This R package can be used to generate artificial data conditionally on pre-specified (simulated or user-defined) relationships between the variables and/or observations. Each observation is drawn from a multivariate Normal distribution where the mean vector and covariance matrix reflect the desired relationships. Outputs can be used to evaluate the performances of variable selection, graphical modelling, or clustering approaches by comparing the true and estimated structures.

The released version of the package can be installed from CRAN with:

install.packages("fake")

The development version can be installed from GitHub:

remotes::install_github("barbarabodinier/fake")

library(fake)

set.seed(1)
simul <- SimulateRegression(n = 100, pk = 20)
head(simul$xdata)
head(simul$ydata)

set.seed(1)
simul <- SimulateRegression(n = 100, pk = 20, family = "binomial")
head(simul$ydata)

set.seed(1)
simul <- SimulateStructural(n = 100, pk = c(3, 2, 3))
head(simul$data)

set.seed(1)
simul <- SimulateGraphical(n = 100, pk = 20)
head(simul$data)

set.seed(1)
simul <- SimulateClustering(n = c(10, 10, 10), pk = 20)
head(simul$data)

The true model structure is returned in the output of any of the main functions in:

simul$theta

The functions print(), summary() and plot() can be used on the outputs from the main functions.

• Barbara Bodinier, Sarah Filippi, Therese Haugdahl Nost, Julien Chiquet and Marc Chadeau-Hyam. Automated calibration for stability selection in penalised regression and graphical models: a multi-OMICs network application exploring the molecular response to tobacco smoking. (2021) arXiv. link

• R scripts to reproduce the simulation study (Bodinier et al. 2021) conducted using the functions in fake link

• R package sharp for stability selection and consensus clustering link