R/HDIofMCMC.R

In MirkoTh/BayesRS: Bayes Factors for Hierarchical Linear Models with Continuous Predictors

HDIofMCMC = function( sampleVec , credMass=0.95 ) {
    # Computes highest density interval from a sample of representative values,
    #   estimated as shortest credible interval.
    # Arguments:
    #   sampleVec
    #     is a vector of representative values from a probability distribution.
    #   credMass
    #     is a scalar between 0 and 1, indicating the mass within the credible
    #     interval that is to be estimated.
    # Value:
    #   HDIlim is a vector containing the limits of the HDI
    sortedPts = sort( sampleVec )
    ciIdxInc = floor( credMass * length( sortedPts ) )
    nCIs = length( sortedPts ) - ciIdxInc
    ciWidth = rep( 0 , nCIs )
    for ( i in 1:nCIs ) {
        ciWidth[ i ] = sortedPts[ i + ciIdxInc ] - sortedPts[ i ]
    }
    HDImin = sortedPts[ which.min( ciWidth ) ]
    HDImax = sortedPts[ which.min( ciWidth ) + ciIdxInc ]
    HDIlim = c( HDImin , HDImax )
    return( HDIlim )
}