From fa080de7afc95aa1c19a6e6fc0e0708ced2eadc4 Mon Sep 17 00:00:00 2001
From: Joseph Hunkeler <jhunkeler@gmail.com>
Date: Wed, 8 Jul 2015 20:46:52 -0400
Subject: Initial commit

---
 math/bevington/linfit.f | 79 +++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 79 insertions(+)
 create mode 100644 math/bevington/linfit.f

(limited to 'math/bevington/linfit.f')

diff --git a/math/bevington/linfit.f b/math/bevington/linfit.f
new file mode 100644
index 00000000..132c62b0
--- /dev/null
+++ b/math/bevington/linfit.f
@@ -0,0 +1,79 @@
+c subroutine linfit.f
+c
+c source
+c   Bevington, pages 104-105.
+c
+c purpose
+c   make a least-squares fit to a data set with a straight line
+c
+c usage
+c   call linfit (x, y, sigmay, npts, mode, a, sigmaa, b, sigmab, r)
+c
+c description of parameters
+c   x      - array of data points for independent variable
+c   y      - array of data points for dependent variable
+c   sigmay - array of standard deviations for y data points
+c   npts   - number of pairs of data points
+c   mode   - determines method of weighting least-squares fit
+c            +1 (instrumental) weight(i) = 1./sigmay(i)**2
+c             0 (no weighting) weight(i) = 1.
+c            -1 (statistical)  weight(i) = 1./y(i)
+c   a      - y intercept of fitted straight line
+c   sigmaa - standard deviation of a
+c   b      - slope of fitted straight line
+c   sigmab - standard deviation of b
+c   r      - linear correlation coefficient
+c
+c subroutines and function subprograms required
+c   none
+c
+      subroutine linfit (x,y,sigmay,npts,mode,a,sigmaa,b,sigmab,r)
+      double precision sum,sumx,sumy,sumx2,sumxy,sumy2
+      double precision xi,yi,weight,delta,varnce
+      dimension x(1),y(1),sigmay(1)
+c
+c accumulate weighted sums
+c
+11    sum=0.
+      sumx=0.
+      sumy=0.
+      sumx2=0.
+      sumxy=0.
+      sumy2=0.
+21    do 50 i=1,npts
+      xi=x(i)
+      yi=y(i)
+      if (mode) 31,36,38
+31    if (yi) 34,36,32
+32    weight=1./yi
+      goto 41
+34    weight=1./(-yi)
+      goto 41
+36    weight=1.
+      goto 41
+38    weight=1./sigmay(i)**2
+41    sum=sum+weight
+      sumx=sumx+weight*xi
+      sumy=sumy+weight*yi
+      sumx2=sumx2+weight*xi*xi
+      sumxy=sumxy+weight*xi*yi
+      sumy2=sumy2+weight*yi*yi
+50    continue
+c
+c calculate coefficients and standard deviations
+c
+51    delta=sum*sumx2-sumx*sumx
+      a=(sumx2*sumy-sumx*sumxy)/delta
+53    b=(sumxy*sum-sumx*sumy)/delta
+61    if (mode) 62,64,62
+62    varnce=1.
+      goto 67
+64    c=npts-2
+      varnce=(sumy2+a*a*sum+b*b*sumx2
+     *-2.*(a*sumy+b*sumxy-a*b*sumx))/c
+67    sigmaa=dsqrt(varnce*sumx2/delta)
+68    sigmab=dsqrt(varnce*sum/delta)
+71    r=(sum*sumxy-sumx*sumy)/
+     *dsqrt(delta*(sum*sumy2-sumy*sumy))
+      return
+      end
-- 
cgit