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Last modified: December 2007

URL: http://cxc.harvard.edu/sherpabeta/covariance.py.html

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AHELP for Sherpa 4.0 Beta covariance Context: py.sherpa

Synopsis

Estimate confidence intervals for selected thawed parameters based on covariance method

Syntax

covar( [ id=1, [otherids], [parameters]] )

Description

Confidence interval bounds are determined for each selected parameter in turn. This method is based on computing the covariance matrix and returning the diagonal elements.

If no arguments are given this function, it is assumed that the data id is the default data id, and that limits should be computed for all thawed parameters.

If arguments are given, each argument is examined to see if it is a Sherpa model parameter. If model parameters are given, it is assumed that limits for only those parameters should be computed. Any argument that is not a model parameter is assumed to be a data id. When additional arguments are given, they may be data set ids or model parameters in any order, even mixed.

Example

sherpa> covar()
sherpa> covar(1)
sherpa> covar(1,2,g.fwhm,g.ampl)
sherpa> covar(1);
sherpa> covar(1,2,g.fwhm,g.ampl);

The covar function can be called with no arguments at all, with arguments that are data set IDs, or arguments that are model parameters. When no model parameters are given, limits for all thawed parameters are computed. When model parameters are given, limits are computed for only those parameters.

Bugs

See the bugs pages on the Sherpa Beta website for an up-to-date listing of known bugs.

See Also

py.sherpa
int_proj, int_unc, projection, reg_proj, reg_unc
Last modified: December 2007


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