Martin,
Pardon the delayed reply.
Bootstrap methods have been around for some time (late seventies?), but
their popularity seems to have exploded in correspondence with computing
technology. You should be able to find more information in most modern
books on statistical inference, but here is a
Dear all,
I am having a hard time to figure out a suitable test for the match
between two nominal classifications of the same set of data.
I have used hierarchical clustering with multiple methods (ward,
k-means,...) to classify my dat into a set number of classesa, and I
would like to compare
There are several statistics used to compare nominal classifications, or
_partitions_ of a data set. A partition isn't quite the same in this
context because partitioned data are not restricted to a fixed number of
classes. However, the statistics used to compare partitions should also
work for
Thanks Mat,
I have in the meantime identified the Rand index, but not the others. I
will also have a look at profdpm, that did not pop-up in my searches.
Indeed, the interpretation is going to be critical... Could you please
elaborate on what you mean by the bootstrap process?
Thanks a lot
On Nov 17, 2010, at 7:33 AM, Martin Tomko wrote:
Dear all,
I am having a hard time to figure out a suitable test for the match between
two nominal classifications of the same set of data.
I have used hierarchical clustering with multiple methods (ward, k-means,...)
to classify my dat into
Another useful measure to compare partitions is the adjusted Rand
index which is implemented in the library(e1071) within the
classAgreement function.
If you have your data partitions to be compared in a matricial form
(where each column is a different partition), the syntax is
Thank you Matta for the great suggestion,
I will try the additional tests. I have just been experimenting with the
e1071 package and the adjustedRand. It works perfectly, The only
outstadning question is interpretation - is there any rule of thumbs for
the level of agreement that needs to be
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