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.