tclust (version 1.4-1)

plot.DiscrFact: plot Method for DiscrFact Objects

Description

The plot method for class DiscrFact: Next to a plot of the tclust object which has been used for creating the DiscrFact object, a silhouette plot indicates the presence of groups with a large amount of doubtfully assigned observations. A third plot similar to the standard tclust plot serves to highlight the identified doubtful observations.

Usage

# S3 method for DiscrFact
plot (x, enum.plots = FALSE, ...)
plot_DiscrFact_p2 (x, xlab = "Discriminant Factor", 
                   ylab = "Clusters", main, xlim, 
                   print.Discr = TRUE, main.pre, ...)
                  
                  
                  
plot_DiscrFact_p3 (x, main = "Doubtful Assignments", col, pch, 
                   col.nodoubt = grey (0.8), by.cluster = FALSE, 
                   ...)

Arguments

x

An object of class "DiscrFact" as from DiscrFact ().

enum.plots

A logical value indicating whether the plots shall be enumerated in their title ("(a)", "(b)", "(c)").

xlab, ylab, xlim

Arguments passed to funcion plot.tclust.

main

Argument passed to funcion plot.

print.Discr

A logical value indicating whether each clusters mean discriminant factor shall be plotted

main.pre

An optional string which is appended to the plot's caption.

pch, col

Arguments passed to function plot.

col.nodoubt

Color of all observations not considered as to be assigned doubtfully.

by.cluster

Logical value indicating whether parameters pch and col refer to observations (FALSE) or clusters (TRUE).

…

Arguments to be passed to or from other methods.

Value

No return value is provided.

Details

plot.DiscrFact.p2 displays a silhouette plot based on the discriminant factors of the observations. A solution with many large discriminant factors is not reliable. Such clusters can be identified with this silhouette plot. Thus plot.DiscrFact.p3 displays the dataset, highlighting observations with discriminant factors greater than the given threshold. Function plot.DiscrFact combines the standard plot of a tclust object, and the two plots introduced here.

References

Garc<ed>a-Escudero, L.A.; Gordaliza, A.; Matr<e1>n, C. and Mayo-Iscar, A. (2010), "Exploring the number of groups in robust model-based clustering." Statistics and Computing, (Forthcoming). Preprint available at www.eio.uva.es/infor/personas/langel.html.

Examples

# NOT RUN {
sig <- diag (2)
cen <- rep (1, 2)
x <- rbind(mvtnorm::rmvnorm(360, cen * 0,   sig),
	       mvtnorm::rmvnorm(540, cen * 5,   sig * 6 - 2),
	       mvtnorm::rmvnorm(100, cen * 2.5, sig * 50)
)

clus.1 <- tclust (x, k = 2, alpha=0.1, restr.fact=12)
clus.2 <- tclust (x, k = 3, alpha=0.1, restr.fact=1)

dsc.1 <- DiscrFact (clus.1)
plot(dsc.1)

dsc.2 <- DiscrFact (clus.2)
plot (dsc.2)

dev.off ()
plot_DiscrFact_p2 (dsc.1)
plot_DiscrFact_p3 (dsc.2)
# }