Calculate Error Contour Maps

Options | Statistical Analysis | s,M rmsd Contour

Options | Statistical Analysis | s1,s2 rmsd Contour

Options | Statistical Analysis | K Contour

These functions automate the repeated fitting required in the evaluation of
the statistical accuracy of the best-fit parameters via F-statistics.
The principle behind the F-statistics error analysis is constraining one
parameter (the one for which the error estimates are being calculated) while
optimizing (floating) all others to achieve the best-fit given this one
constraint. F-statistics can predict the increase of the sum of squares that is
associated e.g. with one standard deviation contour (depending on the number of
data points and the overall best-fit sum of squares). This procedure, as
described in *Bevington: Data Reduction in the Physical Sciences* and in *Press
et al.: Numerical Recipes in C*, incorporates correlation of the fit
parameters into the error estimates, and does not make assumptions about the
shape of error contour map.

The Sedfit functions can be helpful to calculate 2-dimensional confidence
intervals. The functions *s,M rmsd contour*, and *s1,s2 rmsd contour *can
trace a given confidence limit (to be specified by the user as a given rmsd
level) in a 2-parameter space, floating all parameters other than s and M (or
s1, and s2, respectively). Also, limits for the parameter range and the
resolution for which the map is examined have to be provided. If the rmsd for
tracing is specified as 0, all best-fits rmsd in the entire grid of the
specified grid are calculated.

The *K contour* function is analogous to the other contours, but
calculates the rmsd only for a given grid of K-values (association constants),
while floating all other parameters.

The results are stored as a ASCII table in the specified file for further analysis.

Please Note: These functions are very time-consuming and the independent
determination of the confidence level is required (the F-statistics
calculator function can be used). Also, while the algorithm that traces a
given confidence level in the error surface can be extremely useful in reducing
the computation time, it sometimes can loose the trace, or leave the trace
incomplete. This can result in reduced apparent confidence intervals. **For
these reasons, these functions should only be considered as aides, automating
part of the computationally intensive and repetitive fitting procedures. Control
calculations should be performed, and critical evaluation is absolutely crucial.**