optCluster (version 1.3.2)

valPlot: Validation Measure Plots

Description

valPlot displays a plot of the scores for each selected validation measure.

Usage

valPlot(x, measures = measureNames(x), legend = TRUE, 
	legendLoc = "topright", main = NULL, pch = NULL, type = "b", 
	ask = prod(par("mfcol")) < length(measures) && dev.interactive(), ...)

Arguments

x

An object of class "'>optCluster".

measures

Character vector of the names of the validation measures to plot. Any number of choices is allowed.

legend

If TRUE, provides a legend.

legendLoc

Character string specifying the location of the legend.

main

Character string specifying the title of graph.

pch

Specifies the plotting characters to use.

type

A character string specifying the type of plot.

ask

If TRUE, the user is prompted before each plot.

…

Additional plotting parameters.

Details

The the biological homogeneity index (BHI), biological stability index (BSI), Dunn index, and silhouette width measures should all be maximized.

The average proportion of non-overlap (APN), average distance (AD), average distance between means (ADM), figure of merit (FOM), and connectivity measures should all be minimized.

See Also

clValid-class, optCluster-class

Examples

# NOT RUN {
	
	## This example may take a few minutes to compute
	
# }
# NOT RUN {
	## Obtain Dataset	
	data(arabid)	
	
	## Normalize Data with Respect to Library Size	
	obj <- t(t(arabid)/colSums(arabid))
		
	## Analysis of Normalized Data using Internal and Stability Validation Measures
	norm1 <- optCluster(obj, 2:4, clMethods = "all")
	
	## Plots of Internal and Stability Validation Measures
	par(mfrow = c(4,2))
	valPlot(norm1)
	
	## Plots of Internal Validation Measures in a Single Figure
	par(mfrow = c(2,2))
	valPlot(norm1, measure = c("Dunn", "Silhouette", "Connectivity"), legend = FALSE)
	plot(0, type="n", axes=FALSE, xlab = "", ylab = "")
	legend("center", methodNames(norm1), col=1:9, lty=1:9, pch=paste(c(1:9)), 
			cex=0.8)		
	
# }