Dear Jacob,
If you are willing to which to lme4, then you can use ranef(lme1, condVar
= TRUE). See its helpfile for the details.
Best regards,
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature and
Forest
team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
Kliniekstraat 25
1070 Anderlecht
Belgium
To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to say
what the experiment died of. ~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data. ~ Roger Brinner
The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of data.
~ John Tukey
2015-11-18 17:34 GMT+01:00 Jacob Bukoski <jbukoski1 at gmail.com>:
Hi all,
I might be searching for something that doesn't exist -- but is there a
way
to obtain group-specific standard errors for random effect intercept
estimates?
I have hierarchical data grouped by "site," for which I've generated
unique
intercept coefficients. For example:
$random$Site
(Intercept)
ab -9.574204
am -9.149834
ay -2.238734
br 5.073831
...
Is there a way to extract some sort of confidence interval on these
values?
I have attempted using VarCorr(), but am having trouble getting it to
return a standard error beyond that of the standard error across *all*
site
intercept estimates.
If it helps, my model is specified as:
lme1 <- lme(Biomass ~ Basal.area*Latitude - Latitude -1,
random = ~1|Site, method="REML")
?Many kind thanks,
Jacob?
--
Jacob J. Bukoski
Master of Environmental Science Candidate, 2016
School of Forestry and Environmental Studies, Yale University
jbukoski1 at gmail.com | jacob.bukoski at yale.edu | LinkedIn
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