PriorGen (version 2.0)

findbeta_panel: The findbeta (panel) 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) as a vector corresponding to multiple prior mean prevalences from experts or studies.

Usage

findbeta_panel(themean.vec=NULL, themedian.vec=NULL,
 themode.vec=NULL, 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.vec

specify your prior belief about the mean. It takes a vector of means, with values between 0 and 1. Not to be specified if a vector has been given for the median or the mode.

themedian.vec

specify your prior belief about the median. It takes a vector of medians, with values between 0 and 1. Not to be specified if a vector has been given for the mean or the mode.

themode.vec

specify your prior belief about the mode. It takes a vector of modes, with values between 0 and 1. Not to be specified if a vector has been given for the mean or the median.

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 median/mean/mode value for the specificity of a
## test is expected to be equal to 0.1, 0.2, 0.4, 0.04, 0.01, 0.5 based on opinions of 6 experts.

resmed <- findbeta_panel(themedian.vec = c(0.1, 0.2, 0.4, 0.04, 0.01, 0.5))
resmed

resmea <- findbeta_panel(themean.vec = c(0.1, 0.2, 0.4, 0.04, 0.01, 0.5))
resmea

resmod <- findbeta_panel(themode.vec = c(0.1, 0.2, 0.4, 0.04, 0.01, 0.5))
resmod

plot(resmed, lty = 1)
lines(resmea, lty = 2)
lines(resmod, lty = 3)