Nothing
#' Hellinger distance between two MCMC chains for sensitivity studies
#'
#' Determine if two identically dimensioned sets of chains match. This is good for conducting sensitivity studies.
#'
#' @param inputlist1 A list of the combined MCMC chains for all samples from one scenario.
#' @param inputlist2 A list of the combined MCMC chains for all samples from another scenario.
#' @references Boone EL, Merrick JR and Krachey MJ.
#' A Hellinger distance approach to MCMC diagnostics.
#' Journal of Statistical Computation and Simulation,
#' \code{DOI:10.1080/00949655.2012.729588}.
#' @export
#' @examples
#' data(MCMCsamples)
#' bmksensitive(MCMC.one.mean0, MCMC.one.mean1)
#' \dontrun{
#' library(dismo); library(MCMCpack)
#' data(Anguilla_train)
#' b0mean0 <- 0
#' b0mean1 <- 1
#' b0precision <- (1/5)^2
#' mcmclen = 1000
#' burn=10000
#' MCMC.one.mean0 <- MCMClogit(Angaus ~ SegSumT+DSDist+USNative+as.factor(Method)+DSMaxSlope+USSlope,
#' data=Anguilla_train,burnin=burn, mcmc=mcmclen, beta.start=-1,
#' b0=b0mean0, B0=b0precision)
#' MCMC.one.mean1 <- MCMClogit(Angaus ~ SegSumT+DSDist+USNative+as.factor(Method)+DSMaxSlope+USSlope,
#' data=Anguilla_train,burnin=burn, mcmc=mcmclen, beta.start=-.5,
#' b0=b0mean1, B0=b0precision)
#' bmksensitive(one, two)
#' }
bmksensitive = function(inputlist1, inputlist2){
require(functional)
require(plyr)
uncolnames = Compose(colnames, unique)
lenuni = Compose(unique, length)
n.variables = ncol(inputlist1)
n.iter = nrow(inputlist1)
n.var = ncol(inputlist1)
dataset1 = inputlist1
dataset2 = inputlist2
output = matrix(0, nrow = n.var, ncol =1 )
for(i in 1:n.variables){
output[i,1] <- HDistNoSize(dataset1[,i],dataset2[,i])
}
rownames(output) <- colnames(dataset1)
colnames(output) <- "HellingerDist"
output
}
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.