> -----Original Message----- > From: stephen sefick [mailto:ssef...@gmail.com] > Sent: April-01-11 5:44 AM > To: Steven McKinney > Cc: R help > Subject: Re: [R] Linear Model with curve fitting parameter? > > Setting Z=Q-A would be the incorrect dimensions. I could Z=Q/A.
I suspect this is confusion about what Q is. I was presuming that the Q in this following formula was log(Q) with Q from the original data. > >> 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) If the model is Q=K*A*(R^r)*(S^s) then log(Q) = log(K) + log(A) + r*log(R) + s*log(S) Rearranging yields log(Q) - log(A) = log(K) + r*log(R) + s*log(S) so what I labeled 'Z' below is Z = log(Q) - log(A) = log(Q/A) so Z = log(K) + r*log(R) + s*log(S) and a linear model fit of Z ~ log(R) + log(S) will yield parameter estimates for the linear equation E(Z) = B0 + B1*log(R) + B2*log(S) (E(Z) = expected value of Z) so B0 estimate is an estimate of log(K) B1 estimate is an estimate of r B2 estimate is an estimate of s More details and careful notation will eventually lead to a reasonable description and analysis strategy. Best Steve McKinney > 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 <smckin...@bccrc.ca> wrote: > > > >> -----Original Message----- > >> From: r-help-boun...@r-project.org [mailto:r-help-boun...@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 | > >> |___________________________________| > >> | sas0...@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@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. > > > > > > -- > Stephen Sefick > ____________________________________ > | Auburn University | > | Biological Sciences | > | 331 Funchess Hall | > | Auburn, Alabama | > | 36849 | > |___________________________________| > | sas0...@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@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.