uncerPlot {mclust} | R Documentation

## Uncertainty Plot for Model-Based Clustering

### Description

Plots 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 (2002a). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 97:611-631. See http://www.stat.washington.edu/mclust.

C. Fraley and A. E. Raftery (2002b). MCLUST: Software for model-based clustering, density estimation and discriminant analysis. Technical Report, Department of Statistics, University of Washington. See http://www.stat.washington.edu/mclust.

### See Also

`EMclust`, `em`, `me`, `mapClass`

### Examples

    data(iris)
    irisMatrix <- as.matrix(iris[,1:4])

    irisBic <- EMclust(irisMatrix)
    irisSumry3 <-  summary(irisBic, irisMatrix, G = 3)

    uncerPlot(z = irisSumry3$z)

    uncerPlot(z = irisSumry3$z, truth = rep(1:3, rep(50,3)))

    do.call("uncerPlot", c(irisSumry3, list(truth = rep(1:3, rep(50,3)))))

[Package mclust version 2.1-8 Index]