pearson.c File Reference

#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#include "include/pearson.h"

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Functions
int	pearson (double *x, double *y, int nr, double *k, double *kSD, double *b, double *bSD, double *r, double *ySD)
int	pearson2 (double *x, double *y, char *is, int nr, double *k, double *kSD, double *b, double *bSD, double *r, double *ySD)
int	pearson3 (double *x, double *y, int nr, double *k, double *kSD, double *b, double *bSD, double *r, double *ySD)
int	pearson4 (double *x, double *y, int nr, double start, double end, double *k, double *kSD, double *b, double *bSD, double *r, double *ySD)
int	best_pearson (double *x, double *y, int nr, int min_nr, int *first, int *last, double *k, double *kSD, double *b, double *bSD, double *r, double *ySD)
int	mean (double *x, double *y, int nr, double *xmean, double *xsd, double *ymean, double *ysd)
int	regr_line (double *x, double *y, int n, double *m, double *c)
int	highest_slope (double *x, double *y, int n, int slope_n, double *m, double *c, double *xi, double *xh)
int	highest_slope_after (double *x, double *y, int n, int slope_n, double x_start, double *m, double *c, double *xi, double *xh)

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Function Documentation

int best_pearson	(	double *	x,
		double *	y,
		int	nr,
		int	min_nr,
		int *	first,
		int *	last,
		double *	k,
		double *	kSD,
		double *	b,
		double *	bSD,
		double *	r,
		double *	ySD
	)

Find the best linear fit to double data (x[], y[]) with nr points. Data may contain NaN's, which are not used.

Returns:

Returns the nr of points actually used, or 0, in case of error.

Parameters:

x	Data x values
y	Data y values
nr	Number of data sample values
min_nr	Minimum nr of data points to use
first	Index [0..last-2] of the first point to use initially, and after fitting
last	Index [first+1..nr-1] of the last point to use initially, and after fitting
k	slope
kSD	S.D. of slope
b	y axis intercept
bSD	S.D. of y axis intercept
r	Pearson's correlation coefficient, r
ySD	Residual variance of y values

Definition at line 315 of file pearson.c.

References pearson(), and PEARSON_TEST.

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int highest_slope	(	double *	x,
		double *	y,
		int	n,
		int	slope_n,
		double *	m,
		double *	c,
		double *	xi,
		double *	xh
	)

Finds the regression line with the highest slope for x,y data

Returns:

Return 0 if ok.

Parameters:

x	An array of x axis values
y	An array of y axis values
n	The number of values in x and y arrays
slope_n	The number of samples used to fit the line
m	Pointer where calculated slope is written; NULL if not needed
c	Pointer where calculated y axis intercept is written; NULL if not needed
xi	Pointer where calculated x axis intercept is written; NULL if not needed
xh	Pointer where the place (x) of the highest slope is written; NULL if not needed

Definition at line 479 of file pearson.c.

References regr_line().

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int highest_slope_after	(	double *	x,
		double *	y,
		int	n,
		int	slope_n,
		double	x_start,
		double *	m,
		double *	c,
		double *	xi,
		double *	xh
	)

Finds the regression line with the highest slope for x,y data after specified x.

Returns:

Return 0 if ok.

Parameters:

x	An array of x axis values
y	An array of y axis values
n	The number of values in x and y arrays
slope_n	The number of samples used to fit the line
x_start	Estimation start x value, samples with smaller x are ignored; can usually be set to zero
m	Pointer where calculated slope is written; NULL if not needed
c	Pointer where calculated y axis intercept is written; NULL if not needed
xi	Pointer where calculated x axis intercept is written; NULL if not needed
xh	Pointer where the place (x) of the highest slope is written; NULL if not needed

Definition at line 530 of file pearson.c.

References regr_line().

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int mean	(	double *	x,
		double *	y,
		int	nr,
		double *	xmean,
		double *	xsd,
		double *	ymean,
		double *	ysd
	)

Calculates the mean and SD of data. Data (y data) may contain NaN's.

Returns:

Returns !=0 in case of an error.

Parameters:

x	Data x values
y	Data y values
nr	Number of data sample values
xmean	Calculated x mean
xsd	Calculated SD of x mean
ymean	Calculated y mean
ysd	Calculated SD of y mean

Definition at line 402 of file pearson.c.

Referenced by pivot_lep().

int pearson	(	double *	x,
		double *	y,
		int	nr,
		double *	k,
		double *	kSD,
		double *	b,
		double *	bSD,
		double *	r,
		double *	ySD
	)

Calculate slope and intercept of a line and Pearson's correlation coefficient.

Returns:

If successful, function returns value 0.

Parameters:

x	data x values
y	data y values
nr	number of data sample values
k	slope
kSD	S.D. of slope
b	y axis intercept
bSD	S.D. of y axis intercept
r	Pearson's correlation coefficient, r
ySD	Residual variance of y values

Definition at line 82 of file pearson.c.

References PEARSON_TEST.

Referenced by best_pearson(), pearson2(), pearson3(), and pearson4().

int pearson2	(	double *	x,
		double *	y,
		char *	is,
		int	nr,
		double *	k,
		double *	kSD,
		double *	b,
		double *	bSD,
		double *	r,
		double *	ySD
	)

Calculate slope and intercept of a line and Pearson's correlation coefficient Array char is[] specifies whether single (x,y) points are used in the fit.

Returns:

If successful, function returns value 0.

Parameters:

x	data x values
y	data y values
is	Switch values: 0=do not use this point
nr	number of data sample values
k	slope
kSD	S.D. of slope
b	y axis intercept
bSD	S.D. of y axis intercept
r	Pearson's correlation coefficient, r
ySD	Residual variance of y values

Definition at line 175 of file pearson.c.

References pearson().

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int pearson3	(	double *	x,
		double *	y,
		int	nr,
		double *	k,
		double *	kSD,
		double *	b,
		double *	bSD,
		double *	r,
		double *	ySD
	)

Calculate slope and intercept of a line and Pearson's correlation coefficient Data points may contain NaN's.

Returns:

If successful, function returns value 0.

Parameters:

x	data x values
y	data y values
nr	number of data sample values
k	slope
kSD	S.D. of slope
b	y axis intercept
bSD	S.D. of y axis intercept
r	Pearson's correlation coefficient, r
ySD	Residual variance of y values

Definition at line 218 of file pearson.c.

References pearson().

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int pearson4	(	double *	x,
		double *	y,
		int	nr,
		double	start,
		double	end,
		double *	k,
		double *	kSD,
		double *	b,
		double *	bSD,
		double *	r,
		double *	ySD
	)

Calculate slope and intercept of a line and Pearson's correlation coefficient Data points may contain NaN's. Fit start and end times are specified.

Returns:

If successful, function returns value 0.

Parameters:

x	data x values
y	data y values
nr	number of data sample values
start	fit start time
end	fit end time
k	slope
kSD	S.D. of slope
b	y axis intercept
bSD	S.D. of y axis intercept
r	Pearson's correlation coefficient, r
ySD	Residual variance of y values

Definition at line 260 of file pearson.c.

References pearson(), and PEARSON_TEST.

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int regr_line	(	double *	x,
		double *	y,
		int	n,
		double *	m,
		double *	c
	)

Calculates regression line slope (m) and y axis intercept.

Returns:

Returns 0 if ok.

Parameters:

x	An array of x axis values
y	An array of y axis values
n	The number of values in x and y arrays
m	Pointer where calculated slope is written
c	Pointer where calculated y axis intercept

Definition at line 445 of file pearson.c.

Referenced by highest_slope(), and highest_slope_after().