Description Usage Arguments Details Value References Examples

View source: R/e_bayescoring.R

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.

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

`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. |

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).

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.

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

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