Not sure I really like stepwise variable selection, but the function
should work, of course. Compare
data(BostonHousing, package = "mlbench")
lmFit <- train(medv ~ ., data = BostonHousing, "lmStepAIC", scope =
list(lower = ~., upper = ~.^2), direction = "forward")
with
lmFit <- train(medv ~ ., data = BostonHousing, "lmStepAIC", scope =
list(lower = ~., upper = ~.^2), direction = "backward")
and also the version without the "direction" argument where the code
tries to do the right thing.
In summary, my understanding is that your call first fits a y ~ . type
model from which you cannot step backwards with constraints and then it
tries to do 'what you might have meant'.
Hope this helps a little.
Allan
On 05/03/12 01:14, Dan Putka wrote:
I'm looking for guidance on how to implement forward stepwise regression
using lmStepAIC in Caret.
The stepwise "direction" appears to default to "backward". When I try to
use "scope" to provide a lower and upper model, Caret still seems to
default to "backward".
Any thoughts on how I can make this work?
Here is what I tried:
itemonly<- susbstitute(~i1+i2+i3+i4+i5+i6+i7+i8+i9+i10) #this is my full
model
#I want my "lower" model to consist of the intercept only
stepLmFit.i<- train(xtraindata.i, ytraindata,"lmStepAIC",
scope=list(upper=itemonly,lower=~1),direction="forward")
Any guidance on how I can make this work would be greatly appreciated.
Dan
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