Description
Mock Data Generation.
Description
Generation of synthetic data from a real dataset using the combination of rank normal inverse transformation with the calculation of correlation matrix <doi:10.1055/a-2048-7692>. Completely artificial data may be generated through the use of Generalized Lambda Distribution and Generalized Poisson Distribution <doi:10.1201/9781420038040>. Quantitative, binary, ordinal categorical, and survival data may be simulated. Functionalities are offered to generate synthetic data sets according to user's needs.
README.md
modgo
modgo is an R package Mock Data Generation (modgo) that may be used for simulating data from existing study data for continuous, ordinal categorical, and dichotomous variables[reference].
Installation
To install modgo from GitHub, run:
library("devtools")
install_github("https://github.com/GeorgeKoliopanos/modgo")
Usage
To see how to use modgo see ?modgo() in R . Below, we present a simple example on how to run.
library(modgo)
data("Cleveland",package="modgo")
test_modgo <- modgo(data = Cleveland,
bin_variables = c("CAD","HighFastBloodSugar","Sex","ExInducedAngina"),
categ_variables =c("Chestpaintype"))