[R] Nonlinear logistic regression fitting
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
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.
More information about the R-help