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
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