PriorGen (version 2.0)

findbetamupsi_raw: The findbetamupsi (raw) 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 on the raw values of the mean, the within-study variance and the psi (between-study variance) parameters.

Usage

findbetamupsi_raw(themean = 0.2, thevariance = 0.05, thepsi = 0.5,
seed = 280385, nsims = 10000, root.method = "multiroot")

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))

Arguments

themean

specify your prior belief about the mean. It takes a value between 0 and 1.

thevariance

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

thepsi

specify your prior belief about the between-study variance. Large values of psi correspond to low heterogeneity, low values of psi correspond to large heterogeneity.

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.

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 0.20 with a variance
## equal to 0.03, with large heterogeneity i.e. psi equals to 0.15.

findbetamupsi_raw(themean = 0.20, thevariance = 0.03, thepsi = 0.15)
findbetamupsi_raw(
  themean = 0.20, thevariance = 0.03, thepsi = 0.15,
  root.method = "nleqslv"
)