psych (version 1.0-42)

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). Cluster membership may be assigned apriori or may be determined in terms of the highest (absolute) cluster loading for each item.

Usage

cluster.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

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

Value

• Graphical output is presented

See Also

ICLUST,ICLUST.graph

Examples

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

Run the code above in your browser using DataCamp Workspace