R/algorithms__log_prior.R

In feng-li/movingknots: Efficient Bayesian multivariate surface regression

#' A collection of log densities for common priors.
#'
#' The parameters after "..." should be matched exactly.
#'
#' @title logarithm density for priors
#'
#' @param B "matrix".  The paramter that need to be added with a prior. The prior density
#'     is calculated conditional on B. B should be always an one-column matrix,
#' @param priorArgs "list".  when priorArgs$prior_type: is set to "mvnorm", you have to
#'     provide priorArgs$mean: "matrix", the mean of parameter, mu0 should be always an
#'     one-column matrix; priorArgs$covariance: "matrix", the covariance matrix. A
#'     g-prior can be constructed by setting it to X'X, where X is the covariates
#'     matrix.; priorArgs$shrinkage: "numeric", the shrinkage for the covariance.
#'
#' @return "scalor". The log density of priors.
#'
#' @author Feng Li, Department of Statistics, Stockholm University, Sweden.
#' @export
log_prior <- function(B, priorArgs)
{
  if (tolower(priorArgs$prior_type) == "mvnorm") # vecB ~ N(mu0, c0*Sigma0)
    {

      mean <- priorArgs$mean
      covariance <- priorArgs$covariance
      shrinkage <- priorArgs$shrinkage

      log.density <- dmvnorm(t(B), mean, covariance*shrinkage, log = TRUE)

    }# if (tolower(prior_type) == "mvnorm"

  return(log.density)
}