multinomial_mix: Multinomial mixture probabilities
In Selbosh/scrooge: Statistical modelling of heterogeneous citation networks

Description Usage Arguments Details Value See Also Examples

This is equivalent to performing the initial "E" step in expectation maximisation (EM) for a multinomial mixture model.

1	multinomial_mix(target, profiles)

`target`	A vector of citations to be compared
`profiles`	A matrix of `community_profile()`s

Here we are effectively computing the nearest point in the convex hull of community profiles, implicitly using Kullback–Leibler divergence as the distance measure. Alternative distance measures may be used; see nearest_point() and others.

A vector of log-probabilities that each community generated target's citation profile.

nearest_point()

# To which cluster should 'Biometrika' belong?
distances <- as.dist(1 - cor(citations + t(citations)))
clusters <- cutree(hclust(distances), h = 0.8)
profiles <- community_profile(citations, clusters)
Biometrika <- citations[, 'Bka']
w <- multinomial_mix(Biometrika, profiles)
which.max(w) == clusters['Bka']
profiles %*% exp(w) # nearest point