# [R] Linear Model with curve fitting parameter?

stephen sefick ssefick at gmail.com
Fri Apr 1 14:44:01 CEST 2011

```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.

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
>> ____________________________________
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>> |___________________________________|
>>
>> 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
>> and provide commented, minimal, self-contained, reproducible code.
>

--
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

```