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$data=Puromycin; return(m)})
3) Now, evaluate:
lapply(m.list2, eval)
Can anyone point me to a simpler approach?
Regards
S?ren
Define lm/glm object without evaluating them
2 messages · Søren Højsgaard, Gabor Grothendieck
If your models only consists of a formula just use the formula as your specification where evaluation consists of passing the formula to lm with a data argument. # specification fo <- rate ~ conc # evaluation lm(fo, Puromycin) On Tue, Nov 17, 2009 at 4:11 AM, S?ren H?jsgaard
<Soren.Hojsgaard at agrsci.dk> wrote:
For e.g. lm/glm type models I would like to separate model specification and model fitting and then only fit the models later 'when data arrives'. To be specific, I would like make a specification like
m1 <- lm(rate~conc)
m2 <- lm(rate~I(conc^2))
and then later I want to 'put data into' the objects and evaluate (fit the model), e.g. something like
update(m1, data=Puromycin)
update(m2, data=Puromycin)
The 'closest' I can get to what I want is
1) Specification:
m.list <-expression(lm(rate~conc), lm(rate~I(conc^2)))
2) Update with data:
m.list2 <- lapply(m.list, function(m) {m$data=Puromycin; return(m)})
3) Now, evaluate:
lapply(m.list2, eval)
Can anyone point me to a simpler approach?
Regards
S?ren