I have done fitting of data points with a given model that has two parameters (A and B), using NonlinearModelFit
. The result of the fit is the maximum of the likelihood function, aka the best fit, and the best values for parameters A and B, say (A0, B0). On the A-B plot this would be a single point. There is also a standard deviation given for both parameters separately σA and σB. However, the likelihood function should also give a region on A-B diagram, therefore possible values for A and B that give a good fit - the one that is under a certain confidence level, like 68%. This region is not exactly {A-σA, A+σA} × {B-σB, B+σB}. Instead, {A-σA, A+σA} is probably projection of this region on A-axis.
How to get the region of A-B dependence with a specified confidence level (.68, .95, .99) coming from the fit?
"ParameterConfidenceRegion"
in the docs forNonlinearModelFit[]
. $\endgroup$ – J. M.'s ennui♦ Apr 17 '13 at 1:17