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.
Adjacency matrix of locations. All non-zero entries are treated as
if the two locations are adjacent. If
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 post-processing methods.
Type of the CAR structure. Currently implemented are
esicar types are
implemented based on the case study of Max Joseph
bym2 type is implemented based on the case study of Mitzi Morris
An object of class
'car_term', which is a list
of arguments to be interpreted by the formula
parsing functions of brms.
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## 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)
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