I am trying to fit non-linear models with `brms`

. My model is:

```
y = intercept + b1 * (x1 * mean(x2^b2))
```

where:

`x1`

is a measured continuous variable,`x2`

is also a continuous variable nested in`x1`

. For more context,`x1`

is a population size and`x2`

is the mass of a subset of individuals of that population.`intercept`

,`b1`

and`b2`

are parameters to be estimated

A colleague of mine coded it in `rjags`

this way:

```
y1[i] <- x1[i]*(mean(x2_matrix[1:no_per_site[i],i]^b2))
y2[i] <- intercept + y1[i]*b1
```

Where `i`

is the site index, `x2_matrix`

has one row per individual mass (`x2`

) values and one column per site, and where `no_per_site`

is the number of `x2`

measured in each site.

Is there a way to fit this model in `brms`

? How do I manage the `x2`

matrix?

Here is the structure of the data:

```
'data.frame': 31 obs. of 3 variables:
$ population: chr "p1" "p2" "p3" "p4" ...`
$ y2 : num 592 551 1720 5135 3710 ...
$ x1 : int 145 145 72 3173 3173 1262 1262 504 504 777 ...
$ no_per_site: num 104 55 187 102 ...
```

and the x2 matrix:

```
num [1:187, 1:31] 530 600 460 510 325 420 490 430 450 350 ...
```

Thank you!