Competing model fits, for which their associated algorithm and covariance files have been saved, can be ranked, AICc weighted, and their corresponding parameter values and SEs averaged.

This procedure works only for nested models whose saved algorithm files (*.alg) are associated with the same conditions for model initialization, and therefore also have the same number of parameters.

Models can only differ in their parameter values, though the number of free and fixed parameters can vary among competing models.

Be aware that the SE of a fixed parameter is considered to have a value of zero (0).

Another requirement is that all competing models have an associated covariance file (*.cov) that shares its file name (except its suffix) with its algorithm file name, e.g., ‘model_123.alg’ and ‘model_123.cov’.

Model averaging is conducted according to Burnham and Anderson (2002), and averaged parameter values and their standard errors are reported.

Smartstats© will guide you through the procedure for calculating likelihood profiles.

The results of this procedure can be saved in a text file.

A key reference is:

Burnham, K.P. and D.R. Anderson. 2002. Model Selection and Multi-Model Inference: A Practical Information Theoretic Approach. 2nd Edition. Springer. [Burnham] [Anderson] [Springer]