custom algorithm in consensus_cluster

[phiala]

I can't get the example of using a custom clustering algorithm in ?consensus_clustering to work.

The result is identical whether c("pam", "agnes") or just c("pam") is used.

R version 3.4.1 (2017-06-30) Platform: x86_64-redhat-linux-gnu (64-bit) Running under: Fedora 26 (Workstation Edition)

attached base packages: [1] stats graphics grDevices utils datasets methods base

other attached packages: [1] diceR_0.3.1 setwidth_1.0-4 colorout_0.9-9

Here's the code I used:

data(hgsc)
dat <- hgsc[1:100, 1:50]

# Custom distance function
manh <- function(x) {
  stats::dist(x, method = "manhattan")
}

# Custom clustering algorithm
agnes <- function(d, k) {
  return(as.integer(stats::cutree(cluster::agnes(d, diss = TRUE), k)))
}   
assign("agnes", agnes, 1)

cc1 <- consensus_cluster(dat, reps = 6, algorithms = c("pam", "agnes"),
distance = c("euclidean", "manh"), progress = FALSE)
str(cc1)

cc2 <- consensus_cluster(dat, reps = 6, algorithms = c("pam"),
distance = c("euclidean", "manh"), progress = FALSE)
str(cc2)

identical(cc1, cc2)

Using just a custom function gives either an error or empty output object.

What am I missing?

[dchiu911]

Please let me know if you still have unresolved bugs @phiala, thanks for catching this!