From: Peter Ehlers <ehlers_at_ucalgary.ca>

Date: Fri, 01 Apr 2011 09:14:39 -0700

R-help_at_r-project.org mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. Received on Fri 01 Apr 2011 - 16:18:12 GMT

Date: Fri, 01 Apr 2011 09:14:39 -0700

On 2011-04-01 05:44, stephen sefick wrote:

> Setting Z=Q-A would be the incorrect dimensions. I could Z=Q/A. Is

*> fitting a nls model the same as fitting an ols? These data are
**> hydraulic data from ~47 sites. To access predictive ability I am
**> removing one site fitting a new model and then accessing the fit with
**> a myriad of model assessment criteria. I should get the same answer
**> with ols vs nls? Thank you for all of your help.
*

No, ols and nls won't give the same result. If you use ols on the logged data, you're assuming additive errors on the log scale. With nls, you assume additive errors on the original scale. But your model looks simple enough - why not run it through both functions and see what the difference is. Ultimately, everything depends on what assumptions you're comfortable with.

Peter Ehlers

*>
*

> Stephen

*>
**> On Thu, Mar 31, 2011 at 8:34 PM, Steven McKinney<smckinney_at_bccrc.ca> wrote:
**>>
**>>> -----Original Message-----
**>>> From: r-help-bounces_at_r-project.org [mailto:r-help-bounces_at_r-project.org] On Behalf Of stephen sefick
**>>> Sent: March-31-11 3:38 PM
**>>> To: R help
**>>> Subject: [R] Linear Model with curve fitting parameter?
**>>>
**>>> I have a model Q=K*A*(R^r)*(S^s)
**>>>
**>>> A, R, and S are data I have and K is a curve fitting parameter. I
**>>> have linearized as
**>>>
**>>> log(Q)=log(K)+log(A)+r*log(R)+s*log(S)
**>>>
**>>> I have taken the log of the data that I have and this is the model
**>>> formula without the K part
**>>>
**>>> lm(Q~offset(A)+R+S, data=x)
**>>>
**>>> What is the formula that I should use?
**>>
**>> Let Z = Q - A for your logged data.
**>>
**>> Fitting lm(Z ~ R + S, data = x) should yield
**>> intercept parameter estimate = estimate for log(K)
**>> R coefficient parameter estimate = estimate for r
**>> S coefficient parameter estimate = estimate for s
**>>
**>>
**>>
**>> Steven McKinney
**>>
**>> Statistician
**>> Molecular Oncology and Breast Cancer Program
**>> British Columbia Cancer Research Centre
**>>
**>>
**>>
**>>>
**>>> Thanks for all of your help. I can provide a subset of data if necessary.
**>>>
**>>>
**>>>
**>>> --
**>>> Stephen Sefick
**>>> ____________________________________
**>>> | Auburn University |
**>>> | Biological Sciences |
**>>> | 331 Funchess Hall |
**>>> | Auburn, Alabama |
**>>> | 36849 |
**>>> |___________________________________|
**>>> | sas0025_at_auburn.edu |
**>>> | http://www.auburn.edu/~sas0025 |
**>>> |___________________________________|
**>>>
**>>> Let's not spend our time and resources thinking about things that are
**>>> so little or so large that all they really do for us is puff us up and
**>>> make us feel like gods. We are mammals, and have not exhausted the
**>>> annoying little problems of being mammals.
**>>>
**>>> -K. Mullis
**>>>
**>>> "A big computer, a complex algorithm and a long time does not equal science."
**>>>
**>>> -Robert Gentleman
**>>> ______________________________________________
**>>> R-help_at_r-project.org mailing list
**>>> https://stat.ethz.ch/mailman/listinfo/r-help
**>>> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
**>>> and provide commented, minimal, self-contained, reproducible code.
**>>
**>
**>
**>
*

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https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. Received on Fri 01 Apr 2011 - 16:18:12 GMT

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