ae_mnl {bwsTools} R Documentation

## Analytical Estimation of a Multinomial Logit Model for Best-Worst Scaling

### Description

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 coefficientsâ€”as well as their standard errors and confidence intervalsâ€”and choice probabilities.

### Usage

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


### Arguments

 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.

### Value

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

### References

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

### Examples

# 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))



[Package bwsTools version 1.2.0 Index]