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

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

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`

.

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.

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

# 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) # }