Predict polynomial problem

Its the environment thing.

I think you want something like this:

? ? ? ?models[[i]]=lm( bquote( y ~ poly(x,.(i)) ), data=d)

Use
? ? ? ?terms( mmn[[3]] )

both with and without this change and


? ? ? ?ls( env = environment( formula( mmn[[3]] ) ) )
? ? ? ?get("i",env=environment(formula(mmn[[3]])))
? ? ? ?sapply(mmn,function(x) environment( formula( x ) ) )


to see what gives.

Think I see it now. predict involves evaluating poly, and poly here
needs 'i' for the order. If the right 'i' isn't gotten when predict is
called then I get the error. Your fix sticks the right 'i' into the
environment when predict is called.

 I haven't quite got my head round _how_ it does it, and I have no
idea how I could have figured this out for myself. Oh well...

 The following lines are also illustrative:

d = data.frame(x=1:10,y=runif(10))

i=3
#1 naive model:
m1 = lm(y~poly(x,i),data=d)
#2,3 bquote, without or with i-wrapping:
m2 = lm(bquote(y~poly(x,i)),data=d)
m3 = lm(bquote(y~poly(x,.(i))),data=d)

#1 works, gets 'i' from global i=3 above:
predict(m1,newdata=data.frame(x=9:11))
#2 fails - why?
predict(m2,newdata=data.frame(x=9:11))
#3 works, gets 'i' from within:
predict(m3,newdata=data.frame(x=9:11))

rm(i)

#1 now fails because we removed 'i' from top level:
predict(m1,newdata=data.frame(x=9:11))
#2 still fails:
predict(m2,newdata=data.frame(x=9:11))
#3 still works:
predict(m3,newdata=data.frame(x=9:11))

Thanks

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