#include <math/coefficient.h>
#include <math/linreg.h>
#include <math/coefficient.h>
+#include <math/covariance-matrix.h>
#include <math/design-matrix.h>
#include <src/data/category.h>
#include <src/data/variable.h>
}
return true;
}
+static void
+cache_init (pspp_linreg_cache *cache, const struct design_matrix *dm)
+{
+ assert (cache != NULL);
+ cache->dft = cache->n_obs - 1;
+ cache->dfm = cache->n_indeps;
+ cache->dfe = cache->dft - cache->dfm;
+ cache->n_coeffs = dm->m->size2;
+ cache->intercept = 0.0;
+}
+
+static void
+post_sweep_computations (pspp_linreg_cache *cache, const struct design_matrix *dm,
+ gsl_matrix *sw)
+{
+ gsl_matrix *xm;
+ gsl_matrix_view xtx;
+ gsl_matrix_view xmxtx;
+ double m;
+ double tmp;
+ size_t i;
+ size_t j;
+ int rc;
+
+ assert (sw != NULL);
+ assert (cache != NULL);
+ cache->sse = gsl_matrix_get (sw, cache->n_indeps, cache->n_indeps);
+ cache->mse = cache->sse / cache->dfe;
+ /*
+ Get the intercept.
+ */
+ m = cache->depvar_mean;
+ for (i = 0; i < cache->n_indeps; i++)
+ {
+ tmp = gsl_matrix_get (sw, i, cache->n_indeps);
+ cache->coeff[i]->estimate = tmp;
+ m -= tmp * pspp_linreg_get_indep_variable_mean (cache, design_matrix_col_to_var (dm, i));
+ }
+ /*
+ Get the covariance matrix of the parameter estimates.
+ Only the upper triangle is necessary.
+ */
+
+ /*
+ The loops below do not compute the entries related
+ to the estimated intercept.
+ */
+ for (i = 0; i < cache->n_indeps; i++)
+ for (j = i; j < cache->n_indeps; j++)
+ {
+ tmp = -1.0 * cache->mse * gsl_matrix_get (sw, i, j);
+ gsl_matrix_set (cache->cov, i + 1, j + 1, tmp);
+ }
+ /*
+ Get the covariances related to the intercept.
+ */
+ xtx = gsl_matrix_submatrix (sw, 0, 0, cache->n_indeps, cache->n_indeps);
+ xmxtx = gsl_matrix_submatrix (cache->cov, 0, 1, 1, cache->n_indeps);
+ xm = gsl_matrix_calloc (1, cache->n_indeps);
+ for (i = 0; i < xm->size2; i++)
+ {
+ gsl_matrix_set (xm, 0, i,
+ pspp_linreg_get_indep_variable_mean (cache, design_matrix_col_to_var (dm, i)));
+ }
+ rc = gsl_blas_dsymm (CblasRight, CblasUpper, cache->mse,
+ &xtx.matrix, xm, 0.0, &xmxtx.matrix);
+ gsl_matrix_free (xm);
+ if (rc == GSL_SUCCESS)
+ {
+ tmp = cache->mse / cache->n_obs;
+ for (i = 1; i < 1 + cache->n_indeps; i++)
+ {
+ tmp -= gsl_matrix_get (cache->cov, 0, i)
+ * pspp_linreg_get_indep_variable_mean (cache, design_matrix_col_to_var (dm, i - 1));
+ }
+ gsl_matrix_set (cache->cov, 0, 0, tmp);
+
+ cache->intercept = m;
+ }
+ else
+ {
+ fprintf (stderr, "%s:%d:gsl_blas_dsymm: %s\n",
+ __FILE__, __LINE__, gsl_strerror (rc));
+ exit (rc);
+ }
+}
+
/*
Fit the linear model via least squares. All pointers passed to pspp_linreg
are assumed to be allocated to the correct size and initialized to the
int rc;
gsl_matrix *design = NULL;
gsl_matrix_view xtx;
- gsl_matrix *xm;
- gsl_matrix_view xmxtx;
gsl_vector_view xty;
gsl_vector_view xi;
gsl_vector_view xj;
cache->depvar_std = s;
cache->sst = ss;
}
-
- cache->dft = cache->n_obs - 1;
- cache->dfm = cache->n_indeps;
- cache->dfe = cache->dft - cache->dfm;
- cache->n_coeffs = dm->m->size2;
- cache->intercept = 0.0;
-
+ cache_init (cache, dm);
for (i = 0; i < dm->m->size2; i++)
{
if (opts->get_indep_mean_std[i])
Sweep on the matrix sw, which contains XtX, XtY and YtY.
*/
reg_sweep (sw);
- cache->sse = gsl_matrix_get (sw, cache->n_indeps, cache->n_indeps);
- cache->mse = cache->sse / cache->dfe;
- /*
- Get the intercept.
- */
- m = cache->depvar_mean;
- for (i = 0; i < cache->n_indeps; i++)
- {
- tmp = gsl_matrix_get (sw, i, cache->n_indeps);
- cache->coeff[i]->estimate = tmp;
- m -= tmp * pspp_linreg_get_indep_variable_mean (cache, design_matrix_col_to_var (dm, i));
- }
- /*
- Get the covariance matrix of the parameter estimates.
- Only the upper triangle is necessary.
- */
-
- /*
- The loops below do not compute the entries related
- to the estimated intercept.
