You might also want to take a look at the recent paper from the Federmeier group, especially the supplementary materials. There are a few technical inaccuracies (ANOVA is a special case of hierarchical modelling, not the other way around), but they discuss some of the issues involved. And relevant for your work: they model channel as a grouping variable in the random-effects structure.
Payne, B. R., Lee, C.-L., and Federmeier, K. D. (2015). Revisiting the incremental effects of context on word processing: Evidence from single-word event-related brain potentials. Psychophysiology. http://dx.doi.org/10.1111/psyp.12515 Best, Phillip > On 24 Sep 2015, at 22:42, Phillip Alday <phillip.al...@unisa.edu.au> wrote: > > There is actually a fair amount of ERP literature using mixed-effects > modelling, though you may have to branch out from the traditional > psycholinguistics journals a bit (even just more "neurolinguistics" or > language studies published in "psychology" would get you more!). But > just in the traditional psycholinguistics journals, there is a wealth of > literature, see for example the 2008 special issue on mixed models of > the Journal of Memory and Language. > > I would NOT encode the channels/ROIs/other topographic measures as > random effects (grouping variables). If you think about the traditional > ANOVA analysis of ERPs, you'll recall that ROI or some other topographic > measure (laterality, saggitality) are included in the main effects and > interactions. As a rule of thumb, this corresponds to a fixed effect in > random effects models. More specifically, you generally care about > whether the particular levels of the topographic measure (i.e. you care > if an ERP component is located left-anterior or what not) and this is > what fixed effects test. Random effects are more useful when you only > care about the variance introduced by a particular term but not the > specific levels (e.g. participants or items -- we don't care about a > particular participant, but we do care about how much variance there is > between participants, i.e. how the population of participants looks). > > Or, another thought: You may have seen ANOVA by-subjects and by-items, > but I bet you've never seen an ANOVA by-channels. ANOVA "implicitly" > collapses the channels within ROIs and you can do the same with mixed > models. (That's an awkward statement technically, but it should help > with the intuition.) > > There is an another, related important point -- "nuisance parameters" > aren't necessarily random effects. So even if you're not interested in > the per-electrode distribution of the ERP component, that doesn't mean > those should automatically be random effects. It *might* make sense to > add a channel (as in per-electrode) random effect, if you care to model > the variation within a given ROI (as you have done), but I haven't seen > that yet. It is somewhat rare to include a per-channel fixed effect, > just because you lose a lot of information that way and introduce more > parameters into the model, but you could include a more fine-grained > notion of saggital / lateral location based on e.g. the 10-20 system and > make that into an ordered factor. (Or you could be extreme and even use > the spherical coordinates that the 10-20 is based on and have continuous > measures of electrode placement!) The big problem with including > "channel" as a random-effect grouping variable is that the channels > would have a very complicated covariance structure (because adjacent > electrodes are very highly correlated with each other) and I'm not sure > how to model this in a straightforward way with lme4. > > More generally, in considering your random effects structure, you should > look at Barr et al (2013, "Random effects structure for confirmatory > hypothesis testing: Keep it maximal") and the recent reply by Bates et > al (arXiv, "Parsimonious Mixed Models"). You should read up on the GLMM > FAQ on testing random effects -- there are different opinions on this > and not all think that testing them via likelihood-ratio tests makes > sense. > > That wasn't my most coherent response, but maybe it's still useful. And > for questions like this on mixed models, do check out the R Special > Interest Group on Mixed Models. :-) > > Best, > Phillip > > On Thu, 2015-09-24 at 12:00 +0200, r-help-requ...@r-project.org wrote: >> Message: 4 >> Date: Wed, 23 Sep 2015 12:46:46 +0200 >> From: Paolo Canal <paolo.ca...@iusspavia.it> >> To: r-help@r-project.org >> Subject: [R] Appropriate specification of random effects structure for >> EEG/ERP data: including Channels or not? >> Message-ID: <56028316.2050...