densityMclust.diagnostic: Diagnostic plots for `mclustDensity` estimation

Description

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

Usage

densityMclust.diagnostic(object, type = c("cdf", "qq"), 
                         col = c("black", "green4"), 
                         lwd = c(2,2), lty = c(1,2), 
                         legend = TRUE, grid = TRUE, 
                         main = 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.

main

A logical indicating if a title 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

Run this code

# NOT RUN {
x <- faithful$waiting
dens <- densityMclust(x)
plot(dens, x, what = "diagnostic")
# or
densityMclust.diagnostic(dens, type = "cdf")
densityMclust.diagnostic(dens, type = "qq")
# }