These function are deprecated. Please see `car`

for the new
syntax. These functions are constructors for the `cor_car`

class
implementing spatial conditional autoregressive structures.

```
cor_car(W, formula = ~1, type = "escar")
cor_icar(W, formula = ~1)
```

- W
Adjacency matrix of locations. All non-zero entries are treated as if the two locations are adjacent. If

`formula`

contains a grouping factor, the row names of`W`

have to match the levels of the grouping factor.- formula
An optional one-sided formula of the form

`~ 1 | g`

, where`g`

is a 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 post-processing methods.- type
Type of the CAR structure. Currently implemented are

`"escar"`

(exact sparse CAR),`"esicar"`

(exact sparse intrinsic CAR),`"icar"`

(intrinsic CAR), and`"bym2"`

. More information is provided in the 'Details' section.

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://mc-stan.org/users/documentation/case-studies/icar_stan.html).

```
if (FALSE) {
# 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, data = dat,
family = binomial(), autocor = cor_car(W))
summary(fit)
}
```