Arguments
data
A numeric vector, matrix, or data frame of
observations. Categorical
variables are not allowed. If a matrix or data frame, rows
correspond to observations and columns correspond to variables.
labels
A numeric or character vector assigning a class label to
each observation.
emModelNames
A vector of character strings indicating the models to be fitted
in the EM phase of clustering. Possible models:
"E" for spherical, equal variance (one-dimensional)
"V" for spherical, variable variance (one-dimensional)
"EII": spherical, equal volume
G
An integer vector specifying the numbers of Gaussian mixture
components (clusters) for which the BIC is to be calculated (the
same specification is used for all classes). Default: 0:9.
hcModelName
A matrix of merge pairs for hierarchical clustering such as produced
by function hc. The default is to compute a hierarchical
clustering tree by applying function hc with
modelName = .Mclust$hcModelName[1]
equalPro
Logical variable indicating whether or not the mixing
proportions are equal in the model. The default is
.Mclust$equalPro.
noise
A logical vector indicating whether or not observations
are initially estimated to noise in the data. If there is no noise
mclustDAtrain should be use rather than mclustDAtrainN.
Vinv
An estimate of the reciprocal hypervolume of the data region.
The default is determined by applying function
hypvol to the data.