Hello,
As for the first question try
scoreset <- lapply(pcl, function(x) x$scores[, 1])
do.call(cbind, scoreset)
As for the second question, you want to know which columns in 'datasets'
have NA's?
colidx <- apply(datasets, 2, function(x) any(is.na(x)))
datasets[, colidx] # These have NA's
For the column numbers you can do
colnums <- which(colidx)
Hope this helps,
Rui Barradas
Em 12-12-2012 17:14, David Romano escreveu:
Hi everyone,
Suppose I have a 3D array of datasets, where say dimension 1 corresponds to
cases, dimension 2 to datasets, and dimension 3 to observations within a
dataset. As an example, suppose I do the following:
x <- sample(1:20, 48, replace=TRUE)
datasets <- array(x, dim=c(4,3,2))
Here, for each j=1,2,3, I'd like to think of datasets[,j,] as a single data
matrix with four cases and two observations. Now, I'd like to be able to
do the following: apply pca to each dataset, and create a matrix of the
first principal component scores.
In this example, I could do:
pcl<-apply(datasets,2,princomp)
which yields a list of princomp output, one for each dataset, so that the
vector of first principal component scores for dataset 1 is obtained by
score1set1 <- pcl[[1]]$scores[,1]
and I could then obtain the desired matrix by
score1matrix <- cbind( score1set1, score1set2, score1set3)
So my first question is: 1) how could I use *apply to do this? I'm having
trouble because pcl is a list of lists, so I can't use, say, do.call(cbind,
...) without first having a list of the first component score vectors,
which I'm not sure how to produce.
My second question is: 2) Having answered question 1), now suppose there
may be datasets containing NA value -- how could I select the subset of
values from dimension 2 corresponding to the datasets for which this is
true (again using *apply?)?
Thanks in advance for any light you might be able to shed on these
questions!
David Romano
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