search

benbhansen-stats / propertee

Prognostic Regression Offsets with Propagation of ERrors, for Treatment Effect Estimation (IES R305D210029).
https://benbhansen-stats.github.io/propertee/
Other
2 stars 0 forks source link

sharpen cov_adj()'s "partial residuals not implemented" warning #184

Open benthestatistician opened 2 weeks ago

benthestatistician commented 2 weeks ago

cov_adj() has a warning for the circumstance that the user gives a covariance model involving the treatment variable, but as recorded in the Design it's not the sort of treatment variable that the function is equipped to partial out. E.g.,

Warning message: In propertee::cov_adj(cmod, design = des, newdata = as.data.frame(Q_df), : The treatment variable is in the covariance adjustment model, and is neither logical or numeric; for now, partial residuals only implemented for logical or numeric treatments

Problem is, the trigger conditions for this warning don't currently include a check for whether this variable actually contributes to the covariance model; see cov_adj.R#L64-79. I hit it in a case with a covariance model without the treatment variable, and was quite confused.

  1. Let's add such a check. Or perhaps adjust the warning, if adjusting the conditions turns out to be unduly tricky.
  2. From cov_adj()'s perspective, it seems that we prefer that treatments be encoded in Design's as factor rather than character variables. (Of course logicals are better yet, but when there are more than 2 categories, with factors we get to follow the convention of taking factor level 1 to be the control condition, whereas with characters we don't have anything to identify that condition with.) So let's adjust our warning to indicate to the user that for cov_adj()'s purposes it's safer to encode their character treatment variables as factors.
benthestatistician commented 2 weeks ago

Could you take a look, @josherrickson? At your convenience. I've tentatively assigned you. Part 2 relates to #78, and we can discuss it before moving ahead with it, if you think that's appropriate.