cluster.plot: Plot factor/cluster loadings and assign items to clusters by their highest loading.

Description

Cluster analysis and factor analysis are procedures for grouping items in terms of a smaller number of (latent) factors or (observed) clusters. Graphical presentations of clusters typically show tree structures, although they can be represented in terms of item by cluster correlations.

Cluster.plot plots items by their cluster loadings (taken, e.g., from ICLUST) or factor loadings (taken, eg., from fa). Cluster membership may be assigned apriori or may be determined in terms of the highest (absolute) cluster loading for each item.

If the input is an object of class "kmeans", then the cluster centers are plotted.

Usage

cluster.plot(ic.results, cluster = NULL, cut = 0, labels=NULL,
          title = "Cluster plot",pch=18,pos,show.points=TRUE,choose=NULL,...)
fa.plot(ic.results, cluster = NULL, cut = 0, labels=NULL,title, 
    jiggle=FALSE,amount=.02,pch=18,pos,show.points=TRUE,choose=NULL,main=NULL,...)
factor.plot(ic.results, cluster = NULL, cut = 0, labels=NULL,title,jiggle=FALSE,
                  amount=.02,pch=18,pos,show.points=TRUE,...)  #deprecated

Arguments

Value

Graphical output is presented.

Details

Results of either a factor analysis or cluster analysis are plotted. Each item is assigned to its highest loading factor, and then identified by variable name as well as cluster (by color). The cluster assignments can be specified to override the automatic clustering by loading. Both of these functions may be called directly or by calling the generic plot function. (see example).

Examples

Run this code

circ.data <- circ.sim(24,500)
circ.fa <- fa(circ.data,2)
plot(circ.fa,cut=.5)
f5 <- fa(bfi[1:25],5) 
plot(f5,labels=colnames(bfi)[1:25],show.points=FALSE)
plot(f5,labels=colnames(bfi)[1:25],show.points=FALSE,choose=c(1,2,4))