|AHELP for CIAO 4.5 Sherpa v1||
Estimate confidence intervals for selected thawed parameters based on confidence method
conf([id], [otherids], [parameters])
The confidence command computes confidence interval bounds for the specified model parameters in the dataset. A given parameter's value is varied along a grid of values while the values of all the other thawed parameters are allowed to float to new best-fit values. The function may be called as "confidence" or simply "conf".
The confidence command differs from covariance ("ahelp covariance") in that all other thawed parameters are allowed to float to new best-fit values, instead of being fixed to the initial best-fit values. While confidence is more general (e.g. allowing the user to examine the parameter space away from the best-fit point), it is in the strictest sense no more accurate than covariance for determining confidence intervals.
The computationally intensive confidence function is parallelized to make use of multi-core systems (i.e., laptops or desktops with 2 or 4 cores) to provide significant improvements in efficiency compared to previous releases of Sherpa; the 'numcores' option of the set_conf_opt command may be used to specify how the cores should be used when confidence is run.
- id, otherids - the id(s) of the dataset(s) to use; default is all thawed parameters for all datasets which have a model defined; otherids=None
- parameters - model parameters on which confidence should be run; default is all thawed parameters
When running on multiple ids and parameters, the arguments may be given in any order (see the examples); any argument that is not defined as a model parameter is assumed to be a data id.
To control the number of sigma used for the limits (i.e. the change in statistic), set the value of get_conf.sigma ("ahelp get_conf"). The default setting is to calculate +/- 1 sigma limits.
Because confidence estimates intervals for each parameter independently, the relationship between sigma and the change in statistic value delta_S can be particularly simple: sigma = the square root of delta_S for statistics sampled from the chi-square distribution and for the Cash statistic, and is approximately equal to the square root of (2 * delta_S) for fits based on the general log-likelihood.
The confidence function is a replacement for projection. The confidence EstMethod class uses the same infrastructure as the equivalent projection class, so there should be no interface changes (with the exception of the additional options: verbose and openinterval). However, the underlying algorithm used is very different than projection. In the paragraphs to follow, a description of the algorithm used in confidence shall be given.
The resulting output/answer from the confidence function is basically a 1-dimensional root in the translated coordinate system (translated by the value of the statistic at the minimum plus sigma^2). The Taylor series expansion of the multi-dimensional function at the minimum is:
T T f( x + dx ) ~ f( x ) + grad( f(x) ) dx + dx Hessian( f(x) ) dx + ...
Where x is understood to be the n-dimensional vector representing the free parameters to be fitted and the super-script 'T' is the transpose of the row-vector. At or near the minimum, the gradient of the function is zero or negligible, respectively. So the leading term of the expansion is quadratic. The best root finding algorithm for a curve which is approximately parabolic is Muller's method. Muller's method is a generalization of the secant method: the secant method is an iterative root finding method that approximates the function by a straight line through two points, whereas Muller's method is an iterative root finding method that approxmiates the function by a quadratic polynomial through three points.
Three data points are the minimum input to Muller's root finding method. The first point to be submitted to the Muller's root finding method is the point at the minimum. To strategically choose the other two data points, the confidence function uses the output from covariance as the second data point. To generate the third data points for the input to Muller's root finding method, the secant root finding method is used since it only requires two data points to generate the next best approximation of the root.
However, there are cases where confidence cannot locate the root even though the root is bracketed within an interval (perhaps due to the bad resolution of the data). In such cases, when the option openinterval is set to False, the default, the confidence function will print a warning message about not able to find the root within the set tolerance and the function will return the average of the open interval which brackets the root. If the option openinterval is set to True then confidence will print the minimal open interval which brackets the root (not to be confused with the lower and upper bound of the confidence interval). The most accurate thing to do is to return an open interval where the root is localized/bracketed rather then the average of the open interval (since the average of the interval is not a root within the specified tolerance).
For a description of the secant method, see Numerical Recipes in Fortran, 2nd edition, 1986, Press et al., p. 347
Muller, David E., "A Method for Solving Algebraic Equations Using an Automatic Computer," MTAC, 10 (1956), 208-215.
An estimated confidence interval is accurate if and only if:
- the chi-square or logL surface in parameter space is approximately shaped like a multi-dimensional paraboloid, and
- the best-fit point is sufficiently far (~3 sigma) from parameter space boundaries.
One may determine if these conditions hold, for example, by plotting the fit statistic as a function of each parameter's values (the curve should approximate a parabola) and by examining contour plots of the fit statistics made by varying the values of two parameters at a time (the contours should be elliptical, and parameter space boundaries should be no closer than approximately 3 sigma from the best-fit point). The int_proj ("ahelp int_proj"). and reg_proj ("ahelp reg_proj"). can be used to create the contours.
If either of the conditions given above does not hold, then the output from confidence may be meaningless except to give an idea of the scale of the confidence intervals. To accurately determine the confidence intervals, one would have to reparameterize the model, or use Monte Carlo simulations or Bayesian methods.
Run confidence on the default dataset(s), calculating limits for all thawed parameters of all data sets for which the user has defined a model to be fit. The output for one data set with two model parameters looks like:
p1.gamma lower bound: -0.0779263 p1.ampl lower bound: -1.35974e-05 p1.ampl upper bound: 1.35974e-05 p1.gamma upper bound: 0.0785513 Dataset = 1 Confidence Method = confidence Fitting Method = levmar Statistic = chi2gehrels confidence 1-sigma (68.2689%) bounds: Param Best-Fit Lower Bound Upper Bound ----- -------- ----------- ----------- p1.gamma 2.09056 -0.0779263 0.0785513 p1.ampl 0.000210697 -1.35974e-05 1.35974e-05
sherpa> set_conf_opt('numcores', 2) sherpa> conf(2, pl1.gamma)
Use the set_conf_opt command to specify that only two of the available cores on the system should be utilized when the confidence command is run. Compute parameter limits for the model parameter pl1.gamma, which is assigned to dataset 2. The output is:
WARNING: New minimum statistic found while computing confidence limits WARNING: New best-fit parameters: Method = levmar Statistic = chi2gehrels Initial fit statistic = 2.09666 Final fit statistic = 1.94547 at function evaluation 30 Data points = 7 Degrees of freedom = 4 Probability [Q-value] = 0.745788 Reduced statistic = 0.486367 Change in statistic = 0.151189 abs1.nH 0.948597 pl1.gamma 1.913 pl1.ampl 2.9285e-05 pl1.gamma lower bound: -1.02113 pl1.gamma upper bound: 1.2016 Dataset = 2 Confidence Method = confidence Fitting Method = levmar Statistic = chi2gehrels confidence 1-sigma (68.2689%) bounds: Param Best-Fit Lower Bound Upper Bound ----- -------- ----------- ----------- pl1.gamma 1.913 -1.02113 1.2016