logan.c File Reference

Regional Logan plot. More...

Go to the source code of this file.

Functions
int	best_logan_reed (double *x, double *y, double *wx, double *wy, int nr, int min_nr, double *slope, double *ic, double *nwss, double *sslope, double *sic, double *cx, double *cy, int *bnr, int verbose)
int	best_logan_regr (double *x, double *y, double *wx, double *wy, int nr, int min_nr, double *slope, double *ic, double *r, double *sslope, double *sic, int *bnr, int verbose)

Detailed Description

Regional Logan plot.

Estimation of the tracer distribution volume or distribution volume ratio from regional PET data using Logan multiple-time graphical analysis.

Copyright

(c) Turku PET Centre

Author

Vesa Oikonen

Definition in file logan.c.

Function Documentation

best_logan_reed()

int best_logan_reed	(	double *	x,
		double *	y,
		double *	wx,
		double *	wy,
		int	nr,
		int	min_nr,
		double *	slope,
		double *	ic,
		double *	nwss,
		double *	sslope,
		double *	sic,
		double *	cx,
		double *	cy,
		int *	bnr,
		int	verbose )

Finds the best least-squares line to (x,y)-data, leaving points out from the beginning.

Returns

Returns 0, if ok.

Parameters

x	Plot x axis values
y	Plot y axis values
wx	Weighting factors for x
wy	Weighting factors for y
nr	Nr of plot data points
min_nr	Min nr of points to use in the fit; must be >=4
slope	Slope is returned in here
ic	Y axis intercept is returned in here
nwss	sqrt(WSS)/wsum is returned here
sslope	Expected sd of slope at calculated points
sic	Expected sd of intercept at calculated points
cx	Calculated x data points
cy	Calculated y data points
bnr	Number of points in the best fit (incl data with w=0)
verbose	Verbose level; if zero, then nothing is printed to stderr or stdout

Definition at line 717 of file logan.c.

  {
  int from, to, ret, from_min, to_min;
  double *w, lic, lslope, lnwss, nwss_min;
 
  if(verbose>0) printf("best_logan_reed()\n");
  /* Check the data */
  if(x==NULL || y==NULL || nr<min_nr || nr<2) return(1);
  /* Check parameters */
  if(min_nr<4) return(2);
 
  /* Make room for work vector */
  w=(double*)malloc(nr*sizeof(double)); if(w==NULL) return(3);
 
  /* Search the plot range that gives the best nwss */
  nwss_min=9.99E+99; from_min=to_min=-1;
  for(from=0, to=nr-1; from<nr-min_nr; from++) {
    ret=llsqwt(x+from, y+from, (to-from)+1, wx+from, wy+from, 1.0E-10, w,
               &lic, &lslope, &lnwss, NULL, NULL, cx+from, cy+from);
    if(verbose>1) {
      printf("  range: %d-%d ; nwss=%g ; min=%g ; ret=%d\n",
        from, to, lnwss, nwss_min, ret);
    }
    if(ret==0 && lslope>0.0 && lnwss<nwss_min) {
      nwss_min=lnwss; from_min=from; to_min=to;}
  }
  if(from_min<0) {free(w); return(5);}
 
  /* Run llsqwt() again with that range, now with better resolution      */
  /* and this time compute also SD's                                     */
  from=from_min; to=to_min;
  ret=llsqwt(x+from, y+from, (to-from)+1, wx+from, wy+from, 1.0E-12, w,
             ic, slope, nwss, sic, sslope, cx+from, cy+from);
  free(w); if(ret) return(6);
  *bnr=(to-from)+1;
 
  return(0);
}

best_logan_regr()

int best_logan_regr	(	double *	x,
		double *	y,
		double *	wx,
		double *	wy,
		int	nr,
		int	min_nr,
		double *	slope,
		double *	ic,
		double *	r,
		double *	sslope,
		double *	sic,
		int *	bnr,
		int	verbose )

Find the best regression line to (x,y)-data, leaving points out from the beginning.

Returns

Returns 0, if ok.

Parameters

x	Plot x axis values
y	Plot y axis values
wx	Weighting factors for x
wy	Weighting factors for y
nr	Nr of plot data points
min_nr	Min nr of points to use in the fit; must be >=4
slope	Slope is returned in here
ic	Y axis intercept is returned in here
r	Pearson's correlation coefficient is returned here
sslope	Expected sd of slope at calculated points
sic	Expected sd of intercept at calculated points
bnr	Number of points in the best fit (incl data with w=0)
verbose	Verbose level; if zero, then nothing is printed to stderr or stdout

Definition at line 792 of file logan.c.

  {
  int fi, from, to, ret, from_min, to_min, n;
  double lic, lslope, lic_sd, lslope_sd, cv, cv_min, f;
  double *cx, *cy;
 
  if(verbose>0) printf("best_logan_regr()\n");
  /* Check the data */
  if(x==NULL || y==NULL || nr<min_nr || nr<2) return(1);
  /* Check parameters */
  if(min_nr<4) return(2);
 
  /* Construct a checked data with no negative values and weights>0 */
  cx=(double*)malloc(nr*sizeof(double));
  cy=(double*)malloc(nr*sizeof(double));
  if(cx==NULL || cy==NULL) return(3);
  for(fi=n=0; fi<nr; fi++)
    if(wx[fi]>0 && wy[fi]>0 && !isnan(x[fi]) && !isnan(y[fi])) {
      cx[n]=x[fi]; cy[n]=y[fi]; n++;}
  if(n<min_nr) {free(cx); free(cy); return(4);}
 
  /* Search the plot range that gives the lowest CV for slope */
  cv_min=9.99E+99; from_min=to_min=-1;
  for(from=0, to=n-1; from<n-min_nr; from++) {
    /* Calculation of linear regression using pearson() */
    ret=pearson(
      cx+from, cy+from, (to-from)+1,
      &lslope, &lslope_sd, &lic, &lic_sd, r, &f
    );
    if(ret==0 && lslope>0) {
      cv=lslope_sd/lslope;
    } else cv=9.99E+99;
    if(cv<cv_min) {
      cv_min=cv; from_min=from; to_min=to;}
  }
  if(from_min<0) {free(cx); free(cy); return(5);}
 
  /* Run pearson() again with that range */
  from=from_min; to=to_min;
  ret=pearson(
    cx+from, cy+from, (to-from)+1,
    slope, sslope, ic, sic, r, &f
  );
  free(cx); free(cy); if(ret) return(6);
  *bnr=(to-from)+1;
 
  return(0);
}