grouped_cpcc_derivative: Calculates the CPCC derivative for the given input.

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

Description

Calculates the CPCC derivative for the given input. Should not be used by the user.

Usage

grouped_cpcc_derivative(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. combined_indices, the indices that are grouped. 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 derivative

The CPCC derivative of this clustering with the given weights. A vector of length n-1.

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 = TRUE, combined_indices = as.list(1:ncol(mtcars)))
x = rep(1/11,10)
grouped_cpcc_derivative(x,L)

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