psych (version 1.0-77)

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 factor.pa). 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",...)
factor.plot(ic.results, cluster = NULL, cut = 0, labels=NULL,title = "Cluster plot",...)

Arguments

ic.results

A factor analysis or cluster analysis output including the loadings, or a matrix of item by cluster correlations. Or the output from a kmeans cluster analysis.

cluster

A vector of cluster membership

cut

Assign items to clusters if the absolute loadings are > cut

labels

If row.names exist they will be added to the plot, or, if they don't, labels can be specified. If labels =NULL, and there are no row names, then variables are labeled by row number.)

title

Any title

...

Further options to plot

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).

See Also

ICLUST, ICLUST.graph, fa.graph

Examples

circ.data <- circ.sim(24,500)
circ.fa <- factor.pa(circ.data,2)
cluster.plot(circ.fa,cut=.5)