#include "libpspp/message.h"
#include "libpspp/misc.h"
#include "math/categoricals.h"
+#include "math/interaction.h"
+
#include "output/tab.h"
#include "gettext.h"
/* The dependent variable */
const struct variable *dep_var;
- size_t n_predictor_vars;
+ /* The predictor variables (excluding categorical ones) */
const struct variable **predictor_vars;
+ size_t n_predictor_vars;
+
+ /* The categorical predictors */
+ struct interaction **cat_predictors;
+ size_t n_cat_predictors;
/* Which classes of missing vars are to be excluded */
enum mv_class exclude;
/* The weight variable */
const struct variable *wv;
+ /* The dictionary of the dataset */
const struct dictionary *dict;
/* True iff the constant (intercept) is to be included in the model */
double cut_point;
};
+
/* The results and intermediate result of the procedure.
These are mutated as the procedure runs. Used for
temporary variables etc.
*/
struct lr_result
{
+ /* Used to indicate if a pass should flag a warning when
+ invalid (ie negative or missing) weight values are encountered */
bool warn_bad_weight;
-
/* The two values of the dependent variable. */
union value y0;
union value y1;
/* The sum of caseweights */
double cc;
+ /* The number of missing and nonmissing cases */
casenumber n_missing;
casenumber n_nonmissing;
+
+
+ gsl_matrix *hessian;
+
+ /* The categoricals and their payload. Null if the analysis has no
+ categorical predictors */
+ struct categoricals *cats;
+ struct payload cp;
};
/*
- Convert INPUT into a dichotomous scalar. For simple cases, this is a 1:1 mapping
+ Convert INPUT into a dichotomous scalar, according to how the dependent variable's
+ values are mapped.
+ For simple cases, this is a 1:1 mapping
The return value is always either 0 or 1
*/
static double
map_dependent_var (const struct lr_spec *cmd, const struct lr_result *res, const union value *input)
{
- int width = var_get_width (cmd->dep_var);
+ const int width = var_get_width (cmd->dep_var);
if (value_equal (input, &res->y0, width))
return 0;
if (value_equal (input, &res->y1, width))
return 1;
-
+
+ /* This should never happen. If it does, then y0 and/or y1 have probably not been set */
NOT_REACHED ();
return SYSMIS;
}
+static void output_categories (const struct lr_spec *cmd, const struct lr_result *res);
static void output_depvarmap (const struct lr_spec *cmd, const struct lr_result *);
static void output_variables (const struct lr_spec *cmd,
- const gsl_vector *,
+ const struct lr_result *,
const gsl_vector *);
static void output_model_summary (const struct lr_result *,
static void case_processing_summary (const struct lr_result *);
+/* Return the value of case C corresponding to the INDEX'th entry in the
+ model */
+static double
+predictor_value (const struct ccase *c,
+ const struct variable **x, size_t n_x,
+ const struct categoricals *cats,
+ size_t index)
+{
+ /* Values of the scalar predictor variables */
+ if (index < n_x)
+ return case_data (c, x[index])->f;
+
+ /* Coded values of categorical predictor variables (or interactions) */
+ if (cats && index - n_x < categoricals_df_total (cats))
+ {
+ double x = categoricals_get_dummy_code_for_case (cats, index - n_x, c);
+ return x;
+ }
+
+ /* The constant term */
+ return 1.0;
+}
+
+
/*
Return the probability estimator (that is the estimator of logit(y) )
corresponding to the coefficient estimator beta_hat for case C
*/
static double
pi_hat (const struct lr_spec *cmd,
+ struct lr_result *res,
const gsl_vector *beta_hat,
const struct variable **x, size_t n_x,
const struct ccase *c)
{
int v0;
double pi = 0;
- for (v0 = 0; v0 < n_x; ++v0)
+ size_t n_coeffs = beta_hat->size;
+
+ if (cmd->constant)
+ {
+ pi += gsl_vector_get (beta_hat, beta_hat->size - 1);
+ n_coeffs--;
+ }
+
+ for (v0 = 0; v0 < n_coeffs; ++v0)
{
pi += gsl_vector_get (beta_hat, v0) *
- case_data (c, x[v0])->f;
+ predictor_value (c, x, n_x, res->cats, v0);
}
- if (cmd->constant)
- pi += gsl_vector_get (beta_hat, beta_hat->size - 1);
-
pi = 1.0 / (1.0 + exp(-pi));
return pi;
If ALL predicted values derivatives are close to zero or one, then CONVERGED
will be set to true.
