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 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) So I want dose for response = 14 = approx. 0.3 any help would be greatly appreciated! bye for now, Vincent > 1. If you provide a toy data set with, e.g., 5 observations, to accompany > your example, it would be much easier for people to try out ideas and then > give you a more solid response. > > 2. Have you tried something like log(dose+0.5) or I(log(dose+0.5)) in > your model statement in conjunction with "predict" or "predict.glm" on the > output from "glm"? > > hope this helps. spencer graves > > Vincent Philion wrote: >> Hello to all, I'm a biologist trying to tackle a "fish" > (Poisson Regression) which is just too big for my modest > understanding of stats!!! >> >> Here goes... >> >> I want to find good literature or proper mathematical > procedure to calculate a confidence interval for an > inverse prediction of a Poisson regression using R. >> >> I'm currently trying to analyse a "dose-response" > experiment. >> >> I want to calculate the dose (X) for 50% inhibition > of a biological response (Y). My "response" is a "count" > data that fits a Poisson distribution perfectly. >> >> I could make my life easy and calculate: "dose > response/control response" = % of total response... > and then use logistic regression, but somehow, that > doesn't sound right. >> Should I just stick to logistic regression and go > on with my life? Can I be cured of this paranoia? >> ;-) >> >> I thought a Poisson regression would be more > appropriate, but I don't know how to "properly" > calculate the dose equivalent to 50% inhibition. > i/e confidence intervals, etc on the "X" = dose. > Basically an "inverse" prediction problem. >> >> By the way, my data is "graphically" linear for > Log(Y) = log(X) where Y is counts and X is dose. >> >> I use a Poisson regression to fit my dose-response > experiment by EXCLUDING the response for dose = 0, > because of log(0) >> >> Under "R" = >> >>> glm.dose <- glm(response[-1] ~ log(dose[-1]),family=poisson()) >> >> >> (that's why you see the "dose[-1]" term. The > "first" dose in the dose vector is 0. >> >> This is really a nice fit. I can obtain a nice > slope (B) and intercept (A): >> >> log(Y) = B log(x) + A >> >> I do have a biological value for dose = 0 from > my "control". i/e Ymax = some number with a Poisson > error again >> >> So, what I want is EC50x : >> >> Y/Ymax = 0.5 = exp(B log(EC50x) + A) / Ymax >> >> exp((log(0.5) + Log(Ymax)) - A)/B) = EC50x >> >> That's all fine, except I don't have a clue on how > to calculate the confidence intervals of EC50x or even > if I can model this inverse prediction with a Poisson > regression. In OLS linear regression, fitting X based > on Y is not a good idea because of the way OLS calculates > the slope and intercept. Is the same problem found in > GLM/Poisson regression? Moreover, I also have a Poisson > error on Ymax that I would have to consider, right? >> >> Help!!!! >> >> > > > -- Vincent Philion, M.Sc. agr. Phytopathologiste Institut de Recherche et de D�veloppement en Agroenvironnement (IRDA) 3300 Sicotte, St-Hyacinthe Qu�bec J2S 7B8 t�l�phone: 450-778-6522 poste 233 courriel: [EMAIL PROTECTED] Site internet : www.irda.qc.ca ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
