ClusteringAlgo {sharp}

(Weighted) clustering algorithm

Description

Runs the (weighted) clustering algorithm specified in the argument implementation and returns matrices of variable weights, and the co-membership structure. This function is not using stability.

Usage

ClusteringAlgo(
  xdata,
  nc = NULL,
  eps = NULL,
  Lambda = NULL,
  scale = TRUE,
  row = TRUE,
  implementation = HierarchicalClustering,
  ...
)

Arguments

xdata

data matrix with observations as rows and variables as columns.

nc

matrix of parameters controlling the number of clusters in the underlying algorithm specified in implementation. If nc is not provided, it is set to seq(1, nrow(xdata)).

eps

radius in density-based clustering, see dbscan. Only used if implementation=DBSCANClustering.

Lambda

vector of penalty parameters.

scale

logical indicating if the data should be scaled to ensure that all variables contribute equally to the clustering of the observations.

row

logical indicating if rows (if row=TRUE) or columns (if row=FALSE) contain the items to cluster.

implementation

function to use for clustering. Possible functions include HierarchicalClustering (hierarchical clustering), PAMClustering (Partitioning Around Medoids), KMeansClustering (k-means) and GMMClustering (Gaussian Mixture Models). Alternatively, a user-defined function taking xdata and Lambda as arguments and returning a binary and symmetric matrix for which diagonal elements are equal to zero can be used.

...

additional parameters passed to the function provided in implementation.

Value

A list with:

selected

matrix of binary selection status. Rows correspond to different model parameters. Columns correspond to predictors.

weight

array of model coefficients. Rows correspond to different model parameters. Columns correspond to predictors. Indices along the third dimension correspond to outcome variable(s).

comembership

array of model coefficients. Rows correspond to different model parameters. Columns correspond to predictors. Indices along the third dimension correspond to outcome variable(s).

See Also

VariableSelection

Other underlying algorithm functions: CART(), PenalisedGraphical(), PenalisedOpenMx(), PenalisedRegression()

Examples

# Simulation of 15 observations belonging to 3 groups
set.seed(1)
simul <- SimulateClustering(
  n = c(5, 5, 5), pk = 100
)

# Running hierarchical clustering
myclust <- ClusteringAlgo(
  xdata = simul$data, nc = 2:5,
  implementation = HierarchicalClustering
)