double (t, 3, 1, 0, std_err, NULL);
+ tab_double (t, 4, 1, 0, 0.0, NULL);
t_stat = c->intercept / std_err;
- tab_float (t, 5, 1, 0, t_stat, 10, 2);
+ tab_double (t, 5, 1, 0, t_stat, NULL);
pval = 2 * gsl_cdf_tdist_Q (fabs (t_stat), 1.0);
- tab_float (t, 6, 1, 0, pval, 10, 2);
+ tab_double (t, 6, 1, 0, pval, NULL);
for (j = 0; j < c->n_coeffs; j++)
{
struct string tstr;
/*
Regression coefficients.
*/
- tab_float (t, 2, this_row, 0, c->coeff[j]->estimate, 10, 2);
+ tab_double (t, 2, this_row, 0, c->coeff[j]->estimate, NULL);
/*
Standard error of the coefficients.
*/
std_err = sqrt (gsl_matrix_get (c->cov, j + 1, j + 1));
- tab_float (t, 3, this_row, 0, std_err, 10, 2);
+ tab_double (t, 3, this_row, 0, std_err, NULL);
/*
Standardized coefficient, i.e., regression coefficient
if all variables had unit variance.
*/
beta = pspp_coeff_get_sd (c->coeff[j]);
beta *= c->coeff[j]->estimate / c->depvar_std;
- tab_float (t, 4, this_row, 0, beta, 10, 2);
+ tab_double (t, 4, this_row, 0, beta, NULL);
/*
Test statistic for H0: coefficient is 0.
*/
t_stat = c->coeff[j]->estimate / std_err;
- tab_float (t, 5, this_row, 0, t_stat, 10, 2);
+ tab_double (t, 5, this_row, 0, t_stat, NULL);
/*
P values for the test statistic above.
*/
pval =
2 * gsl_cdf_tdist_Q (fabs (t_stat),
(double) (c->n_obs - c->n_coeffs));
- tab_float (t, 6, this_row, 0, pval, 10, 2);
+ tab_double (t, 6, this_row, 0, pval, NULL);
ds_destroy (&tstr);
}
tab_title (t, _("Coefficients"));
tab_text (t, 1, 3, TAB_LEFT | TAT_TITLE, _("Total"));
/* Sums of Squares */
- tab_float (t, 2, 1, 0, c->ssm, 10, 2);
- tab_float (t, 2, 3, 0, c->sst, 10, 2);
- tab_float (t, 2, 2, 0, c->sse, 10, 2);
+ tab_double (t, 2, 1, 0, c->ssm, NULL);
+ tab_double (t, 2, 3, 0, c->sst, NULL);
+ tab_double (t, 2, 2, 0, c->sse, NULL);
/* Degrees of freedom */
tab_text (t, 3, 3, TAB_RIGHT | TAT_PRINTF, "%g", c->dft);
/* Mean Squares */
- tab_float (t, 4, 1, TAB_RIGHT, msm, 8, 3);
- tab_float (t, 4, 2, TAB_RIGHT, mse, 8, 3);
+ tab_double (t, 4, 1, TAB_RIGHT, msm, NULL);
+ tab_double (t, 4, 2, TAB_RIGHT, mse, NULL);
- tab_float (t, 5, 1, 0, F, 8, 3);
+ tab_double (t, 5, 1, 0, F, NULL);
- tab_float (t, 6, 1, 0, pval, 8, 3);
+ tab_double (t, 6, 1, 0, pval, NULL);
tab_title (t, _("ANOVA"));
tab_submit (t);
{
col = (i <= k) ? k : i;
row = (i <= k) ? i : k;
- tab_float (t, k + 2, i, TAB_CENTER,
- gsl_matrix_get (c->cov, row, col), 8, 3);
+ tab_double (t, k + 2, i, TAB_CENTER,
+ gsl_matrix_get (c->cov, row, col), NULL);
}
}
tab_title (t, _("Coefficient Correlations"));
}
lopts.get_depvar_mean_std = 1;
- lopts.get_indep_mean_std = xnmalloc (n_variables, sizeof (int));
+
indep_vars = xnmalloc (n_variables, sizeof *indep_vars);
for (k = 0; k < cmd->n_dependent; k++)
design_matrix_create (n_indep,
(const struct variable **) indep_vars,
n_data);
+ lopts.get_indep_mean_std = xnmalloc (X->m->size2, sizeof (int));
for (i = 0; i < X->m->size2; i++)
{
lopts.get_indep_mean_std[i] = 1;
gsl_vector_free (Y);
design_matrix_destroy (X);
+ free (lopts.get_indep_mean_std);
}
else
{
}
free (mom);
free (indep_vars);
- free (lopts.get_indep_mean_std);
casereader_destroy (input);
return true;