# 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

 ```1 2 3``` ```## 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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29``` ```## 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.