-/*
- src/math/ts/innovations.c
-
- Copyright (C) 2006 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.
- */
+/* PSPP - a program for statistical analysis.
+ Copyright (C) 2006 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
+ the Free Software Foundation, either version 3 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, see <http://www.gnu.org/licenses/>. */
+
/*
Find preliminary ARMA coefficients via the innovations algorithm.
Also compute the sample mean and covariance matrix for each series.
0-387-97429-6. Sections 5.2, 8.3 and 8.4.
*/
+#include <config.h>
#include <gsl/gsl_matrix.h>
#include <gsl/gsl_vector.h>
-#include <gsl/gsl_math.h>
#include <stdlib.h>
-#include <libpspp/alloc.h>
#include <libpspp/compiler.h>
#include <math/coefficient.h>
#include <math/ts/innovations.h>
+#include "xalloc.h"
+
static void
get_mean (const gsl_matrix *data,
struct innovations_estimate **est)
-
+
{
size_t n;
size_t i;
for (n = 0; n < data->size2; n++)
{
tmp = gsl_matrix_get (data, i, n);
- if (!gsl_isnan (tmp))
+ if (!isnan (tmp))
{
est[n]->n_obs += 1.0;
d = (tmp - est[n]->mean) / est[n]->n_obs;
}
}
}
-static void
+static void
update_cov (struct innovations_estimate **est, gsl_vector_const_view x,
gsl_vector_const_view y, size_t lag)
{
{
xj = gsl_vector_get (&x.vector, j);
yj = gsl_vector_get (&y.vector, j);
- if (!gsl_isnan (xj))
+ if (!isnan (xj))
{
- if (!gsl_isnan (yj))
+ if (!isnan (yj))
{
xj -= est[j]->mean;
yj -= est[j]->mean;
}
}
static int
-get_covariance (const gsl_matrix *data,
+get_covariance (const gsl_matrix *data,
struct innovations_estimate **est, size_t max_lag)
{
size_t lag;
{
for (lag = 0; lag <= max_lag && lag < data->size1 - i; lag++)
{
- update_cov (est, gsl_matrix_const_row (data, i),
+ update_cov (est, gsl_matrix_const_row (data, i),
gsl_matrix_const_row (data, i + lag), lag);
}
}
for (j = 1; j <= i; j++)
{
k = i - j;
- theta[i][k] = (est->cov[k+1] -
+ theta[i][k] = (est->cov[k+1] -
innovations_convolve (theta[i] + k + 1, theta[j - 1], est, j))
/ est->scale[j];
}
innovations_update_scale (est, theta[i], i + 1);
- }
+ }
}
static void
get_coef (const gsl_matrix *data,
Let X[m], X[m-1],... denote the original series.
Let X_hat[0] denote the best predicted value of X[0],
X_hat[1] denote the projection of X[1] onto the subspace
- spanned by {X[0] - X_hat[0]}. Let X_hat[m] denote the
+ spanned by {X[0] - X_hat[0]}. Let X_hat[m] denote the
projection of X[m] onto the subspace spanned by {X[m-1] - X_hat[m-1],
X[m-2] - X_hat[m-2],...,X[0] - X_hat[0]}.
+ est->coeff[m-1] * (X[m-2] - X_hat[m-2])
...
+ est->coeff[m-max_lag] * (X[m - max_lag] - X_hat[m - max_lag])
-
- (That is what X_hat[m] SHOULD be, anyway. These routines need
- to be tested.)
*/
pspp_coeff_set_estimate (est[n]->coeff[i], theta[max_lag - 1][i]);
}
}
static void
-innovations_struct_init (struct innovations_estimate *est,
- const struct design_matrix *dm,
+innovations_struct_init (struct innovations_estimate *est,
+ const struct design_matrix *dm,
size_t lag)
{
size_t j;
}
est->max_lag = (double) lag;
}
-
-struct innovations_estimate **
+/*
+ The mean is subtracted from the original data before computing the
+ coefficients. The mean is NOT added back, so if you want to predict
+ a new value, you must add the mean to X_hat[m] to get the correct
+ value.
+ */
+static void
+subtract_mean (gsl_matrix *m, struct innovations_estimate **est)
+{
+ size_t i;
+ size_t j;
+ double tmp;
+
+ for (i = 0; i < m->size1; i++)
+ {
+ for (j = 0; j < m->size2; j++)
+ {
+ tmp = gsl_matrix_get (m, i, j) - est[j]->mean;
+ gsl_matrix_set (m, i, j, tmp);
+ }
+ }
+}
+struct innovations_estimate **
pspp_innovations (const struct design_matrix *dm, size_t lag)
{
struct innovations_estimate **est;
}
get_mean (dm->m, est);
+ subtract_mean (dm->m, est);
get_covariance (dm->m, est, lag);
get_coef (dm->m, est, lag);
-
+
return est;
}
-static void
+static void
pspp_innovations_free_one (struct innovations_estimate *est)
{
size_t i;