[R] Mixed Model analysis instead of repeated measures ANOVA: how to call lmer or lme instead of aov?

[peter_petersen]

Dear R Community!
I try to detect differences in the the reaction times of different
experimental conditions. The treatment has three levels and was manipulated
within each subject. Each subject is tested e.g. 100 times for each
treatment.

Usually, in psychophysics, data like this is analyzed by aggregating the
reaction times of each condition/ participant and subjecting these data to a
 repeated measurements ANOVA. An example call would be 
aov(x ~ expCond + Error(pbn/(expCond)), data = data.file.agg)

How would an equivalent call for mixed models look like?
And how a call which uses the raw data (each 100 measures and not the mean
thereof)?

Please see an example of such a dataset and the procedure of analysis below.


Sincerly,
Henning B.




## Create Datafile
n.pbn = 100
n.expCond = 3
n.trial = 100 # 100 trials per condition

data.file <- matrix(NA, ncol = 3, nrow = n.pbn * n.expCond * n.trial)
data.file <- as.data.frame(data.file)
names(data.file) <- c("pbn", "expCond", "rt")

data.file$pbn <- rep(1:n.pbn, each = n.expCond * n.trial)
data.file$expCond <- rep(rep(1:n.expCond, each = n.trial), n.pbn)

for (i in 1:n.pbn){
	mean.rt <- rnorm(1, mean = 300, sd = 50)
	rt.expCond1 <- rnorm(n.trial, mean = mean.rt, sd = 100)
	rt.expCond2 <- rnorm(n.trial, mean = mean.rt + 20, sd = 100)
	rt.expCond3 <- rnorm(n.trial, mean = mean.rt + 40, sd = 100)
	
	data.file[data.file$pbn == i & data.file$expCond == 1,]$rt <- rt.expCond1
	data.file[data.file$pbn == i & data.file$expCond == 2,]$rt <- rt.expCond2
	data.file[data.file$pbn == i & data.file$expCond == 3,]$rt <- rt.expCond3
}

## Aggregate Datafile
data.file.agg <- evalq(aggregate(rt, list(pbn = pbn, expCond = expCond),
mean), data.file)
data.file.agg$expCond <- factor(data.file.agg$expCond)

## Compute p-value of ANOVA
rt.aov <- aov(x ~ expCond + Error(pbn/(expCond)), data = data.file.agg)
summary(rt.aov)[[3]][[1]][1,"Pr(>F)"]
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