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include <mach.h>
include <math/nlfit.h>
$if (datatype == r)
include "nlfitdefr.h"
$else
include "nlfitdefd.h"
$endif
define COV Mem$t[P2P($1)] # element of COV
# NLERRORS -- Procedure to calculate the reduced chi-squared of the fit
# and the errors of the coefficients. First the variance
# and the reduced chi-squared of the fit are estimated. If these two
# quantities are identical the variance is used to scale the errors
# in the coefficients. The errors in the coefficients are proportional
# to the inverse diagonal elements of MATRIX.
procedure nlerrors$t (nl, z, zfit, w, npts, variance, chisqr, errors)
pointer nl # curve descriptor
PIXEL z[ARB] # data points
PIXEL zfit[ARB] # fitted data points
PIXEL w[ARB] # array of weights
int npts # number of points
PIXEL variance # variance of the fit
PIXEL chisqr # reduced chi-squared of fit (output)
PIXEL errors[ARB] # errors in coefficients (output)
int i, n, nfree
pointer sp, covptr
PIXEL factor
begin
# Allocate space for covariance vector.
call smark (sp)
call salloc (covptr, NL_NPARAMS(nl), TY_PIXEL)
# Estimate the variance and reduce chi-squared of the fit.
n = 0
variance = PIXEL (0.0)
chisqr = PIXEL (0.0)
do i = 1, npts {
if (w[i] <= PIXEL (0.0))
next
factor = (z[i] - zfit[i]) ** 2
variance = variance + factor
chisqr = chisqr + factor * w[i]
n = n + 1
}
# Calculate the reduced chi-squared.
nfree = n - NL_NFPARAMS(nl)
if (nfree > 0) {
variance = variance / nfree
chisqr = chisqr / nfree
} else {
variance = PIXEL (0.0)
chisqr = PIXEL (0.0)
}
# If the variance equals the reduced chi_squared as in the case
# of uniform weights then scale the errors in the coefficients
# by the variance and not the reduced chi-squared
if (abs (chisqr - variance) <= EPSILON$T) {
if (nfree > 0)
factor = chisqr
else
factor = PIXEL (0.0)
} else
factor = 1.
# Calculate the errors in the coefficients.
call aclr$t (errors, NL_NPARAMS(nl))
call nlinvert$t (ALPHA(NL_ALPHA(nl)), CHOFAC(NL_CHOFAC(nl)),
COV(covptr), errors, PLIST(NL_PLIST(nl)), NL_NFPARAMS(nl), factor)
call sfree (sp)
end
# NLINVERT -- Procedure to invert matrix and compute errors
procedure nlinvert$t (alpha, chofac, cov, errors, list, nfit, variance)
PIXEL alpha[nfit,ARB] # data matrix
PIXEL chofac[nfit, ARB] # cholesky factorization
PIXEL cov[ARB] # covariance vector
PIXEL errors[ARB] # computed errors
int list[ARB] # list of active parameters
int nfit # number of fitted parameters
PIXEL variance # variance of the fit
int i, ier
begin
# Factorize the data matrix to determine the errors.
call nl_chfac$t (alpha, nfit, nfit, chofac, ier)
# Estimate the errors.
do i = 1, nfit {
call aclr$t (cov, nfit)
cov[i] = PIXEL (1.0)
call nl_chslv$t (chofac, nfit, nfit, cov, cov)
if (cov[i] >= PIXEL (0.0))
errors[list[i]] = sqrt (cov[i] * variance)
else
errors[list[i]] = PIXEL (0.0)
}
end
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