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
#> # A tibble: 12 x 8
#> fun rate n replications summary_fun proc conf.level coverage
#> <chr> <dbl> <int> <int> <chr> <chr> <dbl> <dbl>
#> 1 rexp 10 10 1 mean t.test 0.8 0.754
#> 2 rexp 10 10 1 mean t.test 0.9 0.855
#> 3 rexp 10 10 1 mean t.test 0.95 0.905
#> 4 rexp 10 50 1 mean t.test 0.8 0.808
#> 5 rexp 10 50 1 mean t.test 0.9 0.905
#> 6 rexp 10 50 1 mean t.test 0.95 0.945
#> 7 rexp 10 100 1 mean t.test 0.8 0.792
#> 8 rexp 10 100 1 mean t.test 0.9 0.895
#> 9 rexp 10 100 1 mean t.test 0.95 0.936
#> 10 rexp 10 1000 1 mean t.test 0.8 0.796
#> 11 rexp 10 1000 1 mean t.test 0.9 0.897
#> 12 rexp 10 1000 1 mean t.test 0.95 0.953
#> Number of data generating functions: 4
#> Number of analyzing procedures: 3
#> Number of replications: 1000
#> Estimated replications per hour: 754228
#> Start of the simulation: 2019-02-01 23:25:01
#> End of the simulation: 2019-02-01 23:25:05Installation
You can install simTool from github with:
install.packages("simTool")
# install.packages("devtools")
devtools::install_github("MarselScheer/simTool")Or from CRAN with:
install.packages("simTool")
