Hi I need to fit a logistic regression model using a saturable Michaelis-Menten function of my predictor x. The likelihood could be expressed as:
L = intercept + emax * x / (EC50+x) Which I guess could be expressed as the following R model ~ emax*x/(ec50+x) As far as I know (please, correct me if I am wrong), fitting such a model is to not doable with glm, since the function is not linear. A Stackoverflow post recommends the bnlr function from the gnlm (https://stackoverflow.com/questions/45362548/nonlinear-logistic-regression-package-in-r)... I would be grateful for any opinion on this package or for any alternative recommendation of package/function. ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.
