Estimates a misclassification probability in a mixture distribution between two mixture components from estimated posterior probabilities regardless of component parameters, see Hennig (2010).
confusion(z,pro,i,j,adjustprobs=FALSE)Estimated probability that an observation generated by component
j is classified to component i by maximum a posteriori rule.
matrix of posterior probabilities for observations (rows) to belong to mixture components (columns), so entries need to sum up to 1 for each row.
vector of component proportions, need to sum up to 1.
integer. Component number.
integer. Component number.
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).
Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en/
Hennig, C. (2010) Methods for merging Gaussian mixture components, Advances in Data Analysis and Classification, 4, 3-34.
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)
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