Shalini, I think your "hill equation" is meant to just be an alternative parameterization of the four parameter logistic (BTW, the "hill *coefficient*" is a function of the slope parameter of the FPL, but I don't believe "hill equation" is standard terminology). Note "conc" is the input in this parameterization, not "log(conc)".
> nls(log(il10)~A+(B-A)/(1+(conc/xmid )^scal),data=test, + start = list(A=3.5, B=15, + xmid=600,scal=1/2.5)) Nonlinear regression model model: log(il10) ~ A + (B - A)/(1 + (conc/xmid)^scal) data: test A B xmid scal 14.7051665 3.7964534 607.9822962 0.3987786 residual sum-of-squares: 0.1667462 To see the equivalence to the other parametrization that you used, note > 1/2.507653 [1] 0.3987793 > log(607.9822962) [1] 6.410146 --Jim > Message: 17 > Date: Mon, 16 Aug 2004 11:25:57 -0500 > From: [EMAIL PROTECTED] > Subject: [R] using nls to fit a four parameter logistic model > To: [EMAIL PROTECTED] > Message-ID: > <[EMAIL PROTECTED]> > Content-Type: text/plain; charset=US-ASCII > > I am working on what appears to be a fairly simple problem for the > following data > > test=data.frame(cbind(conc=c(25000, 12500, 6250, 3125, 1513, 781, 391, > 195, 97.7, 48.4, 24, 12, 6, 3, 1.5, 0.001), > il10=c(330269, 216875, 104613, 51372, 26842, 13256, 7255, 3049, 1849, 743, > 480, 255, 241, 128, 103, 50))) > I am able to fit the above data to the equation > > > nls(log(il10)~A+(B-A)/(1+exp((xmid-log(conc))/scal)),data=test, > + start = list(A=log(0.001), B=log(100000), > + xmid=log(6000),scal=0.8)) > Nonlinear regression model > model: log(il10) ~ A + (B - A)/(1 + exp((xmid - log(conc))/scal)) > data: test > A B xmid scal > 3.796457 14.705159 6.410144 2.507653 > residual sum-of-squares: 0.1667462 > > > But in attempting to achieve a fit to what is commonly known as the hill > equation, which is a four parameter fit that is used widely in biological > data analysis > > nls(log(il10)~A+(B-A)/(1+(log(conc)/xmid )^scal),data=test, > + start = list(A=log(0.001), B=log(100000), xmid=log(6000),scal=0.8)) > > Nonlinear regression model > model: log(il10) ~ A + (B - A)/(1 + (log(conc)/xmid )^scal) > > Error in numericDeriv(form[[3]], names(ind), env) : > Missing value or an Infinity produced when evaluating the model > > > > Please would someone offer a suggestion > > Shalini James A. Rogers Manager, Nonclinical Statistics PGR&D Groton Labs Eastern Point Road (MS 260-1331) Groton, CT 06340 office: (860) 686-0786 fax: (860) 715-5445 LEGAL NOTICE\ Unless expressly stated otherwise, this messag...{{dropped}} ______________________________________________ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html