GORegression

GORegression

Functions

Types and Values

Description

Functions

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".

Parameters

xss

x-vectors (i.e. independent data)

 

dim

number of x-vectors.

 

ys

y-vector. (Dependent data.)

 

n

number of data points.

 

affine

if true, a non-zero constant is allowed.

 

res

output place for constant[0] and slope1[1], slope2[2],... There will be dim+1 results.

 

stat_

non-NULL storage for additional results.

 

Returns

GORegressionResult as above.


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".

Parameters

xss

x-vectors (i.e. independent data)

 

dim

number of x-vectors

 

ys

y-vector (dependent data)

 

n

number of data points

 

affine

if TRUE, a non-one multiplier is allowed

 

res

output place for constant[0] and root1[1], root2[2],... There will be dim+1 results.

 

stat_

non-NULL storage for additional results.

 

Returns

GORegressionResult as above.


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.)

Parameters

xss

x-vectors (i.e. independent data)

 

dim

number of x-vectors

 

ys

y-vector (dependent data)

 

n

number of data points

 

affine

if TRUE, a non-zero constant is allowed

 

res

output place for constant[0] and factor1[1], factor2[2],... There will be dim+1 results.

 

stat_

non-NULL storage for additional results.

 

Returns

GORegressionResult as above.


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".

Parameters

xss

x-vectors (i.e. independent data)

 

dim

number of x-vectors

 

ys

y-vector (dependent data)

 

n

number of data points

 

affine

if TRUE, a non-one multiplier is allowed

 

res

output place for constant[0] and root1[1], root2[2],... There will be dim+1 results.

 

stat_

non-NULL storage for additional results.

 

Returns

GORegressionResult as above.


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.)

Parameters

xs

x-vector (i.e. independent data)

 

ys

y-vector (dependent data)

 

n

number of data points

 

res

output place for sign[0], a[1], b[2], c[3], and sum of squared residuals[4].

 

Returns

GORegressionResult as above.


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_new ()

go_regression_stat_t *
go_regression_stat_new (void);


go_regression_stat_destroy ()

void
go_regression_stat_destroy (go_regression_stat_t *stat_);

Types and Values

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

Members

GO_REG_ok

   

GO_REG_invalid_dimensions

   

GO_REG_invalid_data

   

GO_REG_not_enough_data

   

GO_REG_near_singular_good

   

GO_REG_near_singular_bad

   

GO_REG_singular

   

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;