Comments (1)
"easily lead to running with the same random number seeds" is a bit of an overstatement, at least with respect to the likelihood of the seeds being the same across instances, but point taken. I've exposed the gen_seeds()
function so that users can generate their own sets of unique seeds for this type of application, and you'll find this documented in the same vignette. Below is the idea
design <- SimDesign::createDesign(N=c(10,20,30), var=c(1,2,3,4,5))
seeds <- SimDesign::gen_seeds(design, nsets = 2L)
head(seeds) # first column for computer 1, second for computer 2
# print whether identical number are generated
for(i in 1:nrow(design)){
set.seed(seeds[i,1])
v1 <- runif(100)
set.seed(seeds[i,2])
v2 <- runif(100)
if(length(unique(c(v1, v2))) == 100L)
print("Same numbers generated")
}
set.seed(NULL)
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Related Issues (20)
- Rstudio server crashes when debugging runSimulation() HOT 5
- Is it possible to capture all errors when using multiple analysis functions? HOT 9
- Error in load_packages(packages) : task 1 failed - "could not find function "load_packages"" HOT 6
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- Question / Issue HOT 5
- Variable naming error when using `runSimulation()` HOT 5
- Implement sample size planning functions HOT 2
- Implement MCSE for performance measure functions HOT 2
- runSimulation fails if design and estimate are named the same HOT 1
- Validate function outputs HOT 4
- Improve documentation on parallel processing HOT 5
- Features to support runnign as array jobs on a cluster HOT 15
- aggregate_simulations and selective loading of results HOT 5
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