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Posted 09-15-2015 12:04 PM
(2127 views)

Hello SAS community,

I was reading one of the SAS global proceeding papers related to linear mixed models. That paper suggested to rescaling the data is one of the options to trouble shoot convergence issues in parameter estimation. I'm working on large dataset that consists of multple years and locations, and response variable is yield measured in tons/hectare. Since this dataset requires to estimate a large numbe of parameters, convergence is often a problem to estimate the parameters. I was thinking to rescale yield values to kgs or some other form.

Any suggestions or comments?

Thank you,

RVS

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It might help to post a link to the paper so we can see which approach you may be contemplating.

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Thanks for the response. Below is the link for the paper.

http://support.sas.com/resources/papers/proceedings12/332-2012.pdf

I'm refering to the Section II ("TROUBLE SHOOTING CONVERGENCE FAILURES IN MIXED MODELS") from the above paper.

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I suspect (unfortunately) that the model may be overspecified for the number of observations. Consequently, some random effect values are really sensitive to changes, and thus convergence becomes a problem.

My usual approach is to "sneak up on" the model I want to use, starting with something less complex, and working there until it converges and is meaningful in some sense. Then complexity can be added which, in mixed models, is almost always something in the random (or repeated, if in PROC MIXED) statement.

Steve Denham

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Hi Everyone,

I am having similar problem on the issue of convergence using proc mixed. Three of my traits are in t/ha and the other one is in percentage. Two of the traits converged. I am thinking of rescaling by dividing by 10 to see if that would solve the problem. Is it a good idea?

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