compute_aicc: Compute the corrected Akaike Information Criterion. Assuming...
In pdp10/sbpiper: Data Analysis Functions for 'SBpipe' Package
Description
Usage
Arguments
Value
Examples
Description
Compute the corrected Akaike Information Criterion. Assuming additive Gaussian
measurement noise of width 1, the term -2ln(L(theta|y)) ~ SSR ~ Chi^2
Usage
1
compute_aicc(chi2 = 0, params = 0, data_points = 0)
Arguments
chi2
the Chi^2 for the model
params
the number of model parameters
data_points
the number of data points
Value
the AICc
Examples
1
compute_aicc(chi2=10, params=5, data_points=100)
pdp10/sbpiper documentation built on May 17, 2019, 11:17 p.m.
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Description Usage Arguments Value Examples
Description
Compute the corrected Akaike Information Criterion. Assuming additive Gaussian measurement noise of width 1, the term -2ln(L(theta|y)) ~ SSR ~ Chi^2
Usage
1 | compute_aicc(chi2 = 0, params = 0, data_points = 0)
|
Arguments
chi2 |
the Chi^2 for the model |
params |
the number of model parameters |
data_points |
the number of data points |
Value
the AICc
Examples
1 | compute_aicc(chi2=10, params=5, data_points=100)
|
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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