I'm attempting to use the Adjusted Rand Index to compare different categorizations in my card-sorting experiment. However, as I am attempting to replicate a prior study, I am allowing them to put a single card in multiple piles. However, in the original paper, it looks like Rand expects the cards to be placed into "disjoint" sets. I'm wondering if there is a workaround to this problem. As an example, suppose that you have these two categorizations: Reviewer 1: Cat 1 - Item #s 1,3,5 Cat 2 - Item #s 2,4 Reviewer 2: Cat 1 - Item #s 1,2,3 Cat 2 - Item #s 4,5 #You then convert these into a vector: r1<-c(1,2,1,2,1) r2<-c(1,1,1,2,2) #There are two algorithms that can calculate the adjusted rand index library(mclust) adjustedRandIndex(r1,r2) library(mcclust) arandi(r1,r2, adjust=TRUE) .easy as pie As an example, I have data that looks like this: Reviewer 1: Cat 1 - Item #s 1,3,5 Cat 2 - Item #s 2,4 Cat 3 - Item #s 1,4 Reviewer 2: Cat 1 - Item #s 1,2,3 Cat 2 - Item #s 4,5 However, because of the double categorization for Reviewer 1, it is not trivial to create the vector for reviewer 1. Simply doing: r1<-c(c(1,3),2,1,c(2,3),1) won't work because the input vectors need to be of the same length (and that doesn't do what I want it to either). Is there a way to implement this so that these algorithms will still work? Thanks for your help, -Steve [[alternative HTML version deleted]]