Message-ID: <Pine.LNX.4.31.0208280739440.4413-100000@gannet.stats>
Date: 2002-08-28T06:41:10Z
From: Brian Ripley
Subject: probit etc. for dose-response modeling
In-Reply-To: <20020828062426.GA30325@eckehaat.uft.uni-bremen.de>
That fits by least-squares, which is not optimal. glm fits by
maximum-likelihood. This can matter: the menarche data set (in MASS)
is one example.
On Wed, 28 Aug 2002, Johannes Ranke wrote:
> Hi again
>
> I found that the nonlinear least squares method also works nicely:
>
> library(nls)
> model <- nls(viability ~ pnorm(-log10(conc),-EC50,slope),data=data, \
> start=list(EC50=1.8,slope=0.8))
>
> Is there an advantage of using glm, and how would this work in this case?
--
Brian D. Ripley, ripley at stats.ox.ac.uk
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
University of Oxford, Tel: +44 1865 272861 (self)
1 South Parks Road, +44 1865 272860 (secr)
Oxford OX1 3TG, UK Fax: +44 1865 272595
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