Hello all I have done some fitting of pnorm functions to dose-response data, so I could calculate EC50 values (dose where the response is 0.5). I used the nlm function for this, so I did not get any information about the confidence intervals of the fitted parameters. What would be a good way to do such a probit fit, or is there a package which I could use? Best regards Johannes Ranke -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- 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 _._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._
On Tue, 27 Aug 2002, Johannes Ranke wrote:> Hello all > > I have done some fitting of pnorm functions to dose-response data, so I could > calculate EC50 values (dose where the response is 0.5). I used the nlm function > for this, so I did not get any information about the confidence intervals of > the fitted parameters. > > What would be a good way to do such a probit fit, or is there a package which I > could use?Probit models are generalised linear models so you can use glm(). -thomas -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- 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 _._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._
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? Johannes -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- 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 _._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._
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 _._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._
I do not have binomial data, though - gaussian only allows link="identity" - is there another way? -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- 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 _._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._
Sounds like you need to dichotomize. More generally, R can only fit a fully-specified statistical model. Specifying the model is the hard part of applied statistics. On Wed, 28 Aug 2002, Johannes Ranke wrote:> the "healthy status" of our cells is around 1, and if they are > inactive, they are around 0. Values > 1 and < 0 do occur. > >-- 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 _._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._