Description Usage Arguments Value Author(s) See Also Examples
Bertin display with corresponding cluster anaylsis. Element columns and constructs rows are ordered according to cluster criterion. Various distance measures as well as cluster methods are supported.
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x |
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dmethod |
The distance measure to be used. This must be one of
|
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 |
trim |
The number of characters a construct is trimmed to (default is
|
type |
Type of dendrogram. Either or |
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 |
y.off |
Vertical offset between bertin display and element dendrogram and
(default is |
cex.axis |
|
col.axis |
Color for axis and axis labels, default is |
draw.axis |
Whether to draw axis showing the distance metric for the dendrograms
(default is |
... |
additional parameters to be passed to function |
A list of two hclust
object, for elements and constructs
respectively.
Mark Heckmann
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 | ## Not run:
# 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", "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", NA))
# euclidean distance and single linkage clustering for elements
# no clustering for constructs
bertinCluster(bell2010, cm=c(NA, "single"))
### changing the appearance
# different dendrogram type
bertinCluster(bell2010, type="rectangle")
# no axis drawn for dendrogram
bertinCluster(bell2010, draw.axis=F)
### 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))
## End(Not run)
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