How is Y measured? Is it the number out of 500 who exceed a certain threshold, or is it the average percentage increase in weight of the 500 or what? If it the number out N, with N approximately 500 (and you know N), then you have a logistic regression situation. In that case, section 7.2 in Venables and Ripley (2002) should do what you want. If Y is a percentage increase
When dose = 0, log(dose) = (-Inf). Since 0 is a legitimate dose, log(dose) is not acceptable in a model like this. You need a model like Peter suggested. Depending on you purpose, log(dose+0.015) might be sufficiently close to a model like what Peter suggested to answer your question. If not, perhaps this solution will help you find a better solution.
I previously was able to get dose.p to work in R, and I just now was able to compute from its output. The following worked in both S-Plus 6.1 and R 1.7.1:
> LD50P100p <- print(LD50P100)
Dose SE
p = 14: -2.451018 0.04858572
> exp(LD50P100p[1,1]+c(-2,0,2)*LD50P100p[1,2])-0.015
[1] 0.06322317 0.07120579 0.08000303hope this helps. spencer graves
Vincent Philion wrote:
Hello sir, answers follow...
... 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?
I'm not sure I understand what you mean by "mean", (no pun intended!)
but Y is a biologicial "growth". Only one "observation" for each X. But this observation is from the growth contribution of about 500 individuals, so I guess it is a "mean" response by design.
the log link is for the Poisson regression, so the GLM is "response ~ log(dose), (family=poisson)"
...Response for dose 0 is a "control" = Ymax. So, What I want is the dose for 50% response.
*** Once you observe Ymax, Y is no longer Poisson.
I don't understand this? What do you mean? Please explain.
***What exactly is Ymax? Is it the response at dose 0? Correct. it is measured the same way as for any other Y. (It is also the largest response because the "dose" is always detrimental to growth)
***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's it. that sounds right! How? (Confidence interval on log scale and on real scale, etc) Given that the error on Y is Poisson and not "normal"
***That one is easy.
OK...?
*** I think you are confusing response with mean response, and we can't disentangle them for you.
What else is needed?
bye for now,
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