rsu.sep.rb2st(H = NA, N = NA, n, pstar.c, pstar.u, rg, rr.c, rr.u, 
   ppr.c, ppr.u, se.u)

## EXAMPLE 1:
## You have been asked to provide an assessment of a surveillance program 
## for Actinobacillus hyopneumoniae in pigs. It is known that there are 
## high risk and low risk areas for A. hypopneumoniae in your country with 
## the estimated probability of disease in the high risk area thought to 
## be around 3.5 times that of the probability of disease in the low risk area. 
## It is known that 10% of the 1784 pig herds in the study area are in the 
## high risk area and 90% are in the low risk area.

## The risk of A. hypopneumoniae is dependent on age, with adult pigs around 
## five times more likely to be A. hypopneumoniae positive compared with 
## younger (grower) pigs. 

## Pigs from 20 herds have been sampled: 5 from the low-risk area and 15 from 
## the high-risk area. All of the tested pigs were adults: no grower pigs 
## were tested. 

## The ELISA for A. hypopneumoniae in pigs has a diagnostic sensitivity 
## of 0.95.

## What is the surveillance system sensitivity if we assume a design 
## prevalence of 1 per 100 at the cluster (herd) level and 5 per 100 
## at the surveillance system unit (pig) level?

# There are 1784 herds in the study area:

H <- 1784

# Twenty of the 1784 herds are sampled. Generate 20 herds of varying size:
set.seed(1234)

hsize <- rlnorm(n = 20, meanlog = log(10), sdlog = log(8))
hsize <- round(hsize + 20, digits = 0)

# Generate a matrix listing the number of growers and finishers in each of 
## the 20 sampled herds. Anywhere between 80% and 95% of the animals in 
## each herd are growers:

set.seed(1234)
pctg <- runif(n = 20, min = 0.80, max = 0.95)
ngrow <- round(pctg * hsize, digits = 0)
nfini <- hsize - ngrow
N <- cbind(ngrow, nfini)

# Generate a matrix listing the number of grower and finisher pigs sampled 
## from each herd:

nsgrow <- rep(0, times = 20)
nsfini <- ifelse(nfini <= 15, nfini, 15)
n <- cbind(nsgrow, nsfini)

# The herd-level design prevalence is 0.01 and the individual pig-level design 
## prevalence is 0.05: 

pstar.c <- 0.01
pstar.u <- 0.05

# For herds in the high-risk area the probability being A. hyopneumoniae 
## positive is 3.5 times that of herds in the low-risk area. Ninety 
## percent of herds are in the low risk area and 10% are in the high risk area:

rr.c <- c(1,3.5)
ppr.c <- c(0.9,0.1) 

## We've sampled 5 herds from the low risk area and 15 herds from the 
## high risk area:

rg <- c(rep(1, times = 5), rep(2, times = 15))

## For finishers the probability being A. hyopneumoniae positive is 5 times 
## that of growers:

rr.u <- c(1,5)

## The diagnostic sensitivity of the A. hyopneumoniae ELISA is 0.95:

se.u <- 0.95

rsu.sep.rb2st(H = H, N = N, n = n, 
   pstar.c = pstar.c, pstar.u = pstar.u,
   rg = rg, rr.c = rr.c, rr.u = rr.u,
   ppr.c = ppr.c, ppr.u = NA,
   se.u = se.u)

## The estimated surveillance system sensitivity of this program is 0.31. 


## EXAMPLE 2:
## Repeat these analyses assuming we don't know the total number of pig herds 
## in the population and we have only an estimate of the proportions of 
## growers and finishers in each herd. 

## Generate a matrix listing the proportion of growers and finishers in each
## of the 20 sampled herds:

ppr.u <- cbind(rep(0.9, times = 20), rep(0.1, times = 20))

# Set H (the number of clusters) and N (the number of surveillance units 
## within each cluster) to NA:

rsu.sep.rb2st(H = NA, N = NA, n = n, 
   pstar.c = pstar.c, pstar.u = pstar.u,
   rg = rg, rr.c = rr.c, rr.u = rr.u,
   ppr.c = ppr.c, ppr.u = ppr.u,
   se.u = se.u)

## The estimated surveillance system sensitivity is 0.20.