car {brms}  R Documentation 
Spatial conditional autoregressive (CAR) structures
Description
Set up an spatial conditional autoregressive (CAR) term in brms. The function does not evaluate its arguments – it exists purely to help set up a model with CAR terms.
Usage
car(M, gr = NA, type = "escar")
Arguments
M 
Adjacency matrix of locations. All nonzero entries are treated as
if the two locations are adjacent. If 
gr 
An optional grouping factor mapping observations to spatial locations. If not specified, each observation is treated as a separate location. It is recommended to always specify a grouping factor to allow for handling of new data in postprocessing methods. 
type 
Type of the CAR structure. Currently implemented are

Details
The escar
and esicar
types are
implemented based on the case study of Max Joseph
(https://github.com/mbjoseph/CARstan). The icar
and
bym2
type is implemented based on the case study of Mitzi Morris
(https://mcstan.org/users/documentation/casestudies/icar_stan.html).
Value
An object of class 'car_term'
, which is a list
of arguments to be interpreted by the formula
parsing functions of brms.
See Also
Examples
## Not run:
# generate some spatial data
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 covariates and response data
x1 < rnorm(K)
x2 < rnorm(K)
theta < rnorm(K, sd = 0.05)
phi < rmulti_normal(
1, mu = rep(0, K), Sigma = 0.4 * exp(0.1 * distance)
)
eta < x1 + x2 + phi
prob < exp(eta) / (1 + exp(eta))
size < rep(50, K)
y < rbinom(n = K, size = size, prob = prob)
dat < data.frame(y, size, x1, x2)
# fit a CAR model
fit < brm(y  trials(size) ~ x1 + x2 + car(W),
data = dat, data2 = list(W = W),
family = binomial())
summary(fit)
## End(Not run)