LPL.interval: Lowest Posterior Loss Interval
In LaplacesDemon: Complete Environment for Bayesian Inference

Description Usage Arguments Details Value Author(s) References See Also Examples

This function returns the Lowest Posterior Loss (LPL) interval for one parameter, given samples from the density of its prior distribution and samples of the posterior distribution.

1	LPL.interval(Prior, Posterior, prob=0.95, plot=FALSE, PDF=FALSE)

`Prior`	This is a vector of samples of the prior density.
`Posterior`	This is a vector of posterior samples.
`prob`	This is a numeric scalar in the interval (0,1) giving the Lowest Posterior Loss (LPL) interval, and defaults to 0.95, representing a 95% LPL interval.
`plot`	Logical. When `plot=TRUE`, two plots are produced. The top plot shows the expected posterior loss. The LPL region is shaded black, and the area outside the region is gray. The bottom plot shows LPL interval of theta on the kernel density of theta. Again, the LPL region is shaded black, and the outside area is gray. A vertical, red, dotted line is added at zero for both plots. The `plot` argument defaults to `FALSE`. The plot treats the distribution as if it were unimodal; disjoint regions are not estimated here. If multimodality should result in disjoint regions, then consider using HPD intervals in the `p.interval` function.
`PDF`	Logical. When `PDF=TRUE`, and only when `plot=TRUE`, plots are saved as a .pdf file in the working directory.

The Lowest Posterior Loss (LPL) interval (Bernardo, 2005), or LPLI, is a probability interval based on intrinsic discrepancy loss between prior and posterior distributions. The expected posterior loss is the loss associated with using a particular value theta[i] in theta of the parameter as the unknown true value of theta (Bernardo, 2005). Parameter values with smaller expected posterior loss should always be preferred. The LPL interval includes a region in which all parameter values have smaller expected posterior loss than those outside the region.

Although any loss function could be used, the loss function should be invariant under reparameterization. Any intrinsic loss function is invariant under reparameterization, but not necessarily invariant under one-to-one transformations of data x. When a loss function is also invariant under one-to-one transformations, it is usually also invariant when reduced to a sufficient statistic. Only an intrinsic loss function that is invariant when reduced to a sufficient statistic should be considered.

The intrinsic discrepancy loss is easily a superior loss function to the overused quadratic loss function, and is more appropriate than other popular measures, such as Hellinger distance, Kullback-Leibler divergence (KLD), and Jeffreys logarithmic divergence. The intrinsic discrepancy loss is also an information-theory related divergence measure. Intrinsic discrepancy loss is a symmetric, non-negative loss function, and is a continuous, convex function. Intrinsic discrepancy loss was introduced by Bernardo and Rueda (2002) in a different context: hypothesis testing. Formally, it is:

delta f(p[2],p[1]) = min[kappa(p[2] | p[1]), kappa(p[1] | p[2])]

where delta is the discrepancy, kappa is the KLD, and p[1] and p[2] are the probability distributions. The intrinsic discrepancy loss is the loss function, and the expected posterior loss is the mean of the directed divergences.

The LPL interval is also called an intrinsic credible interval or intrinsic probability interval, and the area inside the interval is often called an intrinsic credible region or intrinsic probability region.

In practice, whether a reference prior or weakly informative prior (WIP) is used, the LPL interval is usually very close to the HPD interval, though the posterior losses may be noticeably different. If LPL used a zero-one loss function, then the HPD interval would be produced. An advantage of the LPL interval over HPD interval (see p.interval) is that the LPL interval is invariant to reparameterization. This is due to the invariant reparameterization property of reference priors. The quantile-based probability interval is also invariant to reparameterization. The LPL interval enjoys the same advantage as the HPD interval does over the quantile-based probability interval: it does not produce equal tails when inappropriate.

Compared with probability intervals, the LPL interval is slightly less convenient to calculate. Although the prior distribution is specified within the Model specification function, the user must specify it for the LPL.interval function as well. A comparison of the quantile-based probability interval, HPD interval, and LPL interval is available here: https://web.archive.org/web/20150214090353/http://www.bayesian-inference.com/credible.

A matrix is returned with one row and two columns. The row represents the parameter and the column names are "Lower" and "Upper". The elements of the matrix are the lower and upper bounds of the LPL interval.

Statisticat, LLC.

Bernardo, J.M. (2005). "Intrinsic Credible Regions: An Objective Bayesian Approach to Interval Estimation". Sociedad de Estadistica e Investigacion Operativa, 14(2), p. 317–384.

Bernardo, J.M. and Rueda, R. (2002). "Bayesian Hypothesis Testing: A Reference Approach". International Statistical Review, 70, p. 351–372.

KLD, p.interval, LaplacesDemon, and PMC.

library(LaplacesDemon)
#Although LPL is intended to be applied to output from LaplacesDemon or
#PMC, here is an example in which p(theta) ~ N(0,100), and
#p(theta | y) ~ N(1,10), given 1000 samples.
theta <- rnorm(1000,1,10)
LPL.interval(Prior=dnorm(theta,0,100^2), Posterior=theta, prob=0.95,
  plot=TRUE)
#A more practical example follows, but it assumes a model has been
#updated with LaplacesDemon or PMC, the output object is called Fit, and
#that the prior for the third parameter is normally distributed with
#mean 0 and variance 100: 
#temp <- Fit$Posterior2[,3]
#names(temp) <- colnames(Fit$Posterior2)[3]
#LPL.interval(Prior=dnorm(temp,0,100^2), Posterior=temp, prob=0.95,
#     plot=TRUE, PDF=FALSE)