On Sat, 6 May 2006, White, Charles E WRAIR-Wash DC wrote: > The model becomes nonlinear when you add the natural response rate. In > R, that means that you switch from using the glm function to using the > nls function.
Not in the usual sense of `linear': it is still just as linear in the explanatory variables as a glm is. > As long as you're willing to use logistic regression > instead of Probit analysis, nls has a 'self starting' option (SSLogis) > for a three parameter logistic model. The third parameter will be your > natural response rate. Unless you are looking at the tails of the > distribution, the Probit and logistic models will agree closely. If you > are highly motivated to use Probit analysis, you can use SSLogis to > figure out how to do that. Quick comment: logistic regression via nls is by least-squares, not the meaning of the term for glm(family=binomial(logit)). If you want the latter, it is easy to adapt the code on MASS4 p.445 to a probit+const link function (and even to estimate the constant). [...] -- Brian D. Ripley, [EMAIL PROTECTED] 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 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595 ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
