Diagnostic plots for mclustDensityBounded estimation

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.

References

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

Author

Luca Scrucca

See also

densityMclustBounded, plot.densityMclustBounded.

Examples

# \donttest{
# 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")

# }