Hi Jackie,
The data are not binomial they are continuous: a beta distribution is
probably most appropriate for continuos observations bounded by 0 and 1.
However, although heritabilities are bounded by 0 and 1, heritability
estimates are not necessarily so, depending on the method of inference (for
example it would be possible to get a negative parent-offspring regression,
either by chance or through certain types of maternal effect).
We have just finished a meta-analysis of h2 estimates and just treated
them as Gaussian. The distribution of the residuals wasn't far off and I
think the conclusions are robust to the distributional assumptions. Have
you checked your residuals - do they look badly non-normal?
Cheers,
Jarrod
Quoting Ken Beath <ken.beath at mq.edu.au> on Wed, 24 Dec 2014 12:30:03
+1100:
If you have the original data giving the numerator and denominator for the
proportion then it is binomial data, and can be modelled in a
met-analysis.
I don't know if this can be done with MCMCglmm but should be possible with
STAN, JAGS or BUGS. All will require a bit of effort in setting up the
model.
On 24 December 2014 at 07:17, Jackie Wood <jackiewood7 at gmail.com> wrote:
Dear R-users,
I am attempting to conduct a meta-analysis to investigate the
relationship
of narrow-sense heritability with population size. In previous work, I
have
used MCMCglmm to conduct a formal meta-analysis which allowed me to
account
for the effect of sampling error through the argument "mev". This was
relatively easy to do for a continuous response variable, however,
heritability is presented as a proportion and is therefore bounded by 0
and
1 which clearly changes the situation.
In fact, I am not actually certain if it possible to conduct a formal
weighted meta-analysis on the heritability data using MCMCglmm. I have
seen
elsewhere where data presented as a proportion (survival, yolk-conversion
efficiency for example) has been logit transformed and fitted using a
Gaussian error distribution (though this was done using REML rather than
Bayesian modelling) but I don't know if this is a legitimate strategy
for a
formal meta-analysis using heritability as a response variable since any
transformation applied to the heritability data would also need to be
applied to the standard errors?
I would greatly appreciate any advice on this matter!
Cheers,
Jackie
--
Jacquelyn L.A. Wood, PhD.
Biology Department
Concordia University
7141 Sherbrooke St. West
Montreal, QC
H4B 1R6
Phone: (514) 293-7255
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