GO_LOGFIT_C_ACCURACY

#define GO_LOGFIT_C_ACCURACY 0.000001

GO_LOGFIT_C_STEP_FACTOR

#define GO_LOGFIT_C_STEP_FACTOR 0.05

GO_LOGFIT_C_RANGE_FACTOR

#define GO_LOGFIT_C_RANGE_FACTOR 100

enum GORegressionResult

typedef enum {
	GO_REG_ok,
	GO_REG_invalid_dimensions,
	GO_REG_invalid_data,
	GO_REG_not_enough_data,
	GO_REG_near_singular_good, /* Probably good result */
	GO_REG_near_singular_bad, 	/* Probably bad result */
	GO_REG_singular
} GORegressionResult;

GORegressionFunction ()

GORegressionResult  (*GORegressionFunction)             (double *x,
                                                         double *params,
                                                         double *f);

go_linear_regression ()

GORegressionResult  go_linear_regression                (double **xss,
                                                         int dim,
                                                         const double *ys,
                                                         int n,
                                                         gboolean affine,
                                                         double *res,
                                                         go_regression_stat_t *stat_);

Performs multi-dimensional linear regressions on the input points. Fits to "y = b + a1 * x1 + ... ad * xd".

go_exponential_regression ()

GORegressionResult  go_exponential_regression           (double **xss,
                                                         int dim,
                                                         const double *ys,
                                                         int n,
                                                         gboolean affine,
                                                         double *res,
                                                         go_regression_stat_t *stat_);

Performs one-dimensional linear regressions on the input points. Fits to "y = b * m1^x1 * ... * md^xd " or equivalently to "log y = log b + x1 * log m1 + ... + xd * log md".

go_logarithmic_regression ()

GORegressionResult  go_logarithmic_regression           (double **xss,
                                                         int dim,
                                                         const double *ys,
                                                         int n,
                                                         gboolean affine,
                                                         double *res,
                                                         go_regression_stat_t *stat_);

This is almost a copy of linear_regression and produces multi-dimensional linear regressions on the input points after transforming xss to ln(xss). Fits to "y = b + a1 * z1 + ... ad * zd" with "zi = ln (xi)". Problems with arrays in the calling function: see comment to gnumeric_linest, which is also valid for gnumeric_logreg.

(Errors: less than two points, all points on a vertical line, non-positive x data.)

go_non_linear_regression ()

GORegressionResult  go_non_linear_regression            (GORegressionFunction f,
                                                         double **xvals,
                                                         double *par,
                                                         double *yvals,
                                                         double *sigmas,
                                                         int x_dim,
                                                         int p_dim,
                                                         double *chi,
                                                         double *errors);

go_power_regression ()

GORegressionResult  go_power_regression                 (double **xss,
                                                         int dim,
                                                         const double *ys,
                                                         int n,
                                                         gboolean affine,
                                                         double *res,
                                                         go_regression_stat_t *stat_);

Performs one-dimensional linear regressions on the input points. Fits to "y = b * x1^m1 * ... * xd^md " or equivalently to "log y = log b + m1 * log x1 + ... + md * log xd".

go_logarithmic_fit ()

GORegressionResult  go_logarithmic_fit                  (double *xs,
                                                         const double *ys,
                                                         int n,
                                                         double *res);

Performs a two-dimensional non-linear fitting on the input points. Fits to "y = a + b * ln (sign * (x - c))", with sign in {-1, +1}. The graph is a logarithmic curve moved horizontally by c and possibly mirrored across the y-axis (if sign = -1).

Fits c (and sign) by iterative trials, but seems to be fast enough even for automatic recomputation.

Adapts c until a local minimum of squared residuals is reached. For each new c tried out the corresponding a and b are calculated by linear regression. If no local minimum is found, an error is returned. If there is more than one local minimum, the one found is not necessarily the smallest (i.e., there might be cases in which the returned fit is not the best possible). If the shape of the point cloud is to different from ``logarithmic'', either sign can not be determined (error returned) or no local minimum will be found.

(Requires: at least 3 different x values, at least 3 different y values.)

go_matrix_invert ()

gboolean            go_matrix_invert                    (double **A,
                                                         int n);

go_matrix_determinant ()

double              go_matrix_determinant               (double *const *const A,
                                                         int n);

go_regression_stat_t

typedef struct {
        double *se;		/* SE for each parameter estimator */
        double *t;  		/* t values for each parameter estimator */
        double sqr_r;
	double adj_sqr_r;
        double se_y; 		/* The Standard Error of Y */
        double F;
        int    df_reg;
        int    df_resid;
        int    df_total;
        double ss_reg;
        double ss_resid;
        double ss_total;
        double ms_reg;
        double ms_resid;
	double ybar;
	double *xbar;
	double var; 		/* The variance of the entire regression: sum(errors^2)/(n-xdim) */
} go_regression_stat_t;

go_regression_stat_new ()

go_regression_stat_t * go_regression_stat_new           (void);

go_regression_stat_destroy ()

void                go_regression_stat_destroy          (go_regression_stat_t *stat_);