Hi Cindy,
you need the summary function
mclustsummary <- summary(mclustBICoutputobject,data)
to get all the information. Some (like best model) is given if you just
print out the summary object. Some other information (like
estimated parameter values) are accessible as components of the summary
object, like
mclustsummary$parameters$...
Try
str(mclustsummary)
to see what's there (unfortunately this is not fully documented).
For more detail see the help pages.
Hope this helps,
Christian
On Sun, 26 Jul 2009, cindy Guo wrote:
Hi, Christian,
Thank you for the reply. I just tried. Does the function mclustBIC only give
the best model, or does it also do EM to get the cluster means and variances
according to the best model it picks? I didn't find it. Is there a way to
automatically select the best number of components and do EM? Because I need
to do the normal mixture model in a loop (one EM at an iteration), so I want
it to do everything automatically.
Thanks,
Cindy
On Sun, Jul 26, 2009 at 3:46 PM, Christian Hennig <chr...@stats.ucl.ac.uk>wrote:
You can use mclustBIC in package mclust (uses the BIC for deciding about
the number of components and hierarchical clustering for initialisation).
Christian
On Sun, 26 Jul 2009, cindy Guo wrote:
Hi, All,
I want to fit a normal mixture model. Which package in R is best for this?
I
was using the package 'mixdist', but I need to group the data into groups
before fitting model, and different groupings seem to lead to different
results. What other package can I use which is stable? And are there
packages that can automatically determine the number of components?
Thank you,
Cindy
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*** --- ***
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University College London, Department of Statistical Science
Gower St., London WC1E 6BT, phone +44 207 679 1698
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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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