plot.ctlcurves {tclust}

The plot method for objects of class ctlcurves

Description

The plot method for class ctlcurves: This function implements a series of plots, which display characteristic values of the each model, computed with different values for k and alpha.

Usage

## S3 method for class 'ctlcurves'
plot(
  x,
  what = c("obj", "min.weights", "doubtful"),
  main,
  xlab,
  ylab,
  xlim,
  ylim,
  col,
  lty = 1,
  ...
)

Arguments

x

The ctlcurves object to be shown

what

A string indicating which type of plot shall be drawn. See the details section for more information.

main

A character-string containing the title of the plot.

xlab, ylab, xlim, ylim

Arguments passed to plot().

col

A single value or vector of line colors passed to lines.

lty

A single value or vector of line colors passed to lines.

...

Arguments to be passed to or from other methods.

Details

These curves show the values of the trimmed classification (log-)likelihoods when altering the trimming proportion alpha and the number of clusters k. The careful examination of these curves provides valuable information for choosing these parameters in a clustering problem. For instance, an appropriate k to be chosen is one that we do not observe a clear increase in the trimmed classification likelihood curve for k with respect to the k+1 curve for almost all the range of alpha values. Moreover, an appropriate choice of parameter alpha may be derived by determining where an initial fast increase of the trimmed classification likelihood curve stops for the final chosen k. A more detailed explanation can be found in García-Escudero et al. (2011).

This function implements a series of plots, which display characteristic values of the each model, computed with different values for k and alpha.

"obj"

Objective function values.

"min.weights"

The minimum cluster weight found for each computed model. This plot is intended to spot spurious clusters, which in general yield quite small weights.

"doubtful"

The number of "doubtful" decisions identified by DiscrFact.

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

 #--- EXAMPLE 1 ------------------------------------------

 sig <- diag (2)
 cen <- rep (1, 2)
 x <- rbind(MASS::mvrnorm(108, cen * 0,   sig),
 	       MASS::mvrnorm(162, cen * 5,   sig * 6 - 2),
 	       MASS::mvrnorm(30, cen * 2.5, sig * 50))

 (ctl <- ctlcurves(x, k = 1:4))

 plot(ctl)