From: Jason Stover Date: Sat, 14 Jun 2008 19:35:03 +0000 (-0400) Subject: moved src/math/linreg.[ch] to src/math X-Git-Tag: v0.7.0~28 X-Git-Url: https://pintos-os.org/cgi-bin/gitweb.cgi?a=commitdiff_plain;h=b5b474193e450bba97610065df0518c08074a7fb;p=pspp-builds.git moved src/math/linreg.[ch] to src/math --- diff --git a/src/language/stats/glm.q b/src/language/stats/glm.q index 2e230c36..f16eff76 100644 --- a/src/language/stats/glm.q +++ b/src/language/stats/glm.q @@ -40,7 +40,7 @@ #include #include #include -#include +#include #include #include diff --git a/src/language/stats/regression.q b/src/language/stats/regression.q index aaf787b5..2c74e3ce 100644 --- a/src/language/stats/regression.q +++ b/src/language/stats/regression.q @@ -41,7 +41,7 @@ #include #include #include -#include +#include #include #include @@ -250,7 +250,7 @@ reg_stats_coeff (pspp_linreg_cache * c) std_err = sqrt (gsl_matrix_get (c->cov, j + 1, j + 1)); tab_float (t, 3, this_row, 0, std_err, 10, 2); /* - 'Standardized' coefficient, i.e., regression coefficient + Standardized coefficient, i.e., regression coefficient if all variables had unit variance. */ beta = gsl_vector_get (c->indep_std, j + 1); diff --git a/src/math/ChangeLog b/src/math/ChangeLog index c1cd1a3a..b045df07 100644 --- a/src/math/ChangeLog +++ b/src/math/ChangeLog @@ -1,3 +1,7 @@ +2008-06-14 Jason Stover + + * linreg/: moved linreg.[ch] to src/math. + 2008-05-15 Ben Pfaff Patch #6512. diff --git a/src/math/automake.mk b/src/math/automake.mk index 5bbf24fa..cff7f960 100644 --- a/src/math/automake.mk +++ b/src/math/automake.mk @@ -1,7 +1,6 @@ ## Process this file with automake to produce Makefile.in -*- makefile -*- include $(top_srcdir)/src/math/ts/automake.mk -include $(top_srcdir)/src/math/linreg/automake.mk noinst_LIBRARIES += src/math/libpspp_math.a @@ -19,6 +18,8 @@ src_math_libpspp_math_a_SOURCES = \ src/math/interaction.h \ src/math/levene.c \ src/math/levene.h \ + src/math/linreg.c \ + src/math/linreg.h \ src/math/merge.c \ src/math/merge.h \ src/math/moments.c src/math/moments.h \ diff --git a/src/math/coefficient.c b/src/math/coefficient.c index 156c782d..fed45b64 100644 --- a/src/math/coefficient.c +++ b/src/math/coefficient.c @@ -19,7 +19,6 @@ */ #include #include -#include #include "src/math/design-matrix.h" #include @@ -47,7 +46,7 @@ pspp_coeff_free (struct pspp_coeff *c) /* Initialize the variable and value pointers inside the - coefficient structures for the linear model. + coefficient structures for the model. */ void pspp_coeff_init (struct pspp_coeff ** c, const struct design_matrix *X) @@ -175,65 +174,3 @@ pspp_coeff_get_value (struct pspp_coeff *c, return NULL; } -/* - Which coefficient is associated with V? The VAL argument is relevant - only to categorical variables. - */ -const struct pspp_coeff * -pspp_linreg_get_coeff (const pspp_linreg_cache * c, - const struct variable *v, const union value *val) -{ - int i; - struct pspp_coeff *result = NULL; - const struct variable *tmp = NULL; - - if (c == NULL) - { - return NULL; - } - if (c->coeff == NULL || c->n_indeps == 0 || v == NULL) - { - return NULL; - } - i = 0; - result = c->coeff[0]; - tmp = pspp_coeff_get_var (result, 0); - while (tmp != v && i < c->n_coeffs) - { - result = c->coeff[i]; - tmp = pspp_coeff_get_var (result, 0); - i++; - } - if (tmp != v) - { - /* - Not found. - */ - return NULL; - } - if (var_is_numeric (v)) - { - return result; - } - else if (val != NULL) - { - /* - If v is categorical, we need to ensure the coefficient - matches the VAL. - */ - while (tmp != v && i < c->n_coeffs - && compare_values (pspp_coeff_get_value (result, tmp), - val, var_get_width (v))) - { /* FIX THIS */ - i++; - result = c->coeff[i]; - tmp = pspp_coeff_get_var (result, 0); - } - if (i == c->n_coeffs && tmp != v) - { - return NULL; - } - return result; - } - return NULL; -} diff --git a/src/math/coefficient.h b/src/math/coefficient.h index 8a82f46b..5fb34c26 100644 --- a/src/math/coefficient.h +++ b/src/math/coefficient.h @@ -19,7 +19,6 @@ #define COEFFICIENT_H #include -#include #include #include @@ -107,10 +106,4 @@ const struct variable *pspp_coeff_get_var (struct pspp_coeff *, */ const union value *pspp_coeff_get_value (struct pspp_coeff *, const struct variable *); - -const struct pspp_coeff *pspp_linreg_get_coeff (const pspp_linreg_cache - *, - const struct variable - *, - const union value *); #endif diff --git a/src/math/linreg.c b/src/math/linreg.c new file mode 100644 index 00000000..6cd02498 --- /dev/null +++ b/src/math/linreg.c @@ -0,0 +1,591 @@ +/* PSPP - a program for statistical analysis. + Copyright (C) 2005 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 . */ + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +/* + Find the least-squares estimate of b for the linear model: + + Y = Xb + Z + + 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. + + This estimate is found via the sweep operator or singular-value + decomposition with gsl. + + + References: + + 1. Matrix Computations, third edition. GH Golub and CF Van Loan. + The Johns Hopkins University Press. 1996. ISBN 0-8018-5414-8. + + 2. Numerical Analysis for Statisticians. K Lange. Springer. 1999. + ISBN 0-387-94979-8. + + 3. Numerical Linear Algebra for Applications in Statistics. JE Gentle. + Springer. 1998. ISBN 0-387-98542-5. +*/ + + +/* + Get the mean and standard deviation of a vector + of doubles via a form of the Kalman filter as + described on page 32 of [3]. + */ +static int +linreg_mean_std (gsl_vector_const_view v, double *mp, double *sp, double *ssp) +{ + size_t i; + double j = 0.0; + double d; + double tmp; + double mean; + double variance; + + mean = gsl_vector_get (&v.vector, 0); + variance = 0; + for (i = 1; i < v.vector.size; i++) + { + j = (double) i + 1.0; + tmp = gsl_vector_get (&v.vector, i); + d = (tmp - mean) / j; + mean += d; + variance += j * (j - 1.0) * d * d; + } + *mp = mean; + *sp = sqrt (variance / (j - 1.0)); + *ssp = variance; + + return GSL_SUCCESS; +} + +/* + Set V to contain an array of pointers to the variables + used in the model. V must be at least C->N_COEFFS in length. + The return value is the number of distinct variables found. + */ +int +pspp_linreg_get_vars (const void *c_, const struct variable **v) +{ + const pspp_linreg_cache *c = c_; + const struct variable *tmp; + int i; + int j; + int result = 0; + + /* + Make sure the caller doesn't try to sneak a variable + into V that is not in the model. + */ + for (i = 0; i < c->n_coeffs; i++) + { + v[i] = NULL; + } + for (j = 0; j < c->n_coeffs; j++) + { + tmp = pspp_coeff_get_var (c->coeff[j], 0); + assert (tmp != NULL); + /* Repeated variables are likely to bunch together, at the end + of the array. */ + i = result - 1; + while (i >= 0 && v[i] != tmp) + { + i--; + } + if (i < 0 && result < c->n_coeffs) + { + v[result] = tmp; + result++; + } + } + return result; +} + +/* + Allocate a pspp_linreg_cache and return a pointer + to it. n is the number of cases, p is the number of + independent variables. + */ +pspp_linreg_cache * +pspp_linreg_cache_alloc (size_t n, size_t p) +{ + 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->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 compute my + residuals. */ + c->get_vars = pspp_linreg_get_vars; /* The procedure that returns + pointers to model + variables. */ + c->resid = NULL; /* The variable storing