principal components analysis is creating correlated axes with nested data

I'm trying to do a principal components analysis with the aim of turning my set of correlated variables into a set of uncorrelated ones (rather than dimension reduction). However, the data are nested, and when I account for this nesting some of my components are highly correlated.

Specifically, my original variables are measured at multiple sites within a number of forest patches. When I run a standard PCA none of the principal components are correlated across the dataset as a whole, but when I model one component as a function of another in a mixed effects model which includes patch as a random factor, the relationship between some pairs of components is highly significant.

Can someone please tell me if there is a way to do a PCA while accounting for nested data structure, or if not, if there is another approach I could use to convert a set of correlated variables into a set of uncorrelated ones while accounting for nesting?

Thank you for your help,

Jay

• Can you please explain a bit more what you mean by "the relationship between some pairs of components is highly significant." Just to clarify, PCA does not give you necessarily "statistically uncorrelated" components; only "orthogonal" ones. (These scenarios might overlap but it is not always the case). Actually, as you present your modelling assumptions it would be worrying if some correlation didn't came forward in certain cases as it would mean that nested structure is practically totally redundant. How big is your sample by the way? Mar 30, 2013 at 0:14
• Hi and thanks for the reply. When I say 'some pairs of components are significantly related' after accounting for nesting, all I mean is that after extracting the components from my original variables, a linear mixed model of eg PC1 ~ PC2 + random intercept [for forest patch] is highly significant. Sample size is ~500 observations across 12 forest patches.
– jay
Mar 31, 2013 at 11:55
• And what I'm really interested in is converting my correlated variables into uncorrelated ones. As you say, the nested structure of the data is making the components correlated. So is there a way ('nested' PCA or otherwise) to create uncorrelated variables from a set of hierarchically structured correlated ones? Cheers
– jay
Mar 31, 2013 at 12:02
• You might want to consider IPCA (Independent Principal Component Analysis, eg. Yao et al. 2012 - so you do PCA, you determine the dimensionality and then you feed the PC to an ICA algorithm) to get a different decomposition but once more I doubt that this will give you uncorrelated nested components. There is some work on phylogenetic PCA (Revell et al. 2009) but then you will have to impose a "dummy tree" structure in your data. Not necessarily a bad thing but I don't know if you want that; it didn't work well for me though... Just to check: lm(PC1 ~ PC2) does come out "rubbish" right? Mar 31, 2013 at 16:14
• Great, I'll look into these. lm(PC1 ~ PC2) came out with the components uncorrelated.... It's only when you add the random effect that they become correlated. Thanks
– jay
Apr 1, 2013 at 1:20

1 Answer

user11852: your suggestion of doing a phylogenetic PCA worked beautifully thank you!

Interesting that the REvell paper and corresponding R package discuss this technique only in terms of correcting for relatedness when comparing traits among species. Seems to be much more widely applicable than this - ie works for nested data in general.

Edit: giving an example of using the approach, as asked for in @nan's comment below:

First, an explanation of the data. My dataframe is called 'Rat', and it consists of rat capture rates and vegetation variables measured in two forest patches, 'B86Grazed' and 'LittleTutu'. Variables were measured at multiple sites within each patch, and my individual sites were named 'LittleTutu-1', 'LittleTutu-2' etc. My aim was to create PCA axes from the vegetation variables that were uncorrelated once the nesting of sites within patches was taken into account. For example, modelling axis1 as a function of axis2 while specifying patch identity as a random factor would find no significant effect of axis2. [Note also that I wouldnt be fitting a random-effect with only two patches; I've simplified the dataset for the example].

This is the code I used:

library(ape) #creating phylogenetic trees
library(phytools) #phylogenetic PCA

#define tree using Newick format, specifying that sites are nested within patches, and assigning all sites a branch length =1 in this example (indicating all sites within a patch are equally related, and equally unrelated to all sites from the other patch):
cat("((B86Grazed-1:1,B86Grazed-2:1,B86Grazed-3:1,B86Grazed-4:1,B86Grazed-5:1)B86Grazed:1,(LittleTutu-1:1,LittleTutu-2:1,LittleTutu-3:1,LittleTutu-4:1,LittleTutu-5:1,LittleTutu-6:1)LittleTutu:1)Patches;", file = "phyloPCAtree.tre", sep = "\n")
myTree <- read.tree(file="phyloPCAtree.tre")

#check tree structure: should show sites nested within patches
plot.phylo(myTree, show.node.label=T)

#create matrix of vegetation variables I want to reduce, and run phylogenetic PCA:
vegMatrix <- as.matrix(Rat[,c(16,17,19,21,30,31)])  #veg variables were #16,#17 etc in dataframe
rownames(vegMatrix) <-Rat$Patch.trap #rownames need to match the names of the tips in myTree exactly ('LittleTutu-1' etc). Rat$Patch.trap is a vector of these names.

phyPCA <- phyl.pca(tree=myTree, Y=vegMatrix, mode="corr")   #based on correlation, rather than covariance, matrix
phyPCA
phyPC1 <- phyPCA\$S[,1]  #extract first PCA axis


This approach produced axes that were correlated with my response variable (rat capture rates) but which were uncorrelated with each other when nesting of sites within patches was taken into account. Note that I haven't been able to make this approach work when the data contains 'singleton' patches (i.e. patches with only one site). Note also that the 'read.tree' command removes whitespace from the tree - so best not to have spaces in patch or site names.

Lastly, when I did this phylogenetic PCA it was for a specific application which required uncorrelated axes after nesting was accounted for. Whether this approach should be done any time a PCA is done on nested data is another question (which I'd be interested to hear people's thoughts on).

• I happy to help that, good luck with the rest of your analysis! I am inclined to say that taking a comment, posting as an answer yourself, and then accepting it sounds odd to say the least. :D Apr 1, 2013 at 16:36
• Yes but can't accept a comment as an answer (?), and think its good to register questions as answered when they have been. If you want to repost your comment as an answer happy to accept that (doesn't look as weird, as you say!). Cheers
– jay
Apr 2, 2013 at 1:33
• No worries, I 'll leave with a little less reputation. :D In general, if a comment works out to be the answer you look for, it is "standard" to ask the commenter to reformulate it and post it as an answer. Apr 2, 2013 at 3:40
• Hi jay, do you have an example of how this phyl.pca function is used in nested data? I don't quite get the parameters, i.e. tree,Y,method,mode.
– nan
May 19, 2014 at 11:27
• Hi nan, have edited answer to give an example. Cheers
– jay
May 20, 2014 at 16:07