pspp_linreg_cache *
pspp_linreg_cache_alloc (size_t n, size_t p)
{
- pspp_linreg_cache *cache;
+ pspp_linreg_cache *c;
- cache = (pspp_linreg_cache *) malloc (sizeof (pspp_linreg_cache));
- cache->param_estimates = gsl_vector_alloc (p + 1);
- cache->indep_means = gsl_vector_alloc (p);
- cache->indep_std = gsl_vector_alloc (p);
- cache->ssx = gsl_vector_alloc (p); /* Sums of squares for the independent
+ c = (pspp_linreg_cache *) malloc (sizeof (pspp_linreg_cache));
+ c->param_estimates = gsl_vector_alloc (p + 1);
+ c->indep_means = gsl_vector_alloc (p);
+ c->indep_std = gsl_vector_alloc (p);
+ c->ssx = gsl_vector_alloc (p); /* Sums of squares for the independent
variables.
*/
- cache->ss_indeps = gsl_vector_alloc (p); /* Sums of squares for the model
- parameters.
- */
- cache->cov = gsl_matrix_alloc (p + 1, p + 1); /* Covariance matrix. */
- cache->n_obs = n;
- cache->n_indeps = p;
+ c->ss_indeps = gsl_vector_alloc (p); /* Sums of squares for the model
+ parameters.
+ */
+ c->cov = gsl_matrix_alloc (p + 1, p + 1); /* Covariance matrix. */
+ c->n_obs = n;
+ c->n_indeps = p;
/*
Default settings.
*/
- cache->method = PSPP_LINREG_SWEEP;
+ c->method = PSPP_LINREG_SWEEP;
- return cache;
+ return c;
}
void
-pspp_linreg_cache_free (pspp_linreg_cache * cache)
+pspp_linreg_cache_free (pspp_linreg_cache * c)
{
- gsl_vector_free (cache->param_estimates);
- gsl_vector_free (cache->indep_means);
- gsl_vector_free (cache->indep_std);
- gsl_vector_free (cache->ss_indeps);
- gsl_matrix_free (cache->cov);
- free (cache);
+ gsl_vector_free (c->param_estimates);
+ gsl_vector_free (c->indep_means);
+ gsl_vector_free (c->indep_std);
+ gsl_vector_free (c->ss_indeps);
+ gsl_matrix_free (c->cov);
+ free (c->coeff);
+ free (c);
}
/*
double m;
double s;
double ss;
- double mse;
if (cache == NULL)
{
cache->dft = cache->n_obs - 1;
cache->dfm = cache->n_indeps;
cache->dfe = cache->dft - cache->dfm;
+ cache->n_coeffs = X->size2 + 1; /* Adjust this later to allow for regression
+ through the origin.
+ */
if (cache->method == PSPP_LINREG_SWEEP)
{
gsl_matrix *sw;
standard deviations of the independent variables here since doing
so would cause a miscalculation of the residual sums of
squares. Dividing by the standard deviation is done GSL's linear
- regression functions, so if the design matrix has a very poor
+ regression functions, so if the design matrix has a poor
condition, use QR decomposition.
- *
+
The design matrix here does not include a column for the intercept
(i.e., a column of 1's). If using PSPP_LINREG_QR, we need that column,
so design is allocated here when sweeping, or below if using QR.
for (i = 0; i < cache->n_indeps; i++)
{
tmp = gsl_matrix_get (sw, i, cache->n_indeps);
+ cache->coeff[i + 1].estimate = tmp;
gsl_vector_set (cache->param_estimates, i + 1, tmp);
m -= tmp * gsl_vector_get (cache->indep_means, i);
}
else
{
/*
- Use QR decomposition via GSL. This section has not been tested.
+ Use QR decomposition via GSL.
*/
design = gsl_matrix_alloc (X->size1, 1 + X->size2);