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It would be nice to have a way to ensure that we have the MOST equal-length bins possible. This could include some sort of algorithm that checks a bunch of different sets of bins and then compares their sds. I'm not sure on the return on investment here, both in terms of developer time and computation time within the function, but I think it might be useful for more statistically-inclined users.
The text was updated successfully, but these errors were encountered:
Agreed. In reality, the current implementation is probably already giving the user the most equal bins for the specified bin size. I think with an exhaustive method, we could have occasions were more equal time bins are drawn, however the mean bin size will likely be further from the specified bin size. The current implementation is grouping based on a cumulative sum, so it is generally giving you the best grouping for the target bin size. This is just something to be aware of. Maybe in an updated approach, the user wouldn't specify a bin size, and a list of different groupings could be returned with the mean/standard deviation of each. The user would then be able to choose what standard deviation they are happy with for given bin sizes. I would like to hear feedback from the community first though! I think there are different ways to think about these things depending on what you prioritise.
It would be nice to have a way to ensure that we have the MOST equal-length bins possible. This could include some sort of algorithm that checks a bunch of different sets of bins and then compares their sds. I'm not sure on the return on investment here, both in terms of developer time and computation time within the function, but I think it might be useful for more statistically-inclined users.
The text was updated successfully, but these errors were encountered: