MixClusUpdateForVisu: MixClusUpdateForVisu 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 provides computes the conditional expectation of the latent vectors related to the Gaussian copulas. It must be used before calling the function MixClusVisu.

Usage

  MixClusUpdateForVisu(output)

Arguments

output

An instance of MixClusResults class

Value

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.