bayesr: Generic model fitting function for Bayesian regression models
In BayesR: Bayesian Regression in R

Usage Arguments Details Value Author(s) References Examples

View source: R/BayesR.R

bayesr(formula, family = gaussian.BayesR,
  data = NULL, knots = NULL, weights = NULL, subset = NULL,
  offset = NULL, na.action = na.fail, contrasts = NULL,
  reference = NULL, parse.input = parse.input.bayesr,
  transform = transformJAGS, setup = setupJAGS,
  sampler = samplerJAGS, results = resultsJAGS,
  cores = NULL, sleep = NULL, combine = TRUE,
  model = TRUE, ...)

`formula`	symbolic description of the model (of type `y ~ x`), also see `formula.gam` and `s`.
`family`	family object specifying the distribution and link to use in fitting etc.?
`data`	a `data.frame` or `list` containing the model response variable and covariates required by the formula. By default the variables are taken from `environment(formula)`: typically the environment from which `bayesx` is called.
`knots`	see function `gam`.
`weights`	prior weights on the data.
`subset`	an optional vector specifying a subset of observations to be used in the fitting process.
`offset`	can be used to supply a model offset for use in fitting.
`na.action`	a function which indicates what should happen when the data contain `NA`'s. The default is set by the `na.action` setting of `options`, and is `na.omit` if set to `NULL`.
`contrasts`	an optional list. See the `contrasts.arg` of `model.matrix.default`.
`reference`	character or integer. The reference category for categorical response models.
`parse.input`	function, used to parse all input parameters, see function `parse.bayesr.input`.
`transform`	function, a transformator function to be applied on smooth term objects.
`setup`	MCMC setup function or named list controlling arguments of the default setup function.
`sampler`	MCMC sampler function or named list controlling arguments of the default MCMC sampler function.
`results`	function, a function that computes the desired results from MCMC samples and data.
`cores`	integer. How many cores should be used? The default is one core if `cores = NULL`. If `cores` > 1, the return value is again a list of class `"bayesr"`, for which all plotting and extractor functions can be applied, see argument `chains`. Note that this option is not available on Windows systems, see the documentation of function `mclapply`.
`sleep`	numeric, when using the multiple core option, sleep determines the waiting time until the next process is started.
`combine`	logical, should samples from different chains/cores already be combined within `bayesr`?
`model`	should the model frame be returned?
`...`	arguments passed to `setup`, `sampler`, `results`.

...

Nikolaus Umlauf, Stefan Lang, Achim Zeileis.

...

## Not run: ## generate some data
set.seed(111)
n <- 200

## regressor
dat <- data.frame(x = runif(n, -3, 3))

## response
dat$y <- with(dat, 1.5 + sin(x) + rnorm(n, sd = 0.6))

## estimate model
b <- bayesr(y ~ s(x), data = dat)

## plot fitted model term
plot(b)

## End(Not run)