Package lib :: Module model_selection
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Module model_selection

source code

Module for the statistical concept of model selection.

Functions [hide private]
float
aic(chi2, k, n)
Akaike's Information Criteria (AIC).
source code
float
aicc(chi2, k, n)
Small sample size corrected AIC.
source code
float
bic(chi2, k, n)
Bayesian or Schwarz Information Criteria.
source code
Variables [hide private]
  __package__ = 'lib'

Imports: log, RelaxError


Function Details [hide private]

aic(chi2, k, n)

source code 

Akaike's Information Criteria (AIC).

The formula is:

   AIC = chi2 + 2k
Parameters:
  • chi2 (float) - The minimised chi-squared value.
  • k (int) - The number of parameters in the model.
  • n (int) - The dimension of the relaxation data set.
Returns: float
The AIC value.

aicc(chi2, k, n)

source code 

Small sample size corrected AIC.

The formula is:

                      2k(k + 1)
   AICc = chi2 + 2k + ---------
                      n - k - 1
Parameters:
  • chi2 (float) - The minimised chi-squared value.
  • k (int) - The number of parameters in the model.
  • n (int) - The dimension of the relaxation data set.
Returns: float
The AIC value.

bic(chi2, k, n)

source code 

Bayesian or Schwarz Information Criteria.

The formula is:

   BIC = chi2 + k ln n
Parameters:
  • chi2 (float) - The minimised chi-squared value.
  • k (int) - The number of parameters in the model.
  • n (int) - The dimension of the relaxation data set.
Returns: float
The AIC value.