[R] Nonlinear logistic regression fitting

[Bert Gunter]

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Bert Gunter

"The trouble with having an open mind is that people keep coming along and
sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )


On Tue, Jul 28, 2020 at 7:25 AM Sebastien Bihorel via R-help <
r-help using r-project.org> wrote:

> 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.
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