Hi Dr. Snow, 

 

I am a graduate student working on analyzing data for my thesis and came
across your post on  an R forum:

 

The default link function for the glm poisson family is a log link, which
means that it is fitting the model:
 
log(mu) ~ b0 + b1 * x
 
But the data that you generate is based on a linear link.  Therefore your
glm analysis does not match with how the data was generated (and therefore
should not necessarily be the best fit).  Either analyze using glm and a
linear link, or generate the data based on a log link (e.g. rpois(40,
exp(seq(1,3, length.out=40))) ).
 
Hope this helps,
 
--
Gregory (Greg) L. Snow Ph.D.
Statistical Data Center
Intermountain Healthcare
greg.snow at imail.org <https://stat.ethz.ch/mailman/listinfo/r-help> 
801.408.8111

 

I am not using R at the moment (working in SPSS, have to love the GUI) but
my question is quite related:

I am running a generalized linear model on data highly skewed to the right
with a bunch of zeroes, so I decided to use the Tweedie distribution. In the
model I ran both untransformed data (with link=log) as well as log(x+1)
transformed data (with link=identity). The latter model had a much smaller
(more negative) AICc value than the untransformed data with link=log. 

Is it valid to run the GLM with log(x+1) transformed data if link=identity?
Or am I violating some kind of assumption about the model?

I really appreciate any advice or thoughts! It seems as if my go-to
statistician has taken a loooong break and any help would be greatly valued!

 

-Emily Bellush

QTGR@IUP.EDU

Indiana University of Pennsylvania


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