hi folks,
I believe that the construction
y ~ A + (A|B)
(where A is a categorical variable for a fixed effect and
B is a categorical variable for a random effect) will generate random
effects for all of the fixed effect coefficients. That is, in addition
fitted a systematic fixed effect coefficient for intercept and
relevant added effects of levels of A, it will fit corresponding
random coefficients for each level of B, including random intercepts,
and relevant added random effects of A, given B (i.e. for each
separate B).
Thus if you think that each family has, in addition to a different
overall mean, also has a different response to levels of A (and you
have the replication to estimate them), then you should include (A|B)
and see if it is better than simply (1|B).
Hank
On Oct 6, 2008, at 11:02 AM, Christian Ritz wrote:
Dear Agnes,
I think your model specification should look like this:
YourModel1 - lmerlmer(y ~ poptype*matingtype + (1|poptype:pop) + (1|
poptype:fam),
data = ...)
The 1 in front of | refers to models that are random intercepts
models as opposed to
general random coefficients models in which case 1 would need to
be replaced by a
variable that is quantitative. So, the (poptype|/pop/fam)
construction is definitely not
relevant to your problem, unless poptype is a quantitative
variable...
The combined factor poptype:pop corresponds to the factor pop, but
taking the nesting
structure into account. Similar for the construction poptype:fam.
From the summary output of the lmer() fit you should be able to
check whether or not the
correct number of groups are used for these random factors.
Kind regards
Christian
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