dot-cluster_count: Return the number of clusters identified in a connectivity...

In oxacclab/adviseR: Advice Exchange Network Modelling

Description

Return the number of clusters identified in a connectivity matrix by Ckmeans.1d.dp

Usage

.cluster_count(g, ..., .full = F)

Arguments

g	connectivity igraph
...	Arguments passed on to Ckmeans.1d.dp::Ckmeans.1d.dp x a numeric vector of data to be clustered. All NA elements must be removed from x before calling this function. The function will run faster on sorted x (in non-decreasing order) than an unsorted input. k either an exact integer number of clusters, or a vector of length two specifying the minimum and maximum numbers of clusters to be examined. The default is c(1,9). When k is a range, the actual number of clusters is determined by Bayesian information criterion. y a value of 1 (default) to specify equal weights of 1 for each element in x, or a numeric vector of unequal non-negative weights for each element in x. It is highly recommended to use positive (instead of zero) weights to account for the influence of every element. The weights have a strong impact on the clustering result. When the number of clusters k is given as a range, the weights should be linearly scaled to sum up to the observed sample size. Currently, Ckmedian.1d.dp only works with an equal weight of 1. method a character string to specify the speedup method to the original cubic runtime dynamic programming. The default is "linear". All methods generate the same optimal results but differ in runtime or memory usage. See Details. estimate.k a character string to specify the method to estimate optimal k. This argument is effective only when a range for k is provided. The default is "BIC". See Details.
.full	return the full Ckmeans.1d.dp output

oxacclab/adviseR documentation built on Oct. 7, 2021, 8:05 p.m.