Hi everyone,
I'm sure this is simple, but I can't seem to figure this out.
Situation. 3 different groups of subjects each submit n X n matrices of
scores. What I want to do is aggregate each group of scores into a summary
n X n matrix. I need the result to be a matrix so that I can calculate a
dissimilarity structure on it. So I thought I would create a
multi-dimensional array and store the data that way.
The first two dimensions represent the data, the third the group membership
(say 3 groups), and forth represents subjects(30 subjects per group)...
subject.data <- array(NA, dim=c(3,3,3,10))
data.1 <- matrix(seq(from=1,to=9,by=1),nrow=3,ncol=3)
data.2 <- matrix(seq(from=11,to=19,by=1),nrow=3,ncol=3)
data.3 <- matrix(seq(from=21,to=29,by=1),nrow=3,ncol=3)
data.4 <- matrix(seq(from=31,to=39,by=1),nrow=3,ncol=3)
data.5 <- matrix(seq(from=41,to=49,by=1),nrow=3,ncol=3)
data.6 <- matrix(seq(from=51,to=59,by=1),nrow=3,ncol=3)
subject.data[,,1,1] <- data.1 # subject 1 group 1
subject.data[,,2,1] <- data.2 # subject 1 group 2
subject.data[,,3,1] <- data.3 # subject 1 group 3
subject.data[,,3,2] <- data.4 # subject 2 group 3
subject.data[,,2,2] <- data.5 # subject 2 group 2
subject.data[,,1,2] <- data.6 # subject 2 group 1
what I want is conceptually:
array.group1 <- apply(subject.data[,,1,],mean)
array.group2 <- apply(subject.data[,,2,],mean)
array.group3 <- apply(subject.data[,,3,],mean)
each of the summary matrices then contain the mean for each cell across all
subjects within a group so that it has the same size as the subject data.
Also, after I've populated subject.data, there will be segments that will
only contain NAs, how do you grab just the portions of the array that
contain data? I know the function is.na() will get me an index I just don't
know how to write it correctly. something like
only.data <- subject.data[!is.na(subject.data)]
Does that make? I would appreciate any help regarding a better way to store
the data and obviously how to get back these summary matrices...
Thanks very much for your help.
emilio
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