The SIGMA-REJECTION optimization method for fits to 1-D data. Alternate names are SIG-REJ and SR.
sigma-rejection [niter] [lrej] [hrej] [grow] [omethod]
The SIGMA-REJECTION optimization method is based upon the
IRAF method SFIT.
It iteratively fits data:
a best-fit to the data is determined using a `regular' optimization method
(e.g. LEVENBERG-MARQUARDT), then
outliers data points are filtered out of the dataset, and the data refit,
etc. Iterations cease when there is no change in the filter from one
iteration to the next, or when the fit has iterated a user-specified maximum
number of times.
Parameter=niter (integer default=5 min=0 max=128)
The maximum number of iterations in the fit; if 0, the fit will
run to convergence (i.e., until there is no change in the filter).
Parameter=lrej (real default=3 min=0 max=10)
Data point rejection criterion in units of
sigma, for data points below the
model (e.g., absorption line data); using lrej is equivalent to
doing LPLOT DELCHI and
filtering out all data points lying below
y = -lrej on that plot.
Parameter=hrej (real default=3 min=0 max=10)
Data point rejection criterion in units of
sigma, for data points above the
model (e.g., absorption line data); using hrej is equivalent to
doing LPLOT DELCHI and
filtering out all data points lying above
y = hrej on that plot.
Parameter=grow (integer default=0 min=0 max=1024)
When a given data point is to be filtered out, this parameter sets
the number of pixels adjacent to that pixel which are also to
be filtered out, i.e., if 0, only the data point itself is filtered
out; if 1, the data point and its two immediate neighbors are
filtered out, etc.
The optimization method to use to perform fits at each iteration.
Note that quotes are required around the name!
- sherpa
-
get_method_expr,
grid,
grid-powell,
levenberg-marquardt,
method,
monte-lm,
monte-powell,
montecarlo,
powell,
simplex,
simul-ann-1,
simul-ann-2,
simul-pow-1,
simul-pow-2,
usermethod
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