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.

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) {
    ret <- with(condition, rnorm(N))
    ret
}

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

Run the simulation

result <- runSimulation(Design, replications = 100, 
                       generate=Generate, analyse=Analyse, summarise=Summarise)

## 
## Design: 1/3;   Replications: 100;   RAM Used: 53.8 Mb;   Total Time: 0.00s 
##  Conditions: N=10
## 
## Design: 2/3;   Replications: 100;   RAM Used: 55 Mb;   Total Time: 0.05s 
##  Conditions: N=20
## 
## Design: 3/3;   Replications: 100;   RAM Used: 55.2 Mb;   Total Time: 0.09s 
##  Conditions: N=30
##

print(result)

## # A tibble: 3 × 9
##       N     bias REPLICATIONS SIM_TIME RAM_USED       SEED COMPLETED      ERRORS
##   <dbl>    <dbl>        <dbl> <chr>    <chr>         <int> <chr>           <int>
## 1    10 0.061138          100 0.05s    55 Mb    1140350788 Thu Nov 14 23…     53
## 2    20 0.014295          100 0.05s    55.2 Mb   312928385 Thu Nov 14 23…     52
## 3    30 0.017927          100 0.04s    55.3 Mb   866248189 Thu Nov 14 23…     42
## # ℹ 1 more variable: WARNINGS <int>

What you’ll immediately notice from this output object is that counts of the error and warning messages have been appended to the result object. This is useful to determine just how problematic the errors and warnings are based on their frequency alone. Furthermore, the specific frequency in which the errors/warnings occurred are also included for each design condition (here the t.test.default() error, where no inputs were supplied, occurred more often than the manually thrown error as well as the invalid-input error) after extracting and inspecting SimExtract(results, what = 'errors') and SimExtract(results, what = 'warnings').

SimExtract(result, what = 'errors')

##    N ERROR:  Error in t.test.default("invalid") : not enough 'x' observations\n
## 1 10                                                                         12
## 2 20                                                                          9
## 3 30                                                                         10
##   ERROR:  Error in t.test.default() : argument "x" is missing, with no default\n
## 1                                                                             31
## 2                                                                             38
## 3                                                                             25
##   ERROR:  Manual error thrown\n
## 1                            10
## 2                             5
## 3                             7

Finally, SimDesign has a built-in safety feature controlled by with max_errors argument to avoid getting stuck in infinite redrawing loops. By default, if more than 50 errors are consecutively returned then the simulation condition will be halted, and a warning message will be printed to the console indicating the last observed fatal error. These safety features are built-in because too many consecutive stop() calls generally indicates a major problem in the simulation code which should be fixed before continuing. However, when encountering fatal errors in a given simulation condition the remainder of the simulation experiment will still be executed as normal, where for the problematic conditions combinations NA placeholders will be assigned to these rows in the final output object. This is so that the entire experiment does not unexpectedly terminate due to one or more problematic row conditions in Design, and instead these conditions can be inspected and debugged at a later time. Of course, if inspecting the code directly, the simulation could be manually halted so that these terminal errors can be attended to immediately (e.g., using Ctrl + c, or clicking the ‘Stop’ icon in Rstudio).

What to do (explicit debugging)

If errors occur too often (but not in a fatal way) then the respective design conditions should either be extracted out of the simulation or further inspected to determine if they can be fixed (e.g., providing better starting values, increasing convergence criteria/number of iterations, etc). For instance, say that the fourth row of the design object raised a number of error messages that should be inspected further. One useful approach then would be to debug the 4th row on the instance that an error is raised, which can be achieved using the following:

runSimulation(..., debug = 'error-4')

The error flag is used to enter R’s debugger on the first instance of an error, while the -4 indicates that only the 4th row of design should be evaluated. This is also one instance where changing warning messages into error messages (i.e., runSimulation(..., extra_options = list(warnings_as_errors=TRUE))) is particularly useful so that the state that generated a warning can be inspected directly. Note that similar arguments can be made for explicitly debugging functions in the generate-analyse-summarise chain (e.g., debug = 'analyse-4'), though these are less useful for debugging (more useful for initial code design).

Manual debugging via `try()`

Failing the above approach, manually wrapping the problematic functions in a try() call. Adding the line if(is(object, 'try-error')) browser() will jump into the location/replication where the object unexpectedly witnessed, though admittedly this is more clunky approach than using debug. Nevertheless, jumping into the exact location where the error occurred, particularly in the case where an analyse() function is throwing multiple error messages, will greatly help you determine what exactly went wrong in the simulation state, allowing you to quickly locate and fix the issue.

