probit etc. for dose-response modeling
That fits by least-squares, which is not optimal. glm fits by maximum-likelihood. This can matter: the menarche data set (in MASS) is one example.
On Wed, 28 Aug 2002, Johannes Ranke wrote:
Hi again I found that the nonlinear least squares method also works nicely: library(nls) model <- nls(viability ~ pnorm(-log10(conc),-EC50,slope),data=data, \ start=list(EC50=1.8,slope=0.8)) Is there an advantage of using glm, and how would this work in this case?
Brian D. Ripley, ripley at stats.ox.ac.uk Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272860 (secr) Oxford OX1 3TG, UK Fax: +44 1865 272595 -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-help-request at stat.math.ethz.ch _._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._