Return the confidence-interval estimation object.
- Return type:
Estimate parameter confidence intervals using the confidence method.
Return one or all of the options for the confidence interval method.
Set an option of the conf estimation object.
The attributes of the confidence-interval object include:
The precision of the calculated limits. The default is 0.01.
Truethen the fit optimization used may be changed from the current setting (only for the error analysis) to use a faster optimization method. The default is
If the reduced chi square is larger than this value, do not use (only used with chi-square statistics). The default is 3.
The maximum number of re-fits allowed (that is, when the
reminfilter is met). The default is 5.
The maximum number of iterations allowed when bracketing limits, before stopping for that parameter. The default is 200.
The number of computer cores to use when evaluating results in parallel. This is only used if
True. The default is to use all cores.
confmethod should cope with intervals that do not converge (that is, when the
maxiterslimit has been reached). The default is
If there is more than one free parameter then the results can be evaluated in parallel, to reduce the time required. The default is
The minimum difference in statistic value for a new fit location to be considered better than the current best fit (which starts out as the starting location of the fit at the time
confis called). The default is 0.01.
What is the error limit being calculated. The default is 1.
Should the search be restricted to the soft limits of the parameters (
True), or can parameter values go out all the way to the hard limits if necessary (
False). The default is
The tolerance for the fit. The default is 0.2.
Should extra information be displayed during fitting? The default is
>>> print(get_conf()) name = confidence numcores = 8 verbose = False openinterval = False max_rstat = 3 maxiters = 200 soft_limits = False eps = 0.01 fast = False maxfits = 5 remin = 0.01 tol = 0.2 sigma = 1 parallel = True
reminfield to 0.05.
>>> cf = get_conf() >>> cf.remin = 0.05