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Implement sample size planning functions #27
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Hi Björn, This sounds quite reasonable to me.
Agreed, this is a nice convention to use in the package, and the respective functions (
I like this idea, but worry about the use of empirical estimates for the purpose of simulation planning. As the empirical variance estimates are themselves a function of the replication size one could quite easily over/under estimate the requisite number of replications to obtain the desired precision, particularly if the replication size was initially too low. Ideally, some type of confidence interval should be included for this type of situation, where either the complete vector of observations used to obtain said empirical variance estimates is passed to the function (where obtaining internal uncertainty quantifiers could be applied, even if from the large sample normal family or via bootstrapping) or the standard error be included by the user to indicate the degree of precision in the empirical variances. I'd be fine with either.
It's unclear to me why this would be necessary within a Summarise() call. As SimDesign stores the results information one could just extract the analyse results out and pass these to
A vignette would be great! Though let me know your thoughts about my above points before proceeding. Thanks! |
Hello Phil, Thank you for your thoughts and willingness to include the idea in your package.
We will prepare a pull request incorporating this functionality. Please excuse that this may take some time. |
Hello Phil,
as we previously briefly discussed via email, it could be useful to have functionality that allows for sample size planning for simulation studies to achieve a desired Monte Carlo Standard Error (MCSE). These functions should allow users to specify their performance measure of interest and the desired precision. The functions then return the number of repetitions needed to achieve said precision. The calculations can be based on the formulas we provide in Siepe et al. (2023).
We (Samuel Pawel, František Bartoš, and I) would like to contribute to this functionality.
Our Suggestions:
plan_*
, where * stands for performance measures such as bias or coverage as implemented in the SimDesign summary functionsplan_*
can be used within theSummarise()
function. Users can then run a pilot simulation study to obtain the empirical variance and return the required sample size for each condition/method.Sketch of what such a function could look like:
For a performance measure with known SE:
For a performance measure with unknown SE:
Depending on your input, we will open a pull request suggesting the functions soon.
Best
Björn
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