c_association: c-association calculates the c-association based on the...

In stops: Structure Optimized Proximity Scaling

c-association calculates the c-association based on the maximal information coefficient We define c-association as the aggregated association between any two columns in confs

Description

c-association calculates the c-association based on the maximal information coefficient We define c-association as the aggregated association between any two columns in confs

Usage

c_association(
  confs,
  aggr = max,
  alpha = 0.6,
  C = 15,
  var.thr = 1e-05,
  zeta = NULL
)

Arguments

confs	a numeric matrix or data frame
aggr	the aggregation function for configurations of more than two dimensions. Defaults to max.
alpha	an optional number of cells allowed in the X-by-Y search-grid. Default value is 0.6
C	an optional number determining the starting point of the X-by-Y search-grid. When trying to partition the x-axis into X columns, the algorithm will start with at most C X clumps. Default value is 15.
var.thr	minimum value allowed for the variance of the input variables, since mine can not be computed in case of variance close to 0. Default value is 1e-5.
zeta	integer in [0,1] (?). If NULL (default) it is set to 1-MIC. It can be set to zero for noiseless functions, but the default choice is the most appropriate parametrization for general cases (as stated in Reshef et al). It provides robustness.

Value

a numeric value; association (aggregated maximal information coefficient MIC, see mine)

Examples

x<-seq(-3,3,length.out=200)
y<-sqrt(3^2-x^2)
z<- sin(y-x)
confs<-cbind(x,y,z)
c_association(confs)

stops documentation built on Dec. 12, 2023, 3:02 a.m.