Re: st: RE: proportion of explained variance with log-transformed outcome

[Buzz Burhans <wsb2@cornell.edu>]

Many thanks to both Rich Goldstein and Nick Cox for their responses to my 
question under the above subject thread.  Both  had useful and 
informative  ideas, for which I am grateful.  Unfortunately, I remain 
unsure of how to describe the extent to which a significant independent 
variable contributes to explaining the variance in the original metric in a 
model of a log transformed dependent variable.  Part of my difficulty 
arises from my use of gllamm with a two level model, because I am 
interested in associated random subject effects as well (else Rich's 
suggestion of -brsq- would work) .  In fact, I am interested in the 
magnitude of the fixed treatment effect relative to the magnitude of the 
variance  at the second level of my 2 level model. The issue is how to best 
form a basis for interpreting / describing the magnitude of the fixed 
effect relative to the  random subject variance.  I am satisfied with how I 
have done this within the transformed metric, but I need to interpret / 
describe it relative to the original metric as well. Basically, I have 
tested the treatment effect, and I now want to describe it  relative to the 
random subject  variance and the overall variance in terms of the original 
values as opposed to the transformed values, which I have already done.

I am considering examining the magnitude of the backtransformed fixed 
effect at given values of the covariates relative to the IQR for the random 
subject effects as an indication or description of how much greater the 
dispersion is in the random effects relative to the smaller difference for 
treatment, however, I would be interested in any further thoughts on how to 
get at this given the model is in a transformed metric and it would be 
advantageous to describe this information in the original metric.

I again thank both Rich Goldstein and Nick Cox for helpful responses.


Buzz Burhans
wsb2@cornell.edu


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