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marthe_haarr

Community Trekker

Joined:

Jun 16, 2016

Variance partitioning in a GLM

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

5 REPLIES
txnelson

Super User

Joined:

Jun 22, 2012

Re: Variance partitioning in a GLM

You can use the Variability platform to do this:

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

Jim
markbailey

Staff

Joined:

Jun 23, 2011

Re: Variance partitioning in a GLM

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:

11964_Effect Tests Report.JPG

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):

11965_Variable Importance in Prediction Profiler.JPG

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

Learn it once, use it forever!
marthe_haarr

Community Trekker

Joined:

Jun 16, 2016

Re: Variance partitioning in a GLM

Thank you so much! This was very helpful.

Ray

Community Member

Joined:

Apr 26, 2018

Re: Variance partitioning in a GLM

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,

markbailey

Staff

Joined:

Jun 23, 2011

Re: Variance partitioning in a GLM

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!