LMERConvenienceFunctions (version 3.0)

relLik: Relative log-likehood.

Description

Calculate the relative log-likehood between two models.

Usage

relLik(x, y, method = c("AIC", "BIC"), ndigits = 6, ...)

Arguments

x,y

Fitted model objects for which there exists a logLik method to extract the corresponding log-likelihood, or objects inheriting from class logLik.

method

Whether to base the comparison on AIC or BIC. Defaults to "AIC".

ndigits

An integer denoting the number of decimal digits in the output.

…

Further arguments to pass to AIC or BIC.

Value

A vector with values:

AIC(x), BIC(x)

The AIC or ]codeBIC value of the first model object.

AIC(y), BIC(y)

The AIC or ]codeBIC value of the second model object.

relLik

The relative likelihood between the two models. Model y will be that much more likely given the data than model x.

Details

The relative log-likelihood is calculated as exp((abs(AIC(x) - AIC(y)))/2) or exp((abs(BIC(x) - BIC(y)))/2), depending on the method.

You can find information regarding differences between AIC and BIC from http://methodology.psu.edu/eresources/ask/sp07.

References

On AIC and relative log-likelihood (which they call evidence ratio):

Symonds, M.R.E and Moussalli, A. (2011). A brief guide to model selection, multimodel inference and model averaging in behavioural ecology using Akaike's information criterion. Behavioral Ecology and Sociobiology, 65, 13--21. doi: 10.1007/s00265-010-1037-6

See Also

logLik; AIC; BIC

Examples

# NOT RUN {
# see example in LMERConvenienceFunctions help page.
# }