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07-05-2011 01:49 PM

Hi,

I am fitting a GLM with gamma log link usng proc genmod log link. I have a difficult time of interpreting the coeffient of independent variables of this model.

For example, if I get a estimate coefficient of - 0.05 for the predictor X under gamma log link model, how can I interpret it as a relationship with the response variable?

Also, what are the criteria for assessing the goodness of fit for gamma log link model using Proc Genmod?

Thank you for your help!

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Solution

2 weeks ago

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Posted in reply to maomiss

07-07-2011 05:26 PM

With a log link and a continuous predictor, you are fitting the model:

ln(mu) = beta0 + beta1*X,

where mu is the expected value. Then, e raised to the left and right sides gives:

mu = exp(beta0 + beta1*X) = exp(beta0)*exp(beta1*X)

In other words, you have a model for the mean as an exponential function of X. With a negative beta1, you have an exponential decay function.

Goodness of fit is not a trivial matter, and one could have a very long discussion on the subject. It usually best done in comparing competing models rather than in an absolute sense. To start with, look at the scaled deviance in the output. The smaller the better (in general), but the goal is not to get it down to 0. The scaled deviance divided by the df (displayed in the output) should be close to 1. There are good ODS GRAPHICS as well.

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2 weeks ago

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Posted in reply to maomiss

07-07-2011 05:26 PM

With a log link and a continuous predictor, you are fitting the model:

ln(mu) = beta0 + beta1*X,

where mu is the expected value. Then, e raised to the left and right sides gives:

mu = exp(beta0 + beta1*X) = exp(beta0)*exp(beta1*X)

In other words, you have a model for the mean as an exponential function of X. With a negative beta1, you have an exponential decay function.

Goodness of fit is not a trivial matter, and one could have a very long discussion on the subject. It usually best done in comparing competing models rather than in an absolute sense. To start with, look at the scaled deviance in the output. The smaller the better (in general), but the goal is not to get it down to 0. The scaled deviance divided by the df (displayed in the output) should be close to 1. There are good ODS GRAPHICS as well.

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07-08-2011 11:00 PM

Hi lvm,

Thanks so much! Now I finally understand the basic components of writing a gamma log link model.

Is there any literature supporting the (scaled deviance)/df =1 as a good measure of goodness of fitness?

Thank you,

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Posted in reply to maomiss

07-09-2011 11:38 AM

The User's Guide for GENMOD (you can get on-line) discusses goodness of fit measures for generalized linear models. Good luck.