extract.mixturepars

output object of <code><a href="/link/summary.mclustBIC?package=fpc&version=2.1-6&to=mclust" rd-options="mclust" data-mini-rdoc="mclust::summary.mclustBIC">summary.mclustBIC</a></code>.

mclustsum

vector of integers. Numbers of mixture components.

compnumbers

logical. Should be <code>TRUE</code> if a noise component was fitted by
    <code><a href="/link/mclustBIC?package=fpc&version=2.1-6&to=mclust" rd-options="mclust" data-mini-rdoc="mclust::mclustBIC">mclustBIC</a></code>.

noise

Extracts parameter of certain mixture components from the output of
  <code><a href="/link/summary.mclustBIC?package=fpc&version=2.1-6&to=mclust" rd-options="mclust" data-mini-rdoc="mclust::summary.mclustBIC">summary.mclustBIC</a></code> and updates proportions so that
  they sum up to 1.

multivariate

cluster

Various methods for clustering and cluster validation.
  Fixed point clustering. Linear regression clustering. Clustering by 
  merging Gaussian mixture components. Symmetric 
  and asymmetric discriminant projections for visualisation of the 
  separation of groupings. Cluster validation statistics
  for distance based clustering including corrected Rand index. 
  Cluster-wise cluster stability assessment. Methods for estimation of 
  the number of clusters: Calinski-Harabasz, Tibshirani and Walther's 
  prediction strength, Fang and Wang's bootstrap stability. 
  Gaussian/multinomial mixture fitting for mixed 
  continuous/categorical variables. Variable-wise statistics for cluster
  interpretation. DBSCAN clustering. Interface functions for many 
  clustering methods implemented in R, including estimating the number of
  clusters with kmeans, pam and clara. Modality diagnosis for Gaussian
  mixtures. For an overview see package?fpc.

extract.mixturepars: Extract parameters for certain components from mclust

Description

Usage

Arguments

Value

Examples