The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

oottest: Out-of-Treatment Testing in R

R-CMD-check Codecov test coverage

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.

Installation

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

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

Example

Input data should be structured in the following way:

Prediction data should be structured in the following way:

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)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.