vreg | R Documentation |
This function is intended to be used on the right hand side of the formula.V
argument to
create_sampler
or generate_data
.
vreg(
formula = NULL,
remove.redundant = FALSE,
sparse = NULL,
X = NULL,
prior = NULL,
Q0 = NULL,
b0 = NULL,
name = ""
)
formula |
a formula for the regression effects explaining the log-variance.
Variable names are looked up in the data frame passed as |
remove.redundant |
whether redundant columns should be removed from the design matrix.
Default is |
sparse |
whether the model matrix associated with |
X |
a (possibly sparse) design matrix can be specified directly, as an alternative to the
creation of one based on |
prior |
prior specification for the coefficients. Currently only
normal priors are supported, specified using function |
Q0 |
prior precision matrix for the regression effects. The default is a
zero matrix corresponding to a noninformative improper prior.
DEPRECATED, please use argument |
b0 |
prior mean for the regression effect. Defaults to a zero vector.
DEPRECATED, please use argument |
name |
the name of the model component. This name is used in the output of the
MCMC simulation function |
An object with precomputed quantities and functions for sampling from prior or conditional posterior distributions for this model component. Intended for internal use by other package functions.
E. Cepeda and D. Gamerman (2000). Bayesian modeling of variance heterogeneity in normal regression models. Brazilian Journal of Probability and Statistics, 207-221.
T.I. Lin and W.L. Wang (2011). Bayesian inference in joint modelling of location and scale parameters of the t distribution for longitudinal data. Journal of Statistical Planning and Inference 141(4), 1543-1553.
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