Bayesian Multilevel Models for Micro-Macro Analysis (Multiple Membership) Using JAGS

The bml package estimates micro-to-macro regressions: Bayesian multiple-membership multilevel models in which the aggregation from member-level records to group-level outcomes is an explicit, estimable object — weights (w), aggregation functions (fn: mean, variance, concentration, thresholds, smooth max/min, CES means, covariance, or a user-written reduction), member/unit random and fixed effects (re, fe), and cross-level or feature-by-feature interactions via named blocks.

JAGS must be installed separately: https://sourceforge.net/projects/mcmc-jags/.

Usage

bml(
  formula,
  data,
  family = stats::gaussian(),
  prior = NULL,
  inits = NULL,
  iter = 1000,
  warmup = 500,
  thin = max(1, floor((iter - warmup)/1000)),
  chains = 3,
  cores = 1,
  seed = NULL,
  run = TRUE,
  monitor = TRUE,
  modelfile = FALSE,
  ...
)

Arguments

formula: A symbolic model formula; see Details.
data: Data frame in member-level (long) format: one row per membership. Must contain all variables referenced in the formula, including the identifiers in id().
family: Model family: a family function (gaussian(), bernoulli(), weibull(), cox()) or a string ("gaussian", "bernoulli", "weibull", "cox"). Cox baseline-hazard intervals travel with the family: cox(intervals = 10).
prior: Prior specifications built with prior and combined with c(); see get_prior for the settable parameters. Raw JAGS strings (e.g. "b.fn.1[1] ~ dnorm(0, 0.1)") pass through untranslated as an escape hatch.
inits: List of initial values for MCMC chains (applied to all chains). Weight and fn shape parameters get stable defaults automatically; user values override.
iter: Total number of MCMC iterations per chain. Default: 1000.
warmup: Number of warmup (burn-in) iterations discarded from the start of each chain. Default: 500.
thin: Thinning rate. Default targets ~1000 retained draws.
chains: Number of MCMC chains. Default: 3.
cores: Number of cores; cores > 1 runs chains in parallel. Default: 1.
seed: Integer random seed for reproducibility.
run: Logical; if FALSE, returns the generated JAGS model string, data, and monitors without fitting (see also make_jagscode).
monitor: Logical; if TRUE (default), store full MCMC chains and fitted/predicted/log-likelihood nodes. Required for most post-estimation methods (as_draws, loo.bml, posterior_predict.bml, monetPlot, mcmcDiag).
modelfile: Logical or character path: TRUE saves the generated JAGS code to modelstring.txt; a path reads JAGS code from that file instead of generating it.
...: Not used; catches removed legacy arguments (n.iter, n.burnin, n.thin, n.chains, parallel, priors, cox_intervals) with a migration message.

Value

A list of class "bml" with elements reg.table (posterior summaries; labeled terms), w (weight matrices per block), re.mm/re.hm (random effects), pred (posterior predictive means), fitted (posterior means of the linear predictor), input (model metadata), and jags.out (full R2jags output when monitor = TRUE).

Details

The general formula structure is


outcome ~ 1 + predictors + mm(...) + hm(...)

where mm defines a multiple-membership block (the micro-macro link) and hm a hierarchical nesting level. Multiple mm() blocks stack features (e.g. mean + variance + concentration); multiple hm() blocks give cross-classification. For survival families use Surv(time, event) on the left-hand side.

Named mm() blocks can be referenced in main-formula interactions:


Y ~ education + Ax:education +
  mm(name = Ax, id = id(task, occ), vars = vars(x), w = w(~ importance), fn = fn("sum"))

Both cross-level (feature x macro variable) and block x block (feature x feature) interactions are supported; the macro variable must also appear as a main effect, and a bare (non-interacted) block reference is an error.

The package is introduced in Rosche (2026), Political Analysis.

References

Rosche, B. (2026). A Multilevel Model for Coalition Governments: Uncovering Party-Level Dependencies Within and Between Governments. Political Analysis.

Browne, W. J., Goldstein, H., & Rasbash, J. (2001). Multiple membership multiple classification (MMMC) models. Statistical Modelling, 1(2), 103-124.

Author

Benjamin Rosche <benrosche@nyu.edu>

Examples

if (FALSE) { # \dontrun{
data(coalgov)

# Additive aggregation with member random effects, country nesting
m1 <- bml(
  Surv(dur_wkb, event_wkb) ~ 1 + majority +
    mm(id = id(pid, gid), vars = vars(cohesion), w = w(~ 1/n), fn = fn("sum"), RE = TRUE) +
    hm(id = id(cid)),
  data = coalgov,
  family = weibull()
)
summary(m1)

# Estimated weights (b0, b1 are free parameters by the one-parameter rule)
m2 <- bml(
  Surv(dur_wkb, event_wkb) ~ 1 + majority +
    mm(id = id(pid, gid), vars = vars(cohesion),
       w = w(~ ilogit(b0 + b1 * pseat), scale = TRUE),
       fn = fn("sum")),
  data = coalgov,
  family = weibull()
)

# Emergent features: variance + concentration, stacked
m3 <- bml(
  sim.y ~ 1 + majority +
    mm(id = id(pid, gid), vars = vars(cohesion), w = w(~ 1/n), fn = fn("var")) +
    mm(id = id(pid, gid), w = w(~ pseat, scale = TRUE), fn = fn("hhi")),
  data = coalgov,
  family = gaussian()
)

# Structured priors
m4 <- bml(
  sim.y ~ 1 + majority +
    mm(id = id(pid, gid), vars = vars(cohesion), w = w(~ 1/n), fn = fn("sum"), RE = TRUE),
  data = coalgov,
  family = gaussian(),
  prior = c(
    prior(normal(0, 5), class = "b"),
    prior(exponential(1), class = "sd")
  )
)
} # }