*/
-static gsl_matrix *
+static void
hessian (const struct lr_spec *cmd,
struct lr_result *res,
struct casereader *input,
const struct variable **x, size_t n_x,
const gsl_vector *beta_hat,
- bool *converged
- )
+ bool *converged)
{
struct casereader *reader;
struct ccase *c;
- gsl_matrix *output = gsl_matrix_calloc (beta_hat->size, beta_hat->size);
double max_w = -DBL_MAX;
+ gsl_matrix_set_zero (res->hessian);
+
for (reader = casereader_clone (input);
(c = casereader_read (reader)) != NULL; case_unref (c))
{
int v0, v1;
- double pi = pi_hat (cmd, beta_hat, x, n_x, c);
+ double pi = pi_hat (cmd, res, beta_hat, x, n_x, c);
double weight = dict_get_case_weight (cmd->dict, c, &res->warn_bad_weight);
double w = pi * (1 - pi);
for (v0 = 0; v0 < beta_hat->size; ++v0)
{
- double in0 = v0 < n_x ? case_data (c, x[v0])->f : 1.0;
+ double in0 = predictor_value (c, x, n_x, res->cats, v0);
for (v1 = 0; v1 < beta_hat->size; ++v1)
{
- double in1 = v1 < n_x ? case_data (c, x[v1])->f : 1.0 ;
- double *o = gsl_matrix_ptr (output, v0, v1);
+ double in1 = predictor_value (c, x, n_x, res->cats, v1);
+ double *o = gsl_matrix_ptr (res->hessian, v0, v1);
*o += in0 * w * in1;
}
}
}
casereader_destroy (reader);
-
if ( max_w < cmd->min_epsilon)
{
*converged = true;
msg (MN, _("All predicted values are either 1 or 0"));
}
-
- return output;
}
(c = casereader_read (reader)) != NULL; case_unref (c))
{
int v0;
- double pi = pi_hat (cmd, beta_hat, x, n_x, c);
+ double pi = pi_hat (cmd, res, beta_hat, x, n_x, c);
double weight = dict_get_case_weight (cmd->dict, c, &res->warn_bad_weight);
for (v0 = 0; v0 < beta_hat->size; ++v0)
{
- double in0 = v0 < n_x ? case_data (c, x[v0])->f : 1.0;
+ double in0 = predictor_value (c, x, n_x, res->cats, v0);
double *o = gsl_vector_ptr (output, v0);
*o += in0 * (y - pi) * weight;
}
return output;
}
+\f
+
+/* "payload" functions for the categoricals.
+ The only function is to accumulate the frequency of each
+ category.
+ */
+
+static void *
+frq_create (const void *aux1 UNUSED, void *aux2 UNUSED)
+{
+ return xzalloc (sizeof (double));
+}
+
+static void
+frq_update (const void *aux1 UNUSED, void *aux2 UNUSED,
+ void *ud, const struct ccase *c UNUSED , double weight)
+{
+ double *freq = ud;
+ *freq += weight;
+}
+
+static void
+frq_destroy (const void *aux1 UNUSED, void *aux2 UNUSED, void *user_data UNUSED)
+{
+ free (user_data);
+}
+
+\f
/*
- Makes an initial pass though the data, checks that the dependent variable is
- dichotomous, and calculates necessary initial values.
+ Makes an initial pass though the data, doing the following:
+
+ * Checks that the dependent variable is dichotomous,
+ * Creates and initialises the categoricals,
+ * Accumulates summary results,
+ * Calculates necessary initial values.
Returns an initial value for \hat\beta the vector of estimators of \beta
*/
if (cmd->constant)
n_coefficients++;
- b0 = gsl_vector_calloc (n_coefficients);
+ /* Create categoricals if appropriate */
+ if (cmd->n_cat_predictors > 0)
+ {
+ res->cp.create = frq_create;
+ res->cp.update = frq_update;
+ res->cp.calculate = NULL;
+ res->cp.destroy = frq_destroy;
+
+ res->cats = categoricals_create (cmd->cat_predictors, cmd->n_cat_predictors,
+ cmd->wv, cmd->exclude, MV_ANY);
+
+ categoricals_set_payload (res->cats, &res->cp, cmd, res);
+ }
res->cc = 0;
for (reader = casereader_clone (input);
}
}
+ /* Accumulate the missing and non-missing counts */
if (missing)
{
res->n_missing++;
continue;
}
-
res->n_nonmissing++;
+ /* Find the values of the dependent variable */
if (!v0set)
{
value_clone (&res->y0, depval, width);
res->cc += weight;
+
+ categoricals_update (res->cats, c);
}
casereader_destroy (reader);
+ categoricals_done (res->cats);
+
sum = sumB;
/* Ensure that Y0 is less than Y1. Otherwise the mapping gets
sum = sumA;
}
