- x
compositional data represented as a data.frame
- k
number of clusters
- method
clustering method. One of Mclust, cmeans, kmeansHartigan,
cmeansUfcl, pam, clara, fanny, ward.D2, single, hclustComplete,
average, mcquitty, median, centroid
- scale
if orthonormal coordinates should be normalized.
- transformation
default are the isometric logratio coordinates. Can only used when distMethod
is not Aitchison.
- distMethod
Distance measure to be used. If “Aitchison”, then transformation should be “identity”.
- iter.max
parameter if kmeans is chosen. The maximum number of iterations allowed
- vals
if cluster validity measures should be calculated
- alt
a known partitioning can be provided (for special cluster validity measures)
- bic
if TRUE then the BIC criteria is evaluated for each single cluster as validity measure
- verbose
if TRUE additional print output is provided
- y
the y coordinates of points in the plot, optional if x is an appropriate structure.
- ...
additional parameters for print method passed through
- normalized
results gets normalized before plotting. Normalization is done by z-transformation
applied on each variable.
- which.plot
currently the only plot. Plot of cluster centers.
- measure
cluster validity measure to be considered for which.plot equals “partMeans”