On Fri, 25 Jul 2003, Vincent Philion wrote: > Hello and thank you for your interest in this problem. > > "real life data" would look like this: > > x y > 0 28 > 0.03 21 > 0.1 11 > 0.3 15 > 1 5 > 3 4 > 10 1 > 30 0 > 100 0 > > x y > 0 30 > 0.0025 30 > 0.02 25 > 0.16 25 > 1.28 10 > 10.24 0 > 81.92 0 > > X Y > 0 35 > 0.00025 23 > 0.002 14 > 0.016 6 > 0.128 5 > 1.024 3 > 8.192 2 > > X Y > 0 43 > 0.00025 35 > 0.002 20 > 0.016 16 > 0.128 11 > 1.024 6 > 8.192 0 > > Where X is dose and Y is response. > the relation is linear for log(response) = b log(dose) + intercept
Is that log(*mean* response), that is a log link and exponential decay with dose? > Response for dose 0 is a "control" = Ymax. So, What I want is the dose > for 50% response. For instance, in example 1: > > Ymax = 28 (this is also an observation with Poisson error) Once you observe Ymax, Y is no longer Poisson. > So I want dose for response = 14 = approx. 0.3 What exactly is Ymax? Is it the response at dose 0? The mean response at dose 0? The largest response? About the only thing I can actually interpret is that you want to fit a curve of mean response vs dose, and find the dose at which the mean response is half of that at dose 0. That one is easy. I think you are confusing response with mean response, and we can't disentangle them for you. -- Brian D. Ripley, [EMAIL PROTECTED] 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 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595 ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
