Latent Class Analysis
Combine clustering results using latent class analysis.
LCA(E, is.relabelled = TRUE, seed = 1)
E |
a matrix of clusterings with number of rows equal to the number of cases to be clustered, number of columns equal to the clustering obtained by different resampling of the data, and the third dimension are the different algorithms. Matrix may already be two-dimensional. |
is.relabelled |
logical; if |
seed |
random seed for reproducibility |
a vector of cluster assignments based on LCA
Derek Chiu
Other consensus functions:
CSPA(),
LCE(),
k_modes(),
majority_voting()
data(hgsc) dat <- hgsc[1:100, 1:50] cc <- consensus_cluster(dat, nk = 4, reps = 6, algorithms = "pam", progress = FALSE) table(LCA(cc[, , 1, 1, drop = FALSE], is.relabelled = FALSE))
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