weight.differenceToIdeal: Calculates attribute weights using the 'objective approach'

In avilesd/productConfig: Tools to analyze behavioral patterns from Product Configurators

Description

This function first normalizes a list of matrices and then calculates the decision weight for each attribute, using the 'objective approach' as given by [1] and [2]. The objective approach, in this case, uses only data gathered from the decision matrix and it does not need a 'subjective' preference matrix from the decision maker. The sum of a weight vector should always equal 1.

Usage

weight.differenceToIdeal(dataset, userid = NULL, attr = NULL,
  rounds = "all", cost_ids = NULL)

Arguments

dataset	data.frame with the user generated data from a product configurator. See decisionMatrix for specifications of the dataset.
userid	a vector of integers that gives the information of which users the matrix should be calculated. Vectorised.
attr	attributes IDs, vector of integer numbers corresponding to the attributes you desire to use; attr are assumed to be 1-indexed. This function will calculate with all attributes and do the subsetting a posteriori. If you want to get the weights for only two attributes you will have to first use decisionMatrix and then pass it on to normalize.altMethod and differenceToIdeal.
rounds	integer vector or text option. Which steps of the configuration process should be taken into account? Defaults are "all" in order to have more data to calculate with. If "first" or "last" are entered there will be only one rounds to gather data from, consequently all attribtues will have the same weight.
cost_ids	argument used to convert selected cost attributes into benefit attributes. Integer vector. This functions uses an alternative normalizing method normalize.altMethod that does not produce negative values.

Details

The result is a list of vectors, each vector with the same length as the number of columns of the input matrices, i.e. each column gets a weight.

This function rewards attributes which values do not change much throughtout the decision matrix, even if the value is the lowest value for that attribute. For an opposite implicit effect see weight.entropy

cost_ids As in the other functions, if you enter a cost_ids that is not in your entered attributes, the functions will calculate the output with all attributes in your data, including the cost(s) attributes and only after the calculations does the function subset the result according to the attr. When the attributes and cost_ids differ, the function allows the calculation but it will throw a warning.

Value

a list of weight vector(s)

References

[1]Ma, J., Fan, Z. P., & Huang, L. H. (1999). A subjective and objective integrated approach to determine attribute weights. European journal of operational research, 112(2), 397-404. [2] Fan, Z. P. (1996). Complicated multiple attribute decision making: theory and applications (Doctoral dissertation, Ph. D. Dissertation, North-eastern University, Shenyang, PRC).

Examples

# Not runnable yet
weight.differenceToIdeal(myData, 15:22)
weight.differenceToIdeal(laptop_data, 40:45, attr= c(1, 3, 4), cost_ids = 4)
savedWeights <- weight.differenceToIdeal(myData, c(6, 15, 18, 20, 26), attr = 1:4, cost_ids = 4, rounds=1:8)
lapply(savedWeights, sum) # Should return 1 for any output of this function

avilesd/productConfig documentation built on May 11, 2019, 4:08 p.m.