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]]

