# epi.ssstrataestb: Sample size to estimate a binary outcome using stratified... In epiR: Tools for the Analysis of Epidemiological Data

 epi.ssstrataestb R Documentation

## Sample size to estimate a binary outcome using stratified random sampling

### Description

Sample size to estimate a binary outcome using stratified random sampling.

### Usage

``````epi.ssstrataestb(strata.n, strata.Py, epsilon, error = "relative",
nfractional = FALSE, conf.level = 0.95)
``````

### Arguments

 `strata.n` vector of integers, the number of individual listing units in each strata. `strata.Py` vector of numbers, the expected proportion of individual listing units with the outcome of interest for each strata. `epsilon` scalar number, the maximum difference between the estimate and the unknown population value expressed in absolute or relative terms. `error` character string. Options are `absolute` for absolute error and `relative` for relative error. `nfractional` logical, return fractional sample size. `conf.level` scalar number, the level of confidence in the computed result.

### Value

A list containing the following:

 `strata.sample` the estimated sample size for each strata. `strata.total` the estimated total size. `strata.stats` `mean` the mean across all strata, `sigma.bx` the among-strata variance, `sigma.wx` the within-strata variance, and `sigma.x` the among-strata variance plus the within-strata variance, `rel.var` the within-strata variance divided by the square of the mean, and `gamma` the ratio of among-strata variance to within-strata variance.

### Author(s)

Mark Stevenson (Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Australia).

Javier Sanchez (Atlantic Veterinary College, University of Prince Edward Island, Charlottetown Prince Edward Island, C1A 4P3, Canada).

### References

Levy PS, Lemeshow S (1999). Sampling of Populations Methods and Applications. Wiley Series in Probability and Statistics, London, pp. 175 - 179.

### Examples

``````## EXAMPLE 1:
## Dairies are to be sampled to determine the proportion of herd managers
## using foot bathes. Herds are stratified according to size (small, medium,
## and large). The number of herds in each strata are 1500, 2500, and 4000
## (respectively). A review of the literature indicates that use of foot bathes
## on farms is in the order of 0.50, with the probability of usage increasing
## as herds get larger. How many dairies should be sampled?

strata.n <- c(1500, 2500, 4000)
strata.Py <- c(0.50, 0.60, 0.70)
epi.ssstrataestb(strata.n, strata.Py, epsilon = 0.20, error = "relative",
nfractional = FALSE, conf.level = 0.95)

## A total of 55 herds should be sampled: 11 small, 18 medium, and 28 large.

``````

epiR documentation built on May 31, 2023, 5:38 p.m.