#include <float.h>
#include <stdbool.h>
+#include <gsl/gsl_math.h>
#include <gsl/gsl_cdf.h>
#include <gsl/gsl_matrix.h>
#include <gl/intprops.h>
-#define REG_LARGE_DATA 1000
-
#define STATS_R 1
#define STATS_COEFF 2
#define STATS_ANOVA 4
return n_indep_vars;
}
-
static double
fill_covariance (gsl_matrix * cov, struct covariance *all_cov,
const struct variable **vars,
/*
STATISTICS subcommand output functions.
*/
-static void reg_stats_r (const linreg *, const struct variable *);
-static void reg_stats_coeff (const linreg *, const gsl_matrix *, const struct variable *, const struct regression *);
-static void reg_stats_anova (const linreg *, const struct variable *);
-static void reg_stats_bcov (const linreg *, const struct variable *);
+static void reg_stats_r (const struct linreg *, const struct variable *);
+static void reg_stats_coeff (const struct linreg *, const gsl_matrix *, const struct variable *, const struct regression *);
+static void reg_stats_anova (const struct linreg *, const struct variable *);
+static void reg_stats_bcov (const struct linreg *, const struct variable *);
static void
-subcommand_statistics (const struct regression *cmd, const linreg * c, const gsl_matrix * cm,
+subcommand_statistics (const struct regression *cmd, const struct linreg * c, const gsl_matrix * cm,
const struct variable *var)
{
if (cmd->stats & STATS_R)
struct casereader *input)
{
size_t i;
- linreg **models;
+ struct linreg **models;
int k;
struct ccase *c;
double n_data = fill_covariance (this_cm, cov, vars, n_indep,
dep_var, all_vars, n_all_vars, means);
models[k] = linreg_alloc (dep_var, vars, n_data, n_indep, cmd->origin);
- models[k]->depvar = dep_var;
for (i = 0; i < n_indep; i++)
{
linreg_set_indep_variable_mean (models[k], i, means[i]);
}
linreg_set_depvar_mean (models[k], means[i]);
- /*
- For large data sets, use QR decomposition.
- */
- if (n_data > sqrt (n_indep) && n_data > REG_LARGE_DATA)
- {
- models[k]->method = LINREG_QR;
- }
-
if (n_data > 0)
{
/*
if (cmd->resid)
{
- double obs = case_data (c, models[k]->depvar)->f;
+ double obs = case_data (c, linreg_dep_var (models[k]))->f;
double res = linreg_residual (models[k], obs, vals, n_indep);
case_data_rw_idx (outc, k * ws->extras + ws->res_idx)->f = res;
}
static void
-reg_stats_r (const linreg * c, const struct variable *var)
+reg_stats_r (const struct linreg * c, const struct variable *var)
{
struct tab_table *t;
int n_rows = 2;
Table showing estimated regression coefficients.
*/
static void
-reg_stats_coeff (const linreg * c, const gsl_matrix *cov, const struct variable *var, const struct regression *cmd)
+reg_stats_coeff (const struct linreg * c, const gsl_matrix *cov, const struct variable *var, const struct regression *cmd)
{
size_t j;
int n_cols = 7;
Display the ANOVA table.
*/
static void
-reg_stats_anova (const linreg * c, const struct variable *var)
+reg_stats_anova (const struct linreg * c, const struct variable *var)
{
int n_cols = 7;
int n_rows = 4;
const double msm = linreg_ssreg (c) / linreg_dfmodel (c);
const double mse = linreg_mse (c);
const double F = msm / mse;
- const double pval = gsl_cdf_fdist_Q (F, c->dfm, c->dfe);
+ const double pval = gsl_cdf_fdist_Q (F, linreg_dfmodel (c),
+ linreg_dferror (c));
struct tab_table *t;
/* Degrees of freedom */
- tab_text_format (t, 3, 1, TAB_RIGHT, "%.*g", DBL_DIG + 1, c->dfm);
- tab_text_format (t, 3, 2, TAB_RIGHT, "%.*g", DBL_DIG + 1, c->dfe);
- tab_text_format (t, 3, 3, TAB_RIGHT, "%.*g", DBL_DIG + 1, c->dft);
+ tab_text_format (t, 3, 1, TAB_RIGHT, "%.*g", DBL_DIG + 1, linreg_dfmodel (c));
+ tab_text_format (t, 3, 2, TAB_RIGHT, "%.*g", DBL_DIG + 1, linreg_dferror (c));
+ tab_text_format (t, 3, 3, TAB_RIGHT, "%.*g", DBL_DIG + 1, linreg_dftotal (c));
/* Mean Squares */
tab_double (t, 4, 1, TAB_RIGHT, msm, NULL, RC_OTHER);
static void
-reg_stats_bcov (const linreg * c, const struct variable *var)
+reg_stats_bcov (const struct linreg * c, const struct variable *var)
{
int n_cols;
int n_rows;
struct tab_table *t;
assert (c != NULL);
- n_cols = c->n_indeps + 1 + 2;
- n_rows = 2 * (c->n_indeps + 1);
+ n_cols = linreg_n_indeps (c) + 1 + 2;
+ n_rows = 2 * (linreg_n_indeps (c) + 1);
t = tab_create (n_cols, n_rows);
tab_headers (t, 2, 0, 1, 0);
tab_box (t, TAL_2, TAL_2, -1, TAL_1, 0, 0, n_cols - 1, n_rows - 1);
col = (i <= k) ? k : i;
row = (i <= k) ? i : k;
tab_double (t, k + 2, i, TAB_CENTER,
- gsl_matrix_get (c->cov, row, col), NULL, RC_OTHER);
+ gsl_matrix_get (linreg_cov (c), row, col), NULL, RC_OTHER);
}
}
tab_title (t, _("Coefficient Correlations (%s)"), var_to_string (var));
Springer. 1998. ISBN 0-387-98542-5.
