bm {bsreg}

Fit a Bayesian model

Description

Fit a Bayesian model

Usage

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.

Value

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)