Author: Robin Denz
simDAG is an R-Package which can be used to generate
data from a known directed acyclic graph (DAG) with associated
information on distributions and causal coefficients. The root nodes are
sampled first and each subsequent child node is generated according to a
regression model (linear, logistic, multinomial, cox, …) or other
function. The result is a dataset that has the same causal structure as
the specified DAG and by expectation the same distributions and
coefficients as initially specified. It also implements a comprehensive
framework for conducting discrete-time simulations in a similar
fashion.
A stable version of this package can be installed from CRAN:
install.packages("simDAG")and the developmental version may be installed from github using the
remotes R-Package:
library(remotes)
remotes::install_github("RobinDenz1/simDAG")If you encounter any bugs or have any specific feature requests, please file an Issue.
Suppose we want to generate data with the following causal structure:
where age is normally distributed with a mean of 50 and
a standard deviation of 4 and sex is bernoulli distributed
with p = 0.5 (equal number of men and women). Both of these
“root nodes” (meaning they have no parents - no arrows pointing into
them) have a direct causal effect on the bmi. The causal
coefficients are 1.1 and 0.4 respectively, with an intercept of 12 and a
sigma standard deviation of 2. death is modeled as a
bernoulli variable, which is caused by both age and
bmi with causal coefficients of 0.1 and 0.3 respectively.
As intercept we use -15.
The following code can be used to generate 10000 samples from these specifications:
dag <- empty_dag() +
node("age", type="rnorm", mean=50, sd=4) +
node("sex", type="rbernoulli", p=0.5) +
node("bmi", type="gaussian", parents=c("age", "sex"), betas=c(1.1, 0.4),
intercept=12, error=2) +
node("death", type="binomial", parents=c("age", "bmi"), betas=c(0.1, 0.3),
intercept=-15)
sim_dat <- sim_from_dag(dag, n_sim=10000)By fitting appropriate regression models, we can check if the data
really does approximately conform to our specifications. First, lets
look at the bmi:
mod_bmi <- glm(bmi ~ age + sex, data=sim_dat, family="gaussian")
summary(mod_bmi)This seems about right. Now we look at death:
mod_death <- glm(death ~ age + bmi, data=sim_dat, family="binomial")
summary(mod_death)The estimated coefficients are also very close to the ones we specified. More examples can be found in the documentation and the vignette.
Use citation("simDAG") to get the relevant citation
information.
© 2023 Robin Denz
The contents of this repository are distributed under the GNU General Public License. You can find the full text of this License in this github repository. Alternatively, see http://www.gnu.org/licenses/.