"What's wrong with that?
(The values you submit as scale in "prior" are not fixed variances, but
parameters of the prior distribtion - your problem may be that you
believe that they are meant to be variances fixed by you!?)"
Yes I did, so I think it is not possible to fix the variance. Anyway, th
I probably don't understand problem. I'd assume that variance$sigmasq are
the three estimated component variances (probably estimated by maximum a
posteriori, but consult the mclust documentation).
What's wrong with that?
(The values you submit as scale in "prior" are not fixed variances, but
Hi,
Thanks a lot for your answer. I effectively was able to get rid of this
message by doing:
> resClust <-
> Mclust(data,G=3,modelName="V",prior=priorControl(scale=c(1.44,0.81,0.49)));
However, I would like to be able to retrieve the variances I defined in the
result. I found:
> resClust$para
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 t
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
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