densityMclustBounded.diagnostic: Diagnostic plots for 'mclustDensityBounded' estimation

In mclustAddons: Addons for the 'mclust' Package

Diagnostic plots for mclustDensityBounded estimation

Description

Diagnostic plots for density estimation of bounded data via transformation-based approach of Gaussian mixtures. Only available for the one-dimensional case.

Usage

densityMclustBounded.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 'mclustDensityBounded' obtained from a call to densityMclustBounded 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).

Value

No return value, called for side effects.

Author(s)

Luca Scrucca

References

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

See Also

densityMclustBounded, plot.densityMclustBounded.

Examples

# univariate case with lower bound
x <- rchisq(200, 3)
dens <- densityMclustBounded(x, lbound = 0)
plot(dens, x, what = "diagnostic")
# or
densityMclustBounded.diagnostic(dens, type = "cdf")
densityMclustBounded.diagnostic(dens, type = "qq")

# univariate case with lower & upper bounds
x <- rbeta(200, 5, 1.5)
dens <- densityMclustBounded(x, lbound = 0, ubound = 1)
plot(dens, x, what = "diagnostic")
# or
densityMclustBounded.diagnostic(dens, type = "cdf")
densityMclustBounded.diagnostic(dens, type = "qq")

mclustAddons documentation built on Jan. 6, 2023, 5:21 p.m.