In MarselScheer/simTool: Conduct Simulation Studies with a Minimal Amount of Source Code

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simTool

An R-Package that facilitates simulation studies. It disengages the researcher from administrative source code.

The simTool package is designed for statistical simulations that have two components. One component generates the data and the other one analyzes the data. The main aims of the simTool package are the reduction of the administrative source code (mainly loops and management code for the results) and a simple applicability of the package that allows the user to quickly learn how to work with the simTool package. Parallel computing is also supported. Finally, convenient functions are provided to summarize the simulation results.

Example

This small simulation (using 4 cores) illustrates how the confidence interval based on the t-distribution performs on exponential distributed random variables. The following lines generate exponential distributed random variables of size 10, 50, 100, and 1000. Afterwards the t.test using confidence levels 0.8, 0.9, 0.95 are applied. This is repeated 1000 times to estimate the coverage:

library(simTool)
dg <- expand_tibble(fun = "rexp", rate = 10, n = c(10L, 50L, 100L, 1000L))
pg <- expand_tibble(proc = "t.test", conf.level = c(0.8, 0.9, 0.95))
et <- eval_tibbles(dg, pg, 
  ncpus = 4,
  replications = 10^3,
  post_analyze = function(ttest) tibble::tibble(
    coverage = ttest$conf.int[1] <= 1 / 10 && 1 / 10 <= ttest$conf.int[2]),
  summary_fun = list(mean = mean)
)
et

Installation

You can install simTool from github with:

remotes::install_github("MarselScheer/simTool")

Or from CRAN with:

install.packages("simTool")

Test suite

With tinytest you can rerun the test for simTool by calling

# install.packages("tinytest")
tinytest::test_package("simTool")

MarselScheer/simTool documentation built on Sept. 8, 2021, 5:55 a.m.