GNU Scientific Library – Reference Manual: Multi-parameter fitting

Next: Robust linear regression, Previous: Linear fitting without a constant term, Up: Least-Squares Fitting [Index]

### 37.4 Multi-parameter fitting

The functions described in this section perform least-squares fits to a general linear model, y = X c where y is a vector of n observations, X is an n by p matrix of predictor variables, and the elements of the vector c are the p unknown best-fit parameters which are to be estimated. The chi-squared value is given by \chi^2 = \sum_i w_i (y_i - \sum_j X_{ij} c_j)^2.

This formulation can be used for fits to any number of functions and/or variables by preparing the n-by-p matrix X appropriately. For example, to fit to a p-th order polynomial in x, use the following matrix, where the index i runs over the observations and the index j runs from 0 to p-1.

To fit to a set of p sinusoidal functions with fixed frequencies \omega_1, \omega_2, …, \omega_p, use, To fit to p independent variables x_1, x_2, …, x_p, use, where x_j(i) is the i-th value of the predictor variable x_j.

The functions described in this section are declared in the header file `gsl_multifit.h`.

The solution of the general linear least-squares system requires an additional working space for intermediate results, such as the singular value decomposition of the matrix X.

Function: gsl_multifit_linear_workspace * gsl_multifit_linear_alloc (size_t n, size_t p)

  This function allocates a workspace for fitting a model to n observations using p parameters.

Function: void gsl_multifit_linear_free (gsl_multifit_linear_workspace * work)

  This function frees the memory associated with the workspace w.

Function: int gsl_multifit_linear (const gsl_matrix * X, const gsl_vector * y, gsl_vector * c, gsl_matrix * cov, double * chisq, gsl_multifit_linear_workspace * work)

  This function computes the best-fit parameters c of the model y = X c for the observations y and the matrix of predictor variables X, using the preallocated workspace provided in work. The p-by-p variance-covariance matrix of the model parameters cov is set to \sigma^2 (X^T X)^{-1}, where \sigma is the standard deviation of the fit residuals. The sum of squares of the residuals from the best-fit, \chi^2, is returned in chisq. If the coefficient of determination is desired, it can be computed from the expression R^2 = 1 - \chi^2 / TSS, where the total sum of squares (TSS) of the observations y may be computed from `gsl_stats_tss`.

  The best-fit is found by singular value decomposition of the matrix X using the modified Golub-Reinsch SVD algorithm, with column scaling to improve the accuracy of the singular values. Any components which have zero singular value (to machine precision) are discarded from the fit.

Function: int gsl_multifit_wlinear (const gsl_matrix * X, const gsl_vector * w, const gsl_vector * y, gsl_vector * c, gsl_matrix * cov, double * chisq, gsl_multifit_linear_workspace * work)

  This function computes the best-fit parameters c of the weighted model y = X c for the observations y with weights w and the matrix of predictor variables X, using the preallocated workspace provided in work. The p-by-p covariance matrix of the model parameters cov is computed as (X^T W X)^{-1}. The weighted sum of squares of the residuals from the best-fit, \chi^2, is returned in chisq. If the coefficient of determination is desired, it can be computed from the expression R^2 = 1 - \chi^2 / WTSS, where the weighted total sum of squares (WTSS) of the observations y may be computed from `gsl_stats_wtss`.

Function: int gsl_multifit_linear_svd (const gsl_matrix * X, const gsl_vector * y, double tol, size_t * rank, gsl_vector * c, gsl_matrix * cov, double * chisq, gsl_multifit_linear_workspace * work)
Function: int gsl_multifit_wlinear_svd (const gsl_matrix * X, const gsl_vector * w, const gsl_vector * y, double tol, size_t * rank, gsl_vector * c, gsl_matrix * cov, double * chisq, gsl_multifit_linear_workspace * work)

  In these functions components of the fit are discarded if the ratio of singular values s_i/s_0 falls below the user-specified tolerance tol, and the effective rank is returned in rank.

Function: int gsl_multifit_linear_usvd (const gsl_matrix * X, const gsl_vector * y, double tol, size_t * rank, gsl_vector * c, gsl_matrix * cov, double * chisq, gsl_multifit_linear_workspace * work)
Function: int gsl_multifit_wlinear_usvd (const gsl_matrix * X, const gsl_vector * w, const gsl_vector * y, double tol, size_t * rank, gsl_vector * c, gsl_matrix * cov, double * chisq, gsl_multifit_linear_workspace * work)

  These functions compute the fit using an SVD without column scaling.

Function: int gsl_multifit_linear_est (const gsl_vector * x, const gsl_vector * c, const gsl_matrix * cov, double * y, double * y_err)

  This function uses the best-fit multilinear regression coefficients c and their covariance matrix cov to compute the fitted function value y and its standard deviation y_err for the model y = x.c at the point x.

Function: int gsl_multifit_linear_residuals (const gsl_matrix * X, const gsl_vector * y, const gsl_vector * c, gsl_vector * r)

  This function computes the vector of residuals r = y - X c for the observations y, coefficients c and matrix of predictor variables X.

Next: Robust linear regression, Previous: Linear fitting without a constant term, Up: Least-Squares Fitting [Index]