Prevent GLM from dropping rows with missings

The @formula macro allows the construction of variables when creating a model matrix.

This is great! I don’t have to add various transformations of variables to a DataFrame simply because I want to try alternative regression specifications. I can write:
@formula(y/x ~ w)
@formula(y - z ~ w + w^2)

But what if I want to run the regression:

GLM.lm( @formula(ismissing(y) ~ x + z) , df)

Uh oh. It doesn’t matter that ismissing(y) is never missing, GLM recognizes that y itself has missing values and drops these rows from the model matrix.

Obviously, I could construct a new variable outside the formula macro:

df[!, :y_is_missing] = ismissing.(df.y)

and run my regression using this. But I don’t want to. Is there any way to turn off the feature that drops missings from the model matrix? A long-term solution would probably have GLM.jl check if the transformed variables are missing, rather than if the variables themselves are missing. In the interim…

This seems unlikely to fly based on prior discussions. But

maybe that could work?