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docs: documentation of covariance and correlated fits extended.

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Fabian Joswig 2022-03-05 08:13:24 +00:00
parent a7ff26ed9c
commit 6bd3868179
3 changed files with 22 additions and 14 deletions
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27
pyerrors/fits.py
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@ -86,7 +86,7 @@ def least_squares(x, y, func, priors=None, silent=False, **kwargs):
```
It is important that all numpy functions refer to autograd.numpy, otherwise the differentiation
will not work
will not work.
priors : list, optional
priors has to be a list with an entry for every parameter in the fit. The entries can either be
Obs (e.g. results from a previous fit) or strings containing a value and an error formatted like
@ -95,24 +95,25 @@ def least_squares(x, y, func, priors=None, silent=False, **kwargs):
If true all output to the console is omitted (default False).
initial_guess : list
can provide an initial guess for the input parameters. Relevant for
non-linear fits with many parameters.
non-linear fits with many parameters.
method : str, optional
can be used to choose an alternative method for the minimization of chisquare.
The possible methods are the ones which can be used for scipy.optimize.minimize and
migrad of iminuit. If no method is specified, Levenberg-Marquard is used.
Reliable alternatives are migrad, Powell and Nelder-Mead.
resplot : bool
If true, a plot which displays fit, data and residuals is generated (default False).
qqplot : bool
If true, a quantile-quantile plot of the fit result is generated (default False).
expected_chisquare : bool
If true prints the expected chisquare which is
corrected by effects caused by correlated input data.
This can take a while as the full correlation matrix
has to be calculated (default False).
correlated_fit : bool
If true, use the full correlation matrix in the definition of the chisquare
(only works for prior==None and when no method is given, at the moment).
If True, use the full inverse covariance matrix in the definition of the chisquare cost function.
For details about how the covariance matrix is estimated see `pyerrors.obs.covariance`.
In practice the correlation matrix is Cholesky decomposed and inverted (instead of the covariance matrix).
This procedure should be numerically more stable as the correlation matrix is typically better conditioned (Jacobi preconditioning).
At the moment this option only works for `prior==None` and when no `method` is given.
expected_chisquare : bool
If True estimates the expected chisquare which is
corrected by effects caused by correlated input data (default False).
resplot : bool
If True, a plot which displays fit, data and residuals is generated (default False).
qqplot : bool
If True, a quantile-quantile plot of the fit result is generated (default False).
'''
if priors is not None:
return _prior_fit(x, y, func, priors, silent=silent, **kwargs)