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Goodness of fit in Binary Logistic Regression

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Goodness of fit in Binary Logistic Regression

Hi can any one help clarify doubt in goodness of fit in binary logistic. I created a model to predict the event response and got excellent c score of about .8 and also an attractive ROC carve. I checked the model by scoring the validation dataset and able to capture more than 70% of my responses in top 4 deciles. Also the KS score looks significant. Only thing I am having a trouble with is HL Hosmer Lemshow statistic coming significant means I need to rethink about the model. My question here is can i ignore the HL test and rely more on predictive power? Also, I am using proc logistic here. Appreciate any help on this.


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‎11-06-2012 04:50 PM
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Re: Goodness of fit in Binary Logistic Regression

The Hosmer-Lemeshow test is not my favorite test; it has low power in smaller samples and can show significance for important deviations in vary large ones.  I much prefer to look at the observed-predicted plots themselves.

If the HL test is significant, it doesn't say that the model you have is "wrong," it says that it can be "improved."  Sometimes "improvement" means a different model, additional variables, or data transformations.  However, if the model seems adequate, I may just "declare victory" and move on.

One caution (that doesn't seem to matter here), if the HL test is significant, then it would be inappropriate to claim that nothing is going on (e.g. to "accept the null").

Doc Muhlbaier

Duke

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‎11-06-2012 04:50 PM
Valued Guide
Posts: 2,108

Re: Goodness of fit in Binary Logistic Regression

The Hosmer-Lemeshow test is not my favorite test; it has low power in smaller samples and can show significance for important deviations in vary large ones.  I much prefer to look at the observed-predicted plots themselves.

If the HL test is significant, it doesn't say that the model you have is "wrong," it says that it can be "improved."  Sometimes "improvement" means a different model, additional variables, or data transformations.  However, if the model seems adequate, I may just "declare victory" and move on.

One caution (that doesn't seem to matter here), if the HL test is significant, then it would be inappropriate to claim that nothing is going on (e.g. to "accept the null").

Doc Muhlbaier

Duke

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