c_clusteredness {stops}

c-clusteredness calculates c-clusteredness as the OPTICS cordillera. The higher the more clustered.

Description

c-clusteredness calculates c-clusteredness as the OPTICS cordillera. The higher the more clustered.

Usage

c_clusteredness(
  confs,
  minpts = 2,
  q = 2,
  epsilon = 2 * max(dist(confs)),
  distmeth = "euclidean",
  dmax = NULL,
  digits = 10,
  scale = 0,
  ...
)

Arguments

confs

a numeric matrix or a dist object

minpts

The minimum number of points that must make up a cluster in OPTICS (corresponds to k in the paper). It is passed to optics where it is called minPts. Defaults to 2.

q

The norm used for the Cordillera. Defaults to 2.

epsilon

The epsilon parameter for OPTICS (called epsilon_max in the paper). Defaults to 2 times the maximum distance between any two points.

distmeth

The distance to be computed if X is not a symmetric matrix or a dist object (otherwise ignored). Defaults to Euclidean distance.

dmax

The winsorization value for the highest allowed reachability. If used for comparisons between different configurations this should be supplied. If no value is supplied, it is NULL (default); then dmax is taken from the data as the either epsilon or the largest reachability, whatever is smaller.

digits

The precision to round the raw Cordillera and the norm factor. Defaults to 10.

scale

Should X be scaled if it is an asymmetric matrix or data frame? Can take values TRUE or FALSE or a numeric value. If TRUE or 1, standardisation is to mean=0 and sd=1. If 2, no centering is applied and scaling of each column is done with the root mean square of each column. If 3, no centering is applied and scaling of all columns is done as X/max(standard deviation(allcolumns)). If 4, no centering is applied and scaling of all columns is done as X/max(rmsq(allcolumns)). If FALSE, 0 or any other numeric value, no standardisation is applied. Defaults to 0.

...

Additional arguments to be passed to cordillera::cordillera

Value

a numeric value; clusteredness (see cordillera)

Examples

delts<-smacof::kinshipdelta
dis<-smacofSym(delts)$confdist
c_clusteredness(dis,minpts=3)