simcases is an R package to simplify the conduct of simulation studies across multiple cases/scenarios.
To install the latest release version from CRAN
install.packages("simcases")To install the latest development version from GitHub
# install.packages("remotes")
remotes::install_github("audrey-b/simcases")First, define the likelihood, constants, parameters and other characteristics of the models to use for the simulations.
library(simcases)
#> Registered S3 method overwritten by 'rjags':
#> method from
#> as.mcmc.list.mcarray mcmcr
lik = "for(i in 1:10){
a[i] ~ dnorm(mu, 1/sigma^2)}"
const = list(mu=0)
sigma1 = list(sigma=1)
sigma2 = list(sigma=2)
all = ".*"
a = "a"Specify the models to use. The first row is a header and the following rows each describe a model.
models_sims = "code constants parameters monitor
lik const sigma1 a
lik const sigma2 a
lik const sigma1 all
lik const sigma2 all"Simulate data. The results are written to files.
set.seed(10)
smc_simulate(models = models_sims,
nsims = 3,
exists = NA,
ask = FALSE)
#> [[1]]
#> [1] TRUE
#>
#> [[2]]
#> [1] TRUE
#>
#> [[3]]
#> [1] TRUE
#>
#> [[4]]
#> [1] TRUEAnalyse data according to specific cases (scenarios).
prior <- "sigma ~ dunif(0, 6)"
sigma <- "sigma"
models_analysis <- "code code.add monitor
lik prior sigma
lik prior sigma"
cases <- "sims analyse
1 1
2 1
3 2
4 2"
smc_analyse(models = models_analysis,
cases = cases,
mode = simanalyse::sma_set_mode("quick"))
#> module dic loaded
#> Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 10
#> Unobserved stochastic nodes: 1
#> Total graph size: 18
#>
#> Initializing model
#>
#> Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 10
#> Unobserved stochastic nodes: 1
#> Total graph size: 18
#>
#> Initializing model
#>
#> Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 10
#> Unobserved stochastic nodes: 1
#> Total graph size: 18
#>
#> Initializing model
#> v data0000001.rds [00:00:00.023]
#> v data0000002.rds [00:00:00.021]
#> v data0000003.rds [00:00:00.029]
#> Success: 3
#> Failure: 0
#> Remaining: 0
#>
#> Module dic unloaded
#> module dic loaded
#> Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 10
#> Unobserved stochastic nodes: 1
#> Total graph size: 18
#>
#> Initializing model
#>
#> Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 10
#> Unobserved stochastic nodes: 1
#> Total graph size: 18
#>
#> Initializing model
#>
#> Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 10
#> Unobserved stochastic nodes: 1
#> Total graph size: 18
#>
#> Initializing model
#> v data0000001.rds [00:00:00.013]
#> v data0000002.rds [00:00:00.012]
#> v data0000003.rds [00:00:00.019]
#> Success: 3
#> Failure: 0
#> Remaining: 0
#>
#> Module dic unloaded
#> module dic loaded
#> Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 10
#> Unobserved stochastic nodes: 1
#> Total graph size: 18
#>
#> Initializing model
#>
#> Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 10
#> Unobserved stochastic nodes: 1
#> Total graph size: 18
#>
#> Initializing model
#>
#> Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 10
#> Unobserved stochastic nodes: 1
#> Total graph size: 18
#>
#> Initializing model
#> v data0000001.rds [00:00:00.028]
#> v data0000002.rds [00:00:00.013]
#> v data0000003.rds [00:00:00.012]
#> Success: 3
#> Failure: 0
#> Remaining: 0
#>
#> Module dic unloaded
#> module dic loaded
#> Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 10
#> Unobserved stochastic nodes: 1
#> Total graph size: 18
#>
#> Initializing model
#>
#> Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 10
#> Unobserved stochastic nodes: 1
#> Total graph size: 18
#>
#> Initializing model
#>
#> Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 10
#> Unobserved stochastic nodes: 1
#> Total graph size: 18
#>
#> Initializing model
#> v data0000001.rds [00:00:00.022]
#> v data0000002.rds [00:00:00.014]
#> v data0000003.rds [00:00:00.014]
#> Success: 3
#> Failure: 0
#> Remaining: 0
#>
#> Module dic unloaded
#> list()Evaluate the performance of the models across the cases (scenarios)
smc_evaluate(cases, monitor="sigma")
#> list()Have a look at the files created.