my residuals. */ + c->pred = NULL; /* The variable storing my predicted values. */ + + return c; +} + +bool +pspp_linreg_cache_free (void *m) +{ + int i; + + pspp_linreg_cache *c = m; + if (c != NULL) + { + gsl_vector_free (c->indep_means); + gsl_vector_free (c->indep_std); + gsl_vector_free (c->ss_indeps); + gsl_matrix_free (c->cov); + gsl_vector_free (c->ssx); + for (i = 0; i < c->n_coeffs; i++) + { + pspp_coeff_free (c->coeff[i]); + } + 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. + */ +int +pspp_linreg (const gsl_vector * Y, const gsl_matrix * X, + const pspp_linreg_opts * opts, pspp_linreg_cache * cache) +{ + int rc; + gsl_matrix *design = NULL; + gsl_matrix_view xtx; + gsl_matrix_view xm; + gsl_matrix_view xmxtx; + gsl_vector_view xty; + gsl_vector_view xi; + gsl_vector_view xj; + gsl_vector *param_estimates; + + size_t i; + size_t j; + double tmp; + double m; + double s; + double ss; + + if (cache == NULL) + { + return GSL_EFAULT; + } + if (opts->get_depvar_mean_std) + { + linreg_mean_std (gsl_vector_const_subvector (Y, 0, Y->size), + &m, &s, &ss); + cache->depvar_mean = m; + cache->depvar_std = s; + cache->sst = ss; + } + for (i = 0; i < cache->n_indeps; i++) + { + if (opts->get_indep_mean_std[i]) + { + linreg_mean_std (gsl_matrix_const_column (X, i), &m, &s, &ss); + gsl_vector_set (cache->indep_means, i, m); + gsl_vector_set (cache->indep_std, i, s); + gsl_vector_set (cache->ssx, i, ss); + } + } + cache->dft = cache->n_obs - 1; + cache->dfm = cache->n_indeps; + cache->dfe = cache->dft - cache->dfm; + cache->n_coeffs = X->size2; + cache->intercept = 0.0; + + if (cache->method == PSPP_LINREG_SWEEP) + { + gsl_matrix *sw; + /* + Subtract the means to improve the condition of the design + matrix. This requires copying X and Y. We do not divide by the + standard deviations of the independent variables here since doing + so would cause a miscalculation of the residual sums of + squares. Dividing by the standard deviation is done GSL's linear + regression functions, so if the design matrix has a poor + condition, use QR decomposition. + + The design matrix here does not include a column for the intercept + (i.e., a column of 1's). If using PSPP_LINREG_QR, we need that column, + so design is allocated here when sweeping, or below if using QR. + */ + design = gsl_matrix_alloc (X->size1, X->size2); + for (i = 0; i < X->size2; i++) + { + m = gsl_vector_get (cache->indep_means, i); + for (j = 0; j < X->size1; j++) + { + tmp = (gsl_matrix_get (X, j, i) - m); + gsl_matrix_set (design, j, i, tmp); + } + } + sw = gsl_matrix_calloc (cache->n_indeps + 1, cache->n_indeps + 1); + xtx = gsl_matrix_submatrix (sw, 0, 0, cache->n_indeps, cache->n_indeps); + + for (i = 0; i < xtx.matrix.size1; i++) + { + tmp = gsl_vector_get (cache->ssx, i); + gsl_matrix_set (&(xtx.matrix), i, i, tmp); + xi = gsl_matrix_column (design, i); + for (j = (i + 1); j < xtx.matrix.size2; j++) + { + xj = gsl_matrix_column (design, j); + gsl_blas_ddot (&(xi.vector), &(xj.vector), &tmp); + gsl_matrix_set (&(xtx.matrix), i, j, tmp); + } + } + + gsl_matrix_set (sw, cache->n_indeps, cache->n_indeps, cache->sst); + xty = gsl_matrix_column (sw, cache->n_indeps); + /* + This loop starts at 1, with i=0 outside the loop, so we can get + the model sum of squares due to the first independent variable. + */ + xi = gsl_matrix_column (design, 0); + gsl_blas_ddot (&(xi.vector), Y, &tmp); + gsl_vector_set (&(xty.vector), 0, tmp); + tmp *= tmp / gsl_vector_get (cache->ssx, 0); + gsl_vector_set (cache->ss_indeps, 0, tmp); + for (i = 1; i < cache->n_indeps; i++) + { + xi = gsl_matrix_column (design, i); + gsl_blas_ddot (&(xi.vector), Y, &tmp); + gsl_vector_set (&(xty.vector), i, tmp); + } + + /* + Sweep on the matrix sw, which contains XtX, XtY and YtY. + */ + reg_sweep (sw); + cache->sse = gsl_matrix_get (sw, cache->n_indeps, cache->n_indeps); + cache->mse = cache->sse / cache->dfe; + /* + Get the intercept. + */ + m = cache->depvar_mean; + for (i = 0; i < cache->n_indeps; i++) + { + tmp = gsl_matrix_get (sw, i, cache->n_indeps); + cache->coeff[i]->estimate = tmp; + m -= tmp * gsl_vector_get (cache->indep_means, i); + } + /* + Get the covariance matrix of the parameter estimates. + Only the upper triangle is necessary. + */ + + /* + The loops below do not compute the entries related + to the estimated intercept. + */ + for (i = 0; i < cache->n_indeps; i++) + for (j = i; j < cache->n_indeps; j++) + { + tmp = -1.0 * cache->mse * gsl_matrix_get (sw, i, j); + gsl_matrix_set (cache->cov, i + 1, j + 1, tmp); + } + /* + Get the covariances related to the intercept. + */ + xtx = gsl_matrix_submatrix (sw, 0, 0, cache->n_indeps, cache->n_indeps); + xmxtx = gsl_matrix_submatrix (cache->cov, 0, 1, 1, cache->n_indeps); + xm = gsl_matrix_view_vector (cache->indep_means, 1, cache->n_indeps); + rc = gsl_blas_dsymm (CblasRight, CblasUpper, cache->mse, + &xtx.matrix, &xm.matrix, 0.0, &xmxtx.matrix); + if (rc == GSL_SUCCESS) + { + tmp = cache->mse / cache->n_obs; + for (i = 1; i < 1 + cache->n_indeps; i++) + { + tmp -= gsl_matrix_get (cache->cov, 0, i) + * gsl_vector_get (cache->indep_means, i - 1); + } + gsl_matrix_set (cache->cov, 0, 0, tmp); + + cache->intercept = m; + } + else + { + fprintf (stderr, "%s:%d:gsl_blas_dsymm: %s\n", + __FILE__, __LINE__, gsl_strerror (rc)); + exit (rc); + } + gsl_matrix_free (sw); + } + else if (cache->method == PSPP_LINREG_CONDITIONAL_INVERSE) + { + /* + Use the SVD of X^T X to find a conditional inverse of X^TX. If + the SVD is X^T X = U D V^T, then set the conditional inverse + to (X^T X)^c = V D^- U^T. D^- is defined as follows: If entry + (i, i) has value sigma_i, then entry (i, i) of D^- is 1 / + sigma_i if sigma_i > 0, and 0 otherwise. Then solve the normal + equations by setting the estimated parameter vector to + (X^TX)^c X^T Y. + */ + } + else + { + gsl_multifit_linear_workspace *wk; + /* + Use QR decomposition via GSL. + */ + + param_estimates = gsl_vector_alloc (1 + X->size2); + design = gsl_matrix_alloc (X->size1, 1 + X->size2); + + for (j = 0; j < X->size1; j++) + { + gsl_matrix_set (design, j, 0, 1.0); + for (i = 0; i < X->size2; i++) + { + tmp = gsl_matrix_get (X, j, i); + gsl_matrix_set (design, j, i + 1, tmp); + } + } + + 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++) + { + cache->coeff[i]->estimate = gsl_vector_get (param_estimates, i + 1); + } + cache->intercept = gsl_vector_get (param_estimates, 0); + if (rc == GSL_SUCCESS) + { + gsl_multifit_linear_free (wk); + gsl_vector_free (param_estimates); + } + else + { + fprintf (stderr, "%s:%d: gsl_multifit_linear returned %d\n", + __FILE__, __LINE__, rc); + } + } + + + cache->ssm = cache->sst - cache->sse; + /* + Get the remaining sums of squares for the independent + variables. + */ + m = 0; + for (i = 1; i < cache->n_indeps; i++) + { + j = i - 1; + m += gsl_vector_get (cache->ss_indeps, j); + tmp = cache->ssm - m; + gsl_vector_set (cache->ss_indeps, i, tmp); + } + + gsl_matrix_free (design); + return GSL_SUCCESS; +} + +/* + Is the coefficient COEF contained in the list of coefficients + COEF_LIST? + */ +static int +has_coefficient (const struct pspp_coeff **coef_list, const struct pspp_coeff *coef, + size_t n) +{ + size_t i = 0; + + while (i < n) + { + if (coef_list[i] == coef) + { + return 1; + } + i++; + } + return 0; +} +/* + Predict the value of the dependent variable with the + new