#' (Weighted) MLE of Inverse Gamma Distribution
#'
#' Inverse Gamma distribution is characterized by the following probability density function,
#' \deqn{f(x;\alpha,\beta) = \frac{\beta^\alpha}{\Gamma(\alpha)} x^{-\alpha-1} \exp\left( -\frac{\beta}{x}\right)}
#' where the domain is \eqn{x \in (0,\infty)} with two parameters \eqn{\alpha > 0} for shape and \eqn{\beta > 0} for scale.
#'
#' @param x a length-\eqn{n} vector of values in \eqn{(0,\infty)}.
#' @param weight a length-\eqn{n} weight vector. If set as \code{NULL}, it gives an equal weight, leading to standard MLE.
#'
#' @return a named list containing (weighted) MLE of \describe{
#' \item{alpha}{shape parameter \eqn{\alpha}.}
#' \item{beta}{scale parameter \eqn{\beta}.}
#' }
#'
#' @examples
#' # generate data from exponential distribution
#' x = stats::rexp(100)
#'
#' # fit unweighted
#' InvGamma(x)
#'
#' \dontrun{
#' # put random weights to see effect of weights
#' niter = 500
#' ndata = 200
#'
#' # generate data as above and fit unweighted MLE
#' x = stats::rexp(ndata)
#' xmle = InvGamma(x)
#'
#' # iterate
#' vec.alpha = rep(0,niter)
#' vec.beta = rep(0,niter)
#' for (i in 1:niter){
#' # random weight
#' ww = abs(stats::rnorm(ndata))
#'
#' MLE = InvGamma(x, weight=ww)
#' vec.alpha[i] = MLE$alpha
#' vec.beta[i] = MLE$beta
#' if ((i%%10) == 0){
#' print(paste0(" iteration ",i,"/",niter," complete.."))
#' }
#' }
#'
#' # distribution of weighted estimates + standard MLE
#' opar <- par(no.readonly=TRUE)
#' par(mfrow=c(1,2))
#' hist(vec.alpha, main="shape 'alpha'")
#' abline(v=xmle$alpha, lwd=3, col="red")
#' hist(vec.beta, main="scale 'beta'")
#' abline(v=xmle$beta, lwd=3, col="blue")
#' par(opar)
#' }
#'
#' @author Kisung You
#' @export
InvGamma <- function(x, weight=NULL){
#############################################
# Preprocessing
x = handle_cts_pos("InvGamma", x) # nonnegative real numbers
nx = length(x)
weight = handle_weight("InvGamma", weight, nx)
maceps = 10*.Machine$double.eps
#############################################
# Optimize : DEoptim
fopt.InvGamma <- function(pars){
# parameters
alpha = pars[1]
beta = pars[2]
# log-likelihood
term1 = alpha*log(beta) - base::lgamma(alpha)
term2 = (-alpha-1)*log(x)
term3 = -beta/x
loglkd = term1+term2+term3
# return
return(-sum(loglkd*weight))
}
sol = DEoptim::DEoptim(fopt.InvGamma, lower=c(maceps, maceps), upper=c(1e+5,1e+5),
control=DEoptim::DEoptim.control(trace=FALSE))$optim$bestmem
#############################################
# Return
output = list()
output$alpha = as.double(sol[1])
output$beta = as.double(sol[2])
return(output)
}
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