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Dec 23, 2019 7:33 AM
(1821 views)

I noticed that JMP invariably reports positive LLs and negative AICs, for example "-2LL = -1406.88" or "AICc = -1451.534". This does not make sense, does it? LL must be negative by definition; AIC can in principle be negative, but with low LLs and low number of parameters it usually is positive.

Am I missing something fundamental here?

Thanks!

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It is from a MLE procedure. The estimation minimizes -2L.

Yes, you can use twice the difference between the log likelihood under the null and the alternative hypotheses for a LRT chi square test.

Learn it once, use it forever!

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Re: positive loglikelihoods and negative AIC's

Created:
Dec 23, 2019 8:03 AM
| Last Modified: Dec 23, 2019 8:04 AM
(1818 views)
| Posted in reply to message from l_yampolsky 12-23-2019

You may have not seen exceptions but they can happen. You are correct that the log-likelihood is always negative because the likelihood is always 0 to 1. Similarly, the -2L is always positive. AICc, though, can be negative or positive.

For example,

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Re: positive loglikelihoods and negative AIC's

Thanks! In the example you give everything is "good". LL negative BIC and AIC positive. So it does not quite answer my question. I see how

So should I assume that this is a type (minus should not be there, see lines marked with >>>>) in a report like this?

Generalized Linear Model Fit

Overdispersion parameter estimated by Maximum Likelihood

Response: O2Cons_ugO2/mgWW

Distribution: Normal

Link: Identity

Estimation Method: Maximum Likelihood

Observations (or Sum Wgts) = 369

Regression Plot

Whole Model Test

>>>>Model -LogLikelihood L-R ChiSquare DF Prob>ChiSq

>>>>Difference 60.566153 121.1323 1 <.0001

>>>>Full -1459.5116

>>>>Reduced -1398.9455

Goodness Of Fit Statistic ChiSquare DF Prob>ChiSq Overdispersion

Pearson 0.0079 367 1.0000 0.0000

Deviance 0.0079 367 1.0000

AICc

-2912.958

Effect Tests

Source DF L-R ChiSquare Prob>ChiSq

assayT 1 121.13231 <.0001

Parameter Estimates

Term Estimate Std Error L-R ChiSquare Prob>ChiSq Lower CL Upper CL

Intercept 0.001544 0.0007616 4.0872942 0.0432 0.0000474 0.0030406

assayT 0.0003546 0.0000296 121.13231 <.0001 0.0002964 0.0004127

Studentized Deviance Residual by Predicted

So should I assume that this is a type (minus should not be there, see lines marked with >>>>) in a report like this?

Generalized Linear Model Fit

Overdispersion parameter estimated by Maximum Likelihood

Response: O2Cons_ugO2/mgWW

Distribution: Normal

Link: Identity

Estimation Method: Maximum Likelihood

Observations (or Sum Wgts) = 369

Regression Plot

Whole Model Test

>>>>Model -LogLikelihood L-R ChiSquare DF Prob>ChiSq

>>>>Difference 60.566153 121.1323 1 <.0001

>>>>Full -1459.5116

>>>>Reduced -1398.9455

Goodness Of Fit Statistic ChiSquare DF Prob>ChiSq Overdispersion

Pearson 0.0079 367 1.0000 0.0000

Deviance 0.0079 367 1.0000

AICc

-2912.958

Effect Tests

Source DF L-R ChiSquare Prob>ChiSq

assayT 1 121.13231 <.0001

Parameter Estimates

Term Estimate Std Error L-R ChiSquare Prob>ChiSq Lower CL Upper CL

Intercept 0.001544 0.0007616 4.0872942 0.0432 0.0000474 0.0030406

assayT 0.0003546 0.0000296 121.13231 <.0001 0.0002964 0.0004127

Studentized Deviance Residual by Predicted

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Re: positive loglikelihoods and negative AIC's

I don't see anything wrong with these results. If you are concerned about the negative AICc, that happens all the time. There really isn't any scale here. Smaller AICc always suggests a better model. So a model with AICc = 1000 is better than a model with AICc = 1200. A model with AICc = -1250 is better than a model with AICc = -1200.

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Re: positive loglikelihoods and negative AIC's

Full model -LogLikelihood = -1459.5116. I.e. LogLikelihood = 1459.5116. >0. Cannot be. Either this is not LL, or it is, but there is a minus in there that should not be there.

Or I am missing something big way.

Thanks

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Re: positive loglikelihoods and negative AIC's

Here is the computation of AICc for a continuous response:

The first term is -2L. So if you have a small SSE (a good fit), then -2L can be negative.

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Re: positive loglikelihoods and negative AIC's

Ahh, got it. This LL is not from a ML procedure. Then my last question: can I still use these values in a log-ratio test or are they not good for that?

Thanks for your help!

Thanks for your help!

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It is from a MLE procedure. The estimation minimizes -2L.

Yes, you can use twice the difference between the log likelihood under the null and the alternative hypotheses for a LRT chi square test.

Learn it once, use it forever!

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Re: positive loglikelihoods and negative AIC's

Created:
Jun 11, 2020 9:55 AM
| Last Modified: Jun 11, 2020 10:07 AM
(728 views)
| Posted in reply to message from l_yampolsky 12-23-2019

Hi,

Can you please explain where the -2LL are reported? I'm only seeing final AICc values on my end. I'm in JMP 14.

UPDATE: Never mind, I figured it out (I needed to do a GLM) and now I can't figure out how to delete this post.

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Re: positive loglikelihoods and negative AIC's

This was in JMP 10. I think it is different on JMP 14, can check.

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