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

plot.densityMclustBounded.Rd

Plots for mclustDensityBounded objects.

Usage

# S3 method for 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.

See also

densityMclustBounded, predict.densityMclustBounded.

Author

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

Examples

# \donttest{
# 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")

# }