Error catching is an important area to consider when creating Monte Carlo simulations. Sometimes, iterative algorithms will ‘fail to converge’, or otherwise crash for other reasons (e.g., sparse data). Moreover, errors may happen in unexpected combinations of the design factors under investigation, which can lead to abrupt termination of a simulation’s execution.

SimDesign makes managing errors much easier because the internal functions are automatically wrapped within try blocks, and therefore simulations will not terminate unexpectedly. This type of information is also collected in the final simulation object since it may be relevant to the writer that something unknown is going wrong in the code-base. Below we demonstrate what happens when errors are thrown and caught, and how this information is tracked in the returned object.

1 Define the functions

As usual, define the functions of interest.

library(SimDesign)
# SimFunctions(comments=FALSE)

Design <- createDesign(N = c(10,20,30))
Generate <- function(condition, fixed_objects = NULL) {
    ret <- with(condition, rnorm(N))
    ret
}

Analyse <- function(condition, dat, fixed_objects = NULL) {
    whc <- sample(c(0,1,2,3), 1, prob = c(.7, .20, .05, .05))
    if(whc == 0){
       ret <- mean(dat)
    } else if(whc == 1){
        ret <- t.test() # missing arguments
    } else if(whc == 2){
        ret <- t.test('invalid') # invalid arguments
    } else if(whc == 3){
        # throw error manually 
        stop('Manual error thrown') 
    }
    # manual warnings
    if(sample(c(TRUE, FALSE), 1, prob = c(.1, .9)))
        warning('This warning happens rarely')
    if(sample(c(TRUE, FALSE), 1, prob = c(.5, .5)))
        warning('This warning happens much more often')
    ret
}

Summarise <- function(condition, results, fixed_objects = NULL) {
    ret <- c(bias = bias(results, 0))
    ret
}

The above simulation is just an example of how errors are tracked in SimDesign, as well as how to throw a manual error in case the data should be re-drawn based on the user’s decision (e.g., when a model converges, but fails to do so before some number of predefined iterations).