View source: R/findbeta_panel.r
findbeta_panel | R Documentation |
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.
findbeta_panel(themean.vec=NULL, themedian.vec=NULL,
themode.vec=NULL, seed=280385, nsims=10000)
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. |
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.
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.
## 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)
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