AIC,MNM-method

Computes the Akaike Information Criterion (AIC) for an object of class <code>"MNM"</code>.
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:<ul>
<li>$\log L$ is the log-likelihood of the model.</li>
<li>$k$ is the number of parameters in the model.</li>
</ul>

Simulating data and fitting multi-species N-mixture models using 'nimble'. Includes features for handling zero-inflation and temporal correlation, Bayesian inference, model diagnostics, parameter estimation, and predictive checks. Designed for ecological studies with zero-altered or time-series data. Mimnagh, N., Parnell, A., Prado, E., & Moral, R. A. (2022) <doi:10.1007/s10651-022-00542-7>. Royle, J. A. (2004) <doi:10.1111/j.0006-341X.2004.00142.x>.

Niamh Mimnagh

MultiNMix

Multi-Species N-Mixture (MNM) Models with 'nimble'

Rafael de Andrade Moral

AIC,MNM-method function

<dl><dt>object</dt>
<dd>An object of class <code>"MNM"</code>.</dd></dl>

Arguments

AIC Method for "MNM" Class — AIC,MNM-method

<dl>

<dt>object</dt>
<dd>An object of class <code>"MNM"</code>.</dd>

</dl>

AIC,MNM-method: AIC Method for "MNM" Class

Description

Usage

Value

Arguments