*/
+struct linreg
+{
+ double n_obs; /* Number of observations. */
+ int n_indeps; /* Number of independent variables. */
+ int n_coeffs; /* The intercept is not considered a
+ coefficient here. */
+
+ /*
+ Pointers to the variables.
+ */
+ const struct variable *depvar;
+ const struct variable **indep_vars;
+
+ double *coeff;
+ double intercept;
+ /*
+ Means and standard deviations of the variables.
+ If these pointers are null when pspp_linreg() is
+ called, pspp_linreg() will compute their values.
+
+ Entry i of indep_means is the mean of independent
+ variable i, whose observations are stored in the ith
+ column of the design matrix.
+ */
+ double depvar_mean;
+ gsl_vector *indep_means;
+ gsl_vector *indep_std;
+
+ /*
+ Sums of squares.
+ */
+ double ssm; /* Sums of squares for the overall model. */
+ double sst; /* Sum of squares total. */
+ double sse; /* Sum of squares error. */
+ double mse; /* Mean squared error. This is just sse /
+ dfe, but since it is the best unbiased
+ estimate of the population variance, it
+ has its own entry here. */
+ /*
+ Covariance matrix of the parameter estimates.
+ */
+ gsl_matrix *cov;
+ /*
+ Degrees of freedom.
+ */
+ double dft;
+ double dfe;
+ double dfm;
+
+ int dependent_column; /* Column containing the dependent variable. Defaults to last column. */
+ int refcnt;
+
+ bool origin;
+};
const struct variable **
-linreg_get_vars (const linreg *c)
+linreg_get_vars (const struct linreg *c)
{
return c->indep_vars;
}
Allocate a linreg and return a pointer to it. n is the number of
cases, p is the number of independent variables.
*/
-linreg *
+struct linreg *
linreg_alloc (const struct variable *depvar, const struct variable **indep_vars,
double n, size_t p, bool origin)
{
- linreg *c;
+ struct linreg *c;
size_t i;
c = xmalloc (sizeof (*c));
/*
Default settings.
*/
- c->method = LINREG_SWEEP;
c->refcnt = 1;
void
-linreg_ref (linreg *c)
+linreg_ref (struct linreg *c)
{
c->refcnt++;
}
void
-linreg_unref (linreg *c)
+linreg_unref (struct linreg *c)
{
if (--c->refcnt == 0)
{
}
static void
-post_sweep_computations (linreg *l, gsl_matrix *sw)
+post_sweep_computations (struct linreg *l, gsl_matrix *sw)
{
double m;
size_t i;
order of the coefficients in the linreg struct.
*/
double
-linreg_predict (const linreg *c, const double *vals, size_t n_vals)
+linreg_predict (const struct linreg *c, const double *vals, size_t n_vals)
{
size_t j;
double result;
}
double
-linreg_residual (const linreg *c, double obs, const double *vals, size_t n_vals)
+linreg_residual (const struct linreg *c, double obs, const double *vals, size_t n_vals)
{
if (vals == NULL || c == NULL)
{
/*
Mean of the independent variable.
*/
-double linreg_get_indep_variable_mean (const linreg *c, size_t j)
+double linreg_get_indep_variable_mean (const struct linreg *c, size_t j)
{
assert (c != NULL);
return gsl_vector_get (c->indep_means, j);
}
-void linreg_set_indep_variable_mean (linreg *c, size_t j, double m)
+void linreg_set_indep_variable_mean (struct linreg *c, size_t j, double m)
{
assert (c != NULL);
gsl_vector_set (c->indep_means, j, m);
}
static void
-linreg_fit_qr (const gsl_matrix *cov, linreg *l)
+linreg_fit_qr (const gsl_matrix *cov, struct linreg *l)
{
double intcpt_coef = 0.0;
double intercept_variance = 0.0;
gsl_vector_free (params);
}
+#define REG_LARGE_DATA 1000
+
/*
Estimate the model parameters from the covariance matrix. This
function assumes the covariance entries corresponding to the
matrix.
