SMEP: Power for Statistical Tests
josef-pkt edited this page Mar 20, 2013
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2 revisions
Status: partially implemented (part of PR #711)
auxiliary code: Effect Sizes
- power and sample size calculation :
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having the power equation, we can solve for any of the variables.
- "non-central tests": equivalence tests, not clear, ?
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example: chisquare gof test, to test that the distance between distribution is larger than a threshold
For more complex models it is difficult to specify the parameters, effect sizes and assumptions.
- explicit:
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Under normal assumption we have explicit formulas for some tests, like t-tests
- Monte Carlo:
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The range of alternatives can be huge. What supporting code can we provide to make it easier?
requirements :
- easy to expand: I don't expect we will add a lot in one big push
- usage for standalone: e.g. for sample size calculations
- attached to test classes/functions: to get the power of a test case
(stubs)
- t-test:
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easy
- f-test:
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easy
- TOST:
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requires a "special" integral, see SAS documentation
- chisquare (gof)
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???
- SAS Manual
- R
pwr: used as benchmark for tests- GPower