plot.DiscrFact {tclust} | R Documentation |
The plot
method for objects of class DiscrFact
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 class 'DiscrFact'
plot(
x,
enum.plots = FALSE,
xlab = "Discriminant Factor",
ylab = "Clusters",
print.DiscrFact = TRUE,
xlim,
col.nodoubt = grey(0.8),
by.cluster = FALSE,
...
)
Arguments
x |
An object of class |
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 |
print.DiscrFact |
A logical value indicating whether each clusters mean discriminant factor shall be plotted |
col.nodoubt |
Color of all observations not considered as to be assigned doubtfully. |
by.cluster |
Logical value indicating whether optional parameters pch and col (if present) refer to observations (FALSE) or clusters (TRUE) |
... |
Arguments to be passed to or from other methods |
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. The function plot.DiscrFact()
combines the standard plot of a tclust
object, and the two plots introduced here.
References
García-Escudero, L.A.; Gordaliza, A.; Matrán, C. and Mayo-Iscar, A. (2011), "Exploring the number of groups in robust model-based clustering." Statistics and Computing, 21 pp. 585-599, <doi:10.1007/s11222-010-9194-z>
Examples
sig <- diag (2)
cen <- rep (1, 2)
x <- rbind(MASS::mvrnorm(360, cen * 0, sig),
MASS::mvrnorm(540, cen * 5, sig * 6 - 2),
MASS::mvrnorm(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)