R: New parameters from merging two Gaussian mixture components

mergeparameters {fpc} | R Documentation

## New parameters from merging two Gaussian mixture components

### Description

Re-computes pointwise posterior probabilities, mean and covariance matrix for a mixture component obtained by merging two mixture components in a Gaussian mixture.

### Usage

      mergeparameters(xdata, j1, j2, probs, muarray,Sigmaarray, z)

### Arguments

`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).

### Value

List with components

`probs` | see above; sum of probabilities for original components `j1` and `j2` is now `probs[j1]`. Note that generally, also for the further components, values for the merged component are in place `j1` and values in place `j2` are not changed. This means that in order to have only the information for the new mixture after merging, the entries in places `j2` need to be suppressed.
`muarray` | see above; weighted mean of means of component `j1` and `j2` is now in place `j1`.
`Sigmaarray` | see above; weighted covariance matrix handled as above.
`z` | see above; original entries for columns `j1` and `j2` are summed up and now in column `j1`.

### Author(s)

Christian Hennig c.hennig@ucl.ac.uk http://www.homepages.ucl.ac.uk/~ucakche/

### References

Hennig, C. (2010) Methods for merging Gaussian mixture components, Advances in Data Analysis and Classification, 4, 3-34.

### Examples

      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

[Package fpc version 2.1-10 Index]