+ n_coefficients += categoricals_df_total (res->cats);
+ b0 = gsl_vector_calloc (n_coefficients);
+
if ( cmd->constant)
{
double mean = sum / res->cc;
+/* Start of the logistic regression routine proper */
static bool
run_lr (const struct lr_spec *cmd, struct casereader *input,
const struct dataset *ds UNUSED)
{
- int i,j;
+ int i;
gsl_vector *beta_hat;
- gsl_vector *se ;
bool converged = false;
+
+ /* Set the likelihoods to a negative sentinel value */
double likelihood = -1;
double prev_likelihood = -1;
double initial_likelihood = -1;
work.n_missing = 0;
work.n_nonmissing = 0;
work.warn_bad_weight = true;
+ work.cats = NULL;
/* Get the initial estimates of \beta and their standard errors */
output_depvarmap (cmd, &work);
- se = gsl_vector_alloc (beta_hat->size);
-
case_processing_summary (&work);
NULL);
+ work.hessian = gsl_matrix_calloc (beta_hat->size, beta_hat->size);
+
/* Start the Newton Raphson iteration process... */
for( i = 0 ; i < cmd->max_iter ; ++i)
{
double min, max;
- gsl_matrix *m ;
gsl_vector *v ;
- m = hessian (cmd, &work, input,
+
+ hessian (cmd, &work, input,
cmd->predictor_vars, cmd->n_predictor_vars,
beta_hat,
&converged);
- gsl_linalg_cholesky_decomp (m);
- gsl_linalg_cholesky_invert (m);
+ gsl_linalg_cholesky_decomp (work.hessian);
+ gsl_linalg_cholesky_invert (work.hessian);
v = xt_times_y_pi (cmd, &work, input,
cmd->predictor_vars, cmd->n_predictor_vars,
{
/* delta = M.v */
gsl_vector *delta = gsl_vector_alloc (v->size);
- gsl_blas_dgemv (CblasNoTrans, 1.0, m, v, 0, delta);
+ gsl_blas_dgemv (CblasNoTrans, 1.0, work.hessian, v, 0, delta);
gsl_vector_free (v);
- for (j = 0; j < se->size; ++j)
- {
- double *ptr = gsl_vector_ptr (se, j);
- *ptr = gsl_matrix_get (m, j, j);
- }
-
- gsl_matrix_free (m);
gsl_vector_add (beta_hat, delta);
casereader_destroy (input);
assert (initial_likelihood >= 0);
- for (i = 0; i < se->size; ++i)
- {
- double *ptr = gsl_vector_ptr (se, i);
- *ptr = sqrt (*ptr);
- }
+ if ( ! converged)
+ msg (MW, _("Estimation terminated at iteration number %d because maximum iterations has been reached"), i );
+
output_model_summary (&work, initial_likelihood, likelihood);
- output_variables (cmd, beta_hat, se);
+ if (work.cats)
+ output_categories (cmd, &work);
+
+ output_variables (cmd, &work, beta_hat);
+
+ gsl_matrix_free (work.hessian);
gsl_vector_free (beta_hat);
- gsl_vector_free (se);
+
+ categoricals_destroy (work.cats);
return true;
}
int
cmd_logistic (struct lexer *lexer, struct dataset *ds)
{
+ /* Temporary location for the predictor variables.
+ These may or may not include the categorical predictors */
+ const struct variable **pred_vars;
+ size_t n_pred_vars;
+
+ int v, x;
struct lr_spec lr;
lr.dict = dataset_dict (ds);
lr.n_predictor_vars = 0;
lr.constant = true;
lr.confidence = 95;
lr.print = PRINT_DEFAULT;
+ lr.cat_predictors = NULL;
+ lr.n_cat_predictors = 0;
+
if (lex_match_id (lexer, "VARIABLES"))
lex_force_match (lexer, T_WITH);
if (!parse_variables_const (lexer, lr.dict,
- &lr.predictor_vars, &lr.n_predictor_vars,
- PV_NO_DUPLICATE | PV_NUMERIC))
+ &pred_vars, &n_pred_vars,
+ PV_NO_DUPLICATE))
goto error;
{
/* This is for compatibility. It does nothing */
}
+ else if (lex_match_id (lexer, "CATEGORICAL"))
+ {
+ lex_match (lexer, T_EQUALS);
+ do
+ {
+ lr.cat_predictors = xrealloc (lr.cat_predictors,
+ sizeof (*lr.cat_predictors) * ++lr.n_cat_predictors);
+ lr.cat_predictors[lr.n_cat_predictors - 1] = 0;
+ }
+ while (parse_design_interaction (lexer, lr.dict,
+ lr.cat_predictors + lr.n_cat_predictors - 1));
+ lr.n_cat_predictors--;
+ }
else if (lex_match_id (lexer, "PRINT"))
{
lex_match (lexer, T_EQUALS);
}
}
+ /* Copy the predictor variables from the temporary location into the
+ final one, dropping any categorical variables which appear there.
+ FIXME: This is O(NxM).
