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GORegression

GORegression

Object Hierarchy

    GBoxed
    ├── go_regression_stat_t
    ╰── go_regression_stat_tl

Description

Functions

GORegressionFunction ()

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

Returns


GORegressionFunctionl ()

GORegressionResult
(*GORegressionFunctionl) (long double *x,
                          long double *params,
                          long double *f);

Returns


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_exponential_regression_as_log ()

GORegressionResult
go_exponential_regression_as_log (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 as go_exponential_regression, but returns the logarithm of the coefficients instead or the coefficients themselves. Fits to "y = b * exp (m1*x1) * ... * exp (md*xd) " or equivalently to "ln y = ln b + x1 * m1 + ... + xd * 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_exponential_regression_as_logl ()

GORegressionResult
go_exponential_regression_as_logl (long double **xss,
                                   int dim,
                                   const long double *ys,
                                   int n,
                                   gboolean affine,
                                   long double *res,
                                   go_regression_stat_tl *stat_);

Returns


go_exponential_regressionl ()

GORegressionResult
go_exponential_regressionl (long double **xss,
                            int dim,
                            const long double *ys,
                            int n,
                            gboolean affine,
                            long double *res,
                            go_regression_stat_tl *stat_);

Returns


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_linear_regressionl ()

GORegressionResult
go_linear_regressionl (long double **xss,
                       int dim,
                       const long double *ys,
                       int n,
                       gboolean affine,
                       long double *res,
                       go_regression_stat_tl *stat_);

Returns


go_linear_regression_leverage ()

GORegressionResult
go_linear_regression_leverage (double **A,
                               double *d,
                               int m,
                               int n);

Returns


go_linear_regression_leveragel ()

GORegressionResult
go_linear_regression_leveragel (long double **A,
                                long double *d,
                                int m,
                                int n);

Returns


go_linear_solve_multiple ()

GORegressionResult
go_linear_solve_multiple (double *const *const A,
                          double **B,
                          int n,
                          int bn);

Returns


go_linear_solve_multiplel ()

GORegressionResult
go_linear_solve_multiplel (long double *const *const A,
                           long double **B,
                           int n,
                           int bn);

Returns


go_linear_solve ()

GORegressionResult
go_linear_solve (double *const *const A,
                 const double *b,
                 int n,
                 double *res);

Returns


go_linear_solvel ()

GORegressionResult
go_linear_solvel (long double *const *const A,
                  const long double *b,
                  int n,
                  long double *res);

Returns


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_logarithmic_fitl ()

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

Returns


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_logarithmic_regressionl ()

GORegressionResult
go_logarithmic_regressionl (long double **xss,
                            int dim,
                            const long double *ys,
                            int n,
                            gboolean affine,
                            long double *res,
                            go_regression_stat_tl *stat_);

Returns


go_matrix_determinant ()

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

Returns


go_matrix_determinantl ()

long double
go_matrix_determinantl (long double *const * const A,
                        int n);

Returns


go_matrix_invert ()

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

Returns


go_matrix_invertl ()

gboolean
go_matrix_invertl (long double **A,
                   int n);

Returns


go_matrix_pseudo_inverse ()

void
go_matrix_pseudo_inverse (double *const * const A,
                          int m,
                          int n,
                          double threshold,
                          double **B);


go_matrix_pseudo_inversel ()

void
go_matrix_pseudo_inversel (long double *const * const A,
                           int m,
                           int n,
                           long double threshold,
                           long double **B);


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);

SYNOPSIS: result = non_linear_regression (f, xvals, par, yvals, sigmas, x_dim, p_dim, &chi, errors) Non linear regression.

Parameters

f

the model function.

[scope call]

xvals

independent values.

 

par

model parameters.

 

yvals

dependent values.

 

sigmas

stahdard deviations for the dependent values.

 

x_dim

Number of data points.

 

p_dim

Number of parameters.

 

chi

Chi Squared of the final result. This value is not very meaningful without the sigmas.

 

errors

MUST ALREADY BE ALLOCATED. These are the approximated standard deviation for each parameter.

 

Returns

the results of the non-linear regression from the given initial values. The resulting parameters are placed back into par .


go_non_linear_regressionl ()

GORegressionResult
go_non_linear_regressionl (GORegressionFunctionl f,
                           long double **xvals,
                           long double *par,
                           long double *yvals,
                           long double *sigmas,
                           int x_dim,
                           int p_dim,
                           long double *chi,
                           long double *errors);

SYNOPSIS: result = non_linear_regression (f, xvals, par, yvals, sigmas, x_dim, p_dim, &chi, errors) Non linear regression.

Parameters

f

the model function.

[scope call]

xvals

independent values.

 

par

model parameters.

 

yvals

dependent values.

 

sigmas

stahdard deviations for the dependent values.

 

x_dim

Number of data points.

 

p_dim

Number of parameters.

 

chi

Chi Squared of the final result. This value is not very meaningful without the sigmas.

 

errors

MUST ALREADY BE ALLOCATED. These are the approximated standard deviation for each parameter.

 

Returns

the results of the non-linear regression from the given initial values. The resulting parameters are placed back into par .


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_power_regressionl ()

GORegressionResult
go_power_regressionl (long double **xss,
                      int dim,
                      const long double *ys,
                      int n,
                      gboolean affine,
                      long double *res,
                      go_regression_stat_tl *stat_);

Returns


go_regression_stat_destroy ()

void
go_regression_stat_destroy (go_regression_stat_t *stat_);


go_regression_stat_destroyl ()

void
go_regression_stat_destroyl (go_regression_stat_tl *stat_);


go_regression_stat_new ()

go_regression_stat_t *
go_regression_stat_new (void);

Returns


go_regression_stat_newl ()

go_regression_stat_tl *
go_regression_stat_newl (void);

Returns

Types and Values

enum GORegressionResult

Members

GO_REG_ok

success.

 

GO_REG_invalid_dimensions

invalid dimensions.

 

GO_REG_invalid_data

invalid data:

 

GO_REG_not_enough_data

not enough data.

 

GO_REG_near_singular_good

probably good result.

 

GO_REG_near_singular_bad

probably bad result.

 

GO_REG_singular

singularity found.

 

GORegressionStat

#define GORegressionStat go_regression_stat_t


GORegressionStatl

#define GORegressionStatl go_regression_stat_tl


GO_LOGFIT_C_ACCURACY

#define GO_LOGFIT_C_ACCURACY 0.000001


GO_LOGFIT_C_RANGE_FACTOR

#define GO_LOGFIT_C_RANGE_FACTOR 100


GO_LOGFIT_C_STEP_FACTOR

#define GO_LOGFIT_C_STEP_FACTOR 0.05


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;

Members

double *se;

SE for each parameter estimator.

 

double *t;

t values for each parameter estimator.

 

double sqr_r;

squared 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_tl

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

Members

long double *se;

SE for each parameter estimator.

 

long double *t;

t values for each parameter estimator.

 

long double sqr_r;

squared R.

 

long double adj_sqr_r;

   

long double se_y;

the Standard Error of Y.

 

long double F;

   

int df_reg;

   

int df_resid;

   

int df_total;

   

long double ss_reg;

   

long double ss_resid;

   

long double ss_total;

   

long double ms_reg;

   

long double ms_resid;

   

long double ybar;

   

long double *xbar;

   

long double var;

the variance of the entire regression: sum(errors^2)/(n-xdim).

 
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