Hi Hans, I hope I can resolve your problems below (Marc, thank you very much for cc'ing me on your initial response!).
Have a look at the following R lines: ## Fitting the model using drm() (from the latest version) m1<- drm(response ~ dose, data = d, fct = LL.4()) summary(m1) plot(m1) ## Checking the fit by using nls() ## (we have very good guesses for the parameter estimates) m2 <- nls(response ~ c + (d - c)/(1 + (dose/e)^b), data=d, start=list(b=-0.95, c=10, d=106, e=1.2745e-06)) summary(m2) The standard errors agree quite well. The minor discrepancies between to two fits are attributable to different numerical approximations of the variance-covariance matrix being used in drm() and nls(). So I would use the latest version of 'drc', especially for datasets with really small doses. One recent change to drm() was to incorporate several layers of scaling prior to estimation (as well as subsequent back scaling after estimation): 1) scaling of parameters with the same scale as the x axis 2) scaling of parameters with the same scale as the y axis 3) scaling of parameters in optim() The effect of scaling is to temporarily "convert" the dataset (and the model) to scales that are more convenient for the estimation procedure. Any feedback on this would be much appreciated. Therefore it should also not be necessary to manually do any scaling prior to using drm() (like what you did). Compare, for instance, your specification of drm() to mine above. Is this explanation useful?! Christian ______________________________________________ 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.
