Diagnostic plots for density estimation. Only available for the one-dimensional case.

```
densityMclust.diagnostic(object, type = c("cdf", "qq"),
col = c("black", "black"),
lwd = c(2,1), lty = c(1,1),
legend = TRUE, grid = TRUE,
...)
```

## Arguments

- object
An object of class `'mclustDensity'`

obtained from a call to `densityMclust`

function.

- type
The type of graph requested:

`"cdf"`

=
a plot of the estimated CDF versus the empirical distribution function.

`"qq"`

=
a Q-Q plot of sample quantiles versus the quantiles obtained from the inverse of the estimated cdf.

- col
A pair of values for the color to be used for plotting, respectively, the estimated CDF and the empirical cdf.

- lwd
A pair of values for the line width to be used for plotting, respectively, the estimated CDF and the empirical cdf.

- lty
A pair of values for the line type to be used for plotting, respectively, the estimated CDF and the empirical cdf.

- legend
A logical indicating if a legend must be added to the plot of fitted CDF vs the empirical CDF.

- grid
A logical indicating if a `grid`

should be added to the plot.

- ...
Additional arguments.

## Details

The two diagnostic plots for density estimation in the one-dimensional case are discussed in Loader (1999, pp- 87-90).

## References

Loader C. (1999), Local Regression and Likelihood. New York, Springer.

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.

## Examples

```
# \donttest{
x <- faithful$waiting
dens <- densityMclust(x)
plot(dens, x, what = "diagnostic")
# or
densityMclust.diagnostic(dens, type = "cdf")
densityMclust.diagnostic(dens, type = "qq")
# }
```