I am currently working on getting the package ready for a submission to CRAN.
The rmm package provides an interface to fit Bayesian multiple membership multilevel models with endogenized weights using JAGS from within R for a variety of outcomes (linear, logit, conditional logit, Cox, Weibull).
Most multilevel analyses examine how lower-level units (e.g. individuals) are affected by their embedding in contextual/aggregate units at a higher level (e.g. neighborhoods) (= macro-micro link). The rmm package uses the multiple membership multilevel model to conceptually reverse this setup. It allows studying how the effect of units at lower levels propagates to a higher level (= micro-macro link).
Previous studies examining micro-macro links either aggregated or disaggregated the data. Both approaches obstruct the inherent aggregation problem, ignore dependencies among observations, which induces excessive Type-I and Type-II error, and cannot separate micro- from macro-level variance. The generalized multiple membership multilevel model (MMMM) is able to overcome these problems by explicitly modeling the aggregation from micro to macro level by including an aggregation function in the regression model. It is a theoretically and statistically sound solution to the study of micro-macro links with regression analysis.
explicitly model how the effect of lower level units propagate to a higher level
examine the fit of different aggregation functions, such as the min, max, mean, or sum
uncover heterogeneity in the effect of lower-level units on higher-level entities
control dependencies that arise from a multiple membership (crisscrossing) data structure
estimate the (residual) variance at the lower and the higher level
This is the data structure that is being modeled:
In this example of coalition governments in Israel, political parties are the level-1 units, governments are the level-2 units, and the relationship between parties and governments is the multiple membership structure.
This is just an example. Multiple membership structures are ubiquitous in the real world. Other applications include:
This is how the
rmm() function looks like:
I welcome contributions to the package! Feel free to submit changes for review or contact me if you have any questions.
If you would like to log an issue or submit a feature request, please create a new issue or comment on an existing issue on GitHub Issues on this repo.