Perform a test that indicates whether a given Generate() function should be executed

This function is designed to prevent specific generate function executions when the design conditions are not met. Primarily useful when the generate argument to runSimulation was input as a named list object, however should only be applied for some specific design condition (otherwise, the data generation moves to the next function in the list).

Usage

GenerateIf(x, condition = NULL)

Arguments

x

logical statement to evaluate. If the statement evaluates to TRUE then the remainder of the defined function will be evaluated

condition

(optional) the current design condition. This does not need to be supplied if the expression in x evaluates to valid logical (e.g., use Attach(condition) prior to using AnalyseIf, or use with(condition, AnalyseIf(someLogicalTest)))

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

See also

Analyse, runSimulation

Author

Phil Chalmers rphilip.chalmers@gmail.com

Examples

if (FALSE) { # \dontrun{

# SimFunctions(nGenerate = 2)

Design <- createDesign(N=c(10,20,30), var.equal = c(TRUE, FALSE))

Generate.G1 <- function(condition, fixed_objects) {
  GenerateIf(condition$var.equal == FALSE) # only run when unequal vars
  Attach(condition)
  dat <- data.frame(DV = c(rnorm(N), rnorm(N, sd=2)),
                    IV = gl(2, N, labels=c('G1', 'G2')))
  dat
}

Generate.G2 <- function(condition, fixed_objects) {
  Attach(condition)
  dat <- data.frame(DV = rnorm(N*2), IV = gl(2, N, labels=c('G1', 'G2')))
  dat
}

# always run this analysis for each row in Design
Analyse <- function(condition, dat, fixed_objects) {
  mod <- t.test(DV ~ IV, data=dat)
  mod$p.value
}

Summarise <- function(condition, results, fixed_objects) {
  ret <- EDR(results, alpha=.05)
  ret
}

#-------------------------------------------------------------------

# append names 'Welch' and 'independent' to associated output
res <- runSimulation(design=Design, replications=1000,
                     generate=list(G1=Generate.G1, G2=Generate.G2),
                     analyse=Analyse,
                     summarise=Summarise)
res

} # }