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
101
300
100
Dear Prof. Ripley M. Philion:
First some commentary then questions for Prof. Ripley and M. Philion.
COMMENTARY
Prof. Ripley said, 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. Unfortunately, it is not
Prof Brian Ripley [EMAIL PROTECTED] writes:
x y
0 28
0.0321
0.1 11
0.3 15
1 5
3 4
10 1
30 0
100 0
Where X is dose and Y is response.
the relation is linear for log(response) =
Hello, and thanks for this.
From www.r-project.org - search - R site search - LD50, I found
dose.p, described on p. 193, sec. 7.2, of Venables and Ripley (2002) Modern
Applied Statistics with S, 4th ed. (Springer).
I found the same, but this is for logistic regression I think, not Poisson.
Ymax is the maximum observation in your example, and also the observation
at zero. I was asking which you meant: if you meant Y at 0 (and I think
you do) then it is somewhat misleading notation.
You have a set of Poisson random variables Y_x at different values of x.
Poisson random variables
The Poisson assumption means that Y is a number of independent events
from a theoretically infinite population occurring in a specific time or
place. The function glm with 'family=poisson' with the default link
= log assumes that the logarithm of the mean of Y is a linear model in
the
Hello again, sorry for the notation. Again, I'm just a biologist!!!
;-)
But I'm enjoying this problem quite a bit! I'm very grateful for all the input. This
is great.
On 2003-07-25 08:38:00 -0400 Prof Brian Ripley [EMAIL PROTECTED] wrote:
Answers:
Ymax is the maximum observation in your
- Original Message -
From: Vincent Philion [EMAIL PROTECTED]
Date: Friday, July 25, 2003 9:25 am
Subject: Re: [R] inverse prediction and Poisson regression
Hi, ... and good morning!
;-)
On 2003-07-25 08:43:35 -0400 Spencer Graves
[EMAIL PROTECTED] wrote:
The Poisson assumption means
of Parameter Estimates:
Ymax
x50 -0.6001
- Original Message -
From: Vincent Philion [EMAIL PROTECTED]
Date: Friday, July 25, 2003 9:25 am
Subject: Re: [R] inverse prediction and Poisson regression
Hi, ... and good morning!
;-)
On 2003-07-25 08:43:35 -0400 Spencer Graves
[EMAIL PROTECTED
Hello to all: first and foremost: thank you for all this input. I only discovered about R last week (!) and I think I will dump my SAS license!!!
;-)
This is a very dynamic listserve!
You R all great! Thank you!
I just hope some day I can help out a student the way you did today.
I will spend
Spencer Graves [EMAIL PROTECTED] writes:
The problem with nls is that it is NOT maximum likelihood for the
Poisson distribution. For the Poisson, the standard deviation is the
square root of the mean, while nls assumes constant standard
deviation. That's why I stayed with glm. The answers
this seem reasonable?
Ravi.
- Original Message -
From: Peter Dalgaard BSA [EMAIL PROTECTED]
Date: Friday, July 25, 2003 3:48 pm
Subject: Re: [R] inverse prediction and Poisson regression
Spencer Graves [EMAIL PROTECTED] writes:
The problem with nls is that it is NOT maximum likelihood
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
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
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