[R-sig-ME] magnitude of random effect vs significance
Mike Dunbar
mdu at ceh.ac.uk
Tue Sep 4 22:09:23 CEST 2007
Hi Peter
Just to check I'm understanding you: So in the generic case where you have a nesting say A/B/C/D, if you want to test by removal of any factor that isn't the lowest in the hierarchy then you have to re-label that factor as including the levels of the next lowest factor. So for example testing by removing A, you must recode B as interaction(A,B) and test that against the full model. If so then I already understood this in the case of POLE and TRANSECT, I'd just forgotten it for the higher level factors.
regards
Mike
>>> Peter Dalgaard <p.dalgaard at biostat.ku.dk> 04/09/2007 20:53 >>>
Mike Dunbar wrote:
> Following on from previous recent post, here is an example of a random effect which is tiny but highly significant. I've got no problem explaining a fixed effect which is tiny but significant (ie precisely estimated), but I'm struggling here!
>
> regards
>
> Mike
>
>
>
> # read in temp3 first below
> varcor.2h.crustacea.hf <- lme(log(crustdens+1) ~ HEIGHT, random=~1|MONTH/TIME/TRANSECT/POLE, data=temp3)
> VarCorr(varcor.2h.crustacea.hf)
>
> varcor.2h.crustacea.nomonth.hf <- lme(log(crustdens+1) ~ HEIGHT, random=~1|TIME/TRANSECT/POLE, data=invdens.bottommiddle)
>
> anova(varcor.2h.crustacea.hf,varcor.2h.crustacea.nomonth.hf)
> # month random effect of very low magnitude, yet it it highly significant: how can I explain this, or have I made a mistake!
>
>
I don't think those models are comparable. Let's ignore TRANSECT and
POLE for now. In one model you have MONTH with 4 groups and TIME %in%
MONTH with 16 groups, and in the other you have TIME with 4 groups. Put
differently the variance for that term in one case means main effect of
TIME and in the other case ditto plus the interaction. If TIME really
only makes sense as nested in MONTH, the former can give a substantially
worse fit to data whether or not there is a MONTH term. For
comparability, try this:
> temp3$MTIME <- interaction(temp3$MONTH,temp3$TIME)>
varcor.2h.crustacea.nomonth2.hf <- lme(log(crustdens+1) ~ HEIGHT,
random=~1|MTIME/TRANSECT/POLE, data=temp3)
> anova(varcor.2h.crustacea.hf,varcor.2h.crustacea.nomonth2.hf)
Model df AIC BIC logLik Test
varcor.2h.crustacea.hf 1 7 1900.187 1929.923 -943.0935
varcor.2h.crustacea.nomonth2.hf 2 6 1898.187 1923.675 -943.0935 1 vs 2
L.Ratio p-value
varcor.2h.crustacea.hf
varcor.2h.crustacea.nomonth2.hf 3.202003e-07 0.9995
--
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