+ */
+ for (v = x = 0; v < n_pred_vars; ++v)
+ {
+ bool drop = false;
+ const struct variable *var = pred_vars[v];
+ int cv = 0;
+ for (cv = 0; cv < lr.n_cat_predictors ; ++cv)
+ {
+ int iv;
+ const struct interaction *iact = lr.cat_predictors[cv];
+ for (iv = 0 ; iv < iact->n_vars ; ++iv)
+ {
+ if (var == iact->vars[iv])
+ {
+ drop = true;
+ goto dropped;
+ }
+ }
+ }
+
+ dropped:
+ if (drop)
+ continue;
+
+ lr.predictor_vars = xrealloc (lr.predictor_vars, sizeof *lr.predictor_vars * (x + 1));
+ lr.predictor_vars[x++] = var;
+ lr.n_predictor_vars++;
+ }
+ free (pred_vars);
+ /* Run logistical regression for each split group */
{
struct casegrouper *grouper;
struct casereader *group;
}
free (lr.predictor_vars);
+ free (lr.cat_predictors);
return CMD_SUCCESS;
error:
free (lr.predictor_vars);
+ free (lr.cat_predictors);
return CMD_FAILURE;
}
/* Show the Variables in the Equation box */
static void
output_variables (const struct lr_spec *cmd,
- const gsl_vector *beta,
- const gsl_vector *se)
+ const struct lr_result *res,
+ const gsl_vector *beta)
{
int row = 0;
const int heading_columns = 1;
int heading_rows = 1;
struct tab_table *t;
- int idx;
- int n_rows = cmd->n_predictor_vars;
-
int nc = 8;
int nr ;
+ int i = 0;
+ int ivar = 0;
+ int idx_correction = 0;
+
if (cmd->print & PRINT_CI)
{
nc += 2;
if (cmd->constant)
nr++;
+ if (res->cats)
+ nr += categoricals_df_total (res->cats) + cmd->n_cat_predictors;
+
t = tab_create (nc, nr);
tab_title (t, _("Variables in the Equation"));
tab_text (t, 9, row, TAB_CENTER | TAT_TITLE, _("Upper"));
}
- if (cmd->constant)
- n_rows++;
-
- for (idx = 0 ; idx < n_rows; ++idx)
+ for (row = heading_rows ; row < nr; ++row)
{
- const int r = idx + heading_rows;
+ const int idx = row - heading_rows - idx_correction;
const double b = gsl_vector_get (beta, idx);
- const double sigma = gsl_vector_get (se, idx);
- const double wald = pow2 (b / sigma);
+ const double sigma2 = gsl_matrix_get (res->hessian, idx, idx);
+ const double wald = pow2 (b) / sigma2;
const double df = 1;
if (idx < cmd->n_predictor_vars)
- tab_text (t, 1, r, TAB_LEFT | TAT_TITLE,
- var_to_string (cmd->predictor_vars[idx]));
+ {
+ tab_text (t, 1, row, TAB_LEFT | TAT_TITLE,
+ var_to_string (cmd->predictor_vars[idx]));
+ }
+ else if (i < cmd->n_cat_predictors)
+ {
+ double wald;
+ bool summary = false;
+ struct string str;
+ const struct interaction *cat_predictors = cmd->cat_predictors[i];
+ const int df = categoricals_df (res->cats, i);
+
+ ds_init_empty (&str);
+ interaction_to_string (cat_predictors, &str);
+
+ if (ivar == 0)
+ {
+ /* Calculate the Wald statistic,
+ which is \beta' C^-1 \beta .
+ where \beta is the vector of the coefficient estimates comprising this
+ categorial variable. and C is the corresponding submatrix of the
+ hessian matrix.
+ */
+ gsl_matrix_const_view mv =
+ gsl_matrix_const_submatrix (res->hessian, idx, idx, df, df);
+ gsl_matrix *subhessian = gsl_matrix_alloc (mv.matrix.size1, mv.matrix.size2);
+ gsl_vector_const_view vv = gsl_vector_const_subvector (beta, idx, df);
+ gsl_vector *temp = gsl_vector_alloc (df);
+
+ gsl_matrix_memcpy (subhessian, &mv.matrix);
+ gsl_linalg_cholesky_decomp (subhessian);
+ gsl_linalg_cholesky_invert (subhessian);
+
+ gsl_blas_dgemv (CblasTrans, 1.0, subhessian, &vv.vector, 0, temp);
+ gsl_blas_ddot (temp, &vv.vector, &wald);
+
+ tab_double (t, 4, row, 0, wald, 0);
+ tab_double (t, 5, row, 0, df, &F_8_0);
+ tab_double (t, 6, row, 0, gsl_cdf_chisq_Q (wald, df), 0);
+
+ idx_correction ++;
+ summary = true;
+ gsl_matrix_free (subhessian);
+ gsl_vector_free (temp);
+ }
+ else
+ {
+ ds_put_format (&str, "(%d)", ivar);
+ }
+
+ tab_text (t, 1, row, TAB_LEFT | TAT_TITLE, ds_cstr (&str));
+ if (ivar++ == df)
+ {
+ ++i; /* next interaction */
+ ivar = 0;
+ }
+
+ ds_destroy (&str);
+
