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:
library(simDAG)
dag <- empty_dag() +
node("age", type="rnorm", mean=50, sd=4) +
node("sex", type="rbernoulli", p=0.5) +
node("bmi", type="gaussian", formula= ~ 12 + age*1.1 + sex*0.4, error=2) +
node("death", type="binomial", formula= ~ -15 + age*0.1 + bmi*0.3)
set.seed(42)
sim_dat <- sim_from_dag(dag, n_sim=100000)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)
#>
#> Call:
#> glm(formula = bmi ~ age + sex, family = "gaussian", data = sim_dat)
#>
#> Deviance Residuals:
#> Min 1Q Median 3Q Max
#> -8.4802 -1.3555 0.0005 1.3423 8.6826
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 11.89194 0.07954 149.51 <2e-16 ***
#> age 1.10220 0.00158 697.41 <2e-16 ***
#> sexTRUE 0.40447 0.01268 31.89 <2e-16 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> (Dispersion parameter for gaussian family taken to be 4.022026)
#>
#> Null deviance: 2361465 on 99999 degrees of freedom
#> Residual deviance: 402190 on 99997 degrees of freedom
#> AIC: 422971
#>
#> Number of Fisher Scoring iterations: 2This seems about right. Now we look at death:
mod_death <- glm(death ~ age + bmi, data=sim_dat, family="binomial")
summary(mod_death)
#>
#> Call:
#> glm(formula = death ~ age + bmi, family = "binomial", data = sim_dat)
#>
#> Deviance Residuals:
#> Min 1Q Median 3Q Max
#> -4.4111 0.0035 0.0066 0.0126 0.2883
#>
#> Coefficients:
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) -14.6833 3.5538 -4.132 3.6e-05 ***
#> age 0.2607 0.1698 1.535 0.125
#> bmi 0.1842 0.1402 1.314 0.189
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> (Dispersion parameter for binomial family taken to be 1)
#>
#> Null deviance: 258.65 on 99999 degrees of freedom
#> Residual deviance: 214.03 on 99997 degrees of freedom
#> AIC: 220.03
#>
#> Number of Fisher Scoring iterations: 13The 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.
© 2024 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/.