If your models only consists of a formula just use the formula as your
specification where evaluation consists of passing the formula to lm
with a data argument.
# specification
fo <- rate ~ conc
# evaluation
lm(fo, Puromycin)
On Tue, Nov 17, 2009 at 4:11 AM, Søren Højsgaard
<soren.hojsga...@agrsci.dk> wrote:
> For e.g. lm/glm type models I would like to separate model specification and
> model fitting and then only fit the models later 'when data arrives'. To be
> specific, I would like make a specification like
> m1 <- lm(rate~conc)
> m2 <- lm(rate~I(conc^2))
>
> and then later I want to 'put data into' the objects and evaluate (fit the
> model), e.g. something like
> update(m1, data=Puromycin)
> update(m2, data=Puromycin)
>
> The 'closest' I can get to what I want is
> 1) Specification:
> m.list <-expression(lm(rate~conc), lm(rate~I(conc^2)))
> 2) Update with data:
> m.list2 <- lapply(m.list, function(m) {m$data=Puromycin; return(m)})
> 3) Now, evaluate:
> lapply(m.list2, eval)
> Can anyone point me to a simpler approach?
> Regards
> Søren
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