R: Misclassification probabilities in mixtures

confusion {fpc} | R Documentation

## 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.

### 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

      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)

[Package fpc version 2.1-10 Index]