# Interpreting log likelihood

I have difficulty interpreting some results. I am doing an hierarchical related regression with ecoreg. If I enter the code I receive output with odds ratios, confidence ratios and a 2x maximized log likelihood.

However, I do not fully understand how to interpret the 2x maximized log likelihood. As far as I know log likelihood is used as a convenient way of calculating a likelihood and it calculates the value of the parameters based on the outcomes. But I do not understand if a higher or lower value is better. I looked at several online sources e.g. https://stackoverflow.com/questions/2343093/what-is-log-likelihood, but I am still stuck.

Call:
eco(formula = cbind(y, N) ~ deprivation + meanIncome, binary = ~fracSmoke +
soclass, data = dfAggPlus, cross = cross)

Aggregate-level odds ratios:
OR        l95        u95
(Intercept) 0.0510475 0.03837276 0.06790878
deprivation 0.9859936 0.88421991 1.09948134
meanIncome  1.0689951 0.95574925 1.19565924

Individual-level odds ratios:
OR       l95      u95
fracSmoke 3.124053 2.0761956 4.700765
soclass   1.001050 0.9930815 1.009083

-2 x log-likelihood:  237.4882


So, how should I interpreted a value of 237.4882 compared to an outcome of 206 or 1083? Help is much appreciated!

• What exactly is unclear for you?
– Tim
May 25, 2016 at 16:08
• Well, I want to understand if a higher log likelihood means the outcome is more reliable or for example less reliable. Furthermore I want to know how I should interpret the differences between several outcomes (e.g. 206 237 or 1083) May 26, 2016 at 14:08
• Possible duplicate of Maximum Likelihood Estimation (MLE) in layman terms
– Tim
May 26, 2016 at 18:09
• I marked your question as a duplicate of another, more general, question that asks what is maximum likelihood estimation -- check it.
– Tim
May 26, 2016 at 18:11
• It seems it's used as a deviance. See <en.wikipedia.org/wiki/Deviance_information_criterion> Aug 24, 2019 at 23:03