On Nov 26, 2009, at 12:14 PM, JVezilier wrote:
Hello !!
I'm recently having a debate with my PhD supervisor regarding how to
write
the result of a likelihood ratio test in an article I'm about to
submit.
I analysed my data using "lme" mixed modelling.
To get some p-values for my fixed effect I used model simplification
and the
typical output R gives looks like this:
model2 = update ( model1,~.-factor A)
anova (model1, model2)
Model df AIC BIC logLik Test
L.Ratio p-value
model 1 1 26 -78.73898 15.29707 65.36949
model 2 2 20 -73.70539 -1.36997 56.85270 1 vs 2
17.03359
0.0092
I thought about presenting it very simply copying/pasting R table and
writing it like: "factor A had a significant effect on the response
variable
(Likelihood ratio test, L-ratio = 17.033, p = 0.0092)"
But my boss argued that it's too unusual (at least in our field of
evolutionary biology) and that I should present instead the LR
statistic
together with the corresponding Chi^2 statistic since the likelihood
ratio
is almost distributed like a Chi2 (df1-df2), and then write down the
p-value
corresponding to this value of Chi.
I looked up in the current litterature but cannot really find a proper
answer to that dilmena.
So, dear evolutionary biologists R users, how would you present it ?
I am not an evolutionary biologist, but presumably your supervisor is
one. Why are you picking a fight not only with him but with your
prospective audience when there is no meaningful difference? Here is
the p-value you would get with his method:
>> 1-pchisq( 2*(65.36949 - 56.85270), df=6)
[1] 0.009160622
--
David Winsemius, just a guy who formerly argued about such matters.
Thank you very much,
Julien.
--
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http://old.nabble.com/How-shall-one-present-LRT-test-statistic-in-a-scientific-journal---tp26532480p26532480.html
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David Winsemius, MD
Heritage Laboratories
West Hartford, CT
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