Learn R Programming

desire (version 1.0.7)

meanDI: Weighted Mean Desirability Index

Description

Computes the weighted mean of a number of desirability functions.

Usage

meanDI(f, ..., weights = 1)

Arguments

f,...
desirability functions.
weights
vector of weights. Weights do not need to sum to one.

Value

returns a function object of the Weighted Mean Desirability Index.

Details

The Desirability Index was introduced by Harrington (1965), and the concept was extended by Derringer and Suich (1980). It is a means for multicriteria (quality) optimization in industrial quality management. All desirability functions of the quality criteria are combined into a univariate global quality criterion which has to be optimized. The Weighted Mean Desirability Index is related to the concept of utility functions.

The function can be used for Harrington as well as Derringer and Suich desirability functions.

References

J. Harrington (1965): The desirability function. Industrial Quality Control, 21: 494-498. G.C. Derringer, D. Suich (1980): Simultaneous optimization of several response variables. Journal of Quality Technology 12 (4): 214-219.

See Also

harrington1 and harrington2 for Harrington type desirability functions; derringerSuich for desirability functions of Derringer and Suich; geometricDI,minimumDI for other types of Desirability indices.

Examples

Run this code
h1 <- harrington1(-2, .9, 2, .1)
h2 <- harrington2(0, 2, 2)

di <- meanDI(h1, h2,weights=c(0.2,0.8))
di(c(0, 1))

## Desirability Index of vector input:
h <- harrington2(3,7,1)
g <- harrington1(-2, .1, 2, .9) 

d <- meanDI(h, g,weights=c(0.3,0.7))

m <- matrix(c(seq(2, 8, 0.1), seq(-2, 4, 0.1)), ncol=2, byrow=FALSE)
apply(m, 1, d) 







Run the code above in your browser using DataLab