Please also provide the following information in addition to your question:

- Operating System: macOS 10.14.5
- brms Version: 2.9.2

I wanted to flag this here before opening an issue on Github in case I’m making a mistake. It appears that the re_formula argument in the fitted and predict functions fail where we specify only a multimembership grouping term with multimembership covariates. In particular, they give the following error:

```
Error: Duplicated group-level effects are not allowed.
Occured for effect 'Intercept' of group 'mmg1g2'.
```

It is possible that I am making a mistake here. But, if not, I suspect that an error to stop people adding, for example, `~ (1 + a | group) + (1 + b | group)`

, is incorrectly flagging multimembership groups (which have more than one intercept by definition) as such cases.

Reproducible example below:

```
library(brms)
# 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),
id = sample(1:3, 100, TRUE)
)
# multi-membership model with level specific covariate values
dat$xc <- (dat$x1 + dat$x2) / 2
fit <- brm(y ~ xc + (1 + mmc(x1, x2) | mm(g1, g2)) + (1 | id),
data = dat,
chains = 2,
cores = 2,
control = list(adapt_delta = .99))
summary(fit)
# simulate new data where cases aren't clustered by id
new_dat <-
data.frame(
y = rnorm(100),
x1 = rnorm(100),
x2 = rnorm(100),
g1 = sample(1:10, 100, TRUE),
g2 = sample(1:10, 100, TRUE)
)
new_dat$xc <- (new_dat$x1 + new_dat$x2) / 2
# compute fitted and predicted values for new data
fitted(fit,
re_formula = ~ (1 + mmc(x1, x2) | mm(g1, g2)),
newdata = new_dat) # Error
predict(fit,
re_formula = ~ (1 + mmc(x1, x2) | mm(g1, g2)),
newdata = new_dat) # Error
```

One of the reasons I think I might be doing something wrong is that this *is not* an issue where there are no multimembership covariates. For example, if we run the exact same code, minus the MMCs, fitted and predict produce no error message.

```
# multi-membership model with level specific covariate values
fit <- brm(y ~ 1 + (1 | mm(g1, g2)) + (1 | id),
data = dat,
chains = 2,
cores = 2,
control = list(adapt_delta = .99))
summary(fit)
# compute fitted and predicted values for new data
fitted(fit,
re_formula = ~ (1 | mm(g1, g2)),
newdata = new_dat) # No error
predict(fit,
re_formula = ~ (1 | mm(g1, g2)),
newdata = new_dat) # No error
```