Sorry, I just realized I didn't send the message below in plain text. -David Romano
On Wed, Dec 12, 2012 at 9:14 AM, David Romano <drom...@stanford.edu> wrote: > > 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 ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
