confusion: Misclassification probabilities in mixtures

In fpc: Flexible Procedures for Clustering

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 [e-mail] 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 documentation built on Sept. 24, 2024, 9:07 a.m.