SimExtract: Function to extract extra information from SimDesign objects

In philchalmers/SimDesign: Structure for Organizing Monte Carlo Simulation Designs

Function to extract extra information from SimDesign objects

Description

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)

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

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")}

Examples

## Not run: 

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

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

Summarise <- function(condition, results, fixed_objects = NULL) {
    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)




## End(Not run)

philchalmers/SimDesign documentation built on May 5, 2024, 6:36 a.m.