ae_mnl: Analytical Estimation of a Multinomial Logit Model for...
In bwsTools: Tools for Case 1 Best-Worst Scaling (MaxDiff) Designs

Description Usage Arguments Value References Examples

This uses Equations 7, 10, 12, 13, and 18 from Lipovetsky & Conklin (2014) to take vectors of total times shown to participants, total times selected as best, and total times selected as worst. It uses their closed-form solution to calculate utility coefficients—as well as their standard errors and confidence intervals—and choice probabilities.

1	ae_mnl(data, totals, bests, worsts, z = 1.96)

`data`	A data.frame where each row represents an item, and three columns represent total times shown to participants, total times selected as bests, and total time selected as worsts.
`totals`	A string that is the name of the column for totals in the data.
`bests`	A string that is the name of the column for bests in the data.
`worsts`	A string that is the name of the column for worsts in the data.
`z`	A z-value to calculate the confidence intervals. Defaults to 1.96, a 95% CI.

A data.frame containing the utility coefficients (with standard error and confidence intervals) and choice probabilities for each item (row) in the data.

Lipovetsky, S., & Conklin, M. (2014). Best-worst scaling in analytical closed-form solution. The Journal of Choice Modelling, 10, 60-68. doi: 10.1016/j.jocm.2014.02.001

# Replicate Table 6 from Lipovetsky & Conklin (2014)
d <- data.frame(
  totals = c(7145, 7144, 7144, 7144, 7145, 7145, 7144, 7146, 8166, 
             7145, 7144, 7144, 7145, 7144, 7145, 7144, 7146),
  bests = c(1733, 968, 5218, 2704, 2307, 692, 1816, 689, 2483, 1422, 
            362, 2589, 4158, 825, 829, 859, 966),
  worsts = c(1324, 2139, 113, 1010, 772, 3986, 1438, 2397, 1041, 1538, 
  4597, 966, 305, 2875, 2256, 2259, 1604)
)
results <- ae_mnl(d, "totals", "bests", "worsts")
(d <- cbind(d, results))