Module model_selection

Module for the statistical concept of model selection.

Functions

float aic(chi2, k, n)
Akaike's Information Criteria (AIC).

float aicc(chi2, k, n)
Small sample size corrected AIC.

float bic(chi2, k, n)
Bayesian or Schwarz Information Criteria.

Variables

__package__ = 'lib'

Imports: log, RelaxError

Function Details

aic(chi2, k, n)

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)

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)

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.