`uncerPlot.Rd`

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

`uncerPlot(z, truth, ...)`

- z
A matrix whose

*[i,k]*th entry is the conditional probability of the ith observation belonging to the*k*th 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`

.

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.

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`

.

```
irisModel3 <- Mclust(iris[,-5], G = 3)
uncerPlot(z = irisModel3$z)
uncerPlot(z = irisModel3$z, truth = iris[,5])
```