Recomputes pointwise posterior probabilities, mean and covariance matrix for a mixture component obtained by merging two mixture components in a Gaussian mixture.
1  mergeparameters(xdata, j1, j2, probs, muarray,Sigmaarray, z)

xdata 
data (something that can be coerced into a matrix). 
j1 
integer. Number of first mixture component to be merged. 
j2 
integer. Number of second mixture component to be merged. 
probs 
vector of component proportions (for all components; should sum up to one). 
muarray 
matrix of component means (rows). 
Sigmaarray 
array of component covariance matrices (third dimension refers to component number). 
z 
matrix of observation (row)wise posterior probabilities of belonging to the components (columns). 
List with components
probs 
see above; sum of probabilities for original components

muarray 
see above; weighted mean of means of component

Sigmaarray 
see above; weighted covariance matrix handled as above. 
z 
see above; original entries for columns 
Christian Hennig c.hennig@ucl.ac.uk http://www.homepages.ucl.ac.uk/~ucakche/
Hennig, C. (2010) Methods for merging Gaussian mixture components, Advances in Data Analysis and Classification, 4, 334.
1 2 3 4 5 6 7 8 9 10 11 12 13  options(digits=3)
set.seed(98765)
require(mclust)
iriss < iris[sample(150,20),5]
irisBIC < mclustBIC(iriss)
siris < summary(irisBIC,iriss)
probs < siris$parameters$pro
muarray < siris$parameters$mean
Sigmaarray < siris$parameters$variance$sigma
z < siris$z
mpi < mergeparameters(iriss,1,2,probs,muarray,Sigmaarray,z)
mpi$probs
mpi$muarray

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