# n.pfree: Sample size to achieve desired (posterior) probability of... In RSurveillance: Design and Analysis of Disease Surveillance Activities

## Description

Calculates the sample size required to achieve a given value for probability of disease freedom

## Usage

 `1` ```n.pfree(pfree, prior, p.intro, pstar, se, N = NA) ```

## Arguments

 `pfree` desired probability of freedom (scalar or vector) `prior` prior probability of freedom before surveillance (scalar or vector of same length as pfree) `p.intro` probability of introduction for time period (scalar or vector of same length as pfree) `pstar` design prevalence (scalar or vector of same length as pfree) `se` unit sensitivity (scalar or vector of same length as pfree) `N` population size (scalar or vector of same length as pfree)

## Value

a list of 3 elements, the first a vector of sample sizes and the second a corresponding vector of population sensitivity values and the third a vector of adjusted priors

## Examples

 ```1 2 3 4 5``` ```# examples for n.pfree n.pfree(0.95, 0.5, 0.01, 0.05, 0.9) n.pfree(0.95, 0.5, 0.01, 0.05, 0.9, N=300) n.pfree(pfree = c(0.9, 0.95, 0.98, 0.99), prior = 0.7, 0.01, 0.01, 0.8, 1000) n.pfree(0.95, 0.7, 0.01, 0.1, 0.96) ```

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