Function to extract extra information from SimDesign objects

Function used to extract any error or warnings messages, the seeds associated with any error or warning messages, and any analysis results that were stored in the final simulation object.

Usage

SimExtract(object, what, fuzzy = TRUE, append = TRUE)

Arguments

object

object returned from runSimulation

what

character indicating what information to extract. Possible inputs include 'errors' to return a tibble object containing counts of any error messages, 'warnings' to return a data.frame object containing counts of any warning messages, 'seeds' for the specified random number generation seeds, 'Random.seeds' for the complete list of .Random.seed states across replications (only stored when runSimulation(..., control = list(store_Random.seeds=TRUE))), 'error_seeds' and 'warning_seeds' to extract the associated .Random.seed values associated with the ERROR/WARNING messages, 'results' to extract the simulation results if the option store_results was passed to runSimulation, 'filename' and 'save_results_dirname' for extracting the saved file/directory name information (if used), and 'summarise' if the Summarise definition returned a named list rather than a named numeric vector.

Note that 'warning_seeds' are not stored automatically in simulations and require passing store_warning_seeds = TRUE to runSimulation.

fuzzy

logical; use fuzzy string matching to reduce effectively identical messages? For example, when attempting to invert a matrix the error message "System is computationally singular: reciprocal condition number = 1.92747e-17" and "System is computationally singular: reciprocal condition number = 2.15321e-16" are effectively the same, and likely should be reported in the same columns of the extracted output

append

logical; append the design conditions when extracting error/warning messages?

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{

Generate <- function(condition, fixed_objects) {
    int <- sample(1:10, 1)
    if(int > 5) warning('GENERATE WARNING: int greater than 5')
    if(int == 1) stop('GENERATE ERROR: integer is 1')
    rnorm(5)
}

Analyse <- function(condition, dat, fixed_objects) {
    int <- sample(1:10, 1)
    if(int > 5) warning('ANALYSE WARNING: int greater than 5')
    if(int == 1) stop('ANALYSE ERROR: int is 1')
    c(ret = 1)
}

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

res <- runSimulation(replications = 100, seed=1234, verbose=FALSE,
                     generate=Generate, analyse=Analyse, summarise=Summarise)
res

SimExtract(res, what = 'errors')
SimExtract(res, what = 'warnings')
seeds <- SimExtract(res, what = 'error_seeds')
seeds[,1:3]

# replicate a specific error for debugging (type Q to exit debugger)
res <- runSimulation(replications = 100, load_seed=seeds[,1], debug='analyse',
                     generate=Generate, analyse=Analyse, summarise=Summarise)



} # }