-
Notifications
You must be signed in to change notification settings - Fork 19
New issue
Have a question about this project? # for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “#”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? # to your account
Allow conditional permutation tests? #5
Comments
That sounds sensible -- have you ever encountered a case where more than 5% On Tue, Jul 19, 2016 at 1:52 PM, Winston Lin notifications@github.com
|
No, it's just a theoretical possibility that occurred to me. :) winston Sent from my phone
|
In the section on permutation tests, we've written, "We recommend attempting the permutation test with mock outcome data and actual covariate data before analyzing the actual outcome data. The mock permutation test may reveal that on some randomizations, the t-statistic cannot be computed because the regressors are collinear or because the HC2 or BM SE is undefined (see the section above on 'Avoiding regression models that do not allow the BM adjustment'). In such cases, covariates should be dropped from the model until the mock permutation test runs without errors."
I'm thinking to change this so that if the t-statistic is uncomputable on only a small % of randomizations (e.g., less than 5%), we do a conditional permutation test (i.e., randomizations where the t-stat is undefined are excluded from both the numerator and the denominator of the p-value).
One situation where this might happen is if the PAP specifies poststratification and there are some randomizations where all units in some poststratum are assigned to one treatment condition.
The text was updated successfully, but these errors were encountered: