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 [[alternative HTML version deleted]] ______________________________________________ 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.