request

What appears to be a preprint copy is available at

  http://www.ats.ucla.edu/stat/paperexamples/atkins/Modeling_Infrequent_Counts_JFP2007_-_WEB.pdf

This article contains what I consider to be unfortunate advice:

"One consequence of this is that using a simple transformation allows us to interpret regression coefficients in the Poisson model as the percentage change in the expected counts:

100*[e?*? ?1] (5) where ? is the regression coefficient from the Poisson regression and ? is the units of change in

the predictor (e.g., for one unit of change in the predictor, ? = 1)."

This advice (which is repeating a misinterpretation foisted by the SAS Stats Manual) fails to recognize that the scale of interpretation is not symmetric upon "inversion" or "complementation" of the predictors. So if the predictor is a 1/0 variable, then one coding of a variable with a coefficient of log(2)  might be "interpreted" as a 100% change(say for "married"==1),  whereas the reverse coding ( alternately coded "not married" ==1)  would be "interpreted" as a 50% change. Ironically this advice occurs immediately after the distinction between linear scales and multiplicative scales. It is the multiplicative scale that invalidates that "percentage change" interpretation simply because the range from a "null"-value of 0 to the low end of possible values os "100%" whereas the range to the high end is unlimited upward.

(The correct interpretation is for the exponentiated coefficient to be seen as a relative risk or a multiplicative factor that creates a predicted count or rate relative to the Intercept or baseline estimate. There is really no need to subtract one if one realizes that a factor of 1 will not change any estimate if the result is being multiplied by a baseline value.)

If the advice were couched in more limited manner such that it were resticted to small coefficients and sufficient caveats about its approximate nature were offers, I would be less offended. It bothers me to see this further source of misinterpretation cited as an authority.

Steven McKinney

Statistician
Molecular Oncology and Breast Cancer Program
British Columbia Cancer Research Centre

________________________________________
From: r-sig-mixed-models-bounces at r-project.org [r-sig-mixed-models-bounces at r-project.org] On Behalf Of Seth Bigelow [seth at swbigelow.net]
Sent: May 28, 2013 7:19 AM
To: 'Champika Shyamalie Kariyawasam'; r-sig-mixed-models at r-project.org
Subject: Re: [R-sig-ME] request

Shyam, I have found Dave Atkin's tutorial to be very helpful in a situation
that may be similar to the one you describe. I was at a loss to understand
the estimated coefficients in poisson regression before reading it. It seems
to be behind a paywall ($11.95), though it was not the last time I checked.
--Seth

A tutorial on count regression and zero-altered count models for
longitudinal substance use data.
By Atkins, David C.; Baldwin, Scott A.; Zheng, Cheng; Gallop, Robert J.;
Neighbors, Clayton
Psychology of Addictive Behaviors, Vol 27(1), Mar 2013, 166-177.

-----Original Message-----
From: r-sig-mixed-models-bounces at r-project.org
[mailto:r-sig-mixed-models-bounces at r-project.org] On Behalf Of Champika
Shyamalie Kariyawasam
Sent: Monday, May 27, 2013 11:50 PM
To: r-sig-mixed-models at r-project.org
Subject: [R-sig-ME] request

Hi all

I am using generalized linear mixed model fit by the laplace approximation
(family poisson) to analize my data. I have seed number (dependent variable)
as a function of study site in two lations in two countries. i ran the model
in R . But i need some assistance to interpret my data. Any source,
reference or result of previous work welcome.

thanks in advance

shyam

David Winsemius
Alameda, CA, USA