bm function

Fit a Bayesian model

bm(x, ...)

## S3 method for class 'formula'
bm(
  x,
  data = NULL,
  n_save = 1000L,
  n_burn = 500L,
  options = set_options(),
  mh = set_mh(),
  verbose = TRUE,
  W,
  X_SLX,
  type = c("lm", "slx", "sar", "sem", "sdm", "sdem", "sv"),
  ...
)

## S3 method for class 'bm'
bm(x, n_save = 1000L, n_burn = 0L, verbose = TRUE, ...)

blm(...)

bslx(...)

bsar(...)

bsem(...)

bsdm(...)

bsdem(...)

bsv(...)

Arguments

• x: Formula or bm object to sample with.
• ...: Not used.
• data: A data.frame containing the variables in the model.
• n_save, n_burn: Integer scalar. Number of draws for the burn-in period and to store for inference.
• options: Settings for the prior setup. See set_options.
• mh: Settings to tune the Metropolis-Hastings step. See set_mh.
• verbose: Logical scalar. Whether to print status updates.
• W: Numeric matrix (or function to construct one) with the spatial connectivities.
• X_SLX: Numeric matrix with explanatory variables that should be lagged spatially.
• type: Character scalar used to specify the desired model.

Returns

Returns a list with draws from the specified Bayesian model and an object to obtain further samples.

Examples

N <- 100L
beta <- 1:5
X <- matrix(rnorm(N * 5), N, 5)
y <- X %*% beta + rnorm(N)

bm(y ~ X, n_burn = 100, n_draw = 100)

# Reproduce the linear model in Kuschnig (2022)
blm(log(sales) ~ log(price / cpi) + log(ndi / cpi) +
  factor(name) + factor(year), data = cigarettes)