Function to set up a multi-membership grouping term in brms. The function does not evaluate its arguments -- it exists purely to help set up a model with grouping terms.

mm(
...,
weights = NULL,
scale = TRUE,
by = NULL,
cor = TRUE,
id = NA,
cov = NULL,
dist = "gaussian"
)

## Arguments

... One or more terms containing grouping factors. A matrix specifying the weights of each member. It should have as many columns as grouping terms specified in .... If NULL (the default), equally weights are used. Logical; if TRUE (the default), weights are standardized in order to sum to one per row. If negative weights are specified, scale needs to be set to FALSE. An optional factor matrix, specifying sub-populations of the groups. It should have as many columns as grouping terms specified in .... For each level of the by variable, a separate variance-covariance matrix will be fitted. Levels of the grouping factor must be nested in levels of the by variable matrix. Logical. If TRUE (the default), group-level terms will be modelled as correlated. Optional character string. All group-level terms across the model with the same id will be modeled as correlated (if cor is TRUE). See brmsformula for more details. An optional matrix which is proportional to the withon-group covariance matrix of the group-level effects. All levels of the grouping factor should appear as rownames of the corresponding matrix. This argument can be used, among others, to model pedigrees and phylogenetic effects. See vignette("brms_phylogenetics") for more details. By default, levels of the same grouping factor are modeled as independent of each other. Name of the distribution of the group-level effects. Currently "gaussian" is the only option.

brmsformula, mmc

## Examples

if (FALSE) {
# simulate some data
dat <- data.frame(
y = rnorm(100), x1 = rnorm(100), x2 = rnorm(100),
g1 = sample(1:10, 100, TRUE), g2 = sample(1:10, 100, TRUE)
)

# multi-membership model with two members per group and equal weights
fit1 <- brm(y ~ x1 + (1|mm(g1, g2)), data = dat)
summary(fit1)

# weight the first member two times for than the second member
dat$w1 <- rep(2, 100) dat$w2 <- rep(1, 100)
fit2 <- brm(y ~ x1 + (1|mm(g1, g2, weights = cbind(w1, w2))), data = dat)
summary(fit2)

# multi-membership model with level specific covariate values
dat$xc <- (dat$x1 + dat\$x2) / 2
fit3 <- brm(y ~ xc + (1 + mmc(x1, x2) | mm(g1, g2)), data = dat)
summary(fit3)
}