- */
- for (i = 0; i < cache->n_indeps; i++)
- for (j = i; j < cache->n_indeps; j++)
- {
- tmp = -1.0 * cache->mse * gsl_matrix_get (sw, i, j);
- gsl_matrix_set (cache->cov, i + 1, j + 1, tmp);
- }
- /*
- Get the covariances related to the intercept.
- */
- xtx = gsl_matrix_submatrix (sw, 0, 0, cache->n_indeps, cache->n_indeps);
- xmxtx = gsl_matrix_submatrix (cache->cov, 0, 1, 1, cache->n_indeps);
- xm = gsl_matrix_calloc (1, cache->n_indeps);
- for (i = 0; i < xm->size2; i++)
- {
- gsl_matrix_set (xm, 0, i,
- pspp_linreg_get_indep_variable_mean (cache, design_matrix_col_to_var (dm, i)));
- }
- rc = gsl_blas_dsymm (CblasRight, CblasUpper, cache->mse,
- &xtx.matrix, xm, 0.0, &xmxtx.matrix);
- gsl_matrix_free (xm);
- if (rc == GSL_SUCCESS)
- {
- tmp = cache->mse / cache->n_obs;
- for (i = 1; i < 1 + cache->n_indeps; i++)
- {
- tmp -= gsl_matrix_get (cache->cov, 0, i)
- * pspp_linreg_get_indep_variable_mean (cache, design_matrix_col_to_var (dm, i - 1));
- }
- gsl_matrix_set (cache->cov, 0, 0, tmp);
-
- cache->intercept = m;
- }
- else
- {
- fprintf (stderr, "%s:%d:gsl_blas_dsymm: %s\n",
- __FILE__, __LINE__, gsl_strerror (rc));
- exit (rc);
- }
+ post_sweep_computations (cache, dm, sw);
gsl_matrix_free (sw);
}
else if (cache->method == PSPP_LINREG_CONDITIONAL_INVERSE)
}
pred = pspp_linreg_predict (predictors, vals, c, n_vals);
- result = gsl_isnan (pred) ? GSL_NAN : (obs->f - pred);
+ result = isnan (pred) ? GSL_NAN : (obs->f - pred);
return result;
}
pspp_coeff_set_mean (coef, m);
}
}
+
+/*
+ Make sure the dependent variable is at the last column, and that
+ only variables in the model are in the covariance matrix.
+ */
+static struct design_matrix *
+rearrange_covariance_matrix (const struct design_matrix *cov, pspp_linreg_cache *c)
+{
+ struct variable **v;
+ struct variable **model_vars;
+ struct variable *tmp;
+ struct design_matrix *result;
+ int n_vars;
+ int found;
+ size_t *columns;
+ size_t i;
+ size_t j;
+ size_t k;
+ size_t dep_col;
+
+ assert (cov != NULL);
+ assert (c != NULL);
+ assert (cov->m->size1 > 0);
+ assert (cov->m->size2 == cov->m->size1);
+ v = xnmalloc (c->n_coeffs, sizeof (*v));
+ model_vars = xnmalloc (c->n_coeffs, sizeof (*model_vars));
+ columns = xnmalloc (cov->m->size2, sizeof (*columns));
+ n_vars = pspp_linreg_get_vars (c, (const struct variable **) v);
+ dep_col = 0;
+ k = 0;
+ for (i = 0; i < cov->m->size2; i++)
+ {
+ tmp = design_matrix_col_to_var (cov, i);
+ found = 0;
+ j = 0;
+ while (!found && j < n_vars)
+ {
+ if (tmp == v[j])
+ {
+ found = 1;
+ if (tmp == c->depvar)
+ {
+ dep_col = j;
+ }
+ else
+ {
+ columns[k] = j;
+ k++;
+ }
+ }
+ j++;
+ }
+ }
+ k++;
+ columns[k] = dep_col;
+ /*
+ K should now be equal to C->N_INDEPS + 1. If it is not, then
+ either the code above is wrong or the caller didn't send us the
+ correct values in C.
+ */
+ assert (k == c->n_indeps + 1);
+ /*
+ Put the model variables in the right order in MODEL_VARS.
+ */
+ for (i = 0; i < k; i++)
+ {
+ model_vars[i] = v[columns[i]];
+ }
+
+ result = covariance_matrix_create (k, model_vars);
+ for (i = 0; i < result->m->size1; i++)
+ {
+ for (j = 0; j < result->m->size2; j++)
+ {
+ gsl_matrix_set (result->m, i, j, gsl_matrix_get (cov->m, columns[i], columns[j]));
+ }
+ }
+ free (columns);
+ free (v);
+ return result;
+}
+/*
+ Estimate the model parameters from the covariance matrix only. This
+ method uses less memory than PSPP_LINREG, which requires the entire
+ data set to be stored in memory.
+*/
+int
+pspp_linreg_with_cov (const struct design_matrix *full_cov,
+ pspp_linreg_cache * cache)
+{
+ struct design_matrix *cov;
+
+ assert (cov != NULL);
+ assert (cache != NULL);
+
+ cov = rearrange_covariance_matrix (full_cov, cache);
+ cache_init (cache, cov);
+ reg_sweep (cov->m);
+ post_sweep_computations (cache, cov, cov->m);
+ covariance_matrix_destroy (cov);
+}
+
+double pspp_linreg_mse (const pspp_linreg_cache *c)
+{
+ assert (c != NULL);
+ return (c->sse / c->dfe);
+}
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