add ash_pois function

NAMESPACE

@@ -49,6 +49,7 @@ S3method(prune,default)
 S3method(summary,ash)
 export(ash)
 export(ash.workhorse)
+export(ash_pois)
 export(ashci)
 export(calc_logLR)
 export(calc_loglik)

R/ash_pois.R

@@ -0,0 +1,23 @@
+#' @title Performs adaptive shrinkage on Poisson data
+#' @description Uses Empirical Bayes to fit the model \deqn{y_j | \lambda_j ~ Poi(c_j \lambda_j)} with \deqn{h(lambda_j) ~ g()}
+#' where \eqn{h} is a specified link function (either "identity" or "log" are permitted). 
+
+#' @details The model is fit in two stages: i) estimate \eqn{g} by maximum likelihood (over the set of symmetric
+#' unimodal distributions) to give estimate \eqn{\hat{g}}; 
+#' ii) Compute posterior distributions for \eqn{\lambda_j} given \eqn{y_j,\hat{g}}.
+#'    Note that the link function \eqn{h} affects the prior assumptions (because, e.g., assuming a unimodal prior on \eqn{\lambda} is
+#'    different from assuming unimodal on \eqn{\log\lambda}), but posterior quantities are always computed for the
+#'    for \eqn{\lambda} and *not* \eqn{h(\lambda)}.
+#' @param y vector of Poisson observations.
+#' @param scale vector of scale factors for Poisson observations: the model is \eqn{y[j]~Pois(scale[j]*lambda[j])}.
+#' @param link string, either "identity" or "log", indicating the link function. 
+#' 
+#' @examples 
+#'    beta = c(rep(0,50),rexp(50))
+#'    y = rpois(100,beta) # simulate Poisson observations
+#'    y.ash = ash_pois(y,scale=1)
+#' @export
+ash_pois = function(y, scale=1, link=c("identity","log"), ...){
+  link = match.arg(link)
+  ash(rep(0,length(y)),1,lik=lik_pois(y,scale,link))
+}