I have questions regarding 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))) 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 I did not understand how these values A=3.5, B=15,xmid=600,scal=1/2.5 were obtained by Jim in the posting here http://www.mail-archive.com/r-help at stat.math.ethz.ch/msg25500.html. I would appreciate a little help here to understand the 4-parameter logisitic regression for processing of standard curve for ELISA/MUltiplex Immunoassays. Thanks and happy holidays sharad -- View this message in context: http://r.789695.n4.nabble.com/understanding-the-4-parameter-logisitc-regression-tp3091588p3091588.html Sent from the R help mailing list archive at Nabble.com.
1Rnwb wrote:> > 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))) > > 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)) > ... > I did not understand how these values A=3.5, B=15,xmid=600,scal=1/2.5 > were obtained by Jim in the posting here > http://www.mail-archive.com/r-help at stat.math.ethz.ch/msg25500.html. > >The easiest way is to plot the function for several parameters with the original data superimposed. It shows you, that either you copied 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))) fn = function(conc, A,B,xmid,scal) { A+(B-A)/(1+(conc/xmid )^scal) } plot(test$conc,fn(test$conc,15,3.5,600,1/2.5),type="l") # looks good #plot(test$conc,fn(test$conc,3.5,15,600,1/2.5),type="l") # bad points(test$conc,log(test$il10)) Which tells you that the example you cited has a typo, or the author had mixed up parameters A and B. Dieter -- View this message in context: http://r.789695.n4.nabble.com/understanding-the-4-parameter-logisitc-regression-tp3091588p3092266.html Sent from the R help mailing list archive at Nabble.com.
Thanks Dieter for the help. This is how I want plot(log(test$conc),fn(test$conc,15,3.5,600,1/2.5),type="l") # looks good points(log(test$conc),log(test$il10)) regards and happy holidays sharad -- View this message in context: http://r.789695.n4.nabble.com/understanding-the-4-parameter-logisitc-regression-tp3091588p3092628.html Sent from the R help mailing list archive at Nabble.com.