This normally happens if the algorithm gets caught in a solution where one
of the components has variance converging to zero.
One way of dealing with this is the use of a prior that penalises too
small variances. This works through the prior argument of Mclust (the
defaultPrior should do the trick but I currently don't have the time to
figure out again how to do this precisely; I have done it before with
success).
Another option is to have a look at the flexmix package.
Best regards,
Christian
On Sun, 4 Sep 2011, Nico902 wrote:
Hi,
I'm trying to use the library mclust for gaussian mixture on a numeric
vector. The function Mclust(data,G=3) is working fine but the fitting is not
optimal and is using modelNames="E". When I'm trying
Mclust(data,G=3,modelName="V") I have the following message:
Error in if (Sumry$G > 1) ans[c(orderedNames, "z")] else ans[orderedNames] :
argument is of length zero
In addition: Warning message:
In pickBIC(object[as.character(G), modelNames, drop = FALSE], k = 3) :
none of the selected models could be fitted
Using variable variance would fit my data better, any idea how to do it?
Thanks a lot.
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Christian Hennig
University College London, Department of Statistical Science
Gower St., London WC1E 6BT, phone +44 207 679 1698
chr...@stats.ucl.ac.uk, www.homepages.ucl.ac.uk/~ucakche
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