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Jun 23, 2016 9:38 PM
(1772 views)

Hi. I am wondering how to determine the portion of variance in the dependent variable that is explained by each independent variable in a GLM. I know this is doable in R, but would prefer to stick with JMP if possible. Thanks, Marthe

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You can use the Variability platform to do this:

Analyze==>Quality and Process==>Variability/Attribute Gauge Chart

Jim

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Jul 7, 2016 11:33 AM
(1656 views)
| Posted in reply to message from marthe_haarr 06/24/2016 12:38 AM

You might use the likelihood ratio chi square (**L-R ChiSquare**) presented in the **Effect Tests** report. This quantity would serve your purpose the same way as the sum of squares for each term would in ordinary least squares linear regression:

You might also use the **Assess Variable Importance** command in the red triangle menu for the **Prediction Profiler** (you have several choices of methods depending on the nature of your predictors):

(Thanks to my colleague, Di Michelson, for thinking of the profiler.)

Learn it once, use it forever!

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Thank you so much! This was very helpful.

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Hi Mark @markbailey,

How can the **L-R ChiSquare** quantities presented in the **Effect Tests** of a **GLM** in **JMP** be converted to the percentage of total variance explained? Can you be more specific? Is there a way to convert these values so that it is known what *percentage* of the variance is explained by each of the independent variables, and also, what percentage is left unexplained?

Thank you,

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- markbailey

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I am not sure about the equivalent to the variance. We use sum of squares with a continuous response and negative log likelihood (-L) with a categorical response. For example, R square for a continuous response is the model SS divided by the corrected total SS. You can also look at the SS associated with the individual terms. For the categorical response, R square is the model -L divided by the reduced model -L.

I don't know if you can use the -L for individual terms to determine the contribution or if this quantity is what you mean by variance.

Learn it once, use it forever!