rank_contributor_pairs | R Documentation |
Separate a 2 person mixture by ranking the possible contributor pairs.
rank_contributor_pairs(contrib_pairs, fit, max_rank = NULL)
contrib_pairs |
A |
fit |
A |
max_rank |
Not used. Reserved for future use. |
A ranked_contrib_pairs
object that is basically an order
vector and the probabilities for each pair (in the same order as given in
contrib_pairs
), found by using fit
. Note, that contributor
order is disregarded so that each contributor pair is only present once (and
not twice as would be the case if taking order into consideration).
contributor_pairs
generate_mixture
disclapmix-package
disclapmix
disclapmixfit
clusterprob
predict.disclapmixfit
print.disclapmixfit
summary.disclapmixfit
simulate.disclapmixfit
disclap
data(danes) db <- as.matrix(danes[rep(1L:nrow(danes), danes$n), 1L:(ncol(danes) - 1L)]) set.seed(1) true_contribs <- sample(1L:nrow(db), 2L) h1 <- db[true_contribs[1L], ] h2 <- db[true_contribs[2L], ] db_ref <- db[-true_contribs, ] h1h2 <- c(paste(h1, collapse = ";"), paste(h2, collapse = ";")) tab_db <- table(apply(db, 1, paste, collapse = ";")) tab_db_ref <- table(apply(db_ref, 1, paste, collapse = ";")) tab_db[h1h2] tab_db_ref[h1h2] rm(db) # To avoid use by accident mixture <- generate_mixture(list(h1, h2)) possible_contributors <- contributor_pairs(mixture) possible_contributors fits <- lapply(1L:5L, function(clus) disclapmix(db_ref, clusters = clus)) best_fit_BIC <- fits[[which.min(sapply(fits, function(fit) fit$BIC_marginal))]] best_fit_BIC ranked_contributors_BIC <- rank_contributor_pairs(possible_contributors, best_fit_BIC) ranked_contributors_BIC plot(ranked_contributors_BIC, top = 10L, type = "b") get_rank(ranked_contributors_BIC, h1)
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