I was reading the helpfile for the dropterm function in the R MASS package, which is one of the main building blocks of the stepAIC function (at least for backwards stepwise regression.)

In it, it states:

Try fitting all models that differ from the current model by dropping a single term, maintaining marginality.

What does maintaining marginality mean in this context? Is it a special treatment of interactions specified in the model formula, e.g. by dropping interactions first before dropping the interacting variables?

Related:

How does step function selects best linear Models which includes polynomial effects and interaction effects in R?

I was reading the helpfile for the dropterm function in the R MASS package, which is one of the main building blocks of the stepAIC function (at least for backwards stepwise regression.) The aim of this function is to identify a variable to remove from a model by sequentially considering models that drop one variable.

In the helpfile, it states:

Try fitting all models that differ from the current model by dropping a single term, maintaining marginality.

What does maintaining marginality mean in this context? Is it a special treatment of interactions specified in the model formula, e.g. by dropping interactions first before dropping the interacting variables?

Related:

How does step function selects best linear Models which includes polynomial effects and interaction effects in R?