plot-methods: Plots of an instance of 'VSLCMresults'

In VarSelLCM: Variable Selection for Model-Based Clustering of Mixed-Type Data Set with Missing Values

Description

This function proposes different plots of an instance of VSLCMresults. It permits to visualize:

• the discriminative power of the variables (type="bar" or type="pie"). The larger is the discriminative power of a variable, the more explained are the clusters by this variable.

• the probabilities of misclassification (type="probs-overall" or type="probs-class").

• the distribution of a signle variable (y is the name of the variable and type="boxplot" or type="cdf").

Usage

## S4 method for signature 'VSLCMresults,character'
plot(x, y, type = "boxplot", ylim = c(1,
  x@data@d))

Arguments

x	instance of VSLCMresults.
y	character. The name of the variable to ploted (only used if type="boxplot" or type="cdf").
type	character. The type of plot ("bar": barplot of the disciminative power, "pie": pie of the discriminative power, "probs-overall": histogram of the probabilities of misclassification, "probs-class": histogram of the probabilities of misclassification per cluster, "boxplot": boxplot of a single variable per cluster, "cdf": distribution of a single variable per cluster).
ylim	numeric. Define the range of the most discriminative variables to considered (only use if type="pie" or type="bar")

Examples

## Not run: 
require(VarSelLCM)

# Data loading:
# x contains the observed variables
# z the known statu (i.e. 1: absence and 2: presence of heart disease)
data(heart)
ztrue <- heart[,"Class"]
x <- heart[,-13]

# Cluster analysis with variable selection (with parallelisation)
res_with <- VarSelCluster(x, 2, nbcores = 2, initModel=40)

# Summary of the probabilities of missclassification
plot(res_with, type="probs-class")

# Discriminative power of the variables (here, the most discriminative variable is MaxHeartRate)
plot(res_with)

# Boxplot for the continuous variable MaxHeartRate
plot(res_with, y="MaxHeartRate")

# Empirical and theoretical distributions (to check that the distribution is well-fitted)
plot(res_with, y="MaxHeartRate", type="cdf")

# Summary of categorical variable
plot(res_with, y="Sex")

## End(Not run)

Example output

Attaching package: 'VarSelLCM'

The following object is masked from 'package:stats':

    predict

VarSelLCM documentation built on May 2, 2019, 4:59 p.m.