+ if (summary)
+ continue;
+ }
+ else
+ {
+ tab_text (t, 1, row, TAB_LEFT | TAT_TITLE, _("Constant"));
+ }
- tab_double (t, 2, r, 0, b, 0);
- tab_double (t, 3, r, 0, sigma, 0);
- tab_double (t, 4, r, 0, wald, 0);
- tab_double (t, 5, r, 0, df, &F_8_0);
- tab_double (t, 6, r, 0, gsl_cdf_chisq_Q (wald, df), 0);
- tab_double (t, 7, r, 0, exp (b), 0);
+ tab_double (t, 2, row, 0, b, 0);
+ tab_double (t, 3, row, 0, sqrt (sigma2), 0);
+ tab_double (t, 4, row, 0, wald, 0);
+ tab_double (t, 5, row, 0, df, &F_8_0);
+ tab_double (t, 6, row, 0, gsl_cdf_chisq_Q (wald, df), 0);
+ tab_double (t, 7, row, 0, exp (b), 0);
if (cmd->print & PRINT_CI)
{
double wc = gsl_cdf_ugaussian_Pinv (0.5 + cmd->confidence / 200.0);
- wc *= sigma;
+ wc *= sqrt (sigma2);
if (idx < cmd->n_predictor_vars)
{
- tab_double (t, 8, r, 0, exp (b - wc), 0);
- tab_double (t, 9, r, 0, exp (b + wc), 0);
+ tab_double (t, 8, row, 0, exp (b - wc), 0);
+ tab_double (t, 9, row, 0, exp (b + wc), 0);
}
}
}
- if ( cmd->constant)
- tab_text (t, 1, nr - 1, TAB_LEFT | TAT_TITLE, _("Constant"));
-
tab_submit (t);
}
tab_submit (t);
}
+static void
+output_categories (const struct lr_spec *cmd, const struct lr_result *res)
+{
+ const struct fmt_spec *wfmt =
+ cmd->wv ? var_get_print_format (cmd->wv) : &F_8_0;
+
+ int cumulative_df;
+ int i = 0;
+ const int heading_columns = 2;
+ const int heading_rows = 2;
+ struct tab_table *t;
+
+ int nc ;
+ int nr ;
+
+ int v;
+ int r = 0;
+
+ int max_df = 0;
+ int total_cats = 0;
+ for (i = 0; i < cmd->n_cat_predictors; ++i)
+ {
+ size_t n = categoricals_n_count (res->cats, i);
+ size_t df = categoricals_df (res->cats, i);
+ if (max_df < df)
+ max_df = df;
+ total_cats += n;
+ }
+
+ nc = heading_columns + 1 + max_df;
+ nr = heading_rows + total_cats;
+
+ t = tab_create (nc, nr);
+ tab_title (t, _("Categorical Variables' Codings"));
+
+ tab_headers (t, heading_columns, 0, heading_rows, 0);
+
+ tab_box (t, TAL_2, TAL_2, -1, TAL_1, 0, 0, nc - 1, nr - 1);
+
+ tab_hline (t, TAL_2, 0, nc - 1, heading_rows);
+ tab_vline (t, TAL_2, heading_columns, 0, nr - 1);
+
+
+ tab_text (t, heading_columns, 1, TAB_CENTER | TAT_TITLE, _("Frequency"));
+
+ tab_joint_text_format (t, heading_columns + 1, 0, nc - 1, 0,
+ TAB_CENTER | TAT_TITLE, _("Parameter coding"));
+
+
+ for (i = 0; i < max_df; ++i)
+ {
+ int c = heading_columns + 1 + i;
+ tab_text_format (t, c, 1, TAB_CENTER | TAT_TITLE, _("(%d)"), i + 1);
+ }
+
+ cumulative_df = 0;
+ for (v = 0; v < cmd->n_cat_predictors; ++v)
+ {
+ int cat;
+ const struct interaction *cat_predictors = cmd->cat_predictors[v];
+ int df = categoricals_df (res->cats, v);
+ struct string str;
+ ds_init_empty (&str);
+
+ interaction_to_string (cat_predictors, &str);
+
+ tab_text (t, 0, heading_rows + r, TAB_LEFT | TAT_TITLE, ds_cstr (&str) );
+
+ ds_destroy (&str);
+
+ for (cat = 0; cat < categoricals_n_count (res->cats, v) ; ++cat)
+ {
+ struct string str;
+ const struct ccase *c = categoricals_get_case_by_category_real (res->cats, v, cat);
+ const double *freq = categoricals_get_user_data_by_category_real (res->cats, v, cat);
+
+ int x;
+ ds_init_empty (&str);
+
+ for (x = 0; x < cat_predictors->n_vars; ++x)
+ {
+ const union value *val = case_data (c, cat_predictors->vars[x]);
+ var_append_value_name (cat_predictors->vars[x], val, &str);
+
+ if (x < cat_predictors->n_vars - 1)
+ ds_put_cstr (&str, " ");
+ }
+
+ tab_text (t, 1, heading_rows + r, 0, ds_cstr (&str));
+ ds_destroy (&str);
+ tab_double (t, 2, heading_rows + r, 0, *freq, wfmt);
+
+ for (x = 0; x < df; ++x)
+ {
+ tab_double (t, heading_columns + 1 + x, heading_rows + r, 0, (cat == x), &F_8_0);
+ }
+ ++r;
+ }
+ cumulative_df += df;
+ }
+
+ tab_submit (t);
+
+}
+
AT_BANNER([LOGISTIC REGRESSION])
dnl These examples are adapted from
error: Dependent variable's values are not dichotomous.
