I have analyzed my data and found out that inverse gamma distribution fits the best to my data. Now, I am trying to figure out the minimum number of data points that will be required to fit this distribution to any data (generated from similar types of experiments). I am not sure how can I perform sample size power analysis in this situation. I am mostly using R for my analysis, so it will be easier if I can find any R package that can help me do this. I would really appreciate any help on the topic
Sorry to late. You should have done this prior to sample collection. Now you can calculate the power of your test for the sample size you used. If this is >0.80 you should be ok. Best , David Booth
BTW G*Power should do the calculation for you. Google the name for it's website URL In the calculation for best results use the inverse gamma distribution. NEXT TIME REMEMBER do this step before data collection. NOW
IF YOU WISH TO CALCULATE n for a similar experiment use G*power as usual but choose several distribution types including the inverse gamma distribution as one of those. If G**Power doesn't do all of this, i expect it will, ask a another question and we'll find a calculator for you. Best Wishes, DB
"pwr" package maybe explored
"Quick-R: Power Analysis" https://www.statmethods.net/stats/power.html
pwr package - RDocumentation" https://www.rdocumentation.org/packages/pwr/versions/1.3-0
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