Is it possible to weight residuals in MCMCglmm?

Hi,

It is not possible currently - the inflexibility of the residual  
structure was due to an oversight when I was writing MCMCglmm which I  
haven't had time to go back and change. As I'm sure you are aware  
there are three options, none of which are exactly what you want (I  
think).

Lets have w as a set of values associated with each observation and  
are proportional to the standard error of each measurement  (m).

The three options are

a) specifying mev=w^2 which fits:

VAR(m_{i}) =  w_{i}^2+VAR(e)

b) specifying random=~leg(sqrt(w), -1, FALSE):units which fits:

VAR(m_{i}) =  (c*w_{i})^2+VAR(e)

where c is the square root of the variance associated with the random effects.

c) specifying rcov=~idh(units):units and  prior=list(diag(w^2), fix=1)  
  which fits:

VAR(m_{i}) =  w_{i}^2

where c is the square root of the variance associated with the random effects.

I think you would like

VAR(m_{i}) =  (c*w_{i})^2

which is not possible


Cheers,

Jarrod


Quoting Szymek Drobniak <geralttee at gmail.com>:

Is there any simple way to fit weighted residuals in MCMCglmm? I'd like to
specify model where I could give different data points different influence
on the fit of the model. Is there any way of doing this beside of course
actually doing sth to the data? I was thinking of modifying the
meta-analytical approach but I got stuck there...

Cheers,
sz.

--
Szymon Drobniak || Population Ecology Group
*Institute of Environmental Sciences, Jagiellonian University
ul. Gronostajowa 7, 30-387 Krak?w, POLAND
*tel.: +48 12 664 52 19 fax: +48 12 664 69 12

www.eko.uj.edu.pl/drobniak

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