Gregory (Greg) L. Snow Ph.D.
Statistical Data Center
Intermountain Healthcare
greg.snow at imail.org
801.408.8111
> -----Original Message-----
> From: r-sig-mixed-models-bounces at r-project.org [mailto:r-sig-mixed-
> models-bounces at r-project.org] On Behalf Of Toby Marthews
> Sent: Thursday, January 20, 2011 9:45 AM
> To: r-sig-mixed-models at r-project.org
> Subject: [R-sig-ME] What to do when a factor term has several p values?
>
> 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.e
> xclude)
>
> > 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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