R help - Aug 2005 - R equivalent to `estimate' in SAS proc mixed

Example: I have the following model

    > model <- lmer(response ~ time * trt * bio + (time|id), data = dat)

    where time = time of observation
           trt = treatment group (0-no treatment / 1-treated)
           bio = biological factor (0-absent / 1-present)

and I would like to obtain an estimate (with standard error) of the change
in response over time for individuals in the treatment group with the
biological factor. The estimate is easy,

    > sum(fixef(model)[c(2,5,6,8)])

    # ie time + time:trt + time:bio + time:trt:bio

but the standard error is a hassle to calculate by hand. Is there some
better way to do this? In SAS for example there is an `estimate' option (see
sample code below) that will calculate the estimate, SE, df, t statistic,
etc... Is there some R equivalent?

Thanks,
Randy


proc mixed data=dat;
  class id;
  model response = time + trt + bio + time*trt + time*bio + trt*bio +
                   time*trt*bio;
  random time;

  estimate "est1" intercept 0 time 1 trt 0 bio 0 time*trt 1 time*bio 1
                  trt*bio 0 time*trt*bio 1;  /* or something like that */
run;

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Randy Johnson
Laboratory of Genomic Diversity
NCI-Frederick
Bldg 560, Rm 11-85
Frederick, MD 21702
(301)846-1304
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Randy Johnson <rjohnson <at> ncifcrf.gov> writes:
>     > model <- lmer(response ~ time * trt * bio + (time|id), data =
dat)
..
> 
> and I would like to obtain an estimate (with standard error) of the change
> in response over time for individuals in the treatment group with the
> biological factor.
Greg Warnes' gmodels package has "estimable", which works for lme
(but probably
not for lmer). You model should work the same with lme with slight syntax
changes, or you could ask Greg to make estimable lmer-aware.

Dieter