-/* lib/linreg/linreg.c
-
- Copyright (C) 2005 Free Software Foundation, Inc.
- Written by Jason H. Stover.
-
- 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
- the Free Software Foundation; either version 2 of the License, or (at
- your option) any later version.
-
- This program is distributed in the hope that it will be useful, but
- WITHOUT ANY WARRANTY; without even the implied warranty of
- MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
- General Public License for more details.
-
- You should have received a copy of the GNU General Public License
- along with this program; if not, write to the Free Software
- Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA
- 02111-1307, USA.
-*/
+/*
+ lib/linreg/linreg.c
+
+ Copyright (C) 2005 Free Software Foundation, Inc. Written by Jason H. Stover.
+
+ 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 the Free
+ Software Foundation; either version 2 of the License, or (at your option)
+ any later version.
+
+ This program is distributed in the hope that it will be useful, but WITHOUT
+ ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
+ FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for
+ more details.
+
+ You should have received a copy of the GNU General Public License along with
+ this program; if not, write to the Free Software Foundation, Inc., 51
+ Franklin Street, Fifth Floor, Boston, MA 02111-1307, USA.
+ */
#include <gsl/gsl_fit.h>
#include <gsl/gsl_multifit.h>
Y = Xb + Z
- where Y is an n-by-1 column vector, X is an n-by-p matrix of
+ where Y is an n-by-1 column vector, X is an n-by-p matrix of
independent variables, b is a p-by-1 vector of regression coefficients,
and Z is an n-by-1 normally-distributed random vector with independent
identically distributed components with mean 0.
/*
Allocate a pspp_linreg_cache and return a pointer
- to it. n is the number of cases, p is the number of
+ to it. n is the number of cases, p is the number of
independent variables.
*/
pspp_linreg_cache *
pspp_linreg_cache *c;
c = (pspp_linreg_cache *) malloc (sizeof (pspp_linreg_cache));
+ c->depvar = NULL;
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.
- */
- c->ss_indeps = gsl_vector_alloc (p); /* Sums of squares for the model
- parameters.
- */
+ 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;
Default settings.
*/
c->method = PSPP_LINREG_SWEEP;
+ c->predict = pspp_linreg_predict;
+ c->residual = pspp_linreg_residual; /* The procedure to comput my residuals. */
+ c->resid = NULL; /* The variable storing my residuals. */
return c;
}
-void
-pspp_linreg_cache_free (pspp_linreg_cache * c)
+bool
+pspp_linreg_cache_free (void * m)
{
+ 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);
free (c);
+ return true;
}
/*
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
- values as indicated by opts.
+ values as indicated by opts.
*/
int
pspp_linreg (const gsl_vector * Y, const gsl_matrix * X,
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.
- */
+ 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;
}
/*
Get the covariance matrix of the parameter estimates.
- Only the upper triangle is necessary.
+ Only the upper triangle is necessary.
*/
/*
}
else
{
+ gsl_multifit_linear_workspace *wk;
/*
Use QR decomposition via GSL.
*/
gsl_matrix_set (design, j, i + 1, tmp);
}
}
- gsl_multifit_linear_workspace *wk =
- gsl_multifit_linear_alloc (design->size1, design->size2);
+
+ wk = gsl_multifit_linear_alloc (design->size1, design->size2);
rc = gsl_multifit_linear (design, Y, param_estimates,
cache->cov, &(cache->sse), wk);
for (i = 0; i < cache->n_coeffs; i++)