OpenRepGrid (version 0.1.9)

cluster: Cluster analysis (of constructs or elements).

Description

cluster is a preliminary implementation of a cluster function. It supports various distance measures as well as cluster methods. More is to come.

Usage

cluster(x, along = 0, dmethod = "euclidean", cmethod = "ward", p = 2, align = TRUE, trim = NA, main = NULL, mar = c(4, 2, 3, 15), cex = 0, lab.cex = 0.8, cex.main = 0.9, print = TRUE, ...)

Arguments

x

repgrid object.

along

Along which dimension to cluster. 1 = constructs only, 2= elements only, 0=both (default).

dmethod

The distance measure to be used. This must be one of "euclidean", "maximum", "manhattan", "canberra", "binary" or "minkowski". Any unambiguous substring can be given. For additional information on the different types type ?dist.

cmethod

The agglomeration method to be used. This should be (an unambiguous abbreviation of) one of "ward", "single", "complete", "average", "mcquitty", "median" or "centroid".

p

The power of the Minkowski distance, in case "minkowski" is used as argument for dmethod.

align

Whether the constructs should be aligned before clustering (default is TRUE). If not, the grid matrix is clustered as is. See Details section for more information.

trim

the number of characters a construct is trimmed to (default is 10). If NA no trimming is done. Trimming simply saves space when displaying the output.

main

Title of plot. The default is a name indicating the distance function and cluster method.

mar

Define the plot region (bottom, left, upper, right).

cex

Size parameter for the nodes. Usually not needed.

lab.cex

Size parameter for the constructs on the right side.

cex.main

Size parameter for the plot title (default is .9).

print

Logical. Wether to print the dendrogram (default is TRUE).

...

Additional parameters to be passed to plotting function from as.dendrogram. Type ?as.dendrogram for further information. This option is usually not needed, except if special designs are needed.

Value

Reordered repgrid object.

Details

align: Aligning will reverse constructs if necessary to yield a maximal similarity between constructs. In a first step the constructs are clustered including both directions. In a second step the direction of a construct that yields smaller distances to the adjacent constructs is preserved and used for the final clustering. As a result, every construct is included once but with an orientation that guarantees optimal clustering. This approach is akin to the procedure used in FOCUS (Jankowicz & Thomas, 1982).

References

Jankowicz, D., & Thomas, L. (1982). An Algorithm for the Cluster Analysis of Repertory Grids in Human Resource Development. Personnel Review, 11(4), 15-22. doi:10.1108/eb055464.

See Also

bertinCluster

Examples

## Not run: 
# 
#   cluster(bell2010)
#   cluster(bell2010, main="My cluster analysis")   # new title
#   cluster(bell2010, type="t")                     # different drawing style
#   cluster(bell2010, dmethod="manhattan")          # using manhattan metric
#   cluster(bell2010, cmethod="single")             # do single linkage clustering
#   cluster(bell2010, cex=1, lab.cex=1)             # change appearance
#   cluster(bell2010, lab.cex=.7,                   # advanced appearance changes
#           edgePar = list(lty=1:2, col=2:1))
# ## End(Not run)