X-Git-Url: https://pintos-os.org/cgi-bin/gitweb.cgi?a=blobdiff_plain;f=src%2Fregression.q;h=30224a6263e46fcf42c62c9facf33ab23f5d9c0f;hb=16aa47dbdde420fe82032f7d2e166fdf4e974df5;hp=d6ebc5200e313878515ac4aef77c6fdca11bd240;hpb=1f8dd363d6c20d07fcca14cb948018465fa5ed8b;p=pspp-builds.git diff --git a/src/regression.q b/src/regression.q index d6ebc520..30224a62 100644 --- a/src/regression.q +++ b/src/regression.q @@ -24,16 +24,24 @@ #include #include "alloc.h" #include "case.h" +#include "casefile.h" +#include "cat.h" +#include "cat-routines.h" +#include "command.h" +#include "design-matrix.h" #include "dictionary.h" +#include "error.h" #include "file-handle.h" -#include "command.h" +#include "gettext.h" #include "lexer.h" +#include +#include "missing-values.h" #include "tab.h" #include "var.h" #include "vfm.h" -#include "casefile.h" -#include -#include "cat.h" + +#define REG_LARGE_DATA 1000 + /* (headers) */ @@ -69,6 +77,11 @@ static struct cmd_regression cmd; */ size_t *indep_vars; +/* + Return value for the procedure. + */ +int pspp_reg_rc = CMD_SUCCESS; + static void run_regression (const struct casefile *, void *); /* STATISTICS subcommand output functions. @@ -94,7 +107,34 @@ static void statistics_keyword_output (void (*)(pspp_linreg_cache *), static void reg_stats_r (pspp_linreg_cache * c) { + struct tab_table *t; + int n_rows = 2; + int n_cols = 5; + double rsq; + double adjrsq; + double std_error; + assert (c != NULL); + rsq = c->ssm / c->sst; + adjrsq = 1.0 - (1.0 - rsq) * (c->n_obs - 1.0) / (c->n_obs - c->n_indeps); + std_error = sqrt ((c->n_indeps - 1.0) / (c->n_obs - 1.0)); + t = tab_create (n_cols, n_rows, 0); + tab_dim (t, tab_natural_dimensions); + tab_box (t, TAL_2, TAL_2, -1, TAL_1, 0, 0, n_cols - 1, n_rows - 1); + tab_hline (t, TAL_2, 0, n_cols - 1, 1); + tab_vline (t, TAL_2, 2, 0, n_rows - 1); + tab_vline (t, TAL_0, 1, 0, 0); + + tab_text (t, 1, 0, TAB_CENTER | TAT_TITLE, _("R")); + tab_text (t, 2, 0, TAB_CENTER | TAT_TITLE, _("R Square")); + tab_text (t, 3, 0, TAB_CENTER | TAT_TITLE, _("Adjusted R Square")); + tab_text (t, 4, 0, TAB_CENTER | TAT_TITLE, _("Std. Error of the Estimate")); + tab_float (t, 1, 1, TAB_RIGHT, sqrt (rsq), 10, 2); + tab_float (t, 2, 1, TAB_RIGHT, rsq, 10, 2); + tab_float (t, 3, 1, TAB_RIGHT, adjrsq, 10, 2); + tab_float (t, 4, 1, TAB_RIGHT, std_error, 10, 2); + tab_title (t, 0, _("Model Summary")); + tab_submit (t); } /* @@ -116,7 +156,8 @@ reg_stats_coeff (pspp_linreg_cache * c) struct tab_table *t; assert (c != NULL); - n_rows = 2 + c->param_estimates->size; + n_rows = c->n_coeffs + 2; + t = tab_create (n_cols, n_rows, 0); tab_headers (t, 2, 0, 1, 0); tab_dim (t, tab_natural_dimensions); @@ -131,7 +172,7 @@ reg_stats_coeff (pspp_linreg_cache * c) tab_text (t, 5, 0, TAB_CENTER | TAT_TITLE, _("t")); tab_text (t, 6, 0, TAB_CENTER | TAT_TITLE, _("Significance")); tab_text (t, 1, 1, TAB_LEFT | TAT_TITLE, _("(Constant)")); - coeff = gsl_vector_get (c->param_estimates, 0); + coeff = c->coeff[0].estimate; tab_float (t, 2, 1, 0, coeff, 10, 2); std_err = sqrt (gsl_matrix_get (c->cov, 0, 0)); tab_float (t, 3, 1, 0, std_err, 10, 2); @@ -141,40 +182,39 @@ reg_stats_coeff (pspp_linreg_cache * c) tab_float (t, 5, 1, 0, t_stat, 10, 2); pval = 2 * gsl_cdf_tdist_Q (fabs (t_stat), 1.0); tab_float (t, 6, 1, 0, pval, 10, 2); - for (j = 0; j < c->n_indeps; j++) + for (j = 1; j <= c->n_indeps; j++) { i = indep_vars[j]; - struct variable *v = cmd.v_variables[i]; - label = var_to_string (v); - tab_text (t, 1, j + 2, TAB_CENTER, label); + label = var_to_string (c->coeff[j].v); + tab_text (t, 1, j + 1, TAB_CENTER, label); /* Regression coefficients. */ - coeff = gsl_vector_get (c->param_estimates, j + 1); - tab_float (t, 2, j + 2, 0, coeff, 10, 2); + coeff = c->coeff[j].estimate; + tab_float (t, 2, j + 1, 0, coeff, 10, 2); /* Standard error of the coefficients. */ - std_err = sqrt (gsl_matrix_get (c->cov, j + 1, j + 1)); - tab_float (t, 3, j + 2, 0, std_err, 10, 2); + std_err = sqrt (gsl_matrix_get (c->cov, j, j)); + tab_float (t, 3, j + 1, 0, std_err, 10, 2); /* 'Standardized' coefficient, i.e., regression coefficient if all variables had unit variance. */ - beta = gsl_vector_get (c->indep_std, j + 1); + beta = gsl_vector_get (c->indep_std, j); beta *= coeff / c->depvar_std; - tab_float (t, 4, j + 2, 0, beta, 10, 2); + tab_float (t, 4, j + 1, 0, beta, 10, 2); /* Test statistic for H0: coefficient is 0. */ t_stat = coeff / std_err; - tab_float (t, 5, j + 2, 0, t_stat, 10, 2); + tab_float (t, 5, j + 1, 0, t_stat, 10, 2); /* P values for the test statistic above. */ pval = 2 * gsl_cdf_tdist_Q (fabs (t_stat), 1.0); - tab_float (t, 6, j + 2, 0, pval, 10, 2); + tab_float (t, 6, j + 1, 0, pval, 10, 2); } tab_title (t, 0, _("Coefficients")); tab_submit (t); @@ -272,7 +312,45 @@ reg_stats_f (pspp_linreg_cache * c) static void reg_stats_bcov (pspp_linreg_cache * c) { + int n_cols; + int n_rows; + int i; + int j; + int k; + int row; + int col; + const char *label; + struct tab_table *t; + assert (c != NULL); + n_cols = c->n_indeps + 1 + 2; + n_rows = 2 * (c->n_indeps + 1); + t = tab_create (n_cols, n_rows, 0); + tab_headers (t, 2, 0, 1, 0); + tab_dim (t, tab_natural_dimensions); + tab_box (t, TAL_2, TAL_2, -1, TAL_1, 0, 0, n_cols - 1, n_rows - 1); + tab_hline (t, TAL_2, 0, n_cols - 1, 1); + tab_vline (t, TAL_2, 2, 0, n_rows - 1); + tab_vline (t, TAL_0, 1, 0, 0); + tab_text (t, 0, 0, TAB_CENTER | TAT_TITLE, _("Model")); + tab_text (t, 1, 1, TAB_CENTER | TAT_TITLE, _("Covariances")); + for (i = 1; i < c->n_indeps + 1; i++) + { + j = indep_vars[(i - 1)]; + struct variable *v = cmd.v_variables[j]; + label = var_to_string (v); + tab_text (t, 2, i, TAB_CENTER, label); + tab_text (t, i + 2, 0, TAB_CENTER, label); + for (k = 1; k < c->n_indeps + 1; k++) + { + col = (i <= k) ? k : i; + row = (i <= k) ? i : k; + tab_float (t, k + 2, i, TAB_CENTER, + gsl_matrix_get (c->cov, row, col), 8, 3); + } + } + tab_title (t, 0, _("Coefficient Correlations")); + tab_submit (t); } static void reg_stats_ses (pspp_linreg_cache * c) @@ -346,7 +424,7 @@ subcommand_statistics (int *keywords, pspp_linreg_cache * c) */ for (i = 0; i < f; i++) { - *(keywords + i) = 1; + keywords[i] = 1; } } else @@ -365,10 +443,10 @@ subcommand_statistics (int *keywords, pspp_linreg_cache * c) */ if (keywords[defaults] | d) { - *(keywords + anova) = 1; - *(keywords + outs) = 1; - *(keywords + coeff) = 1; - *(keywords + r) = 1; + keywords[anova] = 1; + keywords[outs] = 1; + keywords[coeff] = 1; + keywords[r] = 1; } } statistics_keyword_output (reg_stats_r, keywords[r], c); @@ -397,7 +475,7 @@ cmd_regression (void) } multipass_procedure_with_splits (run_regression, &cmd); - return CMD_SUCCESS; + return pspp_reg_rc; } /* @@ -420,123 +498,174 @@ is_depvar (size_t k) } static void -run_regression (const struct casefile *cf, void *cmd_) +run_regression (const struct casefile *cf, void *cmd_ UNUSED) { size_t i; - size_t k; size_t n_data = 0; size_t row; + size_t case_num; int n_indep; + int j = 0; + /* + Keep track of the missing cases. + */ + int *is_missing_case; const union value *val; struct casereader *r; struct casereader *r2; struct ccase c; - const struct variable *v; - struct recoded_categorical_array *ca; - struct recoded_categorical *rc; + struct variable *v; + struct variable **indep_vars; struct design_matrix *X; gsl_vector *Y; pspp_linreg_cache *lcache; pspp_linreg_opts lopts; n_data = casefile_get_case_cnt (cf); + + is_missing_case = xnmalloc (n_data, sizeof (*is_missing_case)); + for (i = 0; i < n_data; i++) + is_missing_case[i] = 0; + n_indep = cmd.n_variables - cmd.n_dependent; - indep_vars = (size_t *) malloc (n_indep * sizeof (*indep_vars)); + indep_vars = xnmalloc (n_indep, sizeof *indep_vars); - Y = gsl_vector_alloc (n_data); lopts.get_depvar_mean_std = 1; - lopts.get_indep_mean_std = (int *) malloc (n_indep * sizeof (int)); + lopts.get_indep_mean_std = xnmalloc (n_indep, sizeof (int)); - lcache = pspp_linreg_cache_alloc (n_data, n_indep); - lcache->indep_means = gsl_vector_alloc (n_indep); - lcache->indep_std = gsl_vector_alloc (n_indep); /* Read from the active file. The first pass encodes categorical - variables. + variables and drops cases with missing values. */ - ca = cr_recoded_cat_ar_create (cmd.n_variables, cmd.v_variables); - for (r = casefile_get_reader (cf); - casereader_read (r, &c); case_destroy (&c)) + j = 0; + for (i = 0; i < cmd.n_variables; i++) { - for (i = 0; i < ca->n_vars; i++) + if (!is_depvar (i)) { - v = (*(ca->a + i))->v; - val = case_data (&c, v->fv); - cr_value_update (*(ca->a + i), val); + v = cmd.v_variables[i]; + indep_vars[j] = v; + j++; + if (v->type == ALPHA) + { + /* Make a place to hold the binary vectors + corresponding to this variable's values. */ + cat_stored_values_create (v); + } + for (r = casefile_get_reader (cf); + casereader_read (r, &c); case_destroy (&c)) + { + row = casereader_cnum (r) - 1; + + val = case_data (&c, v->fv); + cat_value_update (v, val); + if (mv_is_value_missing (&v->miss, val)) + { + if (!is_missing_case[row]) + { + /* Now it is missing. */ + n_data--; + is_missing_case[row] = 1; + } + } + } } } - cr_create_value_matrices (ca); + + Y = gsl_vector_alloc (n_data); X = - design_matrix_create (n_indep, (const struct variable **) cmd.v_variables, - ca, n_data); + design_matrix_create (n_indep, (const struct variable **) indep_vars, + n_data); + lcache = pspp_linreg_cache_alloc (X->m->size1, X->m->size2); + lcache->indep_means = gsl_vector_alloc (X->m->size2); + lcache->indep_std = gsl_vector_alloc (X->m->size2); /* The second pass creates the design matrix. */ + row = 0; for (r2 = casefile_get_reader (cf); casereader_read (r2, &c); case_destroy (&c)) /* Iterate over the cases. */ { - k = 0; - row = casereader_cnum (r2) - 1; - for (i = 0; i < cmd.n_variables; ++i) /* Iterate over the variables + case_num = casereader_cnum (r2) - 1; + if (!is_missing_case[case_num]) + { + for (i = 0; i < cmd.n_variables; ++i) /* Iterate over the variables for the current case. */ - { - v = cmd.v_variables[i]; - val = case_data (&c, v->fv); - /* - Independent/dependent variable separation. The - 'variables' subcommand specifies a varlist which contains - both dependent and independent variables. The dependent - variables are specified with the 'dependent' - subcommand. We need to separate the two. - */ - if (is_depvar (i)) { - if (v->type == NUMERIC) + v = cmd.v_variables[i]; + val = case_data (&c, v->fv); + /* + Independent/dependent variable separation. The + 'variables' subcommand specifies a varlist which contains + both dependent and independent variables. The dependent + variables are specified with the 'dependent' + subcommand. We need to separate the two. + */ + if (is_depvar (i)) { + if (v->type != NUMERIC) + { + msg (SE, + gettext ("Dependent variable must be numeric.")); + pspp_reg_rc = CMD_FAILURE; + return; + } + lcache->depvar = (const struct variable *) v; gsl_vector_set (Y, row, val->f); } else { - errno = EINVAL; - fprintf (stderr, - "%s:%d: Dependent variable should be numeric: %s\n", - __FILE__, __LINE__, strerror (errno)); - err_cond_fail (); - } - } - else - { - if (v->type == ALPHA) - { - rc = cr_var_to_recoded_categorical (v, ca); - design_matrix_set_categorical (X, row, v, val, rc); - } - else if (v->type == NUMERIC) - { - design_matrix_set_numeric (X, row, v, val); + if (v->type == ALPHA) + { + design_matrix_set_categorical (X, row, v, val); + } + else if (v->type == NUMERIC) + { + design_matrix_set_numeric (X, row, v, val); + } + + lopts.get_indep_mean_std[i] = 1; } - - indep_vars[k] = i; - k++; - lopts.get_indep_mean_std[i] = 1; } + row++; } } + /* + Now that we know the number of coefficients, allocate space + and store pointers to the variables that correspond to the + coefficients. + */ + lcache->coeff = xnmalloc (X->m->size2 + 1, sizeof (*lcache->coeff)); + for (i = 0; i < X->m->size2; i++) + { + j = i + 1; /* The first coeff is the intercept. */ + lcache->coeff[j].v = + (const struct variable *) design_matrix_col_to_var (X, i); + assert (lcache->coeff[j].v != NULL); + } + /* + For large data sets, use QR decomposition. + */ + if (n_data > sqrt (n_indep) && n_data > REG_LARGE_DATA) + { + lcache->method = PSPP_LINREG_SVD; + } /* Find the least-squares estimates and other statistics. */ - pspp_linreg ((const gsl_vector *) Y, X->m, &lopts, lcache); + pspp_linreg ((const gsl_vector *) Y, X->m, &lopts, lcache); subcommand_statistics (cmd.a_statistics, lcache); gsl_vector_free (Y); design_matrix_destroy (X); pspp_linreg_cache_free (lcache); free (lopts.get_indep_mean_std); free (indep_vars); + free (is_missing_case); casereader_destroy (r); + return; } /*