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
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