AIC-MNM-method: AIC Method for "MNM" Class
In MultiNMix: Multi-Species N-Mixture (MNM) Models with 'nimble'
AIC,MNM-method R Documentation
AIC Method for "MNM" Class
Description
Computes the Akaike Information Criterion (AIC) for an object of class "MNM".
AIC is a metric used for model comparison, balancing goodness of fit and model complexity.
The formula for AIC is:
AIC = -2 \cdot \log L + 2 \cdot k
where:
-
\log L is the log-likelihood of the model.
-
k is the number of parameters in the model.
Usage
## S4 method for signature 'MNM'
AIC(object)
Arguments
object
An object of class "MNM".
Value
A numeric value representing the AIC of the fitted model.
MultiNMix documentation built on April 3, 2025, 6:10 p.m.
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| AIC,MNM-method | R Documentation |
AIC Method for "MNM" Class
Description
Computes the Akaike Information Criterion (AIC) for an object of class "MNM".
AIC is a metric used for model comparison, balancing goodness of fit and model complexity.
The formula for AIC is:
AIC = -2 \cdot \log L + 2 \cdot k
where:
-
\log Lis the log-likelihood of the model. -
kis the number of parameters in the model.
Usage
## S4 method for signature 'MNM'
AIC(object)
Arguments
object |
An object of class |
Value
A numeric value representing the AIC of the fitted model.
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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