files <- list.files(getwd(), recursive=TRUE, all.files=TRUE)
print(files)
#> [1] "sims1/.sims.rds"
#> [2] "sims1/analysis0000001/.seeds.rds"
#> [3] "sims1/analysis0000001/performance/performance.rds"
#> [4] "sims1/analysis0000001/results/results0000001.rds"
#> [5] "sims1/analysis0000001/results/results0000002.rds"
#> [6] "sims1/analysis0000001/results/results0000003.rds"
#> [7] "sims1/data0000001.rds"
#> [8] "sims1/data0000002.rds"
#> [9] "sims1/data0000003.rds"
#> [10] "sims2/.sims.rds"
#> [11] "sims2/analysis0000001/.seeds.rds"
#> [12] "sims2/analysis0000001/performance/performance.rds"
#> [13] "sims2/analysis0000001/results/results0000001.rds"
#> [14] "sims2/analysis0000001/results/results0000002.rds"
#> [15] "sims2/analysis0000001/results/results0000003.rds"
#> [16] "sims2/data0000001.rds"
#> [17] "sims2/data0000002.rds"
#> [18] "sims2/data0000003.rds"
#> [19] "sims3/.sims.rds"
#> [20] "sims3/analysis0000002/.seeds.rds"
#> [21] "sims3/analysis0000002/performance/performance.rds"
#> [22] "sims3/analysis0000002/results/results0000001.rds"
#> [23] "sims3/analysis0000002/results/results0000002.rds"
#> [24] "sims3/analysis0000002/results/results0000003.rds"
#> [25] "sims3/data0000001.rds"
#> [26] "sims3/data0000002.rds"
#> [27] "sims3/data0000003.rds"
#> [28] "sims4/.sims.rds"
#> [29] "sims4/analysis0000002/.seeds.rds"
#> [30] "sims4/analysis0000002/performance/performance.rds"
#> [31] "sims4/analysis0000002/results/results0000001.rds"
#> [32] "sims4/analysis0000002/results/results0000002.rds"
#> [33] "sims4/analysis0000002/results/results0000003.rds"
#> [34] "sims4/data0000001.rds"
#> [35] "sims4/data0000002.rds"
#> [36] "sims4/data0000003.rds"Load one file.
readRDS(file.path(getwd(), files[3]))
#> term bias mse cpQuantile
#> 1 sigma -0.06750882 0.02581112 0.6666667When a large number of models is used, it can be more convenient to specify models as data frames to facilitate query and manipulation. The example above may be reproduced as follows.
models <- tibble::tribble(
~parameters, ~monitor,
"sigma1", "a",
"sigma2", "all"
)
models <- tidyr::expand(models, parameters, monitor)
models$code <- "lik"
models$constants <- "const"
models
#> # A tibble: 4 x 4
#> parameters monitor code constants
#> <chr> <chr> <chr> <chr>
#> 1 sigma1 a lik const
#> 2 sigma1 all lik const
#> 3 sigma2 a lik const
#> 4 sigma2 all lik const
set.seed(10)
smc_simulate(models = models,
nsims = 3,
fun = identity,
exists = NA,
ask = FALSE)
#> Warning: Deleted 3 sims data files in './sims1'.
#> Warning: Deleted 3 sims data files in './sims2'.
#> Warning: Deleted 3 sims data files in './sims3'.
#> Warning: Deleted 3 sims data files in './sims4'.
#> [[1]]
#> [1] TRUE
#>
#> [[2]]
#> [1] TRUE
#>
#> [[3]]
#> [1] TRUE
#>
#> [[4]]
#> [1] TRUEPlease report any issues.
Pull requests are always welcome.
Please note that this project is released with a Contributor Code of Conduct. By contributing, you agree to abide by its terms.