The simstudy package is a collection of functions that allow users to
generate simulated data sets in order to explore modeling techniques or
better understand data generating processes. The user defines the
distributions of individual variables, specifies relationships between
covariates and outcomes, and generates data based on these
specifications. The final data sets can represent randomized control
trials, repeated measure designs, cluster randomized trials, or
naturally observed data processes. Other complexities that can be added
include survival data, correlated data, factorial study designs, step
wedge designs, and missing data processes.
Simulation using simstudy has two fundamental steps. The user (1)
defines the data elements of a data set and (2) generates the
data based on these definitions. Additional functionality exists to
simulate observed or randomized treatment assignment/exposures, to
create longitudinal/panel data, to create
multi-level/hierarchical data, to create datasets with correlated
variables based on a specified covariance structure, to merge
datasets, to create data sets with missing data, and to create
non-linear relationships with underlying spline curves.
The overarching philosophy of simstudy is to create data generating
processes that mimic the typical models used to fit those types of data.
So, the parameterization of some of the data generating processes may
not follow the standard parameterizations for the specific
distributions. For example, in simstudy gamma-distributed data are
generated based on the specification of a mean μ (or log(μ)) and a
dispersion (d), rather than shape α and rate β parameters that more
typically characterize the gamma distribution. When we estimate the
parameters, we are modeling μ (or some function of μ), so we should
explicitly recover the simstudy parameters used to generate the model,
thus illuminating the relationship between the underlying data
generating processes and the models.
You can install the released version of simstudy from CRAN with:
install.packages("simstudy")And the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("kgoldfeld/simstudy")Here is some simple sample code, much more in the vignettes:
library(simstudy)
set.seed(1965)
def <- defData(varname="x", formula = 10, variance = 2, dist = "normal")
def <- defData(def, varname="y", formula = "3 + 0.5 * x", variance = 1, dist = "normal")
dd <- genData(250, def)
dd <- trtAssign(dd, nTrt = 4, grpName = "grp", balanced = TRUE)
dd
#> id x y grp
#> 1: 1 11.191960 8.949389 4
#> 2: 2 10.418375 7.372060 2
#> 3: 3 8.512109 6.925844 4
#> 4: 4 11.361632 9.850340 4
#> 5: 5 9.928811 6.515463 2
#> ---
#> 246: 246 8.220609 7.898416 4
#> 247: 247 8.531483 8.681783 4
#> 248: 248 10.507370 8.552350 4
#> 249: 249 8.621339 6.652300 1
#> 250: 250 9.508164 7.083845 4If you find a bug or need help, please file an issue with a reprex on Github. We are happy to accept contributions to simstudy. More information on how to propose changes or fix bugs can be found here.
Please note that the simstudy project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.