PriorGen (version 2.0)

findbeta_raw: The findbeta (raw) function

Description

A function to estimate the parameters alpha and beta of a Beta distribution based on the existing prior beliefs (data and/or expert opinion). Information should be provided on the raw values of the mean (or the median or the mode) and a corresponding scale metric, either the variance or the range of the parameter.

Usage

findbeta_raw(themean=NULL,themedian=NULL,themode=NULL,
 thevariance=NULL, therange=c(0,1), seed=280385, nsims=10000)

Value

parameters The beta distribution parameters Beta(a,b)

summary A basic summary of the elicited prior

input The initial input value that produced the above prior.

Arguments

themean

specify your prior belief about the mean. It takes a value between 0 and 1. Not to be specified if a value has been given for the median or the mode.

themedian

specify your prior belief about the median. It takes a value between 0 and 1. Not to be specified if a value has been given for the mean or the mode.

themode

specify your prior belief about the mode. It takes a value between 0 and 1. Not to be specified if a value has been given for the mean or the median.

thevariance

specify your prior belief about the variance. If the selected variance is larger than possible, the variance will be adjusted downwards to create comply with the range of a probability.

therange

specify your prior belief about the range. It should be a two number vector that c(ul,ll), where ul>0, ll<1 and ul<ll. This scale metric applies for themode and themedian options.

seed

A fixed seed for replication purposes.

nsims

Number of simulations for the creation of various summary metrics of the elicited prior.

References

Branscum, A. J., Gardner, I. A., & Johnson, W. O. (2005): Estimation of diagnostic test sensitivity and specificity through Bayesian modeling. Preventive veterinary medicine, 68, 145--163.

Examples

## Example 1
## Based on the available literature the mean value for the sensitivity of a test
## is expected to be 0.90 and its variance equals to 0.1.

findbeta_raw(themean = 0.90, thevariance = 0.1)

## Example 2
## Based on the available literature the median value for the specificity of a
## test is expected to be 0.99 and its range between 0.1 and 1.

findbeta_raw(themedian = 0.70, therange = c(0.1, 1))


# Mode

findbeta_raw(themode = 0.70, therange = c(0.1, 1))