rbind.SimDesign {SimDesign}

Combine two separate SimDesign objects by row

Description

This function combines two Monte Carlo simulations executed by SimDesign's runSimulation function which, for all intents and purposes, could have been executed in a single run. This situation arises when a simulation has been completed, however the Design object was later modified to include more levels in the defined simulation factors. Rather than re-executing the previously completed simulation combinations, only the new combinations need to be evaluated into a different object and then rbind together to create the complete object combinations.

Usage

## S3 method for class 'SimDesign'
rbind(...)

Arguments

...

two or more SimDesign objects that should be combined by rows

Value

same object that is returned by runSimulation

Author(s)

Phil Chalmers [e-mail]

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. 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. doi:10.1080/10691898.2016.1246953

Examples

## Not run: 

# modified example from runSimulation()

Design <- createDesign(N = c(10, 20),
                       SD = c(1, 2))

Generate <- function(condition, fixed_objects) {
    dat <- with(condition, rnorm(N, 10, sd=SD))
    dat
}

Analyse <- function(condition, dat, fixed_objects) {
    ret <- mean(dat) # mean of the sample data vector
    ret
}

Summarise <- function(condition, results, fixed_objects) {
    ret <- c(mu=mean(results), SE=sd(results)) # mean and SD summary of the sample means
    ret
}

Final1 <- runSimulation(design=Design, replications=1000,
                       generate=Generate, analyse=Analyse, summarise=Summarise)
Final1

###
# later decide that N = 30 should have also been investigated. Rather than
# running the following object ....
newDesign <- createDesign(N = c(10, 20, 30),
                          SD = c(1, 2))

# ... only the new subset levels are executed to save time
subDesign <- subset(newDesign, N == 30)
subDesign

Final2 <- runSimulation(design=subDesign, replications=1000,
                       generate=Generate, analyse=Analyse, summarise=Summarise)
Final2

# glue results together by row into one object as though the complete 'Design'
# object were run all at once
Final <- rbind(Final1, Final2)
Final

summary(Final)


## End(Not run)