The whole of idea of 'level' in mixed models is confusing to some.
Professor Snijders (who teaches our students) and Professor Bates label
from opposite ends.
But, assuming this is my work in package MASS (Master Harwood: it is
childish, to put it mildly, to fail to give due credit), it follows lme
in package nlme: glmmPQL's predict method just passes this on to nlme,
and the documentation is identical.
So not only was it unfair to fail to mention which package and whose
work this was, it was even more unfair to attribute personal lack of
understanding to my work and not package nlme.
Just because R and many contributed packages are free does not entitle
you to treat them as zero-value: very much to the contrary.
On 10/01/2012 16:38, Ben Bolker wrote:
Mike Harwood<harwood262<at> gmail.com> writes:
Is the labeling/naming of levels in the documentation for the
predict.glmmPQL function "backwards"? The documentation states "Level
values increase from outermost to innermost grouping, with level zero
corresponding to the population predictions". Taking the sample in
the documentation:
fit<- glmmPQL(y ~ trt + I(week> 2), random = ~1 | ID,
family = binomial, data = bacteria)
head(predict(fit, bacteria, level = 0, type="response"))
[1] 0.9680779 0.9680779 0.8587270 0.8587270 0.9344832 0.9344832
head(predict(fit, bacteria, level = 1, type="response"))
X01 X01 X01 X01 X02 X02
0.9828449 0.9828449 0.9198935 0.9198935 0.9050782 0.9050782
head(predict(fit, bacteria, type="response")) ## population prediction
X01 X01 X01 X01 X02 X02
0.9828449 0.9828449 0.9198935 0.9198935 0.9050782 0.9050782
The returned values for level=1 and level=<unspecified> match, which
is not what I expected based upon the documentation.
Well, the documentation says: "Defaults to the highest or innermost level of
grouping", which is level 1 in this case -- right?
Exponentiating
the intercept coefficients from the fitted regression, the level=0
values match when the random effect intercept is included
Do you mean "is NOT included" here?
0.9680779 (no random effect, below) matches the level=0 prediction above
0.9828449 (include random effect, below) matches the level=1 prediction,
which is also the default prediction, above.
1/(1+exp(-3.412014)) ## only the fixed effect
[1] 0.9680779
1/(1+exp(-1*(3.412014+0.63614382))) ## fixed and random effect intercepts
[1] 0.9828449
This all matches my expectations. If your expectations still go
in the other direction, could you explain in more detail?
By the way, I recommend r-sig-mixed-mod...@r-project.org for
mixed model questions in general ...
Ben Bolker
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--
Brian D. Ripley, rip...@stats.ox.ac.uk
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
University of Oxford, Tel: +44 1865 272861 (self)
1 South Parks Road, +44 1865 272866 (PA)
Oxford OX1 3TG, UK Fax: +44 1865 272595
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