fpc: confusion – R documentation

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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.

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en/

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)

fpc

Flexible Procedures for Clustering

v2.2-9

GPL

Authors

Christian Hennig <christian.hennig@unibo.it>

Initial release

2020-12-06