*/
#include <math/linreg/linreg.h>
-#include <math/linreg/coefficient.h>
+#include <math/coefficient.h>
#include <gsl/gsl_errno.h>
#include <linreg/sweep.h>
/*
return GSL_SUCCESS;
}
+
/*
Set V to contain an array of pointers to the variables
used in the model. V must be at least C->N_COEFFS in length.
int result = 0;
/*
- Make sure the caller doesn't try to sneak a variable
- into V that is not in the model.
+ Make sure the caller doesn't try to sneak a variable
+ into V that is not in the model.
*/
for (i = 0; i < c->n_coeffs; i++)
{
v[i] = NULL;
}
/*
- Start at c->coeff + 1 to avoid the intercept.
+ Start at c->coeff[1] to avoid the intercept.
*/
- v[result] = (struct variable *) pspp_linreg_coeff_get_var (c->coeff + 1, 0);
+ v[result] = (struct variable *) pspp_linreg_coeff_get_var (c->coeff[1], 0);
result = (v[result] == NULL) ? 0 : 1;
- for (coef = c->coeff + 2; coef < c->coeff + c->n_coeffs; coef++)
+ for (coef = c->coeff[2]; coef < c->coeff[c->n_coeffs]; coef++)
{
tmp = pspp_linreg_coeff_get_var (coef, 0);
assert (tmp != NULL);
/* Repeated variables are likely to bunch together, at the end
- of the array. */
+ of the array. */
i = result - 1;
while (i >= 0 && (v[i]->index != tmp->index))
{
c->indep_std = gsl_vector_alloc (p);
c->ssx = gsl_vector_alloc (p); /* Sums of squares for the
independent variables.
- */
+ */
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;
*/
c->method = PSPP_LINREG_SWEEP;
c->predict = pspp_linreg_predict;
- c->residual = pspp_linreg_residual; /* The procedure to compute my
- residuals. */
- c->get_vars = pspp_linreg_get_vars; /* The procedure that returns
- pointers to model
- variables. */
- c->resid = NULL; /* The variable storing my residuals. */
- c->pred = NULL; /* The variable storing my predicted values. */
+ c->residual = pspp_linreg_residual; /* The procedure to compute my
+ residuals. */
+ c->get_vars = pspp_linreg_get_vars; /* The procedure that returns
+ pointers to model
+ variables. */
+ c->resid = NULL; /* The variable storing my residuals. */
+ c->pred = NULL; /* The variable storing my predicted values. */
return c;
}
bool
-pspp_linreg_cache_free (void * m)
+pspp_linreg_cache_free (void *m)
{
+ int i;
+
pspp_linreg_cache *c = m;
gsl_vector_free (c->indep_means);
gsl_vector_free (c->indep_std);
gsl_vector_free (c->ss_indeps);
gsl_matrix_free (c->cov);
- pspp_linreg_coeff_free (c->coeff);
+ for (i = 0; i < c->n_coeffs; i++)
+ {
+ pspp_linreg_coeff_free (c->coeff[i]);
+ }
free (c);
return true;
}
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;
for (i = 0; i < cache->n_indeps; i++)
{
tmp = gsl_matrix_get (sw, i, cache->n_indeps);
- cache->coeff[i + 1].estimate = tmp;
+ cache->coeff[i + 1]->estimate = tmp;
m -= tmp * gsl_vector_get (cache->indep_means, i);
}
/*
}
gsl_matrix_set (cache->cov, 0, 0, tmp);
- cache->coeff[0].estimate = m;
+ cache->coeff[0]->estimate = m;
}
else
{
cache->cov, &(cache->sse), wk);
for (i = 0; i < cache->n_coeffs; i++)
{
- cache->coeff[i].estimate = gsl_vector_get (param_estimates, i);
+ cache->coeff[i]->estimate = gsl_vector_get (param_estimates, i);
}
if (rc == GSL_SUCCESS)
{