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

Source: R/AnalyseIf.R

This function is designed to prevent specific analysis function executions when the design conditions are not met. Primarily useful when the analyse argument to runSimulation was input as a named list object, however some of the analysis functions are not interesting/compatible with the generated data and should therefore be skipped.

AnalyseIf(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

Author

Phil Chalmers rphilip.chalmers@gmail.com

Examples

if (FALSE) { # \dontrun{

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

Generate <- 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
Analyse1 <- function(condition, dat, fixed_objects) {
  mod <- t.test(DV ~ IV, data=dat)
  mod$p.value
}

# Only perform analysis when variances are equal and N = 20 or 30
Analyse2 <- function(condition, dat, fixed_objects) {
  AnalyseIf(var.equal && N %in% c(20, 30), condition)
  mod <- t.test(DV ~ IV, data=dat, var.equal=TRUE)
  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=100, generate=Generate,
                     analyse=list(Welch=Analyse1, independent=Analyse2),
                     summarise=Summarise)
res

# leave results unnamed
res <- runSimulation(design=Design, replications=100, generate=Generate,
                     analyse=list(Analyse1, Analyse2),
                     summarise=Summarise)


} # }