Summarizing the fitted model takes more RAM than
On Mon, Dec 15, 2008 at 10:16 AM, Gabor Grothendieck
<ggrothendieck at gmail.com> wrote:
Note that the fitted method in lme had a level= argument that is no longer available in lmer presumably because lmer does not assume a hierarchy -- but do we have or will we have an easy way to get the same effect as fitted(..., level=) in lmer?
One would need to define such a method carefully. If the factors defining random effects form a strictly nested sequence then there is an interpretation of level. If you do not have a strictly nested sequence then I can only make sense of having all the random effects defining fitted values (which is what the method for fitted returns now) or having none of them. The second is using the fixed-effects coefficients only.
library(nlme) # example from plot.lme fm1 <- lme(distance ~ age, Orthodont, random = ~ age | Subject) fit0 <- fitted(fm1, level = 0) fit1 <- fitted(fm1, level = 1) (Maybe this is a bad example since its actually not so hard: fitted(lmer(distance ~ age + (age|Subject), Orthodont)) gives level 1 and fitted(lm(distance ~ age, Orthodont)) gives level 0 but even here it involved the complexity of using different approaches to get them whereas with lme one could do it in a unified manner.)
I don't know if fitted(lm(distance ~ age, Orthodont)) produces the desired result. Removing the random effects from the prediction is not always the same as removing the random effects from the fit. I would get the fitted values for the fixed effects only using as.vector(model.matrix(fm1) %*% fixef(fm1))
On Mon, Dec 15, 2008 at 10:19 AM, Douglas Bates <bates at stat.wisc.edu> wrote:
I believe you are using the terminology of multilevel modeling where one characterizes factors as being at the first level, the second level, etc. One can fit multilevel models using lmer but one can also