ClusVis-package {ClusVis}

Gaussian-Based Visualization of Gaussian and Non-Gaussian Model-Based Clustering.

Description

The main function for parameter inference is clusvis. Moreover, specific functions clusvisVarSelLCM and clusvisMixmod are implemented to visualize the results of the R package VarSelLCM and Rmixmod. After parameter inference, visualization is done with function plotDensityClusVisu.

Details

Package:	ClusVis
Type:	Package
Version:	1.1.0
Date:	2018-04-18
License:	GPL-3
LazyLoad:	yes

Author(s)

Biernacki, C. and Marbac, M. and Vandewalle, V.

Examples

## Not run: 

 ## First example: R package Rmixmod
 # Package loading
 require(Rmixmod)

 # Data loading (categorical data)
 data("congress")
 # Model-based clustering with 4 components
 set.seed(123)
 res <- mixmodCluster(congress[,-1], 4, strategy = mixmodStrategy(nbTryInInit = 500, nbTry=25))

 # Inference of the parameters used for results visualization
 # (specific for Rmixmod results)
 # It is better because probabilities of classification are generated
 # by using the model parameters
 resvisu <- clusvisMixmod(res)

 # Component interpretation graph
 plotDensityClusVisu(resvisu)

 # Scatter-plot of the observation memberships
 plotDensityClusVisu(resvisu,  add.obs = TRUE)


## Second example: R package Rmixmod
# Package loading
require(Rmixmod)
 
# Data loading (categorical data)
data(birds)

# Model-based clustering with 3 components
resmixmod <- mixmodCluster(birds, 3)

# Inference of the parameters used for results visualization (general approach)
# Probabilities of classification are not sampled from the model parameter,
# but observed probabilities of classification are used for parameter estimation
resvisu <- clusvis(log(resmixmod@bestResult@proba),
                   resmixmod@bestResult@parameters@proportions)

# Inference of the parameters used for results visualization
# (specific for Rmixmod results)
# It is better because probabilities of classification are generated
# by using the model parameters
resvisu <- clusvisMixmod(resmixmod)

# Component interpretation graph
plotDensityClusVisu(resvisu)

# Scatter-plot of the observation memberships
plotDensityClusVisu(resvisu,  add.obs = TRUE)

## Third example: R package VarSelLCM
# Package loading
require(VarSelLCM)

# Data loading (categorical data)
data("heart")
# Model-based clustering with 3 components
res <- VarSelCluster(heart[,-13], 3)

# Inference of the parameters used for results visualization
# (specific for VarSelLCM results)
# It is better because probabilities of classification are generated
# by using the model parameters
resvisu <- clusvisVarSelLCM(res)

# Component interpretation graph
plotDensityClusVisu(resvisu)

# Scatter-plot of the observation memberships
plotDensityClusVisu(resvisu,  add.obs = TRUE)

## End(Not run)