Print the results of a Monte Carlo Simulation run by future_mc()
Usage
# S3 method for mc
print(x, ...)Examples
test_func <- function(param = 0.1, n = 100, x1 = 1, x2 = 2){
data <- rnorm(n, mean = param) + x1 + x2
stat <- mean(data)
stat_2 <- var(data)
if (x2 == 5){
stop("x2 can't be 5!")
}
return(list(mean = stat, var = stat_2))
}
param_list <- list(param = seq(from = 0, to = 1, by = 0.5),
x1 = 1:2)
set.seed(101)
test_mc <- future_mc(
fun = test_func,
repetitions = 1000,
param_list = param_list,
n = 10,
x2 = 2
)
#> Running single test-iteration for each parameter combination...
#>
#> Test-run successfull: No errors occurred!
#> Running whole simulation: Overall 6 parameter combinations are simulated ...
#>
#> Simulation was successfull!
#> Running time: 00:00:00.970688
test_mc
#> Monte Carlo simulation results for the specified function:
#>
#> function (param = 0.1, n = 100, x1 = 1, x2 = 2)
#> {
#> data <- rnorm(n, mean = param) + x1 + x2
#> stat <- mean(data)
#> stat_2 <- var(data)
#> if (x2 == 5) {
#> stop("x2 can't be 5!")
#> }
#> return(list(mean = stat, var = stat_2))
#> }
#>
#> The following 6 parameter combinations:
#> # A tibble: 6 × 2
#> param x1
#> <dbl> <int>
#> 1 0 1
#> 2 0.5 1
#> 3 1 1
#> 4 0 2
#> 5 0.5 2
#> 6 1 2
#> are each simulated 1000 times.
#>
#> The Running time was: 00:00:00.970688
#>
#> Parallel: TRUE
#>
#> The following parallelisation plan was used:
#> $strategy
#> multisession:
#> - args: function (..., workers = availableCores(), lazy = FALSE, rscript_libs = .libPaths(), envir = parent.frame())
#> - tweaked: FALSE
#> - call: NULL
#>
#>
#> Seed: TRUE