Package 'oottest'

Title: Out-of-Treatment Testing
Description: Implements the out-of-treatment testing from Kuelpmann and Kuzmics (2020) <doi:10.2139/ssrn.3441675> based on the Vuong Test introduced in Vuong (1989) <doi:10.2307/1912557>. Out-of treatment testing allows for a direct, pairwise likelihood comparison of theories, calibrated with pre-existing data.
Authors: Philipp Külpmann [aut, cre] , Panagiota Charalampidou [aut], University of Vienna [cph]
Maintainer: Philipp Külpmann <[email protected]>
License: MIT + file LICENSE
Version: 0.9.0
Built: 2024-11-24 05:25:43 UTC
Source: https://github.com/philippkuelpmann/oottest

Help Index


Data from the three-action games

Description

This is the choice data from Kuelpmann and Kuzmics (2022) for the three-action games, i.e., Hawk-Middle-Dove and Rock-Paper-Scissor. For an explanation of the different treatments and the games, please refer to the paper.

Usage

data_three_action_games

Format

Need to reformat anyway ...

rows

actions

columns

treatments

cells

number of subjects who chose each action on each treatment

...

Source

https://homepage.univie.ac.at/philipp.kuelpmann/hdg.html


Data from the two-action games

Description

This is the choice data from Kuelpmann and Kuzmics (2022) for the two-action games, i.e., Hawk-Dove and Matching Pennies. For an explanation of the different treatments and the games, please refer to the paper.

Usage

data_two_action_games

Format

rows

actions

columns

treatments

cells

number of subjects who chose each action on each treatment

...

Source

https://homepage.univie.ac.at/philipp.kuelpmann/hdg.html


Predictions for the three-action games

Description

These are the prediction of every theory considered in Kuelpmann and Kuzmics (2022) for the three-action games. For a explanation of the different theories, treatments and the games, please refer to the paper.

Usage

predictions_three_action_games

Format

rows

predicted probabilities of actions

columns

treatments

tables

different theories

cells

probability of choosing an action on each treatment depending on the theory

...

Source

https://homepage.univie.ac.at/philipp.kuelpmann/hdg.html


Predictions for the two-action games

Description

These are the prediction of every theory considered in Kuelpmann and Kuzmics (2022) for the two-action games. For a explanation of the different theories, treatments and the games, please refer to the paper.

Usage

predictions_two_action_games

Format

rows

predicted probabilities of actions

columns

treatments

tables

different theories

cells

probability of choosing an action on each treatment depending on the theory

...

Source

https://homepage.univie.ac.at/philipp.kuelpmann/hdg.html


Simple Test Data

Description

This is just very simple test data, mostly used for testing and demonstration purposes.

Usage

simple_test_data

Format

A matrix with 3 rows and 2 columns

rows

actions

columns

treatments

cells

number of subjects who chose each action on each treatment

...


Getting a vuong matrix

Description

Getting a vuong matrix

Usage

vuong_matrix(data, theories)

Arguments

data

matrix rows: choices, columns: treatments

theories

list of matrices each in the same format as above

Value

Table of z-scores for all comparisons

Examples

vuong_matrix(data_two_action_games, predictions_two_action_games)

Implementation of the Vuong test for our setting

Description

Implementation of the Vuong test for our setting

Usage

vuong_statistic(data, pred_i, pred_j)

Arguments

data

Data of the experiment (matrix of ints/data.frame): columns are treatments, rows are actions, values are number of subjects who chose each action on each treatment

pred_i

Model prediction for a theory (matrix of doubles/data.frame): rows are prediction of playing action 1,2,3..., columns are treatments

pred_j

Model prediction for a theory (matrix of doubles/data.frame): rows are prediction of playing action 1,2,3..., columns are treatments

Value

The z score of testing theory I against theory J, given the data

Examples

vuong_statistic(
  data_two_action_games,
  predictions_two_action_games[, , 1],
  predictions_two_action_games[, , 2]
)