R/binom.PP.FB.MC.R

In StudyPrior: Distributions for Historical Information

#' Full Bayes Power Prior
#'
#' @param x number of historical successes
#' @param n number historical patients
#' @param verbose Print messages
#' @param length Number of points to evaluate density at
#' @param mc.cores Number of cores for parallel
#' @param samples Number of Monte Carlo samples
#' @param d.prior.a shape1 parameter for beta prior on weights
#' @param d.prior.b shape2 parameter for beta prior on weights
#' @param focus List of triples specifying regions to focus on, eg peaks. Specified as list(c(lower, upper, length))
#'
#' @return A density function
#' @export
#'

binom.PP.FB.MC <- function(x, n, verbose=FALSE, length=30, d.prior.a=1, d.prior.b=1, mc.cores=1, samples=10000, focus){
  n.hist <- length(x)

  ddbinom <- function(x, size, prob, delta) dbinom(x,size,prob)^delta

  cex <- function(d, x, n) integrate(function(p) sapply(p, function(PROB)
    prod(mapply(ddbinom, x=x, size=n, delta=d, prob=PROB))),
    lower=0,upper=1 )

  p <- seq(0, 1, len=length)
  if(!missing(focus)){
    p2 <- unlist(sapply(focus, function(f) seq(from=f[1], to=f[2], length.out = f[3])))
    p <- unique(sort(c(p,p2)))
  }



  # if(missing(dprior)) dprior <- function(d) (d>=0 & d<=1)*1

  dens <- mclapply(p, function(PROB){
    # print(PROB)

    eval.f <- function(d) {
      prod(mapply(ddbinom, x=x, size=n, delta=d, prob=PROB))/
      cex(d,x,n)$value}

    mean(apply(
      matrix(rbeta(samples*n.hist, d.prior.a, d.prior.b),ncol=n.hist),
      1,eval.f))
  },mc.cores=mc.cores)

  f <- splinefun(smooth.spline(x=p, y=dens))

  k <- integrate(f, 0,1)$value

  f <- splinefun(x=p, y=unlist(dens)/k)

  g <- function(p,X) ifelse(0<=p&p<=1, f(p),0)

  return(g)

}