[R] Starting estimates for nls Exponential Fit

[Anto]

I have tried the method proposed by Dave, and I must say it works very well.
Not to yield starting estimates for an nls-fit, but as an independent method
for calculating E (which is, by the way, the only paramater that I am
actually interested in).

The calculated values for E (Esp[3]) are on average 1.40e-06 lower than the
values produced by the nls fit (calculated over 96 reactions). I am using
the found E values to compenstate for differences between reaction
efficiency in genetic quantification assays. An efficiency difference of a
few %'s (+/- 0.05 in absolute value) can cause quantification differences of
several dozen percentages. Since I am in the process of comparing different
efficiency calculation methods (of which exopnential fit is one) I'll
compare the nls-fit results with the results by Dave's method to see if they
yield any significant differnces. I'll probably post the results by the end
of next week (I have an alarming number of reports still due, for which I am
recieving increasingly frequent angry looks by my coworkers. I had the
revision of an article as an excuse for further procrastination, but now
that it has been accepted I'll temporarly have to shift the focus of my
work).

thanks a lot for all your time & effort

Antoon




dave fournier wrote:
> 
> Thanks to Dennis Murphy who pointed out that ExponCycles
> is undefined. It is an R gotcha. I had shortened the name but R still
> remembered it so the script worked but only on my computer.
> This should fix that.
> 
> ExponValues=c(2018.34,2012.54,2018.85,2023.52,2054.58,2132.61,2247.17,2468.32,27
> 78.47)
> Expon=c(17,18,19,20,21,22,23,24,25)
> # Example starting estimate calculation
> E=1000.0
> y1=2018
> yn=2778.47
> nobs=9
> 
> 
> #keep y1 and yn fixed and get initial value for E
> Esp1 <- optim(c(E=E),method ="BFGS",
>   function(x)
>   {
>     E=x[1]
>     a=(yn-y1)/(E^Expon[nobs]-E^Expon[1])
>     Y0=y1-a*E^Expon[1];
>     diff=ExponValues-(Y0+a*E^Expon)
>     return(1000*sum(diff*diff))
>   })$par
> 
> E=Esp1[1]
> 
> Esp <- optim(c(y1=y1,yn=yn,E=E),method ="BFGS",
>   function(x)
>   {
>     E=x[3]
>     a=(x[2]-x[1])/(E^Expon[nobs]-E^Expon[1])
>     Y0=x[1]-a*E^Expon[1];
>     diff=ExponValues-(Y0+a*E^Expon)
>     return(1000*sum(diff*diff))
>   })$par
> 
> y1=Esp[1]
> y2=Esp[2]
> E=Esp[3]
> 
> a=(y2-y1)/(E^Expon[nobs]-E^Expon[1])
> Y0=y1-a*E^Expon[1];
> 
> 
> 
> 
> -- 
> David A. Fournier
> P.O. Box 2040,
> Sidney, B.C. V8l 3S3
> Canada
> Phone/FAX 250-655-3364
> http://otter-rsch.com
> 
> ______________________________________________
> 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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