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mclust (version 6.0.0)

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", "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.

Author

Luca Scrucca

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.

See Also

densityMclust, plot.densityMclust.

Examples

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

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