On Jan 15, 2010, at 4:50 PM, Shawn Morrison wrote:
Is there a readily available function to calculate the effect of
variables from a poisson GLM on the response variable?
My situation is as follows:
I have developed a poisson GLM model and have obtained the
coefficients, SEs, etc However, I am somewhat stuck on interpreting
a coefficient in everyday language.
For example:
Y = dependent variable (count data)
A = independent variable (continuous)
B = independent variable (continuous)
The hypothetical regression equation is:
[I used natural logs for A]
Assuming that -0.19 was an estimated coefficient in a glm model
specified with a formula of:
Y ~ A + log(B+1) , then you most likely got a model fit with a log
link (the default for Poisson models) in addition to the log transform
you applied . So you may have unnecessarily used log transforms.
Then the expected value of Y|log(B+1) for E(Y|log(B+1)=1), would be
exp(-0.19) times that of E(Y|log(B+1)=0). You may have confused things
a bit by using log(B+1).
a) Did you have zero values for B?
b) Was there really a need to transform A and B in that manner? You
ended up with a log(log()) transform.
I want to be able to say that changing B by one unit has a
corresponding ___% decrease in Y.
How do I calculate the % change in Y caused by changes in B? Is
there an R function, or a bit of code that will do the trick? How do
these calculations affect the SEs?
Thank you,
and provide commented, minimal, self-contained, reproducible code.
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Code. We want code.
--
David Winsemius, MD
Heritage Laboratories
West Hartford, CT
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