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

You can install the development version of oottest fromGitHub 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 eachtreatment

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 treatmentdepending on the theory

Here is a basic example on how you can use the vuong_statistic usingpredictions 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 fromtwo 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)