Statistical Simulation in R

hello! in general, the goal of this project is to develop a simulation in the R programming statistical environment to investigate the assumptions that drive mixed-effects/random coefficients/multilevel/hierarchical linear models (so many names to describe the same thing).

in the ideal setting, the user should be able to specify:


- the number of simulation runs (as expected)
- the sample size
- the regression coefficients (so one can specify them and see if violations of the assumptions biases them)
- the intra-class or intra-cluster or intra-group correlation coefficient (again many names for the same concept)
- the probability distribution of the residuals/errors (they are always assumed to be multivariate normal. one should be ablet to change that to other skewed distributions and whatnot)

the analysis would ideally be done in the R package lme4, but if you are only familiar with nlme we can accommodate that.

my ideal candidate for this project would be a statistician/mathematician, economist or a quantitative methodologist with a very good knowledge of R, and who is also familiar with mixed-effects/random-coefficients/multilevel/heirarchical linear models. i consider myself to be an intermediate-level R user (i have ran my own simulations, albeit simpler) with a fair ammount of statistics under my belt, so i can communicate efficiently and know very clearly what i need.

a well-commented code is a *must*, since i also want to understand how things are done.

i have access to a sizeable grant so, ideally, this would be the first of a long line of simulations ideally, i hope to find someone (or a group of people) who i can give several simulation projects to work on during the year.

i sincerely appreciate your time!

- Adam