1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
|
.TH GRADLS 3M
.SH NAME
gradls
.SH DESCRIPTION
subroutine gradls.f
source
Bevington, pages 220-222.
purpose
make gradient-search least-squares fit to data with a
specified function which is not linear in coefficients
usage
call gradls (x, y, sigmay, npts, nterms, mode, a, deltaa,
yfit, chisqr)
description of parameters
x - array of data points for independent variable
y - array of data points for dependent variable
sigmay - array of standard deviations for y data points
npts - number of pairs of data points
nterms - number of parameters
mode - determines method of weighting least-squares fit
+1 (instrumental) weight(i) = 1./sigmay(i)**2
0 (no weighting) weight(i) = 1.
-1 (statistical) weight(i) = 1./y(i)
a - array of parameters
deltaa - array of increments for parameters a
yfit - array of calculated values of y
chisqr - reduced chi square for fit
subroutines and function subprograms required
functn (x, i, a)
evaluates the fitting function for the ith term
fchisq (y, sigmay, npts, nfree, mode, yfit)
evaluates reduced chi squared for fit to data
comments
valid for nterms up to 10
|
