SimErrors: Extract Simulation Errors

In SimDesign: Structure for Organizing Monte Carlo Simulation Designs

Extract Simulation Errors

Description

Extractor function in situations where runSimulation returned a simulation with detected ERRORS.

Usage

SimErrors(obj, seeds = FALSE, subset = TRUE)

Arguments

obj	object returned from runSimulation containing an ERRORS column
seeds	logical; locate .Random.seed state that caused the error message?
subset	logical; take a subset of the design object showing only conditions that returned errors?

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. \Sexpr[results=rd]{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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/10691898.2016.1246953")}

See Also

SimWarnings, SimExtract

Examples

sample_sizes <- c(10, 20)
standard_deviations <- 1

Design <- createDesign(N1=sample_sizes,
                       N2=sample_sizes,
                       SD=standard_deviations)
Design

Generate <- function(condition, fixed_objects){
    Attach(condition)
    group1 <- rnorm(N1)
    group2 <- rnorm(N2, sd=SD)
    dat <- data.frame(group = c(rep('g1', N1), rep('g2', N2)),
                      DV = c(group1, group2))
    dat
}

Analyse <- function(condition, dat, fixed_objects){

  # raise errors with unequal sample sizes only
  if(with(condition, N1 != N2)){
    if(runif(1, 0, 1) < .9) t.test('char')
    if(runif(1, 0, 1) < .9) aov('char')
    if(runif(1, 0, 1) < .2) stop('my error')
  }

  welch <- t.test(DV ~ group, dat)
  ind <- stats::t.test(DV ~ group, dat, var.equal=TRUE)
  ret <- c(welch = welch$p.value, independent = ind$p.value)
  ret
}

Summarise <- function(condition, results, fixed_objects) {
  ret <- EDR(results)
  ret
}

# print any error messages and their frequency
res <- runSimulation(design=Design, replications=3, generate=Generate,
                     analyse=Analyse, summarise=Summarise, max_errors = Inf)
res |> select(N1, N2, SD, ERRORS)
SimErrors(res)
SimErrors(res, subset=FALSE)

# for specific seeds (organized list of SEEDS returned)
(seeds <- SimErrors(res, seeds=TRUE))
names(seeds$SEEDS[[1]]) # first row errors

# extract one .Random.seed state for the first design condition where
# error occurred, pointing to the second uniquely recorded error message
seeds$SEEDS[[1]][[2]][, 1] -> seed_state
seed_state     # note that Design_row_2 is where the error occurred

## Not run: 
# pass to runSimulation() to replicate issue (not run as this calls debug())
runSimulation(design=Design, replications=3, generate=Generate,

## End(Not run)

SimDesign documentation built on July 28, 2026, 1:07 a.m.