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- Variance partitioning in a GLM

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Jun 23, 2016 9:38 PM
(1293 views)

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Jul 5, 2016 3:21 PM
(1177 views)

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
(1177 views)

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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Jul 7, 2016 12:01 PM
(1177 views)

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Apr 26, 2018 9:31 AM
(205 views)

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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Apr 27, 2018 4:13 PM
(183 views)

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.

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