Extracting error seeds for hard-to-find bugs

An alternative approach to locating errors in general is to use information stored within the SimDesign objects at the time of completion. By default, all .Random.seed states associated with errors are stored within the final object, and these can be extracted using the SimExtract(..., what='error_seeds') option. This function returns a data.frame object with each seed stored column-wise, where the associated error message is contained in the column name itself (and allowed to be coerced into a valid column name to make it easier to use the $ operator). For example,

seeds <- SimExtract(result, what = 'error_seeds')
head(seeds[,1:3])

## # A tibble: 6 × 3
##   Design_row_1.1..Error.in.t.tes…¹ Design_row_1.2..Manu…² Design_row_1.3..Erro…³
##                              <int>                  <int>                  <int>
## 1                            10403                  10403                  10403
## 2                              624                     21                     65
## 3                       -159038368             -230613222             -230613222
## 4                       1905303777              203707493              203707493
## 5                       -371375826             1161141503             1161141503
## 6                      -1012234281              549195142              549195142
## # ℹ abbreviated names:
## #   ¹Design_row_1.1..Error.in.t.test.default.....argument..x..is.missing..with.no.default.,
## #   ²Design_row_1.2..Manual.error.thrown.,
## #   ³Design_row_1.3..Error.in.t.test.default.....argument..x..is.missing..with.no.default.

Given these seeds, replicating an exact error can be achieved by a) extracting a single column into an integer vector, and b) passing this vector to the load_seed input. For example, replicating the first error message can be achieved as follows, where it makes the most sense to immediately go into the debugging mode via the debug inputs.

Note: It is important to manually select the correct Design row using this error extraction approach; otherwise, the seed will clearly not replicate the exact problem state.

picked_seed <- seeds$Design_row_1.1..Error.in.t.test.default..invalid.....not.enough..x..observations.

# debug analyse() for first row of Design object via debug='analyse-1'
runSimulation(Design, replications = 100, load_seed=picked_seed, debug='analyse-1',
              generate=Generate, analyse=Analyse, summarise=Summarise)

The .Random.seed state will be loaded at this exact state, and will always be related at this state as well (in case c is typed in the debugger, or somehow the error is harder to find while walking through the debug mode). Hence, users must type Q to exit the debugger after they have better understood the nature of the error message first-hand.

Converting warings to errors explicitly

On occasion functions will return warning message that either border on (or should be treated as) error messages if they influence the veracity of the simulation results. Such examples may include models that appear to ‘converge’ but do so with non-nonsensical parameter estimates (e.g., negative variances, non-positive definite correlation matrices, maximum iterations reached in an numerical searching algorithm, etc). However, because such issues are non-fatal third-party software (i.e., functions not written by the developer of the simulation) may simply raise a warning message for further inspection, whereas in a Monte Carlo simulation experiment such issues should generally be treated as invalid (though their frequency of occurrence should still be tracked, as is the default in SimDesign).

To resolve this issue, and to avoid using a more nuclear option such as setting option(warn=2) to treat all warnings as errors in the simulation, the function manageWarnings() can be used to explicit convert known warning message strings into errors that disrupt the code execution while allowing other warning messages to continue to be raised.

For example, if a function utilized in a simulation was

myfun <- function() {
    if(sample(c(TRUE, FALSE), 1, prob = c(.1, .9)))
        warning('This warning is serious')
    if(sample(c(TRUE, FALSE), 1, prob = c(.5, .5)))
        warning('This warning is no big deal')
    return(1)
}

set.seed(1)
out <- myfun()

set.seed(2)
out <- myfun()

## Warning in myfun(): This warning is no big deal

set.seed(7)
out <- myfun()

## Warning in myfun(): This warning is serious

then whenever the serious warning message is raised it could be explicitly converted to an error using an internal grepl() test.

set.seed(1)
out1 <- manageWarnings(myfun(), 
                        warning2error='This warning is serious')
out1

## [1] 1

set.seed(2)
out2 <- manageWarnings(myfun(), 
                        warning2error='This warning is serious')

## Warning in myfun(): This warning is no big deal

out2

## [1] 1

set.seed(7)
out3 <- manageWarnings(myfun(), 
                        warning2error='This warning is serious')

## Error: This warning is serious

which now converts the previous warning message into an error message, thereby correctly disrupting the flow of the Monte Carlo simulation experiment and prompting a new call to Generate(). Of course, all warning and error messages are tallied in the resulting runSimulation() object, though now the serious warnings will be tallied as disruptive errors instead of warnings that continued normally.