overlapGOM | R Documentation |

Computes the generalized overlap as defined by R. Maitra.

overlapGOM(Pi, Mu, S, eps = 1e-06, lim = 1e06)

`Pi` |
vector of mixing proprtions (length K). |

`Mu` |
matrix consisting of components' mean vectors (K * p). |

`S` |
set of components' covariance matrices (p * p * K). |

`eps` |
error bound for overlap computation. |

`lim` |
maximum number of integration terms (Davies, 1980). |

Returns the value of goMega.

Volodymyr Melnykov, Wei-Chen Chen, and Ranjan Maitra.

Maitra, R. (2010) “A re-defined and generalized percent-overlap-of-activation measure for studies of fMRI reproducibility and its use in identifying outlier activation maps”, NeuroImage, 50, 124-135.

Melnykov, V., Chen, W.-C., and Maitra, R. (2012) “MixSim: An R Package for Simulating Data to Study Performance of Clustering Algorithms”, Journal of Statistical Software, 51:12, 1-25.

Davies, R. (1980) “The distribution of a linear combination of chi-square random variables”, Applied Statistics, 29, 323-333.

`MixSim`

, `MixGOM`

, and `overlap`

.

data("iris", package = "datasets") p <- ncol(iris) - 1 id <- as.integer(iris[, 5]) K <- max(id) # estimate mixture parameters Pi <- prop.table(tabulate(id)) Mu <- t(sapply(1:K, function(k){ colMeans(iris[id == k, -5]) })) S <- sapply(1:K, function(k){ var(iris[id == k, -5]) }) dim(S) <- c(p, p, K) overlapGOM(Pi = Pi, Mu = Mu, S = S)

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