psych (version 1.3.10.12)

iclust.diagram: Draw an ICLUST hierarchical cluster structure diagram

Description

Given a cluster structure determined by ICLUST, create a graphic structural diagram using graphic functions in the psych package To create dot code to describe the ICLUST output with more precision, use ICLUST.graph. If Rgraphviz has been successfully installed, the alternative is to use ICLUST.rgraph.

Usage

iclust.diagram(ic, labels = NULL, short = FALSE, digits = 2, cex = NULL, min.size = NULL,
  e.size =1,colors=c("black","blue"), main = "ICLUST diagram",cluster.names=NULL)

Arguments

ic
Output from ICLUST
labels
labels for variables (if not specified as rownames in the ICLUST output
short
if short=TRUE, variable names are replaced with Vn
digits
Round the path coefficients to digits accuracy
cex
The standard graphic control parameter for font size modifications. This can be used to make the labels bigger or smaller than the default values.
min.size
Don't provide statistics for clusters less than min.size
e.size
size of the ellipses with the cluster statistics.
colors
postive and negative
main
The main graphic title
cluster.names
Normally, clusters are named sequentially C1 ... Cn. If cluster.names are specified, then these values will be used instead.

Value

  • Graphical output summarizing the hierarchical cluster structure. The graph is drawn using the diagram functions (e.g., dia.curve, dia.arrow, dia.rect, dia.ellipse ) created as a work around to Rgraphviz.

Details

iclust.diagram provides most of the power of ICLUST.rgraph without the difficulties involved in installing Rgraphviz. It is called automatically from ICLUST.

Following a request by Michael Kubovy, cluster.names may be specified to replace the normal C1 ... Cn names.

If access to a dot language graphics program is available, it is probably better to use the iclust.graph function to get dot output for offline editing.

References

Revelle, W. Hierarchical Cluster Analysis and the Internal Structure of Tests. Multivariate Behavioral Research, 1979, 14, 57-74.

See Also

ICLUST

Examples

Run this code
v9 <- sim.hierarchical()
v9c <- ICLUST(v9)
test.data <- Harman74.cor$cov
ic.out <- ICLUST(test.data)
#now show how to relabel clusters
ic.bfi <- iclust(bfi[1:25],beta=3) #find the clusters
cluster.names <- rownames(ic.bfi$results) #get the old names
#change the names to the desired ones
cluster.names[c(16,19,18,15,20)] <- c("Neuroticism","Extra-Open","Agreeableness",
      "Conscientiousness","Open")
#now show the new names
iclust.diagram(ic.bfi,cluster.names=cluster.names,min.size=4,e.size=1.75)

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