eval.plot {nomclust}

Visualization of Evaluation Criteria

Description

The function visualizes the values of up to eight evaluation criteria for the range of cluster solutions defined by the user in the nomclust, evalclust or nomprox functions. It also indicates the optimal number of clusters determined by these criteria. The charts for the evaluation criteria in the nomclust package.

Usage

eval.plot(
  x,
  criteria = "all",
  style = "greys",
  opt.col = "red",
  main = "Cluster Evaluation",
  ...
)

Arguments

x

An output of the "nomclust" object containing the eval and opt components.

criteria

A character string or character vector specifying the criteria that are going to be visualized. It can be selected one particular criterion, a vector of criteria, or all the available criteria by typing "all".

style

A character string or a vector of colors defines the graphical style of the produced plots. There are two predefined styles in the nomclust package, namely "greys" and "dark", but a custom color scheme can be set by a user as a vector of a length four.

opt.col

An argument specifying a color that is used for the optimal number of clusters identification.

main

A character string with the chart title.

...

Other graphical arguments compatible with the generic plot() function.

Details

The function can display up to eight evaluation criteria. Namely, Within-cluster mutability coefficient (WCM), Within-cluster entropy coefficient (WCE), Pseudo F Indices based on the mutability (PSFM) and the entropy (PSFE), Bayesian (BIC), and Akaike (AIC) information criteria for categorical data, the BK index, and the silhouette index (SI).

Value

The function returns a series of up to eight plots with evaluation criteria values and the graphical indication of the optimal numbers of clusters (for AIC, BIC, BK, PSFE, PSFM, SI).

Author(s)

Jana Cibulkova and Zdenek Sulc.
Contact: [e-mail]

See Also

dend.plot, nomclust, evalclust, nomprox.

Examples

# sample data
data(data20)

# creating an object with results of hierarchical clustering 
hca.object <- nomclust(data20, measure = "iof", eval = TRUE)

# a default series of plots
eval.plot(hca.object)

# changing the color indicating the optimum number of clusters
eval.plot(hca.object, opt.col= "darkorange")

# selecting only AIC and BIC criteria with the dark style
eval.plot(hca.object, criteria = c("AIC", "BIC"), style = "dark")

# selecting only SI
eval.plot(hca.object, criteria = "SI")