Timeline for Variance (maybe?) of categorical data
Current License: CC BY-SA 4.0
11 events
| when toggle format | what | by | license | comment | |
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| Nov 22, 2023 at 9:24 | review | Suggested edits | |||
| Nov 22, 2023 at 13:20 | |||||
| Nov 21, 2023 at 12:27 | comment | added | Doong | jTables, @Tim Thank you for your question, answer and discussion. I wonder if the entropy value acquired using the above needs a unit when I report it. | |
| Nov 21, 2023 at 12:13 | review | Suggested edits | |||
| Nov 21, 2023 at 13:09 | |||||
| Aug 8, 2019 at 20:25 | vote | accept | jTables | ||
| Aug 8, 2019 at 20:25 | comment | added | jTables | That is absolutely true for the description I gave. I added an update and I don't think it be comparable within sample between loci as the N would be different for each locus (13-160). Thank you again for helping me out, I appreciate the time you are putting in! I'm going to accept your answer as it was correct for how I originally framed the question. | |
| Aug 8, 2019 at 20:25 | history | edited | Tim | CC BY-SA 4.0 |
added 81 characters in body
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| Aug 8, 2019 at 20:18 | comment | added | Tim | @jTables still, if you choose a single constant for all the data, all the estimates of entropy would be off by the same quantity. The smoothing would make the counts more uniform, the degree on how much would it impact the counts depends on the choice of $\alpha$. You can always choose a small value, since the only point of this in your case would be to get rid of zeros. Entropy is a natural choice of measure for this kind of problems, the only problem are the zeroes. | |
| Aug 8, 2019 at 20:13 | comment | added | jTables | Unfortunately these are just toy examples. Between samples the sparsity varies greatly, and there are up too 160 categories. I'm going to edit my question to include a better summary of the problem as I think I over simplified it and there is more than I can communicate in a comment | |
| Aug 8, 2019 at 20:00 | comment | added | Tim | @jTables looking at your numbers, incrementing them by 1 would not seem to have big impact on their magnitude, nor on their relative differences, so should not be an issue if you only want to compare the distributions. Using $\alpha=1$ is equivalent to using uniform prior for Bayesian estimator. | |
| Aug 8, 2019 at 19:56 | comment | added | jTables | Thanks for the quick response! While entropy would be a decent metric you're right I would have to implement some sort of smoothing. I addition I would have to normalize my counts as entropy of raw counts will not be comparable between samples. While neither of these are prohibitive to using Entropy as my metric I'm worried that I will make poor choices on how to normalize my counts and how to smooth the counts, as there are many options for both. (and I clearly am not a statistician) | |
| Aug 8, 2019 at 19:32 | history | answered | Tim | CC BY-SA 4.0 |