[R-sig-ME] What to do when a factor term has several p values?
Toby Marthews
toby.marthews at ouce.ox.ac.uk
Thu Jan 20 17:45:19 CET 2011
Dear Very-patient Mixed-modelling list,
Thank you very much for your replies to my nesting question earlier today. EXTREMEly helpful! It seems I'm tripping over a lot of basic misconceptions with this LME application.
I am running an lme fit with two categorical fixed effects (in this case roostsitu which is roosting situation of some birds - nestbox, tree, inside or other - and mnth=Jan,Nov) and I am trying to simplify the model, i.e. considering whether there is a significant interaction between mnth and roostsitu when measuring the mass of these birds. According to the Fixed effects table of the summary.lme I have 3 p-values (0.1802, 0.3683 and 0.5474) so there's no significant interaction for any of the levels of roostsitu (readout below).
I have tried and failed to create an example to show this, but say there were another factor FF in the LME model and I were trying to follow a model simplification process based on these p-values. Further suppose that the p-value of roostsitu:FF were 0.400. There's a question here whether I would remove roostsitu:FF or roostsitu:mnth from the model first during my model simplification process.
(1) If I'm always supposed to consider the maximum p-value across all levels of a factor, then roostsitu:mnth scores 0.5474 which is >0.400 and it goes out first
(2) If I'm always supposed to take the mean p-value then roostsitu:mnth will score mean(c(0.1802,0.3683,0.5474))=0.3653 which is <0.400 so roostsitu:FF will go out first.
(3) Or some other calculation?
Is there a basic principle or rule I'm missing here regarding what to do in the case of multi-level factors? I would really appreciate someone telling me which option is the right one. I have just spent >1 hour searching a large number of websites and leafed through Pinheiro & Bates again but can't find an answer to this. Lots of websites say to use p-values (referencing Crawley generally) but I need a bit more detail than is in Crawley, it seems.
Thanks very much!
Toby Marthews
> lmeres=lme(fixed=stmass~mnth*roostsitu,random=~1|subject,na.action=na.exclude)
> summary(lmeres)
Linear mixed-effects model fit by REML
Data: NULL
AIC BIC logLik
449.6082 472.3749 -214.8041
Random effects:
Formula: ~1 | subject
(Intercept) Residual
StdDev: 0.5868961 4.165333
Fixed effects: stmass ~ mnth * roostsitu
Value Std.Error DF t-value p-value
(Intercept) 83.6 1.330205 36 62.84747 0.0000
mnthJan 7.2 1.862793 36 3.86516 0.0004
roostsitunest-box -4.2 1.881193 36 -2.23263 0.0319
roostsituinside -5.0 1.881193 36 -2.65789 0.0117
roostsituother -8.2 1.881193 36 -4.35893 0.0001
mnthJan:roostsitunest-box 3.6 2.634388 36 1.36654 0.1802
mnthJan:roostsituinside 2.4 2.634388 36 0.91103 0.3683
mnthJan:roostsituother 1.6 2.634388 36 0.60735 0.5474
Correlation:
(Intr) mnthJn rstst- rststn rststt mntJ:- mnthJn:rststn
mnthJan -0.700
roostsitunest-box -0.707 0.495
roostsituinside -0.707 0.495 0.500
roostsituother -0.707 0.495 0.500 0.500
mnthJan:roostsitunest-box 0.495 -0.707 -0.700 -0.350 -0.350
mnthJan:roostsituinside 0.495 -0.707 -0.350 -0.700 -0.350 0.500
mnthJan:roostsituother 0.495 -0.707 -0.350 -0.350 -0.700 0.500 0.500
Standardized Within-Group Residuals:
Min Q1 Med Q3 Max
-1.75548143 -0.76870435 -0.08640394 0.70218233 2.16928300
Number of Observations: 80
Number of Groups: 40
> anova(lmeres)
numDF denDF F-value p-value
(Intercept) 1 36 31143.554 <.0001
mnth 1 36 95.458 <.0001
roostsitu 3 36 10.614 <.0001
mnth:roostsitu 3 36 0.657 0.5838
>
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