# confusion: Misclassification probabilities in mixtures

## Description

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

## Usage

confusion(z,pro,i,j,adjustprobs=FALSE)

## 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.

i

integer. Component number.

j

integer. Component number.

adjustprobs

logical. If `TRUE`

, probabilities are
initially standardised so that those for components `i`

and
`j`

add up to one (i.e., if they were the only components).

## Value

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.

## Examples

# NOT RUN {
set.seed(12345)
m <- rpois(20,lambda=5)
dim(m) <- c(5,4)
pro <- apply(m,2,sum)
pro <- pro/sum(pro)
m <- m/apply(m,1,sum)
round(confusion(m,pro,1,2),digits=2)
# }