makebugs function

Make BUGS model from fitted DAG

Make BUGS model from fitted DAG

makebugs(dag, data.dists, coefs, stderrors)

Arguments

  • dag: named adjacency matrix representing the DAG. Names correspond to node names.
  • data.dists: list of node distributions.
  • coefs: a list named by the node names containing for each element a matrix with the nodes' coefficients.
  • stderrors: a list named by the node names containing for each element a matrix with the nodes' standard errors

Returns

Bugs model returned as stdout.

Examples

## Prepare data and arguments mydists <- list(a="gaussian", b="multinomial", c="binomial", d="poisson") mydag <- matrix(0, 4, 4, byrow = TRUE, dimnames = list(c("a", "b", "c", "d"), c("a", "b", "c", "d"))) mydag[2,1] <- mydag[3,2] <- mydag[4,3] <- 1 # plotAbn(mydag, data.dists = mydists) mycoefs <- list("a"=matrix(-6.883383e-17, byrow = TRUE, dimnames = list(NULL, "a|intercept")), "b"=matrix(c(2.18865, 3.133928, 3.138531, 1.686432, 3.134161, 5.052104), nrow= 1, byrow = TRUE, dimnames = list(c(NULL), c("b|intercept.2", "b|intercept.3", "b|intercept.4", "a.2", "a.3", "a.4"))), "c"=matrix(c(1.11, 2.22, 3.33, 4.44, 5.55), nrow= 1, byrow = TRUE, dimnames = list(c(NULL), c("c|intercept", "b1", "b2", "b3", "b4"))), "d"=matrix(c(3.33, 4.44), nrow= 1, byrow = TRUE, dimnames = list(c(NULL), c("d|intercept", "c")))) mymse <- c("a"=0,"b"=1,"c"=2,"d"=3) ## Make BUGS model makebugs(dag = mydag, data.dists = mydists, coefs = mycoefs, stderrors = mymse)

See Also

simulateAbn gauss_bugs bern_bugs categorical_bugs pois_bugs