utils_newalphbet: Computes Beta Parameters given a Mean and Variance

In EpiBayes: Implements Hierarchical Bayesian Models for Epidemiological Applications

Description

This function is designed to approximate a density of a bounded (0, 1) function with a beta distribution by equating the mean and variance to the first and second shape parameters. It does do by using the fact that:

μ = α / (α + β)

σ^2 = (α β) / [(α + β)^2 + (α + β + 1)]

Usage

utils_newalphbet(mu, sigma2)

Arguments

mu	The mean of the function to be approximated.
sigma2	The variance of the function to be approximated.

Details

Some nonsensical answers could be returned if one simply uses the formulas for the first and second shape parameters ourtight, especially concerning beta distributions at the extreme ends of the (0, 1) interval. To account for this, the function handles two cases: (1) if the mean is greater than or equal to 1 or is less than or equal to 0, it is arbitrarily set to 0.99 or 0.01, respectively, and (2) if either of the two shape parameters would be returned as a negative value, the variance is increased for 1,000 iterations to try to force positive shape parameters. After those 1,000 iterations, an error is returned.

Value

The returned values are the first and second shape parameters of the fitted beta distribution, stored in a 2 x 1 vector.

Examples

## Say we have that the tau posterior distribution from EpiBayes_ns() has mean and
## variance 0.01, and 0.015, respectively. The corresponding beta parameters will be:
utils_newalphbet(0.01, 0.015)

## If we provide a mean of 1, gives meaningful results
utils_newalphbet(1, 1)

## If we provide a mean of 0, gives meaningful results
utils_newalphbet(0, 1)

## Not run: 
## Returns an error message if shape parameters cannot be coerced to be positive
utils_newalphbet(1, -1)

## End(Not run)

EpiBayes documentation built on May 30, 2017, 4:01 a.m.