walkscoring: Walkscoring Method to Calculate Individual Best-Worst Scores
In bwsTools: Tools for Case 1 Best-Worst Scaling (MaxDiff) Designs

Description Usage Arguments Details Value References Examples

Calculate best-worst scores for each respondent-item combination. This uses the walkscoring method described in White (2019).

1	walkscoring(data, id, block, item, choice, walks = 10000, 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.
`walks`	Integer indicating how many random walks to simulate.
`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 "walk" column that indicates the best worst score. If 'wide = TRUE', then each item has its own column and the walkscore is filled-in those columns.

White, M. H., II. (2019). bwsTools: An R package for case 1 best-worst scaling. Retrieved from https://osf.io/xftvq/

## Not run: 
data(indiv)
head(indiv)
# use more than 100 walks; only using 100 here for speed
walkscoring(indiv, "id", "block", "label", "value", 100)

## End(Not run)