Hi,

I have a (somewhat) a meta-analysis problem, where I want to estimate the effect and incorporate the standard errors.

*The toy data are in a dput under this question*

My (simplified) model looks something like this:

```
fit1 <- brm(estimate0 | se(ses0, sigma = TRUE) ~ 0 + Intercept,
data = test_data, chains = 2, cores = 2,
backend = "cmdstanr")
```

However, I would also like to have multivariate estimation for several parameters and their related standard errors.

So, something like this:

```
test_formula <- bf(
estimate0 | se(ses0) ~ 0 + Intercept,
estimate1 | se(ses1) ~ 0 + Intercept,
estimate2 | se(ses2) ~ 0 + Intercept
)
fit2 <- brm(test_formula,
data = test_data, chains = 2, cores = 2,
backend = "cmdstanr")
```

But if I run it, I get the

```
Error: The parameter 'estimate1' is not a valid distributional or non-linear parameter. Did you forget to set 'nl = TRUE'?
```

error. And I am not entirely sure where should I put the `nl = TRUE`

parameter. If I append it after the formula, I get the same error message.

Is this even possible to do in brms? Maybe I’m trying to do something that is simply not doable?

Thanks!

```
MacOS
brms_2.20.1
```

```
structure(list(estimate0 = c(-2.74876373618311, -2.97829517727648,
-3.31587179960804, -2.74622067026356, -1.97689255696711, -2.38463858769863,
-2.657051516499, -3.5771158198616), estimate1 = c(3.30028118767104,
3.09510917773597, 2.47541717757146, 3.03921954392546, 2.73840806363676,
2.53757808570781, 3.54507678579056, 4.46954149336329), estimate2 = c(1.72890925213394,
2.75946765179713, 1.65250309785428, 0.949840140911668, -0.793179529482053,
0.128334536390859, -0.984729153591692, 2.30698570416319), ses0 = c(0.729243965561863,
0.591756617157621, 0.670143362916103, 0.389684055042382, 0.459217661595079,
0.364738955731592, 0.261757424628417, 0.495091983220812), ses1 = c(0.943796250030539,
0.617538169919119, 0.68466894862678, 0.533789454127428, 0.70084072236695,
0.540122226154277, 0.471813632890032, 0.629923958348362), ses2 = c(1.87350515405701,
2.01540207975469, 1.29377800859925, 1.27629805597475, 1.002161114072,
0.985230655070162, 0.65625735868238, 1.80747985819466)), row.names = c(NA,
-8L), class = c("tbl_df", "tbl", "data.frame"))
```