In naecker-lab/revealedPrefsExtra: revealedPrefsExtra

library(revealedPrefs)
library(revealedPrefsExtra)
library(dplyr)

set.seed(1000)

The revealedPrefsExtra package allows users to quickly check for revealed preference axiom violations in consumer choice data. The package relies heavily on the revealedPrefs package for the work under the hood, but provides an API that is intended for easy use with dplyr-based workflows.

Examples

We need some hypothetical data to work with.

experiment_data <- tibble(x1 = c(1:4),
                          x2 = c(5:8),
                          p1 = c(5:8),
                          p2 = c(5:8))

Then we can check whether the given data pass any of the revealed preference axioms. Note the functions take the data as the first argument, then the consumption amounts, then the prices, all unquoted.

checkWarpExtra(experiment_data, x1, x2, p1, p2)

checkGarpExtra(experiment_data, x1, x2, p1, p2)

checkSarpExtra(experiment_data, x1, x2, p1, p2)

If you forget to specify columns, or give a different number of consumption and price variables, you will get an error.

# checkWarpExtra(experiment_data)
# checkWarpExtra(experiment_data, x1, x2, p1)

Note that the functions return a list that includes a logical value for whether the test was passed, and a string indicatig the type of test run. Note that the logical FALSE if there is no violation, and TRUE if there is a violation.

str(checkWarpExtra(experiment_data, x1, x2, p1, p2))

The calculate_CCEI function allows for calculated the Critical Cost Efficiency Index (also known as the Afriat index).

calculate_CCEI(experiment_data, step = 0.01, x1, x2, p1, p2)

Integration with pipe-based workflow

The functions above are compatible with a pipe-based workflow, which allows for easy grouping by subject- or agent-level responses.

experiment_data %>%
  mutate(id = c(1, 1, 2, 2)) %>%
  group_by(id) %>%
  summarize(
    fail_GARP = checkGarpExtra(., x1, x2, p1, p2)$violation,
    CCEI      = calculate_CCEI(., step = 0.01, x1, x2, p1, p2)
  )