])
-AT_CLEANUP
\ No newline at end of file
+AT_CLEANUP
+
+
+
+dnl An example to check the behaviour of LOGISTIC REGRESSION with a categorical
+dnl variable. This examṕle was inspired from that at:
+dnl http://www.ats.ucla.edu/stat/spss/dae/logit.htm
+AT_SETUP([LOGISTIC REGRESSION with categorical])
+
+AT_DATA([lr-cat.data], [dnl
+ 620 3.07 2 4
+ 800 4.00 3 9
+ 580 3.40 2 4
+ 600 3.13 2 4
+ 540 2.70 2 4
+ 660 3.31 4 4
+ 480 3.58 1 9
+ 620 4.00 1 9
+ 680 3.98 2 9
+ 580 3.40 4 4
+ 760 3.35 3 4
+ 700 3.72 2 4
+ 460 3.64 1 9
+ 540 3.28 3 4
+ 680 3.48 3 4
+ 740 3.31 1 4
+ 460 3.77 3 4
+ 740 3.54 1 4
+ 600 3.63 3 4
+ 620 3.05 2 4
+ 560 3.04 3 4
+ 520 2.70 3 4
+ 640 3.35 3 4
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+ 660 3.70 4 9
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+ 640 3.50 2 4
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+ 620 3.37 2 9
+ 620 3.63 2 4
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+ 640 4.00 3 4
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+ 620 3.21 4 4
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+ 660 3.34 3 4
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+ 360 3.27 3 4
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+ 720 3.45 4 4
+ 580 3.06 2 4
+ 580 3.02 2 4
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+ 580 3.12 3 9
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+ 560 4.00 3 4
+ 560 2.65 3 9
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+ 680 3.31 2 4
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+ 560 3.59 2 4
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+ 620 3.95 3 9
+ 740 3.86 2 9
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+ 700 3.65 2 4
+ 800 3.22 1 9
+ 560 3.59 2 9
+ 800 3.15 4 4
+ 520 3.90 3 9
+ 520 3.74 4 9
+ 480 2.55 1 4
+ 800 4.00 4 4
+ 620 3.09 4 4
+ 560 3.49 4 4
+ 500 3.17 3 4
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+ 580 3.58 1 9
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+ 480 3.45 2 4
+ 440 3.17 2 4
+ 660 3.32 1 4
+ 500 3.08 3 4
+ 660 3.94 2 4
+ 720 3.31 1 4
+ 460 3.64 3 9
+ 500 2.93 4 4
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+ 500 2.98 3 4
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+ 560 3.24 4 4
+ 560 2.42 2 4
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+ 500 3.23 4 4
+ 680 2.42 1 9
+ 580 3.46 3 4
+ 800 3.91 3 4
+ 700 2.90 4 4
+ 520 3.12 2 4
+ 300 2.92 4 4
+ 560 3.43 3 4
+ 620 3.63 3 4
+ 500 2.79 4 4
+ 360 3.14 1 4
+ 640 3.94 2 9
+ 460 3.99 3 9
+ 300 3.01 3 4
+ 520 2.73 2 4
+ 600 3.47 2 9
+ 580 3.25 1 4
+ 520 3.10 4 4
+ 620 3.43 3 4
+ 380 2.91 4 4
+ 660 3.59 3 4
+ 660 3.95 2 9
+ 540 3.33 3 4
+ 740 4.00 3 4
+ 640 3.38 3 4
+ 600 3.89 3 4
+ 720 3.88 3 4
+ 580 4.00 3 4
+ 420 2.26 4 4
+ 520 4.00 2 9
+ 800 3.70 1 9
+ 700 4.00 1 9
+ 480 3.43 2 4
+ 660 3.45 4 4
+ 520 3.25 3 4
+ 560 2.71 3 4
+ 600 3.32 2 4
+ 580 2.88 2 4
+ 660 3.88 2 9
+ 600 3.22 1 4
+ 580 4.00 1 4
+ 660 3.60 3 9
+ 500 3.35 2 4
+ 520 2.98 2 4
+ 660 3.49 2 9
+ 560 3.07 2 4
+ 500 3.13 2 9
+ 720 3.50 3 9
+ 440 3.39 2 9
+ 640 3.95 2 9
+ 380 3.61 3 4
+ 800 3.05 2 9
+ 520 3.19 3 9
+ 600 3.40 3 4
+])
+
+AT_DATA([lr-cat.sps], [dnl
+set format=F20.3.
+
+data list notable list file='lr-cat.data' /b1 b2 bcat y.
+
+logistic regression
+ y with b1 b2 bcat
+ /categorical = bcat
+ .