set of predictors. PREDICTORS must point to a list + of variables, each of whose values are stored in VALS, + in the same order. + */ +double +pspp_linreg_predict (const struct variable **predictors, + const union value **vals, const void *c_, int n_vals) +{ + const pspp_linreg_cache *c = c_; + int j; + size_t next_coef = 0; + const struct pspp_coeff **coef_list; + const struct pspp_coeff *coe; + double result; + double tmp; + + if (predictors == NULL || vals == NULL || c == NULL) + { + return GSL_NAN; + } + if (c->coeff == NULL) + { + /* The stupid model: just guess the mean. */ + return c->depvar_mean; + } + coef_list = xnmalloc (c->n_coeffs, sizeof (*coef_list)); + result = c->intercept; + + /* + The loops guard against the possibility that the caller passed us + inadequate information, such as too few or too many values, or + a redundant list of variable names. + */ + for (j = 0; j < n_vals; j++) + { + coe = pspp_linreg_get_coeff (c, predictors[j], vals[j]); + if (!has_coefficient (coef_list, coe, next_coef)) + { + tmp = pspp_coeff_get_est (coe); + if (var_is_numeric (predictors[j])) + { + tmp *= vals[j]->f; + } + result += tmp; + coef_list[next_coef++] = coe; + } + } + free (coef_list); + + return result; +} + +double +pspp_linreg_residual (const struct variable **predictors, + const union value **vals, + const union value *obs, const void *c, int n_vals) +{ + double pred; + double result; + + if (predictors == NULL || vals == NULL || c == NULL || obs == NULL) + { + return GSL_NAN; + } + pred = pspp_linreg_predict (predictors, vals, c, n_vals); + + result = gsl_isnan (pred) ? GSL_NAN : (obs->f - pred); + return result; +} + +/* + Which coefficient is associated with V? The VAL argument is relevant + only to categorical variables. + */ +const struct pspp_coeff * +pspp_linreg_get_coeff (const pspp_linreg_cache * c, + const struct variable *v, const union value *val) +{ + int i; + struct pspp_coeff *result = NULL; + const struct variable *tmp = NULL; + + if (c == NULL) + { + return NULL; + } + if (c->coeff == NULL || c->n_indeps == 0 || v == NULL) + { + return NULL; + } + i = 0; + result = c->coeff[0]; + tmp = pspp_coeff_get_var (result, 0); + while (tmp != v && i < c->n_coeffs) + { + result = c->coeff[i]; + tmp = pspp_coeff_get_var (result, 0); + i++; + } + if (tmp != v) + { + /* + Not found. + */ + return NULL; + } + if (var_is_numeric (v)) + { + return result; + } + else if (val != NULL) + { + /* + If v is categorical, we need to ensure the coefficient + matches the VAL. + */ + while (tmp != v && i < c->n_coeffs + && compare_values (pspp_coeff_get_value (result, tmp), + val, var_get_width (v))) + { /* FIX THIS */ + i++; + result = c->coeff[i]; + tmp = pspp_coeff_get_var (result, 0); + } + if (i == c->n_coeffs && tmp != v) + { + return NULL; + } + return result; + } + return NULL; +} diff --git a/src/math/linreg.h b/src/math/linreg.h new file mode 100644 index 00000000..834b0922 --- /dev/null +++ b/src/math/linreg.h @@ -0,0 +1,211 @@ +/* PSPP - a program for statistical analysis. + 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 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 . */ + +#ifndef LINREG_H +#define LINREG_H +#include +#include +#include +#include + +struct variable; +struct pspp_coeff; +union value; + +enum +{ + PSPP_LINREG_CONDITIONAL_INVERSE, + PSPP_LINREG_QR, + PSPP_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: + + Y = Xb + Z + + 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. + + This estimate is found via the sweep operator or singular-value + decomposition with gsl. + + + References: + + 1. Matrix Computations, third edition. GH Golub and CF Van Loan. + The Johns Hopkins University Press. 