MixClusClustering: MixClusClustering function

In MixCluster: MixCluster performs the clustering of mixed-type data sets (data sets with different natures of variables) with missing values.

Description

This function performs the cluster analysis of mixed data sets with missing values by using the mixture model of Gaussian copulas.

Usage

  MixClusClustering(x, g, model="hetero", kind=NULL, nbalgo=1, burn_in=100, nbiter=1000, param)

Arguments

x	Input data as matrix or data-frame. The binary and the ordinal variables have to use a numeric coding as follows: 0,1,2,...,number of modalities.
g	Integer specifying the number of classes.
model	One of the following models: "hetero"= mixture of Gaussian copulas without constraint, "homo"= mixture of Gaussian copulas with equal correlation matrices, "indpt"=locally independent mixture model.
kind	Vector indicating the nature of the variables as follows: 1=continuous, 2=integer, 3=ordinal. If this input is not specified, the function automatically detects the nature of each variables.
nbalgo	Number of MCMC chains.
burn_in	Number of iterations for the burn-in of the Gibbs sampler.
nbiter	Number of iterations for the parameter estimation performed via the Gibbs sampler.
param	An instance of MixClusParam to initialize the Gibbs sampler. If it is not specified, the Gibbs sampler is initialized in the maximum likelihood estimates of the locally independent mixture model.

Value

Return an instance of MixClusResults class.

Examples

## Not run: 
# Loading of a dataset simulated from a bi-component mixture model of Gaussian copulas
# (see Example 2.2 page 6)
# The first column indicates the class membership
# The last three column are used for the clustering
data(simu)

# Cluster analysis by the bi-component mixture model of Gaussian copulas
# without constrain between the correlation matrices
res.mixclus <- MixClusClustering(simu[,-1], 2)

# Confusion matrix between the estimated (row) and the true (column) partition
table(res.mixclus@data@partition, simu[,1])

# Summary of the model
summary(res.mixclus)

# Visualisation
# Update of the results (computing the conditional expectations of the latent vectors
# related to the Gaussian copulas)
res.mixclus <- MixClusUpdateForVisu(res.mixclus)

# Scatterplot of the individuals  (Figure 1.(a)) described by three variables:
# one continuous (abscissa), one integer (ordiate) and one binary (symbol).
# Colors indicate the component memberships
plot(simu[,2:3], col=simu[,1], pch=16+simu[,4], xlab=expression(x^1), ylab=expression(x^2))

# Scatterplot of the individuals in the first PCA-map of the first-component of the model
MixClusVisu(res.mixclus, class = 2, figure = "scatter", xlim=c(-10,4), ylim=c(-4,4))

# Correlation circle of the first PCA-map of the first-component of the model
MixClusVisu(res.mixclus, class = 2, figure = "circle")


## End(Not run)

MixCluster documentation built on May 2, 2019, 5:49 p.m.