cluster | R Documentation |
cluster
is a preliminary implementation of a cluster function. It supports various distance measures as well as
cluster methods. More is to come.
cluster(
x,
along = 0,
dmethod = "euclidean",
cmethod = "ward.D",
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,
...
)
x |
|
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 |
cmethod |
The agglomeration method to be used. This should be (an unambiguous abbreviation of) one of
|
p |
The power of the Minkowski distance, in case |
align |
Whether the constructs should be aligned before clustering (default is |
trim |
the number of characters a construct is trimmed to. If |
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 |
print |
Logical. Whether to print the dendrogram (default is |
... |
Additional parameters to be passed to plotting function from |
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).
Reordered repgrid
object.
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.
bertinCluster()
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, edgePar = list(lty = 1:2, col = 2:1)) # advanced appearance changes
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