MCAPfit: Model based clustering via adaptive (linear) projections

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

Description

"MCAPfit" performs model based clustering using full variance Gaussian mixtures in a lower dimensional projected space obtained via adaptive (linear) projections. Projection variants include PCA-based and random projection.

Usage

MCAPfit(xx, k, projection = "PCA", true_labels = NULL,
  centering_per_group = FALSE, parallel = FALSE, verbose = FALSE,
  ...)

Arguments

xx	The data matrix (n x p).
k	The number of clusters.
projection	Projection method ("PCA", "gaussian", "achlioptas" or "li"). Default: "PCA".
true_labels	Vector of true cluster assignments (when provided, it is used to compute the Rand index).
centering_per_group	Logical, when true: mean centre input matrix (if true labels are provided: centre data per group)
parallel	Logical, when true: perform line search over projection dimension in parallel.
verbose	Logical, when true: print some progress information.
...	Additional options for OptDimClusterStability and GMMwrapper.

Details

...

Value

fit_gmm	Model fit (GMM output of mixglasso), including BIC, MMDL and adj. Rand index (when true_labels is provided).
fit_q_opt	Output of OptDimClusterStability.

Author(s)

Bernd Taschler: [e-mail]

Sach Mukherjee: [e-mail]

References

Taschler, B., Dondelinger, F. and Mukherjee, S. (2019) Model based clustering via adaptive projections https://arxiv.org/pdf/??.pdf

See Also

GMMwrapper, OptDimClusterStability, ClusterStability

Examples

  ## small standard Normal matrix with random labels:
  MCAPfit(xx=matrix(rnorm(200),20,10), k=2, projection='PCA', 
          true_labels = round(runif(20)), parallel=TRUE)
  
  ## Not run: 
  ## sparse random projection:
  MCAPfit(xx=rbind(matrix(rnorm(5e4),100,500), matrix(rnorm(5e4,mean=1),100,500)),
          k=2, projection='li, 
          true_labels=c(rep(0,100),rep(1,100)), parallel=TRUE)
          
  ## sparse random projection, removing the mean signal:
  MCAPfit(xx=rbind(matrix(rnorm(5e4),100,500), matrix(rnorm(5e4,mean=1),100,500)),
          k=2, projection='li, centering_per_group=TRUE,
          true_labels=c(rep(0,100),rep(1,100)), parallel=TRUE)
  
## End(Not run) 

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