Latent Class Analysis — LCA • diceR

Combine clustering results using latent class analysis.

Usage

LCA(E, is.relabelled = TRUE, seed = 1)

Arguments

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 FALSE the data will be relabelled using the first clustering as the reference.
seed: random seed for reproducibility

Value

a vector of cluster assignments based on LCA

See also

Other consensus functions: CSPA(), LCE(), k_modes(), majority_voting()

Author

Derek Chiu

Examples

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))
#> 
#>  1  2  3  4 
#> 21 38  9 32