[R] Nonlinear logistic regression fitting

Sebastien Bihorel Seb@@t|en@B|hore| @end|ng |rom cogn|gencorp@com
Tue Jul 28 16:13:07 CEST 2020


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.

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