Hello,
Glad it can be of help.
Post example data then, like this it's rather difficult to answer
accurately.
Rui Barradas
�s 17:59 de 28/07/2020, Sebastien Bihorel escreveu:
> Hi Rui,
>
> Thanks for your input.
>
> In my analysis, the MM model is not intended to fit continuous data
> but must be used within a logistic regression model of binary data.
> So, while useful in itself, the suggested example does not exactly apply.
>
> I appreciate your time
>
> ------------------------------------------------------------------------
> *From:* Rui Barradas <ruipbarra...@sapo.pt>
> *Sent:* Tuesday, July 28, 2020 12:42
> *To:* Sebastien Bihorel <sebastien.biho...@cognigencorp.com>;
> r-help@r-project.org <r-help@r-project.org>
> *Subject:* Re: [R] Nonlinear logistic regression fitting
> Hello,
>
> glm might not be the right tool for the MM model but nls is meant to fit
> non-linear models.
> And, after an on-line search, there is also package drc, function drm.
>
> I will use the data and examples in the links below. (The second gave me
> right, it uses nls.)
>
>
> #install.packages("drc")
> library(drc)
>
> #--- data
>
> # substrate
> S <- c(0,1,2,5,8,12,30,50)
>
> # reaction rate
> v <- c(0,11.1,25.4,44.8,54.5,58.2,72.0,60.1)
> kinData <- data.frame(S, v)
>
>
> #--- package drc fit
>
> # use the two parameter MM model (MM.2)
> drm_fit <- drm(v ~ S, data = kinData, fct = MM.2())
>
> #--- nls fit
> MMcurve <- formula(v ~ Vmax*S/(Km + S))
> nls_fit <- nls(MMcurve, kinData, start = list(Vmax = 50, Km = 2))
>
> coef(drm_fit)
> coef(nls_fit)
>
> #--- plot
>
> SconcRange <- seq(0, 50, 0.1)
> nls_Line <- predict(nls_fit, list(S = SconcRange))
>
> plot(drm_fit, log = '', pch = 17, col = "red", main = "Fitted MM curve")
> lines(SconcRange, nls_Line, col = "blue", lty = "dotted")
>
>
> [1]
> https://davetang.org/muse/2013/05/17/fitting-a-michaelis-mentens-curve-using/
> [2]
> http://rforbiochemists.blogspot.com/2015/05/plotting-and-fitting-enzymology-data.html
>
>
> Hope this helps,
>
> Rui Barradas
>
> �s 15:13 de 28/07/2020, Sebastien Bihorel via R-help escreveu:
> > 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
> <http://www.R-project.org/posting-guide.html>
> > and provide commented, minimal, self-contained, reproducible code.
>
>
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