*/
void
-linreg_fit (const gsl_matrix *cov, linreg *l)
+linreg_fit (const gsl_matrix *cov, struct linreg *l)
{
assert (l != NULL);
assert (cov != NULL);
l->sst = gsl_matrix_get (cov, cov->size1 - 1, cov->size2 - 1);
- if (l->method == LINREG_SWEEP)
+
+ if ((l->n_obs * l->n_obs > l->n_indeps) && ( l->n_obs > REG_LARGE_DATA))
+ {
+ /*
+ For large data sets, use QR decomposition.
+ */
+ linreg_fit_qr (cov, l);
+ }
+ else
{
- gsl_matrix *params;
- params = gsl_matrix_calloc (cov->size1, cov->size2);
+ gsl_matrix *params = gsl_matrix_calloc (cov->size1, cov->size2);
gsl_matrix_memcpy (params, cov);
reg_sweep (params, l->dependent_column);
post_sweep_computations (l, params);
gsl_matrix_free (params);
}
- else if (l->method == LINREG_QR)
- {
- linreg_fit_qr (cov, l);
- }
}
-double linreg_mse (const linreg *c)
+double linreg_mse (const struct linreg *c)
{
assert (c != NULL);
return (c->sse / c->dfe);
}
-double linreg_intercept (const linreg *c)
+double linreg_intercept (const struct linreg *c)
{
return c->intercept;
}
const gsl_matrix *
-linreg_cov (const linreg *c)
+linreg_cov (const struct linreg *c)
{
return c->cov;
}
double
-linreg_coeff (const linreg *c, size_t i)
+linreg_coeff (const struct linreg *c, size_t i)
{
return (c->coeff[i]);
}
const struct variable *
-linreg_indep_var (const linreg *c, size_t i)
+linreg_indep_var (const struct linreg *c, size_t i)
{
return (c->indep_vars[i]);
}
+int
+linreg_n_indeps (const struct linreg *c)
+{
+ return c->n_indeps;
+}
+
+
+const struct variable *
+linreg_dep_var (const struct linreg *c)
+{
+ return c->depvar;
+}
+
+
size_t
-linreg_n_coeffs (const linreg *c)
+linreg_n_coeffs (const struct linreg *c)
{
return c->n_coeffs;
}
double
-linreg_n_obs (const linreg *c)
+linreg_n_obs (const struct linreg *c)
{
return c->n_obs;
}
double
-linreg_sse (const linreg *c)
+linreg_sse (const struct linreg *c)
{
return c->sse;
}
double
-linreg_ssreg (const linreg *c)
+linreg_ssreg (const struct linreg *c)
{
return (c->sst - c->sse);
}
-double linreg_sst (const linreg *c)
+double linreg_sst (const struct linreg *c)
{
return c->sst;
}
double
-linreg_dfmodel ( const linreg *c)
+linreg_dfmodel ( const struct linreg *c)
{
return c->dfm;
}
+double
+linreg_dferror ( const struct linreg *c)
+{
+ return c->dfe;
+}
+
+double
+linreg_dftotal ( const struct linreg *c)
+{
+ return c->dft;
+}
+
void
-linreg_set_depvar_mean (linreg *c, double x)
+linreg_set_depvar_mean (struct linreg *c, double x)
{
c->depvar_mean = x;
}
double
-linreg_get_depvar_mean (const linreg *c)
+linreg_get_depvar_mean (const struct linreg *c)
{
return c->depvar_mean;
}
#ifndef LINREG_H
#define LINREG_H
-#include <gsl/gsl_math.h>
#include <gsl/gsl_matrix.h>
-#include <gsl/gsl_vector.h>
#include <stdbool.h>
-enum
-{
- LINREG_CONDITIONAL_INVERSE,
- LINREG_QR,
- LINREG_SWEEP,
-};
-
-
-
-/*
- Options describing what special values should be computed.
- */
-struct pspp_linreg_opts_struct
-{
- int get_depvar_mean_std;
- int *get_indep_mean_std; /* Array of booleans
- dictating which
- independent variables need
- their means and standard
- deviations computed within
- pspp_linreg. This array
- MUST be of length
- n_indeps. If element i is
- 1, pspp_linreg will
- compute the mean and
- variance of indpendent
- variable i. If element i
- is 0, it will not compute
- the mean and standard
- deviation, and assume the
- values are stored.
