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Interpreting Poisson GLM coefficients into everyday language

2 messages · Shawn Morrison, David Winsemius

#
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:

Y ~ constant + 0.25*A - 0.19*(log(B+1)) [I used natural logs for A]

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,

***************
Shawn Morrison
Edmonton, Alberta
#
On Jan 15, 2010, at 4:50 PM, Shawn Morrison wrote:

            
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.

        
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