SimDesign (version 2.17.1)

expandDesign: Create the simulation design object

Description

Repeat each design row the specified number of times. This is primarily used for cluster computing where jobs are distributed with batches of replications and later aggregated into a complete simulation object (see runArraySimulation and SimCollect).

Usage

expandDesign(Design, repeat_conditions)

Value

a tibble or data.frame containing the simulation experiment conditions to be evaluated in runSimulation

Arguments

Design

object created by createDesign which should have its rows repeated for optimal HPC schedulers

repeat_conditions

integer vector used to repeat each design row the specified number of times. Can either be a single integer, which repeats each row this many times, or an integer vector equal to the number of total rows in the created object.

This argument is useful when distributing independent row conditions to cluster computing environments, particularly with different replication information. For example, if 1000 replications in total are the target but the condition is repeated over 4 rows then only 250 replications per row would be required across the repeated conditions. See SimCollect for combining the simulation objects once complete

Author

Phil Chalmers

References

Chalmers, R. P., & Adkins, M. C. (2020). Writing Effective and Reliable Monte Carlo Simulations with the SimDesign Package. The Quantitative Methods for Psychology, 16(4), 248-280. tools:::Rd_expr_doi("10.20982/tqmp.16.4.p248")

Sigal, M. J., & Chalmers, R. P. (2016). Play it again: Teaching statistics with Monte Carlo simulation. Journal of Statistics Education, 24(3), 136-156. tools:::Rd_expr_doi("10.1080/10691898.2016.1246953")

See Also

createDesign, SimCollect, runArraySimulation

Examples

if (FALSE) {

# repeat each row 4 times (for cluster computing)
Design <- createDesign(N = c(10, 20),
                       SD.equal = c(TRUE, FALSE))
Design4 <- expandDesign(Design, 4)
Design4

# repeat first two rows 2x and the rest 4 times (for cluster computing
#   where first two conditions are faster to execute)
Design <- createDesign(SD.equal = c(TRUE, FALSE),
                       N = c(10, 100, 1000))
Design24 <- expandDesign(Design, c(2,2,rep(4, 4)))
Design24

}