PriorGen (version 2.0)

findbeta: The findbeta 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 about the mean (or the median or the mode) and whether it is lower or greater that a certain value with a pre-specified certainty (usually set at 95\

Usage

findbeta(themean=NULL, themedian=NULL, themode=NULL,
 percentile=0.95,lower.v=F, percentile.value,
 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.

percentile

specify the level of confidence that the true value of the mean (or the median or the mode) is greater or lower than the percentile.value. It takes a value between 0 and 1 and the default =0.95.

lower.v

logical, if TRUE the specified percentile.value is the upper limit for the mean (or the median or the mode) at the specified confidence level (percentile). If FALSE the specified percentile.value is the lower limit for the mean (or the median or the mode) at the specified confidence level (percentile). The default is FALSE.

percentile.value

specify the upper or lower limit for the mean (or the median or the mode) at the specified level of confidence (percentile). It takes a value between 0 and 1.

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 we can be 95\% sure that it is higher than 0.80.

findbeta(
  themean = 0.90, percentile = 0.95, lower.v = FALSE,
  percentile.value = 0.80, seed = 280385, nsims = 10000
)

## Example 2
## Based on the available literature the median value for the specificity of a
## test is expected to be 0.99 and we can be 95\% sure that it is higher than 0.90.

findbeta(
 themedian = 0.99, percentile = 0.95, lower.v = FALSE,
 percentile.value = 0.90
)

##Example 3
##The most probable value (mode) for the prevalence of a disease/infection in a
##population is expected to be 0.15 and we are 90\% sure that it is less than 0.40.

findbeta(themode=0.15, percentile=0.90,lower.v=TRUE,
percentile.value=0.40)