rsu.adjrisk(rr, ppr)

## EXAMPLE 1:
## The relative risk of a given disease in an area of your country is 5
## compared with a known reference 'low risk' area. A recent census shows that 
## 10% of the population are resident in the high risk area and 90% 
## are resident in the low risk area.   . 

## Calculate the adjusted relative risks for each area. 

rsu.adjrisk(rr = c(5,1), ppr = c(0.10,0.90))

## The adjusted relative risks for the high and low risk areas are 3.6 and 
## 0.7, respectively.


## EXAMPLE 2:
## Re-calculate the adjusted relative risks assuming there are 'high', 
## 'medium' and 'low' risk areas. The relative risks for the high, medium
## and low risk areas are 5, 3 and 1, respectively. Population proportions for 
## each area are 0.10, 0.10 and 0.80, respectively.

rsu.adjrisk(rr = c(5,3,1), ppr = c(0.10,0.10,0.80))
 
## The adjusted relative risks for the high, medium and low risk areas are 
## 3.1, 1.9 and 0.6, respectively.


## EXAMPLE 3:
## Consider now the situation where we are not certain of our relative risk
## estimates for the high, medium and low risk areas described in Example 2 
## so we ask a group of experts for their opinion. Minimum, mode and maximum 
## relative risk estimates for the high and medium risk areas are defined 
## using a PERT distribution. For the high risk area the mode of the 
## relative risk is 5 with a minimum of 3 and a maximum of 20. For the medium
## risk area the mode of the relative risk is 3 with a minimum of 2 and a 
## maximum of 20. As before, the population proportions for each area are 
## 0.10, 0.10 and 0.80, respectively. Take 10 random draws from a PERT 
## distribution (using the rpert function in package mc2d) and calculate 
## the adjusted relative risks for each draw: 

## Not run: 
## Set up an empty matrix to collect the simulated relative risk values:
nsims <- 10; nrcat <- 3
rr <- matrix(NA, nrow = nsims, ncol = nrcat)

## Use the mc2d package to take nsims random draws from the PERT distribution:
rr[,1] <- mc2d::rpert(n = nsims, min = 3, mode = 5, max = 20)
rr[,2] <- mc2d::rpert(n = nsims, min = 2, mode = 3, max = 5)

## The low risk area is the reference, so its relative risk values are 1:
rr[,3] <- 1

## Population proportions:
ppr <- c(0.10,0.10,0.80)

rval.df <- rsu.adjrisk(rr, ppr)
summary(rval.df)

## The median adjusted relative risks for the high, medium and low risk area
## are 3.6, 1.6 and 0.5 (respectively). The minimum adjusted relative risks 
## are 2.5, 1.3 and 0.39, repectively. The maximum adjusted relative risks 
## are 5.5, 2.3 and 0.72, respectively.

## End(Not run)