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12-11-2013 01:08 PM

I am estimating nutrient requirement of animals using NLMIXED. When I convert from Imperial to Metric, NLMIXED is estimating different requirement. That does not make sense biologically of course, why I am having this problem? Is there a way to fix it?

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12-11-2013
03:06 PM

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Posted in reply to marcioadg

12-11-2013 03:06 PM

What model are you using to estimate requirements--a five parameter logistic? It may be that you are dealing with a scaling/centering problem. If you express the independent variable in dimensionless units, then this should not occur.

Steve Denham

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12-11-2013
03:06 PM

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Posted in reply to marcioadg

12-11-2013 03:06 PM

What model are you using to estimate requirements--a five parameter logistic? It may be that you are dealing with a scaling/centering problem. If you express the independent variable in dimensionless units, then this should not occur.

Steve Denham

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Posted in reply to marcioadg

12-11-2013 03:12 PM

I am not sure that is a five parameter logistic. I was using the NLMIXED. And the problem is exactly what you said. Initially I was using 500 grams i.e., now I changed to 0.500 kg and it is exactly the same.

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Posted in reply to marcioadg

12-11-2013 03:19 PM

Oh. That can happen too, with non-linear models, just a scaling problem. No, I was thinking of a five parameter logistic dose response curve to various nutrient levels. What model are you fitting with NLMIXED? Standard nonlinear growth curve (Gompertz curve)? (You see, you have hit on my favorite mathematical biology/statistics area, so we may need to go off-line to talk.)

Steve Denham

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Posted in reply to SteveDenham

12-11-2013 03:25 PM

Interesting Steve. I am using a 7-point titration, if that is what you mean. Gompertz is an asymptotic model, correct? I am using the broken-line linear and curvilinera (NLMIXED) and a quadratic polynomial model (MIXED). Is my understanding based on the literature that there is not really the 'best' model, there are dozens of types out there. Let me know your thoughts.

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Posted in reply to marcioadg

12-12-2013 09:13 AM

It is my belief (mine, but I have seen others) that the models you choose from should proceed from your knowledge of the physiological processes involved. There has been enough work done fitting linear regressions or polynomial regressions in this area, and the likelihood of discovering new insights by fitting models of those types, other than as a reference to the literature, is not high. Broken line/hockey stick etc. models are richer, but physiological processes really don't seem to operate on a basis of stark discontinuities--there is a shift between processes that is smooth and averaged over cells, organs and animals. A couple of really good reads are J. D. Murray's *Mathematical Biology *and D. S. Riggs' *The Mathematical Aroach to Physiological Problems. *Both are older (Murray-1980 (my version is the 1990 corrected second printing), Riggs-1963 (my version is 1967)), but very helpful.

Steve Denham

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Posted in reply to SteveDenham

12-12-2013 10:54 AM

Really appreciate your input. I agree. I will check out these references.