grouped_cpcc: Calculates the CPCC for the given input with grouped...

In Jeroentjeh/opthierarch: Hiearchical clustering with optimised Gower's metric's weights

Description

Calculates the CPCC for the given input with grouped variables. Should not be used by the user.

Usage

grouped_cpcc(x, var_list)

Arguments

x	The weight vector of length n-1, where n is the number of variables, for the first n-1 . See the reference in find_grouped_weights for the explanation.
var_list	A list of variables containing the following: data, the relevant data. method, the clustering method. bounds, the bounds on the weights. MMM, Minimal memory mode: see find_grouped_weights for details. fk, which only exists if MMM is TRUE: see find_grouped_weights for details.

Value

CPCC	The cophenetic correlation coefficient of this clustering with the given weights.

Author(s)

Jeroen van den Hoven

References

Clustering with optimised weights for Gower's metric: Using hierarchical clustering and Quasi-Newton methods to maximise the cophenetic correlation coefficient, Jeroen van den Hoven.

Examples

data(mtcars)
L = list(data = mtcars, method = "average",
    bounds = c(0,1), MMM = FALSE, combined_indices = as.list(1:ncol(mtcars)))
x = rep(1/11,10)
grouped_cpcc(x,L)

Jeroentjeh/opthierarch documentation built on May 26, 2019, 7:28 a.m.