GNU Scientific Library – Reference Manual: Multi-parameter regression

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### 38.3 Multi-parameter regression

This section describes routines which perform least squares fits to a linear model by minimizing the cost function

    \chi^2 = \sum_i w_i (y_i - \sum_j X_ij c_j)^2 = || y - Xc ||_W^2

where y is a vector of n observations, X is an n-by-p matrix of predictor variables, c is a vector of the p unknown best-fit parameters to be estimated, and ||r||_W^2 = r^T W r. The matrix W = diag(w_1,w_2,...,w_n) defines the weights or uncertainties of the observation vector.

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,

    X_{ij} = x_i^j

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,

    X_{ij} = sin(\omega_j x_i)

To fit to p independent variables x_1, x_2, …, x_p, use,

    X_{ij} = x_j(i)

where x_j(i) is the i-th value of the predictor variable x_j.

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.

These functions are declared in the header file `gsl_multifit.h`.

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

  This function allocates a workspace for fitting a model to a maximum of n observations using a maximum of p parameters. The user may later supply a smaller least squares system if desired. The size of the workspace is O(np + p^2).

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_svd (const gsl_matrix * X, gsl_multifit_linear_workspace * work)

  This function performs a singular value decomposition of the matrix X and stores the SVD factors internally in work.

Function: int gsl_multifit_linear_bsvd (const gsl_matrix * X, gsl_multifit_linear_workspace * work)

  This function performs a singular value decomposition of the matrix X and stores the SVD factors internally in work. The matrix X is first balanced by applying column scaling factors to improve the accuracy of the singular values.

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_linear_tsvd (const gsl_matrix * X, const gsl_vector * y, const double tol, gsl_vector * c, gsl_matrix * cov, double * chisq, size_t * rank, 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 a truncated SVD expansion. Singular values which satisfy s_i \le tol \times s_0 are discarded from the fit, where s_0 is the largest singular value. 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. The effective rank (number of singular values used in solution) is returned in rank. 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`.

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_wlinear_tsvd (const gsl_matrix * X, const gsl_vector * w, const gsl_vector * y, const double tol, gsl_vector * c, gsl_matrix * cov, double * chisq, size_t * rank, 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 a truncated SVD expansion. Singular values which satisfy s_i \le tol \times s_0 are discarded from the fit, where s_0 is the largest singular value. 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. The effective rank of the system (number of singular values used in the solution) is returned in rank. 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_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.

Function: size_t gsl_multifit_linear_rank (const double tol, const gsl_multifit_linear_workspace * work)

  This function returns the rank of the matrix X which must first have its singular value decomposition computed. The rank is computed by counting the number of singular values \sigma_j which satisfy \sigma_j > tol \times \sigma_0, where \sigma_0 is the largest singular value.

Next: Regularized regression, Previous: Linear regression, Up: Least-Squares Fitting [Index]