plot.densityMclustBounded {mclustAddons}

Plotting method for model-based mixture density estimation for bounded data

Description

Plots for mclustDensityBounded objects.

Usage

## S3 method for class 'densityMclustBounded'
plot(x, what = c("BIC", "density", "diagnostic"), 
     data = NULL, ...)

Arguments

x

An object of class "densityMclustBounded" obtained from a call to densityMclustBounded.

what

The type of graph requested:

"BIC" =

a plot of BIC values for the estimated models versus the number of components.

"density" =

a plot of estimated density; if data is also provided the density is plotted over data points.

"diagnostic" =

diagnostic plots (only available for the one-dimensional case).

data

Optional data points.

...

Further available arguments.

• For 1-dimensional data:

  hist.col = "lightgrey", hist.border = "white", breaks = "Sturges"

• For 2-dimensional data:

  type = c("contour", "hdr", "image", "persp")
transformation = c("none", "log", "sqrt")
grid = 100, nlevels = 11, levels = NULL
prob = c(0.25, 0.5, 0.75)
col = grey(0.6), color.palette = blue2grey.colors
points.col = 1, points.cex = 0.8, points.pch = 1

• For d > 2-dimensional data:

  type = c("contour", "hdr"), gap = 0.2
grid = 100, nlevels = 11, levels = NULL
prob = c(0.25, 0.5, 0.75)
col = grey(0.6), color.palette = blue2grey.colors
points.col = 1, points.cex = 0.8, points.pch = 1

Value

No return value, called for side effects.

Author(s)

Luca Scrucca

References

Scrucca L. (2019) A transformation-based approach to Gaussian mixture density estimation for bounded data. Biometrical Journal, 61:4, 873–888. https://doi.org/10.1002/bimj.201800174

See Also

densityMclustBounded, predict.densityMclustBounded.

Examples

# univariate case with lower bound
x <- rchisq(200, 3)
dens <- densityMclustBounded(x, lbound = 0)
plot(dens, what = "BIC")
plot(dens, what = "density", data = x, breaks = 15)

# univariate case with lower & upper bound
x <- rbeta(200, 5, 1.5)
dens <- densityMclustBounded(x, lbound = 0, ubound = 1)
plot(dens, what = "BIC")
plot(dens, what = "density", data = x, breaks = 9)

# bivariate case with lower bounds
x1 <- rchisq(200, 3)
x2 <- 0.5*x1 + sqrt(1-0.5^2)*rchisq(200, 5)
x <- cbind(x1, x2)
dens <- densityMclustBounded(x, lbound = c(0,0))
plot(dens, what = "density")
plot(dens, what = "density", data = x)
plot(dens, what = "density", type = "hdr")
plot(dens, what = "density", type = "persp")