CheckUnsystematic {beezdemand}

Systematic Purchase Task Data Checker

Description

Applies Stein, Koffarnus, Snider, Quisenberry, & Bickel's (2015) criteria for identification of nonsystematic purchase task data.

Usage

CheckUnsystematic(dat, deltaq = 0.025, bounce = 0.1, reversals = 0, ncons0 = 2)

Arguments

dat

Dataframe in long form. Colums are id, x, y.

deltaq

Numeric vector of length equal to one. The criterion by which the relative change in quantity purchased will be compared. Relative changes in quantity purchased below this criterion will be flagged. Default value is 0.025.

bounce

Numeric vector of length equal to one. The criterion by which the number of price-to-price increases in consumption that exceed 25% of initial consumption at the lowest price, expressed relative to the total number of price increments, will be compared. The relative number of price-to-price increases above this criterion will be flagged. Default value is 0.10.

reversals

Numeric vector of length equal to one. The criterion by which the number of reversals from number of consecutive (see ncons0) 0s will be compared. Number of reversals above this criterion will be flagged. Default value is 0.

ncons0

Numer of consecutive 0s prior to a positive value is used to flag for a reversal. Value can be either 1 (relatively more conservative) or 2 (default; as recommended by Stein et al., (2015).

Details

This function applies the 3 criteria proposed by Stein et al., (2015) for identification of nonsystematic purchase task data. The three criteria include trend (deltaq), bounce, and reversals from 0. Also reports number of positive consumption values.

Value

Dataframe

Author(s)

Brent Kaplan <[e-mail]>

Examples

## Using all default values
CheckUnsystematic(apt, deltaq = 0.025, bounce = 0.10, reversals = 0, ncons0 = 2)
## Specifying just 1 zero to flag as reversal
CheckUnsystematic(apt, deltaq = 0.025, bounce = 0.10, reversals = 0, ncons0 = 1)