[R-sig-ME] Related fixed and random factors and planned comparisons in a 2x2 design
paul
graftedlife at gmail.com
Fri Jun 3 14:28:59 CEST 2016
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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