# n.rb.varse: Risk-based sample size for varying unit sensitivity In RSurveillance: Design and Analysis of Disease Surveillance Activities

## Description

Calculates sample size for risk-based sampling for a single risk factor and varying unit sensitivity, using binomial method

## Usage

 `1` ```n.rb.varse(pstar, rr, ppr, spr, se, spr.rg, sep) ```

## Arguments

 `pstar` design prevalence (scalar) `rr` relative risk values (vector, length equal to the number of risk strata) `ppr` population proportions for each risk group, vector of same length as rr `spr` planned surveillance proportions for each risk group, vector of same length as rr `se` unit sensitivities (vector of group values) `spr.rg` proportions of samples for each sensitivity value in each risk group (matrix with rows = risk groups, columns = sensitivity values), row sums must equal 1 `sep` required population sensitivity (scalar)

## Value

list of 3 elements, a matrix of sample sizes for each risk and sensitivity group, a vector of EPI values and a vector of mean sensitivity for each risk group

## Examples

 ```1 2 3 4 5 6 7 8 9``` ```# examples for n.rb.varse m<- rbind(c(0.8, 0.2), c(0.5, 0.5), c(0.7, 0.3)) n.rb.varse(0.01, c(5, 3, 1), c(0.1, 0.1, 0.8), c(0.4, 0.4, 0.2), c(0.92, 0.8), m, 0.95) m<- rbind(c(0.8, 0.2), c(0.6, 0.4)) n.rb.varse(0.05, c(3, 1), c(0.2, 0.8), c(0.7, 0.3), c(0.95, 0.8), m, 0.95) m<- rbind(c(1), c(1)) n.rb.varse(0.05, c(3, 1), c(0.2, 0.8), c(0.7, 0.3), c(0.95), m, 0.99) ```

RSurveillance documentation built on May 29, 2017, 11:52 p.m.