
The subject says it all really.

Question 1.
Here is some code created to illustrate my problem, can anyone spot where I'm going wrong?

Question 2.
The reason I'm following a manual specification of models relates to the fact that in reality I am using mgcv::gam, and I'm not aware that dredge is able to separate individual smooth terms out of say s(a,b). Hence an additional request, if anyone has example code for using gam in a multimodel inference framework, especially with bivariate smooths, I'd be most grateful.

Cheers and Thanks in Advance
Mike

require(MuMIn)
data(Cement)
# option 1, create model.selection object using dredge
fm0 <- lm(y ~ ., data = Cement)
print(dd <- dredge(fm0))
fm1 <- lm(formula = y ~ X1 + X2, data = Cement)
fm2 <- lm(formula = y ~ X1 + X2 + X4, data = Cement)
fm3 <- lm(formula = y ~ X1 + X2 + X3, data = Cement)
fm4 <- lm(formula = y ~ X1 + X4, data = Cement)
fm5 <- lm(formula = y ~ X1 + X3 + X4, data = Cement)
# ranked with AICc by default
# obviously this works
model.avg(get.models(dd, delta < 4))

#  option 2: the aim is to produce a model selection object comparable to that from get.models(dd, delta < 4)
# but from a manually-specified list of models
my.manual.selection <- mod.sel(list(fm1, fm2, fm3, fm4, fm5))
# works
model.avg(list(fm1, fm2, fm3, fm4, fm5)) # or jut model.avg(fm1, fm2, fm3, fm4, fm5)
# doesn't work
model.avg(my.manual.selection)
# hence this doesn't work
get.models(my.manual.selection, delta < 4)


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