[R-sig-ME] Related fixed and random factors and planned comparisons in a 2x2 design
Houslay, Tom
T.Houslay at exeter.ac.uk
Mon Jun 6 21:51:26 CEST 2016
Hi Paul,
I think you're right here in that actually you don't want to nest channel inside participant (which led to that error message - sorry, should have seen that coming!).
It's hard to know without seeing data plotted, but my guess from your email is that you probably see some clustering both at individual level and at channel level? Perhaps separate random effects, ie (1|Participant) + (1|Channel), is the way to go (and then you shouldn't have the problem as regards number of observations - instead you'll have an intercept deviation for each of your N individuals, and also intercept deviations for each of your 9 channels). You certainly want to keep the participant intercept in though, as each individual gets both items (right?), so you need to model that association. You can use your variance components output from lmer to determine what proportion of the phenotypic variance (conditional on your fixed effects) is explained by each of these components, eg V(individual)/(V(individual) + V(channel) + V(residual) would give you the proportion explained by differences among individuals in their voltage. It would be cool to know if differences among individuals, or among channels, is driving the variation that you find. I think using the sjplot function for lmer would be useful to look at the levels of your random effects:
http://strengejacke.de/sjPlot/sjp.lmer/
As for 'contrasts', again I haven't used that particular package, but from a brief glance it looks like you're on the right track - binary coding is the 'simple coding' as set out here:
http://www.ats.ucla.edu/stat/r/library/contrast_coding.htm
Good luck!
Tom
________________________________
From: paul <graftedlife at gmail.com>
Sent: 06 June 2016 20:06:02
To: Houslay, Tom
Cc: r-sig-mixed-models at r-project.org
Subject: Re: Related fixed and random factors and planned comparisons in a 2x2 design
Dear Tom,
Many thanks for these very helpful comments and suggestions! Would you just allow me to ask some further questions:
1. I've been considering whether to cross or to nest the random effects for quite a while. Data from the same channel across participants do show corresponding trends (thus a bit different from the case when, e.g., sampling nine neurons from the same individual). Would nesting channel within participant deal with that relationship?
2. I actually also tried nesting channel within participant. However, when I proceeded to run planned comparisons (I guess I'd better have them done because of their theoretical significance) based on this mixed-effect modeling approach (as illustrated in the earlier mail but with the random factor as (1|participant/channel), to maintain consistency of analytical methods), R gave me an error message:
Error: number of levels of each grouping factor must be < number of observations
I think this is because in my data, each participant only contributes one data point per channel and thus the data points are not enough. I guess that probably means I can't go on in this direction to run the planned comparisons... (?) I'm not pretty sure how contrasts based on binary dummy variables may be done and will try to further explore that. But before I establish the mixed model I already set up orthogonal contrasts for group and item in the dataset using the function contrasts(). Does this have anything to do with what you meant?
3. I worried about pseudoreplicability when participant ID is not included. Concerning this point, later it came to me that pseudoreplicability usually occurred in cases when multiple responses from the same individual are grouped in the same cell, rendering the data within the same cell non-independent (similar to the case of repeated-measure ANOVA? sorry if I got a wrong understanding...). But as mentioned earlier in my data, each participant only contributes one data point per channel, when channel alone is already modeled as a random factor, would that mean all data points within a cell all come from different participants and thus in this case may deal with the independence assumption? (Again I'm sorry if my concept is wrong and would appreciate instructions on this point...)
Many, many thanks!
Paul
2016-06-06 19:10 GMT+02:00 Houslay, Tom <T.Houslay at exeter.ac.uk<mailto:T.Houslay at exeter.ac.uk>>:
Hi Paul,
I don't think anyone's responded to this yet, but my main point would be that you should check out Schielzeth & Nakagawa's 2012 paper 'Nested by design' ( http://onlinelibrary.wiley.com/doi/10.1111/j.2041-210x.2012.00251.x/abstract ) for a nice rundown on structuring your model for this type of data.
It may also be worth thinking about how random intercepts work in a visual sense; there are a variety of tools that help you do this from a model (packages sjplot, visreg, broom), or you can just plot different levels yourself (eg consider plotting the means for AP, AQ, BP, BQ; the same with mean values from each individual overplotted around these group means; and even the group means with all points shown, perhaps coloured by individual - ggplot is really useful for getting this type of figure together quickly).
As to some of your other questions:
1) You need to keep participant ID in. I'm not 100% on your data structure from the question, but you certainly seem to have repeated measures for individuals (I'm assuming that groups A and B each contain multiple individuals, none of whom were in both groups, and each of which were shown both objects P and Q, in a random order). It's not surprising that the effects of group are weakened if you remove participant ID, because you're then effectively entering pseudoreplication into your model (ie, telling your model that all the data points within a group are independent, when that isn't the case).
2) I think channel should be nested within individual, with a model something like model <- lmer(voltage ~ group * item + (1|participant/channel), data = ...)
3) This really depends on what your interest is. If you simply want to show that there is an overall interaction effect, then your p-value from a likelihood ratio test of the model with/without the interaction term gives significance of this interaction, and then a plot of predicted values for the fixed effects (w/ data overplotted if possible) should show the trends. You could also use binary dummy variables to make more explicit contrasts, but it's worth reading up on these a bit more. I don't really use these type of comparisons very much, so I can't comment further I'm afraid.
4) Your item is like treatment in this case - you appear to be more interested in the effect of different items (rather than how much variation 'item' explains), so keep this as a fixed effect and not as random.
Hope some of this is useful,
Tom
________________________________________
Message: 1
Date: Fri, 3 Jun 2016 14:28:59 +0200
From: paul <graftedlife at gmail.com<mailto:graftedlife at gmail.com>>
To: r-sig-mixed-models at r-project.org<mailto:r-sig-mixed-models at r-project.org>
Subject: [R-sig-ME] Related fixed and random factors and planned
comparisons in a 2x2 design
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Dear All,
I am trying to use mixed-effect modeling to analyze brain wave data from
two groups of participants when they were presented with two distinct
stimulus. The data points (scalp voltage) were gathered from the same set
of 9 nearby channels from each participant. And so I have the following
factors:
- voltage: the dependent variable
- group: the between-participant/within-item variable for groups A and B
- item: the within-participant variable (note there are exactly only 2
items, P and Q)
- participant: identifying each participant across the two groups
- channel: identifying each channel (note that data from these channels
in a nearby region tend to display similar, thus correlated, patterns in
the same participant)
The hypothesis is that only group B will show difference between P and Q
(i.e., there should be an interaction effect). So I established a
mixed-effect model using the lme4 package in R:
model <- lmer(voltage~1+group+item+(group:item)+(1|participant)+(1|channel),
data=data, REML=FALSE)
Questions:
1.
I'm not sure if it is reasonable to add in participant as a random
effect, because it is related to group and seems to weaken the effects of
group. Would it be all right if I don't add it in?
2.
Because the data from nearby channels of the same participant tend to be
correlated, I'm not sure if modeling participant and channel as crossed
random effects is all right. But meanwhile it seems also strange if I treat
channel as nested within participant, because they are the same set of
channels across participants.
3.
The interaction term is significant. But how should planned comparisons
be done (e.g., differences between groups A and B for P) or is it even
necessary to run planned comparisons? I saw suggestions for t-tests,
lsmeans, glht, or for more complicated methods such as breaking down the
model and subsetting the data:
data[, P_True:=(item=="P")]
posthoc<-lmer(voltage~1+group
+(1|participant)+1|channel)
, data=data[item=="P"]
, subset=data$P_True
, REML=FALSE)
But especially here comparing only between two groups while modeling
participant as a random effect seems detrimental to the group effects. And
I'm not sure if it is really OK to do so. On the other hand, because the
data still contain non-independent data points (from nearby channels), I'm
not sure if simply using t-tests is all right. Will non-parametric tests
(e.g., Wilcoxon tests) do in such cases?
4.
I suppose I don't need to model item as a random effect because there
are only two of them, one for each level, right?
I would really appreciate your help!!
Best regards,
Paul
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