Well, you are dealing with probability based clustering, so for each
bird you will get a probability of belonging to each cluster. If your
clusters are well defined, then each bird should have a very high
probability of belonging to one of the clusters. You can get this
probability matrix from your mclust object.
For the iris dataset example,
my.clusters=Mclust(iris[,-5])
This will give you the probability matrix
my.clusters$z
You can assign membership based on these probabilities (i.e. each bird
belongs to the cluster with highest probability). You can obtain this
membership by doing
my.clusters$membership
Hope this helps,
Julian
cnagy wrote:> I'm trying to test a method of identifying individuals (birds) based on
> measured data (their calls).
>
> I have test data from known individual birds, and I am using the Mclust
> package to see if the program can correctly identify which calls come from
> different birds.
>
> So far, mclust has correctly ID'd the number of birds in the test data
set
> (i.e., the correct # of clusters). However I also need to correctly assign
> each call to the right bird (i.e., data rows (calls) 1 - 10 are in cluster
> (bird) 1; rows 2 - 20 are in cluster 2, etc.).
>
> Is there a way to get mclust to show the cluster assignments of each
> observation?
>
> Thank you
>
>
>
>