# Specification of scaling factor in brms BYM2 model

Hi Stan community,

I’m relatively new to the syntax of `brms`, and unfortunately I can’t find any information about this on the page detailing how to specify a CAR term within a brms model.

I am trying to specify a BYM2 model (a variant of the ICAR model) in `brms`. This is relatively easy to do as detailed here: Spatial conditional autoregressive (CAR) structures — car • brms

Following the guide, I set such a term in my model. Since it’s complex, I’m using a simplified example here. (In my actual model I do specify `gr` in my CAR term).

For the example, I’m using the same setup as in the guide linked above, except I generate a dummy count variable for the response and set `family=poisson()`. I set the priors for `rhocar` and `sdcar` according to the case studies that the BYM2 model in `brms` is based on: Spatial Models in Stan: Intrinsic Auto-Regressive Models for Areal Data

However, the article also mentions a scaling factor, and I’m wondering whether I need to set this myself in the model. I tried two ways of doing so, both of which returned the error that `car_scale` is not a recognised argument. (I named it `car_scale` because after calling `make_stancode`, I saw that the underlying Stan code has an argument named `car_scale` for setting this parameter).

``````library(brms)

east <- north <- 1:10
Grid <- expand.grid(east, north)
K <- nrow(Grid)

# set up distance and neighbourhood matrices
distance <- as.matrix(dist(Grid))
W <- array(0, c(K, K))
W[distance == 1] <- 1

# generate the covariate and response data
x <- rnorm(K)

lambda <- 4
y <- rpois(K,lambda)

df <- data.frame(y,x)

prior_bym <- c(prior(normal(0,5), class=Intercept),
prior(normal(0,1),class=b,coef="x"),
prior(beta(0.5,0.5),class=rhocar),
prior(exponential(1),class=sdcar)
)

fit <- brm(y ~ x + car(W, type="bym2"),
data = df, data2 = list(W = W),
family = poisson(),
prior = prior_bym,
iter = 500, warmup = 200, chains = 2, # some arbitrary amount
)

``````

So far, I have tried to set `car_scale = scaling factor` in the CAR term, but without success:

``````scaling_factor = 0.5 # just an example

fit <- brm(y ~ x + car(W, type="bym2",car_scale = scaling_factor), # doesn't work
data = df, data2 = list(W = W),
family = poisson(),
prior = prior_bym,
iter = 500, warmup = 200, chains = 2, # some arbitrary amount
)
``````

I also tried setting it in `brm`, likewise without success:

``````fit <- brm(y ~ x + car(W, type="bym2"),
data = df, data2 = list(W = W),
car_scale = scaling_factor, # doesn't work
family = poisson(),
prior = prior_bym,
iter = 500, warmup = 200, chains = 2, # some arbitrary amount