It seems the current implementation of gRim::stepwise.iModel() only supports starting model-selection from a full main effects model gRim::dmod(~.^1, data = data) if one wants to include all the variables in the selection procedure.
Is there any theoretical reason for this? In other words, couldn't we just as well start from log p(i) = mu and let AIC do the variable selection?
Reason I ask is that starting from a full main effects model limits model selection to low dimensional contingency tables. (e.g. table() will not work on data.frames with more than 31 binary columns since the amount of cells explodes to 2^31.)
It seems the current implementation of
gRim::stepwise.iModel()only supports starting model-selection from a full main effects modelgRim::dmod(~.^1, data = data)if one wants to include all the variables in the selection procedure.Is there any theoretical reason for this? In other words, couldn't we just as well start from log p(i) = mu and let AIC do the variable selection?
Reason I ask is that starting from a full main effects model limits model selection to low dimensional contingency tables. (e.g.
table()will not work on data.frames with more than 31 binary columns since the amount of cells explodes to 2^31.)