compute_aic: Compute the Akaike Information Criterion. Assuming additive...
In sbpiper: Data Analysis Functions for 'SBpipe' Package
Description
Usage
Arguments
Value
Examples
Description
Compute the Akaike Information Criterion. Assuming additive Gaussian
measurement noise of width 1, the term -2ln(L(theta|y)) ~ SSR ~ Chi^2
Usage
1
compute_aic(chi2 = 0, params = 0)
Arguments
chi2
the Chi^2 for the model
params
the number of model parameters
Value
the AIC
Examples
1
compute_aic(chi2=10, params=5)
sbpiper documentation built on May 2, 2019, 8:53 a.m.
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Description Usage Arguments Value Examples
Description
Compute the Akaike Information Criterion. Assuming additive Gaussian measurement noise of width 1, the term -2ln(L(theta|y)) ~ SSR ~ Chi^2
Usage
1 | compute_aic(chi2 = 0, params = 0)
|
Arguments
chi2 |
the Chi^2 for the model |
params |
the number of model parameters |
Value
the AIC
Examples
1 | compute_aic(chi2=10, params=5)
|
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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