fpc (version 2.2-9)

# zmisclassification.matrix: 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.

## References

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

## See Also

`confusion`

## Examples

```# NOT RUN {
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)
# }
```