Hello all,

I’m performing a meta-analysis with correlated data (various effect sizes from the same subjects), predictors with missing data and with measurement error (i.e., sd), and a combination of both. As shown in the example below:

```
library(brms)
#covariance matrix of outcomes
cor_matrix <- function(x, r, v = rep(1, length(x)), na.rm = FALSE) {
mat <- diag(v)
se <- sqrt(v)
se[is.na(se)] <- 0
if (length(x) > 1L) {
for (i in 2:nrow(mat)) {
for (j in 1:(i-1)) {
if (x[i] == x[j]) {
mat[i, j] <- mat[j, i] <- r * se[i] * se[j]
}
}
}
}
dimnames(mat) <- list(1:nrow(mat), 1:ncol(mat))
if (na.rm) {
keep <- !is.na(diag(mat))
mat <- mat[keep, keep]
}
mat
}
#data
dat <- data.frame(
yi = rnorm(10),
x_1 = c(1, 1, NA, NA, NA, 1, NA, 3, 5, 1),
x_2 = rnorm(10),
x_2_sd = runif(n = 10, min = 0.5, max = 1.5),
x_3 = rnorm(10),
effect_ID = factor(1:10),
study_ID = factor(c(1:4,5,5,6,6,7:8)),
vi = runif(n = 10, min = .25, max = .85)
)
#matrix
V <- cor_matrix(dat$study_ID, r = 0.9, v = dat$vi)
#model
m1_f <-
bf(yi|mi() ~ 1 + mi(x_1) + mi(x_2) + x_3 +
(1|effect_ID) +
fcor(V),
sigma = 1) +
bf(x_1|mi() ~ 1) +
bf(x_2|mi(x_2_sd) ~ 1) +
set_rescor(FALSE)
m1 <-
brm(m1_f,
family = gaussian(),
data2 = list(V = V),
data = dat,
chains = 2,
save_pars = save_pars())
```

Since I have a combination of other predictors, I want to check the best-fitting model. So, to cross-validate, I used only the complete data (excluded the missing data):

```
#loo CV
m1_loo <- add_criterion(m1, newdata = na.omit(m1$data), criterion = "loo",
resp = "yi")
```

But as the variance-covariance matrix was inserted into the model when adding the “loo” criterion, the following error message appears:

```
Error: Dimensions of 'M' for FCOR terms must be equal to the number of observations.
```

My question is if is there a way to cross-validate these models, given the circumstances described above?

- Operating System: Windows 10
- brms Version: 2.16.8