Plot final PAC values across range of k to find optimal number of clusters.
pac_landscape(pac_res, n_shade = max(pac_res$iteration)/5)
The data.frame output by consensus_cluster.
The PAC values across the last n_shade iterations will be shaded to illustrate the how stable the PAC score is.
A ggplot2 object with the final PAC vs k plot. A local minimum in the landscape indicates an especially stable value of k.
pac.res = consensus_cluster(iris[,1:4], k_max=20)
#> Calculating consensus clustering
pac_landscape(pac.res)