bayes.lm.conjugate {spBayes} | R Documentation |
Given an lm
object, the bayes.lm.conjugate
function fits a
simple Bayesian linear model with Normal and inverse-Gamma priors.
bayes.lm.conjugate(formula, data = parent.frame(), n.samples, beta.prior.mean, beta.prior.precision, prior.shape, prior.rate, ...)
formula |
for a univariate model, this is a symbolic description of the regression model to be fit. See example below. |
data |
an optional data frame containing the variables in the
model. If not found in data, the variables are taken from
environment(formula) , typically the environment from which sp.lm is called. |
n.samples |
the number of posterior samples to collect. |
beta.prior.mean |
beta multivariate normal mean vector hyperprior. |
beta.prior.precision |
beta multivariate normal precision matrix hyperprior. |
prior.shape |
sigma.sq inverse-Gamma shape hyperprior. |
prior.rate |
sigma.sq inverse-Gamma 1/scale hyperprior. |
... |
currently no additional arguments. |
A CODA mcmc
matrix object with columns corresponding to each
parameter and posterior samples held in the rows.
Sudipto Banerjee sudiptob@biostat.umn.edu,
Andrew O. Finley finleya@msu.edu
## Not run: data(FORMGMT.dat) n = nrow(FORMGMT.dat) p = 7 ##an intercept and six covariates n.samples <- 500 ## Below we demonstrate the conjugate function in the special case ## with improper priors. The results are the same as for the above, ## up to MC error. beta.prior.mean <- rep(0, times=p) beta.prior.precision <- matrix(0, nrow=p, ncol=p) prior.shape <- -p/2 prior.rate <- 0 m.1 <- bayes.lm.conjugate(Y ~ X1+X2+X3+X4+X5+X6, data = FORMGMT.dat, n.samples, beta.prior.mean, beta.prior.precision, prior.shape, prior.rate) summary(m.1) ## End(Not run)