akaike_info_criterion_lsq

astropy.stats.akaike_info_criterion_lsq(ssr, n_params, n_samples)[source] [edit on github]

Computes the Akaike Information Criterion assuming that the observations are Gaussian distributed.

In this case, AIC is given as

\[\mathrm{AIC} = n\ln\left(\dfrac{\mathrm{SSR}}{n}\right) + 2k\]

In case that the sample size is not “large enough”, a correction is applied, i.e.

\[\mathrm{AIC} = n\ln\left(\dfrac{\mathrm{SSR}}{n}\right) + 2k + \dfrac{2k(k+1)}{n-k-1}\]

in which \(n\) is the sample size, \(k\) is the number of free parameters and \(\mathrm{SSR}\) stands for the sum of squared residuals between model and data.

This is applicable, for instance, when the parameters of a model are estimated using the least squares statistic.

Parameters:

ssr : float

Sum of squared residuals (SSR) between model and data.

n_params : int

Number of free parameters of the model, i.e., the dimension of the parameter space.

n_samples : int

Number of observations.

Returns:

aic : float

Akaike Information Criterion.

References

[R33]Akaike Information Criteria <http://avesbiodiv.mncn.csic.es/estadistica/ejemploaic.pdf>
[R34]Hu, S. Akaike Information Criterion. <http://www4.ncsu.edu/~shu3/Presentation/AIC.pdf>
[R35]Origin Lab. Comparing Two Fitting Functions. <http://www.originlab.com/doc/Origin-Help/PostFit-CompareFitFunc>

Examples

This example is based on Astropy Modeling webpage, Compound models section.

>>> import numpy as np
>>> from astropy.modeling import models, fitting
>>> from astropy.stats.info_theory import akaike_info_criterion_lsq
>>> np.random.seed(42)
>>> # Generate fake data
>>> g1 = models.Gaussian1D(.1, 0, 0.2) # changed this to noise level
>>> g2 = models.Gaussian1D(.1, 0.3, 0.2) # and added another Gaussian
>>> g3 = models.Gaussian1D(2.5, 0.5, 0.1)
>>> x = np.linspace(-1, 1, 200)
>>> y = g1(x) + g2(x) + g3(x) + np.random.normal(0., 0.2, x.shape)
>>> # Fit with three Gaussians
>>> g3_init = (models.Gaussian1D(.1, 0, 0.1)
...            + models.Gaussian1D(.1, 0.2, 0.15)
...            + models.Gaussian1D(2., .4, 0.1))
>>> fitter = fitting.LevMarLSQFitter()
>>> g3_fit = fitter(g3_init, x, y)
>>> # Fit with two Gaussians
>>> g2_init = (models.Gaussian1D(.1, 0, 0.1) +
...            models.Gaussian1D(2, 0.5, 0.1))
>>> g2_fit = fitter(g2_init, x, y)
>>> # Fit with only one Gaussian
>>> g1_init = models.Gaussian1D(amplitude=2., mean=0.3, stddev=.5)
>>> g1_fit = fitter(g1_init, x, y)
>>> # Compute the mean squared errors
>>> ssr_g3 = np.sum((g3_fit(x) - y)**2.0)
>>> ssr_g2 = np.sum((g2_fit(x) - y)**2.0)
>>> ssr_g1 = np.sum((g1_fit(x) - y)**2.0)
>>> akaike_info_criterion_lsq(ssr_g3, 9, x.shape[0]) 
-660.41075962620482
>>> akaike_info_criterion_lsq(ssr_g2, 6, x.shape[0]) 
-662.83834510232043
>>> akaike_info_criterion_lsq(ssr_g1, 3, x.shape[0]) 
-647.47312032659499

Hence, from the AIC values, we would prefer to choose the model g2_fit. However, we can considerably support the model g3_fit, since the difference in AIC is about 2.4. We should reject the model g1_fit.