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.
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,marg=c(.5,.5,1.5,.5))Output from ICLUST
labels for variables (if not specified as rownames in the ICLUST output
if short=TRUE, variable names are replaced with Vn
Round the path coefficients to digits accuracy
The standard graphic control parameter for font size modifications. This can be used to make the labels bigger or smaller than the default values.
Don't provide statistics for clusters less than min.size
size of the ellipses with the cluster statistics.
postive and negative
The main graphic title
Normally, clusters are named sequentially C1 ... Cn. If cluster.names are specified, then these values will be used instead.
Sets the margins to be narrower than the default values. Resets them upon return
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.
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.
Revelle, W. Hierarchical Cluster Analysis and the Internal Structure of Tests. Multivariate Behavioral Research, 1979, 14, 57-74.
# NOT RUN {
v9 <- sim.hierarchical()
v9c <- ICLUST(v9)
test.data <- Harman74.cor$cov
ic.out <- ICLUST(test.data)
#now show how to relabel clusters
ic.bfi <- iclust(psychTools::bfi[1:25],beta=3) #find the clusters
cluster.names <- rownames(ic.bfi$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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