+])
+
+AT_CHECK([pspp -O format=csv lr-cat.sps], [0],
+ [dnl
+Table: Dependent Variable Encoding
+Original Value,Internal Value
+4.000,0
+9.000,1
+
+Table: Case Processing Summary
+Unweighted Cases,N,Percent
+Included in Analysis,400,100.000
+Missing Cases,0,.000
+Total,400,100.000
+
+note: Estimation terminated at iteration number 4 because parameter estimates changed by less than 0.001
+
+Table: Model Summary
+Step 1,-2 Log likelihood,Cox & Snell R Square,Nagelkerke R Square
+,458.517,.098,.138
+
+Table: Categorical Variables' Codings
+,,,Parameter coding,,
+,,Frequency,(1),(2),(3)
+bcat,1.000,61,1,0,0
+,2.000,151,0,1,0
+,3.000,121,0,0,1
+,4.000,67,0,0,0
+
+Table: Variables in the Equation
+,,B,S.E.,Wald,df,Sig.,Exp(B)
+Step 1,b1,.002,.001,4.284,1,.038,1.002
+,b2,.804,.332,5.872,1,.015,2.235
+,bcat,,,20.895,3,.000,
+,bcat(1),1.551,.418,13.788,1,.000,4.718
+,bcat(2),.876,.367,5.706,1,.017,2.401
+,bcat(3),.211,.393,.289,1,.591,1.235
+,Constant,-5.541,1.138,23.709,1,.000,.004
+])
+
+AT_CLEANUP
+
+
+
+dnl This example is inspired by http://www.ats.ucla.edu/stat/spss/output/logistic.htm
+AT_SETUP([LOGISTIC REGRESSION with cat var 2])
+
+AT_DATA([lr-cat2.data], [dnl
+ 60.00 1.00 8.00 50.00
+ 47.00 .00 9.00 42.00
+ 57.00 1.00 7.00 53.00
+ 60.00 .00 8.00 53.00
+ 68.00 .00 8.00 66.00
+ 63.00 .00 8.00 55.00
+ 65.00 .00 8.00 63.00
+ 52.00 .00 8.00 61.00
+ 34.00 .00 9.00 42.00
+ 37.00 .00 8.00 39.00
+ 68.00 1.00 9.00 69.00
+ 60.00 .00 9.00 61.00
+ 44.00 .00 9.00 58.00
+ 42.00 .00 8.00 47.00
+ 57.00 1.00 7.00 61.00
+ 55.00 1.00 8.00 50.00
+ 55.00 .00 9.00 58.00
+ 44.00 .00 8.00 63.00
+ 50.00 1.00 9.00 66.00
+ 44.00 .00 8.00 39.00
+ 55.00 .00 8.00 58.00
+ 44.00 .00 8.00 50.00
+ 47.00 1.00 7.00 34.00
+ 48.00 .00 8.00 44.00
+ 45.00 .00 7.00 31.00
+ 43.00 .00 8.00 50.00
+ 39.00 .00 8.00 42.00
+ 63.00 .00 9.00 50.00
+ 47.00 .00 8.00 58.00
+ 42.00 .00 7.00 50.00
+ 50.00 .00 9.00 36.00
+ 47.00 .00 7.00 33.00
+ 60.00 .00 9.00 61.00
+ 47.00 .00 7.00 42.00
+ 68.00 1.00 9.00 69.00
+ 52.00 .00 8.00 54.00
+ 63.00 1.00 9.00 61.00
+ 65.00 1.00 9.00 61.00
+ 63.00 1.00 9.00 53.00
+ 57.00 .00 8.00 51.00
+ 34.00 .00 8.00 36.00
+ 50.00 .00 8.00 39.00
+ 52.00 1.00 7.00 56.00
+ 45.00 .00 7.00 34.00
+ 47.00 1.00 7.00 53.00
+ 34.00 .00 7.00 39.00
+ 50.00 1.00 8.00 55.00
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+ 35.00 .00 7.00 51.00
+ 50.00 .00 8.00 58.00
+ 68.00 .00 8.00 63.00
+ 41.00 .00 9.00 34.00
+ 47.00 .00 8.00 47.00
+ 76.00 .00 9.00 64.00
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+ 47.00 1.00 8.00 50.00
+ 50.00 .00 7.00 53.00
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+ 39.00 .00 7.00 44.00
+ 50.00 .00 8.00 55.00
+ 52.00 .00 9.00 61.00
+ 47.00 .00 8.00 53.00
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+ 55.00 1.00 9.00 49.00
+ 68.00 1.00 8.00 50.00
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+ 55.00 .00 8.00 58.00
+ 57.00 .00 8.00 55.00
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+ 60.00 1.00 9.00 61.00
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+ 57.00 1.00 7.00 54.00
+ 42.00 .00 9.00 50.00
+ 42.00 .00 8.00 47.00
+ 57.00 .00 8.00 50.00
+ 47.00 .00 7.00 45.00
+ 44.00 .00 7.00 40.00
+ 43.00 .00 9.00 55.00
+ 31.00 .00 8.00 39.00
+ 37.00 .00 7.00 33.00
+ 63.00 1.00 7.00 63.00
+ 47.00 .00 8.00 39.00
+ 57.00 1.00 8.00 63.00
+ 52.00 .00 8.00 44.00
+ 44.00 .00 7.00 35.00
+ 52.00 .00 7.00 55.00
+ 55.00 .00 7.00 69.00
+ 52.00 .00 8.00 53.00
+ 55.00 .00 9.00 61.00
+ 65.00 1.00 9.00 63.00
+ 55.00 .00 8.00 44.00
+ 63.00 .00 7.00 65.00
+ 44.00 .00 7.00 39.00
+ 47.00 .00 7.00 36.00
