GMMwrapper: Wrapper to do clustering with Gaussian mixture models

In btaschler/mcap: Model-Based Clustering in High Dimensions via Adaptive Projections

Description

Full covariance Gaussian mixture modelling based on the nethet package. The wrapper performs multiple restarts in case true labels are provided (in order to optimise cluster assignments w.r.t. the Rand index).

Usage

GMMwrapper(xx, k, criterion = "mmdl", true_labels = NULL,
  verbose = FALSE)

Arguments

xx	The data matrix (n x p).
k	The number of clusters.
criterion	Optimisation criterion ("bic" or "mmdl"). Default: "mmdl".
true_labels	Vector of true cluster assignments (when provided, it is used to compute the Rand index).
verbose	Logical, when true: print progress information.

Value

model_fit	Model fit (output of mixglasso).
aRI	Adjusted Rand index (when true_labels is provided).

Author(s)

Bernd Taschler: [e-mail]

See Also

MCAPfit, OptDimClusterStability, ClusterStability

adjustedRandIndex

Examples

  ## standard Normal 100x5 matrix, 2 clusters:
  GMMwrapper(xx = matrix(rnorm(500),100), k = 2) 

btaschler/mcap documentation built on May 26, 2019, 1:31 a.m.