- cache->indep_mean[i] is
- the mean and
- cache->indep_std[i] is the
- sample standard deviation. */
-};
-typedef struct pspp_linreg_opts_struct pspp_linreg_opts;
-
-
/*
Find the least-squares estimate of b for the linear model:
Springer. 1998. ISBN 0-387-98542-5.
*/
+struct variable;
+
+struct linreg *linreg_alloc (const struct variable *, const struct variable **,
+ double, size_t, bool);
+
+void linreg_unref (struct linreg *);
+void linreg_ref (struct linreg *);
-struct linreg_struct
-{
- double n_obs; /* Number of observations. */
- int n_indeps; /* Number of independent variables. */
- int n_coeffs; /* The intercept is not considered a
- coefficient here. */
-
- /*
- Pointers to the variables.
- */
- const struct variable *depvar;
- const struct variable **indep_vars;
-
- double *coeff;
- double intercept;
- int method; /* Method to use to estimate parameters. */
- /*
- Means and standard deviations of the variables.
- If these pointers are null when pspp_linreg() is
- called, pspp_linreg() will compute their values.
-
- Entry i of indep_means is the mean of independent
- variable i, whose observations are stored in the ith
- column of the design matrix.
- */
- double depvar_mean;
- gsl_vector *indep_means;
- gsl_vector *indep_std;
-
- /*
- Sums of squares.
- */
- double ssm; /* Sums of squares for the overall model. */
- double sst; /* Sum of squares total. */
- double sse; /* Sum of squares error. */
- double mse; /* Mean squared error. This is just sse /
- dfe, but since it is the best unbiased
- estimate of the population variance, it
- has its own entry here. */
- /*
- Covariance matrix of the parameter estimates.
- */
- gsl_matrix *cov;
- /*
- Degrees of freedom.
- */
- double dft;
- double dfe;
- double dfm;
-
- int dependent_column; /* Column containing the dependent variable. Defaults to last column. */
- int refcnt;
-
- bool origin;
-};
-
-typedef struct linreg_struct linreg;
-
-
-
-linreg *linreg_alloc (const struct variable *, const struct variable **,
- double, size_t, bool);
-
-void linreg_unref (linreg *);
-void linreg_ref (linreg *);
+int linreg_n_indeps (const struct linreg *c);
/*
- 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.
- */
-void linreg_fit (const gsl_matrix *, linreg *);
+ Fit the linear model via least squares.
+*/
+void linreg_fit (const gsl_matrix *, struct linreg *);
-double linreg_predict (const linreg *, const double *, size_t);
-double linreg_residual (const linreg *, double, const double *, size_t);
-const struct variable ** linreg_get_vars (const linreg *);
+double linreg_predict (const struct linreg *, const double *, size_t);
+double linreg_residual (const struct linreg *, double, const double *, size_t);
+const struct variable ** linreg_get_vars (const struct linreg *);
/*
Mean of the independent variable.
*/
-double linreg_get_indep_variable_mean (const linreg *, size_t);
-void linreg_set_indep_variable_mean (linreg *, size_t, double);
-
-double linreg_mse (const linreg *);
-
-double linreg_intercept (const linreg *);
-
-const gsl_matrix * linreg_cov (const linreg *);
-double linreg_coeff (const linreg *, size_t);
-const struct variable * linreg_indep_var (const linreg *, size_t);
-size_t linreg_n_coeffs (const linreg *);
-double linreg_n_obs (const linreg *);
-double linreg_sse (const linreg *);
-double linreg_ssreg (const linreg *);
-double linreg_dfmodel (const linreg *);
-double linreg_sst (const linreg *);
-void linreg_set_depvar_mean (linreg *, double);
-double linreg_get_depvar_mean (const linreg *);
+double linreg_get_indep_variable_mean (const struct linreg *, size_t);
+void linreg_set_indep_variable_mean (struct linreg *, size_t, double);
+
+double linreg_mse (const struct linreg *);
+
+double linreg_intercept (const struct linreg *);
+
+const gsl_matrix * linreg_cov (const struct linreg *);
+double linreg_coeff (const struct linreg *, size_t);
+const struct variable * linreg_indep_var (const struct linreg *, size_t);
+const struct variable * linreg_dep_var (const struct linreg *);
+size_t linreg_n_coeffs (const struct linreg *);
+double linreg_n_obs (const struct linreg *);
+double linreg_sse (const struct linreg *);
+double linreg_ssreg (const struct linreg *);
+double linreg_dfmodel (const struct linreg *);
+double linreg_dferror (const struct linreg *);
+double linreg_dftotal (const struct linreg *);
+double linreg_sst (const struct linreg *);
+void linreg_set_depvar_mean (struct linreg *, double);
+double linreg_get_depvar_mean (const struct linreg *);
+
#endif
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