R: Matrix of misclassification probabilities between mixture...

zmisclassification.matrix {fpc} | R Documentation

## Matrix of misclassification probabilities between mixture components

### Description

Matrix of misclassification probabilities in a mixture distribution between two mixture components from estimated posterior probabilities regardless of component parameters, see Hennig (2010).

### Usage

    zmisclassification.matrix(z,pro=NULL,clustering=NULL,
                                          ipairs="all",symmetric=TRUE,
                                          stat="max")

### Arguments

`z` | matrix of posterior probabilities for observations (rows) to belong to mixture components (columns), so entries need to sum up to 1 for each row.
`pro` | vector of component proportions, need to sum up to 1. Computed from `z` as default.
`clustering` | vector of integers giving the estimated mixture components for every observation. Computed from `z` as default.
`ipairs` | `"all"` or list of vectors of two integers. If `ipairs="all"`, computations are carried out for all pairs of components. Otherwise, ipairs gives the pairs of components for which computations are carried out.
`symmetric` | logical. If `TRUE`, the matrix is symmetrised, see parameter `stat`.
`stat` | `"max"` or `"mean"`. The statistic by which the two misclassification probabilities are aggregated if `symmetric=TRUE`.

### Value

A matrix with the (symmetrised, if required) misclassification probabilities between each pair of mixture components. If `symmetric=FALSE`, matrix entry `[i,j]` is the estimated probability that an observation generated by component `j` is classified to component `i` by maximum a posteriori rule.

### 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.

### See Also

`confusion`

### Examples

      set.seed(12345)
      m <- rpois(20,lambda=5)
      dim(m) <- c(5,4)
      m <- m/apply(m,1,sum)
      round(zmisclassification.matrix(m,symmetric=FALSE),digits=2)

[Package fpc version 2.1-10 Index]