bm: Fit a Bayesian model

In bsreg: Bayesian Spatial Regression Models

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)

bsreg documentation built on March 18, 2022, 5:36 p.m.