overlap {MixSim} | R Documentation |
Computes misclassification probabilities and pairwise overlaps for finite mixture models with Gaussian components. Overlap is defined as sum of two misclassification probabilities
overlap(Pi, Mu, S, eps = 1e-06, acc = 1e-06, lim = 1e06)
Pi |
vector of mixing proprtions (length K) |
Mu |
matrix consisting of components' mean vectors (K x p) |
S |
set of components' covariance matrices (p x p x K) |
eps |
error bound for overlap computation |
acc |
error bound for integration (Davies, 1980) |
lim |
maximum number of integration terms (Davies, 1980) |
OmegaMap |
matrix of misclassification probabilities (K x K); OmegaMap[i,j] is the probability that X coming from the i-th component is classified to the j-th component |
BarOmega |
value of average overap |
MaxOmega |
value of maximum overap |
rcMax |
row and column numbers for the pair of components producing maximum overlap 'MaxOmega' |
Melnykov, V., Chen, W.-C., Maitra, R.
Maitra, R. and Melnykov, V. (200?) "Simulating data to study performance of finite mixture modeling and clustering algorithms", The Journal of Computational and Graphical Statistics.
Davies, R. (1980) "The distribution of a linear combination of chi-square random variables", Applied Statistics, 29, 323-333.
MixSim, pdplot, simdataset
data(iris) p <- dim(iris)[2] - 1 n <- dim(iris)[1] K <- 3 id <- as.numeric(iris[,5]) Pi <- NULL Mu <- NULL S <- array(rep(0, p * p * K), c(p, p, K)) # estimate mixture parameters for (k in 1:K){ Pi <- c(Pi, sum(id == k) / n) Mu <- rbind(Mu, apply(iris[id == k,-5], 2, mean)) S[,,k] <- var(iris[id == k,-5]) } overlap(Pi = Pi, Mu = Mu, S = S)