findbetamupsi_panel {PriorGen}

The findbetamupsi (panel) function

Description

A function to estimate (a) the parameters of a Beta distribution for the expected mean of a proportion - usually the prevalence of disease/infection for the units in an area/region and (b) the parameters of a Gamma distribution expressing our prior belief about the variability of the prevalence estimates across the units of the area/region under consideration. Information should be provided about the mean as a vector corresponding to multiple prior mean prevalences from experts or studies.

Usage

findbetamupsi_panel(themean.vec, psi.percentile=0.90,
percentile.median, percentile95value,
seed = 280385, nsims = 10000, root.method = "multiroot")

Arguments

themean.vec

specify the multiple sources prior belief about the mean as a vector. Each mean should take a value between 0 and 1.

psi.percentile

specify the level of confidence that a certain fraction of the units under study has a prevalence less than the percentile.median. It takes a value between 0 and 1 and the default is 0.90.

percentile.median

specify the median value that corresponds to the defined psi.percentile. It takes a value between 0 and 1 and has to be higher than both themean and the percentile.

percentile95value

specify the value that the percentile.median does not exceed with 95% confidence. It takes a value between 0 and 1 and has to be higher than the percentile.median.

seed

A fixed seed for replication purposes.

nsims

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

root.method

Choose between two alternatives to solve the two non-linear equations to identify the hyperparameters of psi. root.method="multiroot" involves the basic function of the rootSolve package, root.method="nleqslv" involves the base functions of the nleqslv package.

Value

param_beta: The beta distribution parameters Beta(a,b)

param_gamma: The gamma distribution parameters gamma(a,b)

summary: A basic summary of the elicited prior

input: The initial input value that produced the above prior.

param_upper: simulated mu and psi of Beta(mu psi,psi(1-mu))

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
## The mean prevalence of a disease/infection for the units within an area/region
## is thought to be 8%, 20%, 10%, 15% 20% , 22%, 10%, 2%, 2%, 4%, 5%,
## we are also confident that 90% of all units have a prevalence
## less or equal to 0.60 and we are 95% certain that it does not exceed 0.70

findbetamupsi_panel(
  themean.vec = c(
    0.4, 0.2, 0.1, 0.3,
    0.4, 0.5, 0.1, 0.02, 0.04, 0.05
  ), psi.percentile = 0.90,
  percentile.median = 0.60, percentile95value = 0.70
)

findbetamupsi_panel(
  themean.vec = c(
    0.4, 0.2, 0.1, 0.3,
    0.4, 0.5, 0.1, 0.02, 0.04, 0.05
  ), psi.percentile = 0.90,
  percentile.median = 0.60, percentile95value = 0.70,
  root.method = "nleqslv"
)