@iusspavia.it> >> Content-Type: text/plain; charset="UTF-8" >> >> Dear r-help list, >> >> I work with EEG/ERP data and this is the first time I am using LMM to >> analyze my data (using lme4). >> The experimental design is a 2X2: one manipulated factor is >> agreement, >> the other is noun (agreement being within subjects and items, and >> noun >> being within subjects and between items). >> >> The data matrix is 31 subjects * 160 items * 33 channels. In ERP >> research, the distribution of the EEG amplitude differences (in a >> time >> window of interest) are important, and we care about knowing whether >> a >> negative difference is occurring in Parietal or Frontal electrodes. >> At >> the same time information from single channel is often too noisy and >> channels are organized in topographic factors for evaluating >> differences >> in distribution. In the present case I have assigned each channel to >> one >> of three levels of two factors, i.e., Longitude (Anterior, Central, >> Parietal) and Medial (Left, Midline, Right): for instance, one >> channel >> is Anterior and Left. With traditional ANOVAs channels from the same >> level of topographic factors are averaged before variance is >> evaluated >> and this also has the benefit of reducing the noise picked up by the >> electrodes. >> >> I have troubles in deciding the random structure of my model. Very >> few >> examples on LMM on ERP data exist (e.g., Newman, Tremblay, Nichols, >> Neville & Ullman, 2012) and little detail is provided about the >> treatment of channel. I feel it is a tricky term but very important >> to >> optimize fit. Newman et al say "data from each electrode within an >> ROI >> were treated as repeated measures of that ROI". In Newman et al, the >> ROIs are the 9 regions deriving from Longitude X Medial >> (Anterior-Left, >> Anterior-Midline, Anterior-Right, Central-Left ... and so on), so in >> a >> way they treated each ROI separately and not according to the >> relevant >> dimensions of Longitude and Medial. >> >> We used the following specifications in lmer: >> >> [fixed effects specification: ?V ~ Agreement * Noun * Longitude * >> Medial >> * (cov1 + cov2 + cov3 + cov4)] (the terms within brackets are a >> series >> of individual covariates, most of which are continuous variables) >> >> [random effects specification: (1+Agreement*Type of Noun | subject) + >> (1+Agreement | item) + (1|longitude:medial:channel)] >> >> What I care the most about is the last term >> (1|longitude:medial:channel). I chose this specification because I >> thought that allowing each channel to have different intercepts in >> the >> random structure would affect the estimation of the topographic fixed >> effects (Longitude and Medial) in which channel is nested. >> Unfortunately >> a reviewer commented that since "channel is not included in the fixed >> effects I would probably leave that out". >> >> But each channel is a repeated measure of the eeg amplitude inside >> the >> two topographic factors, and random terms do not have to be in the >> fixed >> structure, otherwise we would also include subjects and items in the >> fixed effects structure. So I kind of feel that including channels as >> random effect is correct, and having them nested in longitude:medial >> allows to relax the assumption that the effect in the EEG has always >> the >> same longitude:medial distribution. But I might be wrong. >> >> I thus tested differences in fit (ML) with anova() between >> (1|longitude:medial:channel) and the same model without the term, and >> a >> third model with the model with a simpler (1|longitude:medial). >> >> Fullmod vs Nochannel: >> >> Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq) >> modnoch 119 969479 970653 -484621 969241 >> fullmod 120 968972 970156 -484366 968732 508.73 1 < 2.2e-16 *** >> >> Differences in fit is remarkable (no variance components with >> estimates >> close to zero; no correlation parameters with values close to ?1). >> >> Fullmod vs SimplerMod: >> >> Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq) >> >> fullmod 120 968972 970156 -484366 968732 >> simplermod 120 969481 970665 -484621 969241 0 0 1 >> >> Here the number of parameters to estimate in fullmod and simplermod >> is >> the same but the increase in fit is very consistent (-509 BIC). So I >> guess although the chisquare is not significant we do have a string >> increase in fit. As I understand this, a model with better fit will >> find >> more accurate estimates, and I would be inclined to keep the fullmod >> random structure. >> >> But perhaps I am missing something or I am doing something wrong. >> Which >> is the correct random structure to use? >> >> Feedbacks are very much appreciated. I often find answers in the >> list, >> and this is the first time I post a question. >> Thanks, >> Paolo >> >> >> >> > ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