+ 63.00 1.00 9.00 55.00
+ 68.00 .00 8.00 66.00
+ 34.00 .00 8.00 39.00
+ 47.00 .00 9.00 50.00
+ 50.00 .00 9.00 58.00
+ 63.00 .00 8.00 66.00
+ 44.00 .00 7.00 34.00
+ 44.00 .00 8.00 50.00
+ 50.00 .00 8.00 53.00
+ 47.00 1.00 9.00 69.00
+ 65.00 .00 9.00 58.00
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+ 39.00 .00 8.00 39.00
+ 47.00 .00 8.00 53.00
+ 50.00 1.00 7.00 63.00
+ 50.00 .00 8.00 50.00
+ 63.00 .00 9.00 53.00
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+ 36.00 .00 7.00 61.00
+ 57.00 1.00 8.00 55.00
+ 53.00 1.00 8.00 57.00
+ 63.00 .00 7.00 66.00
+ 50.00 .00 8.00 34.00
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+ 42.00 .00 8.00 42.00
+ 42.00 .00 9.00 31.00
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+ 46.00 .00 8.00 44.00
+ 55.00 .00 8.00 42.00
+ 42.00 .00 8.00 47.00
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+ 44.00 .00 9.00 39.00
+ 73.00 1.00 8.00 69.00
+ 71.00 1.00 9.00 58.00
+ 50.00 .00 9.00 49.00
+ 63.00 1.00 7.00 54.00
+ 42.00 .00 8.00 36.00
+ 47.00 .00 7.00 42.00
+ 39.00 .00 9.00 26.00
+ 63.00 .00 8.00 58.00
+ 50.00 .00 8.00 55.00
+ 65.00 1.00 8.00 55.00
+ 76.00 1.00 9.00 67.00
+ 71.00 1.00 8.00 66.00
+ 39.00 .00 9.00 47.00
+ 47.00 1.00 9.00 63.00
+ 60.00 .00 7.00 50.00
+ 63.00 .00 9.00 55.00
+ 54.00 1.00 9.00 55.00
+ 55.00 1.00 8.00 58.00
+ 57.00 .00 8.00 61.00
+ 55.00 1.00 9.00 63.00
+ 42.00 .00 7.00 50.00
+ 50.00 .00 8.00 44.00
+ 55.00 .00 8.00 42.00
+ 42.00 .00 7.00 50.00
+ 34.00 .00 8.00 39.00
+ 65.00 .00 9.00 46.00
+ 52.00 .00 7.00 58.00
+ 44.00 .00 8.00 39.00
+ 65.00 1.00 9.00 66.00
+ 47.00 .00 8.00 42.00
+ 41.00 .00 7.00 39.00
+ 68.00 .00 9.00 63.00
+ 63.00 1.00 8.00 72.00
+ 52.00 .00 8.00 53.00
+ 57.00 .00 8.00 50.00
+ 68.00 .00 8.00 55.00
+ 42.00 .00 8.00 56.00
+ 47.00 .00 8.00 48.00
+ 73.00 1.00 9.00 58.00
+ 39.00 .00 8.00 50.00
+ 63.00 1.00 9.00 69.00
+ 60.00 .00 8.00 55.00
+ 65.00 1.00 9.00 66.00
+ 73.00 1.00 8.00 63.00
+ 52.00 .00 8.00 55.00
+ 36.00 .00 8.00 42.00
+ 28.00 .00 7.00 44.00
+ 47.00 .00 8.00 44.00
+ 57.00 .00 7.00 47.00
+ 34.00 .00 7.00 29.00
+ 47.00 .00 9.00 66.00
+ 57.00 .00 8.00 58.00
+ 60.00 1.00 9.00 50.00
+ 50.00 .00 9.00 47.00
+ 73.00 1.00 9.00 55.00
+ 52.00 1.00 8.00 47.00
+ 55.00 .00 8.00 53.00
+ 47.00 .00 8.00 53.00
+ 50.00 .00 8.00 61.00
+ 61.00 .00 7.00 44.00
+ 52.00 .00 9.00 53.00
+ 47.00 .00 7.00 40.00
+ 47.00 .00 7.00 50.00
+])
+
+AT_DATA([stringcat.sps], [dnl
+set format=F20.3.
+data list notable file='lr-cat2.data' list /read honcomp wiz science *.
+
+string ses(a1).
+recode wiz (7 = "a") (8 = "b") (9 = "c") into ses.
+
+logistic regression honcomp with read science ses
+ /categorical = ses.
+
+])
+
+AT_CHECK([pspp -O format=csv stringcat.sps], [0],
+ [dnl
+Table: Dependent Variable Encoding
+Original Value,Internal Value
+.000,0
+1.000,1
+
+Table: Case Processing Summary
+Unweighted Cases,N,Percent
+Included in Analysis,200,100.000
+Missing Cases,0,.000
+Total,200,100.000
+
+note: Estimation terminated at iteration number 5 because parameter estimates changed by less than 0.001
+
+Table: Model Summary
+Step 1,-2 Log likelihood,Cox & Snell R Square,Nagelkerke R Square
+,165.701,.280,.408
+
+Table: Categorical Variables' Codings
+,,,Parameter coding,
+,,Frequency,(1),(2)
+ses,a,47,1,0
+,b,95,0,1
+,c,58,0,0
+
+Table: Variables in the Equation
+,,B,S.E.,Wald,df,Sig.,Exp(B)
+Step 1,read,.098,.025,15.199,1,.000,1.103
+,science,.066,.027,5.867,1,.015,1.068
+,ses,,,6.690,2,.035,
+,ses(1),.058,.532,.012,1,.913,1.060
+,ses(2),-1.013,.444,5.212,1,.022,.363
+,Constant,-9.561,1.662,33.113,1,.000,.000
+])
+
+AT_CLEANUP
projects / pspp / commitdiff