I have difficulty interpreting some results. I am doing an hierarhical related regression with ecoreg. If I enter the code I receive output with oddsratio's, confidence ratio's and a 2x maximized log likelihood.

However, I do not fully understand how to interpreted the 2x maximized log likelihood. As far as I know log likelihood is used as a convenient way to calculate 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.

Below the outcome I receive:

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!

  • 1
    $\begingroup$ Wrong site for the question. You'd better try Cross Validated. $\endgroup$ May 25, 2016 at 10:53
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    $\begingroup$ It was a cross-post of of Interpreting log likelihood $\endgroup$
    – Tim
    May 29, 2016 at 20:19

1 Answer 1


A higher value of likelihood or log-likelihood (since log is an increasing function) is better, since it says that the parameters are more likely given the observation/data.

I suggest the wiki article on maximum likelihood estimation for further reference. (https://en.wikipedia.org/wiki/Maximum_likelihood)


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