1996. ISBN 0-8018-5414-8. + + 2. Numerical Analysis for Statisticians. K Lange. Springer. 1999. + ISBN 0-387-94979-8. + + 3. Numerical Linear Algebra for Applications in Statistics. JE Gentle. + Springer. 1998. ISBN 0-387-98542-5. +*/ + + +struct pspp_linreg_cache_struct +{ + int n_obs; /* Number of observations. */ + int n_indeps; /* Number of independent variables. */ + int n_coeffs; /* The intercept is not considered a + coefficient here. */ + + /* + The variable struct is ignored during estimation. It is here so + the calling procedure can find the variable used in the model. + */ + const struct variable *depvar; + + gsl_vector *residuals; + struct pspp_coeff **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; + double depvar_std; + gsl_vector *indep_means; + gsl_vector *indep_std; + + /* + Sums of squares. + */ + double ssm; /* Sums of squares for the overall model. */ + gsl_vector *ss_indeps; /* Sums of squares from each + independent variable. */ + 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. */ + gsl_vector *ssx; /* Centered sums of squares for independent + variables, i.e. \sum (x[i] - mean(x))^2. */ + double ssy; /* Centered sums of squares for dependent + variable. + */ + /* + Covariance matrix of the parameter estimates. + */ + gsl_matrix *cov; + /* + Degrees of freedom. + */ + double dft; + double dfe; + double dfm; + + /* + 'Hat' or Hessian matrix, i.e. (X'X)^{-1}, where X is our + design matrix. + */ + gsl_matrix *hat; + + double (*predict) (const struct variable **, const union value **, + const void *, int); + double (*residual) (const struct variable **, + const union value **, + const union value *, const void *, int); + /* + Returns pointers to the variables used in the model. + */ + int (*get_vars) (const void *, const struct variable **); + struct variable *resid; + struct variable *pred; + +}; + +typedef struct pspp_linreg_cache_struct pspp_linreg_cache; + + + +/* + Allocate a pspp_linreg_cache and return a pointer + to it. n is the number of cases, p is the number of + independent variables. + */ +pspp_linreg_cache *pspp_linreg_cache_alloc (size_t n, size_t p); + +bool pspp_linreg_cache_free (void *); + +/* + 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. + */ +int +pspp_linreg (const gsl_vector * Y, const gsl_matrix * X, + const pspp_linreg_opts * opts, pspp_linreg_cache * cache); + +double +pspp_linreg_predict (const struct variable **, const union value **, + const void *, int); +double +pspp_linreg_residual (const struct variable **, const union value **, + const union value *, const void *, int); +/* + All variables used in the model. + */ +int pspp_linreg_get_vars (const void *, const struct variable **); + +const struct pspp_coeff *pspp_linreg_get_coeff (const pspp_linreg_cache + *, + const struct variable + *, + const union value *); +#endif diff --git a/src/ui/gui/automake.mk b/src/ui/gui/automake.mk index 651f5905..8130f5cf 100644 --- a/src/ui/gui/automake.mk +++ b/src/ui/gui/automake.mk @@ -58,7 +58,6 @@ src_ui_gui_psppire_LDADD = \ src/output/charts/libcharts.a \ src/output/liboutput.a \ src/math/libpspp_math.a \ - src/math/linreg/libpspp_linreg.a \ lib/linreg/liblinreg.a \ lib/gsl-extras/libgsl-extras.a \ src/data/libdata.a \ diff --git a/src/ui/terminal/automake.mk b/src/ui/terminal/automake.mk index a19c9738..ae6d3a5e 100644 --- a/src/ui/terminal/automake.mk +++ b/src/ui/terminal/automake.mk @@ -26,7 +26,6 @@ src_ui_terminal_pspp_LDADD = \ src/output/charts/libcharts.a \ src/output/liboutput.a \ src/math/libpspp_math.a \ - src/math/linreg/libpspp_linreg.a \ src/ui/libuicommon.a \ lib/linreg/liblinreg.a \ lib/gsl-extras/libgsl-extras.a \