

Dear all,

I kindly ask the help of this community with the following issue. My research question is about individual-level variance of employment stability in several European countries. The number of countries is small (N=13), therefore MCMC is a more proper estimation method than ML. Variance on the country level doesn't interest me as much, but I do have to take it into account of course. 
When I run my model in R/MLwiN via R2MLwiN package, its formula is specified as approximately follows: 
y ~ (0|cons+careertype+gender) + (1|cons+careertype) + (2|cons)
To decode: there fixed part contains the intercept, career type (more or less job mobility) and gender. Random effects on the individual level entail the constant and career type, whereas random effects on level to include only the constant. Therefore it is a random intercept model with heteroskedasticity on level one. For more details on this model please see http://stats.stackexchange.com/questions/83148/specifying-a-multilevel-model-in-mcmcglmm-r-that-is-heteroskedastic-at-level, I have included a screenshot there to give a better idea. 
Is it possible at all to estimate a similar model in MCMCglmm? I cannot figure out how to specify variance at level one to be dependent on one of the predictors. 
Thank you so much in advance for any tips!
Kind regards,Maxim  		 	   		  
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