oottest implements the out-of-treatment testing from Kuelpmann and Kuzmics (2020). Out-of treatment testing allows for a direct, pairwise likelihood comparison of theories, calibrated with pre-existing data.

You can install the development version of oottest from GitHub with:

# install.packages("devtools")
devtools::install_github("PhilippKuelpmann/oottest")

Input data should be structured in the following way:

• columns represent different treatments

• rows represent actions

• cells record the number of subjects who chose each action on each treatment

Prediction data should be structured in the following way:

• columns represent different treatments

• rows represent the predicted probability of each action

• the different tables represent the different theories

• cells record the probability of choosing an action on each treatment depending on the theory

Here is a basic example on how you can use the vuong_statistic using predictions from two theories:

library(oottest)
data_experiment <- c(1,2,3)
prediction_theory_1 <- c(1/3,1/3,1/3)
prediction_theory_2 <- c(1/4,1/4,1/2)
vuong_statistic(data_experiment, pred_I = prediction_theory_1, pred_J = prediction_theory_2)

Here is a basic example how to compare three theories, using data from two treatments:

library(oottest)
treatment_1 <- c(1,2,3)
treatment_2 <- c(3,2,1)
data_experiment <- data.frame(treatment_1, treatment_2)
theory_1 <- matrix(c(1/3,1/3,1/3, 1/3, 1/3, 1/3), nrow = 3, ncol=2)
theory_2 <- matrix(c(1/4,1/4,1/2,1/2,1/4,1/4), nrow = 3, ncol=2)
theory_3 <- matrix(c(1/3,1/3,1/3, 1/4,1/4,1/2), nrow = 3, ncol=2)
theories <- array(c(theory_1,theory_2,theory_3), dim=c(3,2,3))
vuong_matrix(data_experiment, theories)