e_bayescoring {bwsTools}

Empirical Bayes Method to Calculate Individual Best-Worst Scores

Description

Individual utilities from empirical bayes estimations. Instead of doing the computationally-expensive hierarchical Bayesian multinomial logistic regression model, Lipovetsky & Conklin (2015) show an empirical Bayes way to calculate this analytically. This function calculates choice probabilities shown using Equation 10 in Lipovetsky & Conklin (2015) and transforms them to be on a linear regression coefficient scale. Default values for the E and alpha parameters are those performing best in their empirical example.

Usage

e_bayescoring(data, id, block, item, choice, E = 0.1, alpha = 1, wide = FALSE)

Arguments

data

A data.frame of the type described in details.

id

A string of the name of the id column.

block

A string of the name of the block column.

item

A string of the name of the item column.

choice

A string of the name of the choice column.

E

Value of precision shown in Equation 8 of Lipovetsky & Conklin (2015). If the naive estimate for a choice probability is 0, it is replaced with E; If the naive estimate for the choice probability is 1, i is replaced with 1 - E.

alpha

The mixing parameter shown in Equation 10 of Lipovetsky & Conklin (2015). This shapes how much the naive individual estimate and how much of the aggregate estimate influences the resulting estimate.

wide

Logical of whether or not one wants the data returned in long (each row is an item-respondent combination and all best-worst scores are in the same column) format (FALSE) or in wide format (where each row is a respondent, and the best-worst scores for the items are in their own columns). See the 'indiv' data as an example.

Details

This function requires data to be in a specified format. Each row must represent a respondent-block-label combination. That is, it indicates the person, the block (or trial), the item that was judged, and a column indicating whether it was chosen as best (+1), worst (-1), or wasn't selected as either (0).

Value

A data.frame containing the id and item columns as well as a "b_ebayes" column that indicates the utility coefficient. If 'wide = TRUE', then each item has its own column and the coefficient is filled-in those columns.

References

Lipovetsky, S., & Conklin, M. (2015). MaxDiff priority estimations with and without HB-MNL. Advances in Adaptive Data Analysis, 7(1). doi: 10.1142/S1793536915500028

Examples

data(indiv)
head(indiv)
e_bayescoring(indiv, "id", "block", "label", "value")