R: Uncertainty Plot for Model-Based Clustering

uncerPlot {mclust} | R Documentation

## Uncertainty Plot for Model-Based Clustering

### Description

Displays the uncertainty in converting a conditional probablility from EM to a classification in model-based clustering.

### Usage

    uncerPlot(z, truth, ...)

### Arguments

`z` | A matrix whose [i,k]th entry is the conditional probability of the ith observation belonging to the kth component of the mixture.
`truth` | A numeric or character vector giving the true classification of the data.
`...` | Provided to allow lists with elements other than the arguments can be passed in indirect or list calls with `do.call`.

### Details

When `truth` is provided and the number of classes is compatible with `z`, the function `compareClass` is used to to find best correspondence between classes in `truth` and `z`.

### Value

A plot of the uncertainty profile of the data, with uncertainties in increasing order of magnitude. If `truth` is supplied and the number of classes is the same as the number of columns of `z`, the uncertainty of the misclassified data is marked by vertical lines on the plot.

### References

C. Fraley and A. E. Raftery (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 97:611-631.

C. Fraley, A. E. Raftery, T. B. Murphy and L. Scrucca (2012). mclust Version 4 for R: Normal Mixture Modeling for Model-Based Clustering, Classification, and Density Estimation. Technical Report No. 597, Department of Statistics, University of Washington.

### See Also

`mclustBIC`, `em`, `me`, `mapClass`

### Examples

    irisModel3 <-  Mclust(iris[,-5], G = 3)

    uncerPlot(z = irisModel3$z)

    uncerPlot(z = irisModel3$z, truth = iris[,5])

[Package mclust version 5.3 Index]