On Nov 26, 2009, at 12:46 PM, Peter Dalgaard wrote:
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)
As I understood the question, it *is* purely formalistic. I.e., what to
write, not what to do.
I'd say "L-ratio" is plain wrong, since this is not a ratio, but the log
of a ratio. "-2lnQ" or "-2logQ" is what my old teachers would write, but
pragmatically, I'd expect the best chances with editors and reviewers to
be "LRT: chi-square=17.03, df=6, p=0.092", possibly with LRT spelled
out. (Some journals like to have the df because it allows reviewers to
catch glaring mistakes like categorical variables treated as numeric.)
I wonder about the phrase "used model simplification". Wouldn't that
raise a question about the proper degrees of freedom to use? If terms
were dropped from the model based simply on the basis of
"non-significance" shouldn't there be some appropriate penalization of
subsequent tests of significance?