[R-sig-ME] lme or lmer?
Antoine Tremblay
trea26 at gmail.com
Sat Jan 21 17:55:50 CET 2012
Hey Giorgio!
I have one published paper in JoCN where we analyzed ERP data using
LMER, another one is on the way, and a methods paper is almost finished
and submitted to NeuroImage regarding the addition of by-item random
intercepts and slopes to the model. I'm sending you a copy of the JoCN
paper (proofs for now, it'll be officially out any time soon) and a copy
of the almost finished NeuroImage paper. If others are interested,
please let me know and I can send them to you (trea26 at gmail dot com).
So LMER-wise, adding (1+Electtrode|Subject), which you could simply
write (Electrode|Subject) is correct, but fitting a model with such a
random effect will take forever.
In NewmanTremblay2011 paper we collapsed Electrode into 9 ROIs: left
anterior, midline anterior, right anterior, left central, midline
central, right central, left posterior, midline posterior, and right
posterior and use that new variable (ROI) instead of Electrode. Note
that we are not averaging at all! The main reason for this is
computation time: With 9 levels of ROI in the model as (ROI|Subject) it
takes A LOT of time to fit, tried once with (Electrode|Subject) and
would run for sooooo long, actually killed it after like two or three
days. Even the model with ROI takes what seems to be forever.
Then, once fitted, you'll see with print(model) that in the random
effects portion of the summary there's a table of correlations between
levels of ROI, something like this from one of our models (incomplete):
Linear mixed model fit by REML
Formula: Ampl ~ rProficiency * Condition * ROI * Group + (Condition |
Subject) + (1 | Subject) + (ROI | Subject)
Data: dat
AIC BIC logLik deviance REMLdev
1011929 1013147 -505843 1011340 1011685
Random effects:
Groups Name Variance Std.Dev. Corr
Subject (Intercept) 2.5852e-01 0.508447
ConditionGood 1.4045e+00 1.185133 -0.681
Subject (Intercept) 1.8638e-04 0.013652
Subject (Intercept) 2.8105e+00 1.676449
ROILcent 9.4985e-01 0.974604 -0.423
ROILpost 3.2123e+00 1.792281 -0.644 0.833
ROIMant 3.3728e-01 0.580755 -0.240 0.150 0.258
ROIMcent 1.8586e+00 1.363301 -0.249 0.687 ...
ROIMpost 2.8172e+00 1.678445 -0.577 0.746 ...
ROIRant 7.5682e-01 0.869957 -0.421 -0.171 ...
ROIRcent 1.4031e+00 1.184519 -0.840 0.531 ...
ROIRpost 3.0983e+00 1.760201 -0.797 0.578 ...
Residual 3.3207e+01 5.762530
Number of obs: 159374, groups: Subject, 44
I'm attaching a complete report (using Sweave) for your reference.
There's one other paper where they used lme with a corSpher function in
Davidson2007 (attached here, see page 90). The problem i see with using
lme is that you can't really add crossed by-item random effect, which
you should as demonstrated in Tremblay2012. Basically, adding by-item
random effects substantially decreases the amount of (partial)
autocorrelation in the model residuals (i.e., better approximation of
the assumption of independence of errors. Note that the data used in
that paper is available on CRAN (data package LCFdata) and I can send
you the .Rnw file that contains the R code used for data manipulation,
analysis and plotting in Tremblay2012, so it's fully replicable. Note
that for simplicity, analyses in Tremblay2012 are on a single electrode,
and that I'm not comparing (ROI|Subject) in LMER to corSpher in LME.
Would like to eventually look a variograms to see how well the two take
care of spatial correlation and also compare them with a model that
doesn't account for this correlation.
Note that, as I demonstrate in Tremblay2012, by-item random effect
should be added if warranted (by Log-likelihood Ratio Test or other; I
suspect it will always be).
Cheers,
Antoine
On 12-01-20 07:00 AM, r-sig-mixed-models-request at r-project.org wrote:
> ------------------------------
>
> Message: 3
> Date: Fri, 20 Jan 2012 11:53:37 +0100
> From: Giorgio Arcara<giorgio.arcara at gmail.com>
> To: r-sig-mixed-models at r-project.org
> Subject: [R-sig-ME] lme or lmer?
> Message-ID:<43825DBA-F86F-4732-AFE1-4B139B572331 at gmail.com>
> Content-Type: text/plain
>
>
> I would like to use mixed models in R to analyze EEG data, but I don't
> know if it is more correct to use lme or lmer.
> My data have the following structure
>
>
> Subject Electrode Interval Trial Condition Ampl
> 1 Fp1 200-300 1 A 3.5
> 1 Fp1 200-300 2 B 4.2
> 1 Fp2 400-600 1 A 6.5
> 1 Fp2 400-600 2 B 3.3
> 2 Fp1 200-300 1 A 2.1
> 2 Fp1 200-300 2 B -5.4
> 2 Fp2 400-600 1 A -5.6
> 2 Fp2 400-600 2 B -3.2
> .
> .
> .
>
> For sake of simplicity, here I include only 2 Trials but in the real
> dataset they are many more.
> In this hypothetical dataset Ampl is the depentent variable. Electrode
> and Interval are two predictors. I expect that levels of Electrode
> will be highly correlated as well the levels of Interval.
> My goal is to study if Condition influence Ampl and if interact with
> Electrode variable and Interval Variable.
>
>
> I would fit a model on these data with lmer with the following structure
>
> mod=lmer(Ampl~Electrode*Interval*Condition+(1+Electrode|Subject)
> +(1+Interval|Subject))
>
> If I'm correct the corresponding lme model would be
>
> mod=lme(Ampl~Electrode*Interval*Condition, random=list(~1+condition|
> Subject, ~1+Interval|Subject))
>
>
> So my questions are:
> Are these specification corrects?
> Should I use lmer or lme?
> Any suggestion for covariance matrix specification in lme?
>
> Thanks in advance!!!
>
>
>
>
>
>
>
>
>
>
>
>
>
>
> ___________
>
> Giorgio Arcara
> Ph.D.
>
> Department of General Psychology, University of Padua
> Via Venezia 15, 35131 Padova - Italy
> e-mail: giorgio.arcara at unipd.it
> Phone: +39 049 8276149
> http://lcnl.psy.unipd.it/people/arcara.htm
>
>
> [[alternative HTML version deleted]]
>
>
>
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