Element columns and constructs rows are ordered according to cluster criterion. Various distance measures as well as cluster methods are supported.

## Arguments

- x
`repgrid`

object.- dmethod
The distance measure to be used. This must be one of

`"euclidean"`

,`"maximum"`

,`"manhattan"`

,`"canberra"`

,`"binary"`

, or`"minkowski"`

. Default is`"euclidean"`

. Any unambiguous substring can be given (e.g.`"euc"`

for`"euclidean"`

). A vector of length two can be passed if a different distance measure for constructs and elements is wanted (e.g.`c("euclidean", "manhattan")`

). This will apply euclidean distance to the constructs and manhattan distance to the elements. For additional information on the different types see`?dist`

.- cmethod
The agglomeration method to be used. This should be (an unambiguous abbreviation of) one of

`"ward.D"`

,`"ward.D2"`

,`"single"`

,`"complete"`

,`"average"`

,`"mcquitty"`

,`"median"`

or`"centroid"`

. Default is`"ward.D"`

. A vector of length two can be passed if a different cluster method for constructs and elements is wanted (e.g.`c("ward.D", "euclidean")`

). This will apply ward clustering to the constructs and single linkage clustering to the elements. If only one of either constructs or elements is to be clustered the value`NA`

can be supplied. E.g. to cluster elements only use`c(NA, "ward.D")`

.- p
The power of the Minkowski distance, in case

`"minkowski"`

is used as argument for`dmethod`

.`p`

can be a vector of length two if different powers are wanted for constructs and elements respectively (e.g.`c(2,1)`

).- align
Whether the constructs should be aligned before clustering (default is

`TRUE`

). If not, the grid matrix is clustered as is. See Details section in function`cluster()`

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.- type
Type of dendrogram. Either or

`"triangle"`

(default) or`"rectangle"`

form.- xsegs
Numeric vector of normal device coordinates (ndc i.e. 0 to 1) to mark the widths of the regions for the left labels, for the bertin display, for the right labels and for the vertical dendrogram (i.e. for the constructs).

- ysegs
Numeric vector of normal device coordinates (ndc i.e. 0 to 1) to mark the heights of the regions for the horizontal dendrogram (i.e. for the elements), for the bertin display and for the element names.

- x.off
Horizontal offset between construct labels and construct dendrogram and (default is

`0.01`

in normal device coordinates).- y.off
Vertical offset between bertin display and element dendrogram and (default is

`0.01`

in normal device coordinates).- cex.axis
`cex`

for axis labels, default is`.6`

.- col.axis
Color for axis and axis labels, default is

`grey(.4)`

.- draw.axis
Whether to draw axis showing the distance metric for the dendrograms (default is

`TRUE`

).- ...
additional parameters to be passed to function

`bertin()`

.

## Value

A list of two `hclust()`

object, for elements and constructs
respectively.

## Examples

```
# default is euclidean distance and ward clustering
bertinCluster(bell2010)
### applying different distance measures and cluster methods
# euclidean distance and single linkage clustering
bertinCluster(bell2010, cmethod = "single")
# manhattan distance and single linkage clustering
bertinCluster(bell2010, dmethod = "manhattan", cm = "single")
# minkowksi distance with power of 2 = euclidean distance
bertinCluster(bell2010, dm = "mink", p = 2)
### using different methods for constructs and elements
# ward clustering for constructs, single linkage for elements
bertinCluster(bell2010, cmethod = c("ward.D", "single"))
# euclidean distance measure for constructs, manhatten
# distance for elements
bertinCluster(bell2010, dmethod = c("euclidean", "man"))
# minkowski metric with different powers for constructs and elements
bertinCluster(bell2010, dmethod = "mink", p = c(2, 1))
### clustering either constructs or elements only
# euclidean distance and ward clustering for constructs no
# clustering for elements
bertinCluster(bell2010, cmethod = c("ward.D", NA))
# euclidean distance and single linkage clustering for elements
# no clustering for constructs
bertinCluster(bell2010, cm = c(NA, "single"), align = FALSE)
### changing the appearance
# different dendrogram type
bertinCluster(bell2010, type = "rectangle")
# no axis drawn for dendrogram
bertinCluster(bell2010, draw.axis = FALSE)
### passing on arguments to bertin function via ...
# grey cell borders in bertin display
bertinCluster(bell2010, border = "grey")
# omit printing of grid scores, i.e. colors only
bertinCluster(bell2010, showvalues = FALSE)
### changing the layout
# making the vertical dendrogram bigger
bertinCluster(bell2010, xsegs = c(0, .2, .5, .7, 1))
# making the horizontal dendrogram bigger
bertinCluster(bell2010, ysegs = c(0, .3, .8, 1))
```