[R-sig-ME] Another case of -1.0 correlation of random effects
Viechtbauer Wolfgang (STAT)
Wolfgang.Viechtbauer at STAT.unimaas.nl
Fri Apr 9 15:16:32 CEST 2010
Maybe I am totally off here, but wouldn't it help if you make what is currently Dose = 0 equal to Dose = -8 and then have what is currently Dose = 8 be equal to Dose = 0? This should help to decrease the correlation between the intercepts and the slopes.
Best,
--
Wolfgang Viechtbauer http://www.wvbauer.com/
Department of Methodology and Statistics Tel: +31 (43) 388-2277
School for Public Health and Primary Care Office Location:
Maastricht University, P.O. Box 616 Room B2.01 (second floor)
6200 MD Maastricht, The Netherlands Debyeplein 1 (Randwyck)
----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 Kevin E.
Thorpe Sent: Friday, April 09, 2010 13:04 To:
r-sig-mixed-models at r-project.org Subject: [R-sig-ME] Another case of
-1.0 correlation of random effects
> Hello.
>
> I know this has come up a couple times recently, but I'm still not
> sure
> what to do about it in my data. Note that my sessionInfo() will be at
> the bottom.
>
> My data come from a crossover trial and are balanced.
>
> > str(gluc)
> 'data.frame': 96 obs. of 4 variables:
> $ Subject : int 1 2 3 5 6 7 10 11 12 13 ...
> $ Treatment: Factor w/ 2 levels "Barley","Oat": 1 1 1 1 1 1 1 1 1 1
> ... $ Dose : int 8 8 8 8 8 8 8 8 8 8 ...
> $ iAUC : num 110 256 129 207 244 ...
>
> > xtabs(~Treatment+Dose,data=gluc)
> Dose
> Treatment 0 2 4 8
> Barley 12 12 12 12
> Oat 12 12 12 12
>
> I plot the data (attached as gluc.pdf, if it comes through).
>
> From the plot, I think I want to fit the model as:
>
> lmer(iAUC~Treatment+Dose+(Treatment|Subject)+(Dose|Subject),data=gluc)
>
> It could possibly be argued that the (Treatment|Subject) part is not
> needed. When I fit this, I got -1.0 correlation within the Dose
> random
> effects. To simplify, I will fit a simpler model, since the issue
> persists.
>
> > lmer(iAUC~Dose+(Dose|Subject),data=gluc,subset=Treatment=="Oat")
> Linear mixed model fit by REML
> Formula: iAUC ~ Dose + (Dose | Subject)
> Data: gluc
> Subset: Treatment == "Oat"
> AIC BIC logLik deviance REMLdev
> 562.6 573.9 -275.3 563.1 550.6
> Random effects:
> Groups Name Variance Std.Dev. Corr
> Subject (Intercept) 8274.324 90.9633
> Dose 16.214 4.0266 -1.000
> Residual 4862.319 69.7303
> Number of obs: 48, groups: Subject, 12
>
> Fixed effects:
> Estimate Std. Error t value
> (Intercept) 309.352 30.539 10.130
> Dose -14.424 3.596 -4.012
>
> Correlation of Fixed Effects:
> (Intr)
> Dose -0.647
>
> Now, a plot created by (and attached as lmlist.pdf):
>
> plot(confint(lmList(iAUC~Dose|Subject,data=gluc,subset=Treatment=="Oat"),pooled=TRUE),order=1)
>
> shows (I think) a strong negative correlation between the intercept
> and
> slope random effects for Dose.
>
> So, I would appreciate some advice on how I might specify these random
> effects correctly.
>
> One last thing I tried. If I treat Dose as a factor (which might be
> reasonable) rather than numeric, I don't get any -1.0 correlations.
>
> > lmer(iAUC~dose+(dose|Subject),data=gluc,subset=Treatment=="Oat")
> Linear mixed model fit by REML
> Formula: iAUC ~ dose + (dose | Subject)
> Data: gluc
> Subset: Treatment == "Oat"
> AIC BIC logLik deviance REMLdev
> 545.2 573.3 -257.6 547 515.2
> Random effects:
> Groups Name Variance Std.Dev. Corr
> Subject (Intercept) 7509.9 86.660
> dose2 11993.0 109.513 -0.321
> dose4 6399.5 79.997 0.043 0.873
> dose8 6051.7 77.793 -0.743 0.433 0.306
> Residual 1206.4 34.733
> Number of obs: 48, groups: Subject, 12
>
> Fixed effects:
> Estimate Std. Error t value
> (Intercept) 293.567 26.951 10.893
> dose2 6.692 34.648 0.193
> dose4 -39.975 27.099 -1.475
> dose8 -105.517 26.558 -3.973
>
> Correlation of Fixed Effects:
> (Intr) dose2 dose4
> dose2 -0.380
> dose4 -0.103 0.786
> dose8 -0.724 0.443 0.360
>
> Thanks in advance and here is my sessionInfo().
>
> > sessionInfo()
> R version 2.10.1 Patched (2009-12-29 r50852)
> i686-pc-linux-gnu
>
> locale:
> [1] LC_CTYPE=en_US LC_NUMERIC=C LC_TIME=en_US
> [4] LC_COLLATE=C LC_MONETARY=C LC_MESSAGES=en_US
> [7] LC_PAPER=en_US LC_NAME=C LC_ADDRESS=C
> [10] LC_TELEPHONE=C LC_MEASUREMENT=en_US LC_IDENTIFICATION=C
>
> attached base packages:
> [1] stats graphics grDevices utils datasets methods base
>
> other attached packages:
> [1] lme4_0.999375-32 Matrix_0.999375-33 lattice_0.17-26
>
> loaded via a namespace (and not attached):
> [1] grid_2.10.1
>
>
> --
> Kevin E. Thorpe
> Biostatistician/Trialist, Knowledge Translation Program Assistant
> Professor, Dalla Lana School of Public Health University of Toronto
> email: kevin.thorpe at utoronto.ca Tel: 416.864.5776 Fax: 416.864.3016
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