epi.sssimpleestc {epiR} R Documentation

## Sample size to estimate a continuous outcome using simple random sampling

### Description

Sample size to estimate a continuous outcome using simple random sampling.

### Usage

```epi.sssimpleestc(N = 1E+06, xbar, sigma, epsilon, error = "relative",
nfractional = FALSE, conf.level = 0.95)
```

### Arguments

 `N` scalar integer, representing the total number of individual listing units in the population. `xbar` scalar number, the expected mean of the continuous variable to be estimated. `sigma` scalar number, the expected standard deviation of the continuous variable to be estimated. `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

Returns an integer defining the required sample size.

### Note

If `epsilon.r` equals the relative error the sample estimate should not differ in absolute value from the true unknown population parameter `d` by more than `epsilon.r * d`.

### References

Levy PS, Lemeshow S (1999). Sampling of Populations Methods and Applications. Wiley Series in Probability and Statistics, London, pp. 70 - 75.

Scheaffer RL, Mendenhall W, Lyman Ott R (1996). Elementary Survey Sampling. Duxbury Press, New York, pp. 95.

Otte J, Gumm I (1997). Intra-cluster correlation coefficients of 20 infections calculated from the results of cluster-sample surveys. Preventive Veterinary Medicine 31: 147 - 150.

### Examples

```## EXAMPLE 1:
## A city contains 20 neighbourhood health clinics and it is desired to take a
## sample of clinics to estimate the total number of persons from all these
## clinics who have been given, during the past 12 month period, prescriptions
## for a recently approved antidepressant. If we assume that the average number
## of people seen at these clinics is 1500 per year with the standard deviation
## equal to 300, and that approximately 5% of patients (regardless of clinic)
## are given this drug, how many clinics need to be sampled to yield an estimate
## that is within 20% of the true population value?

pmean <- 1500 * 0.05; psigma <- (300 * 0.05)
epi.sssimpleestc(N = 20, xbar = pmean, sigma = psigma, epsilon = 0.20,
error = "relative", nfractional = FALSE, conf.level = 0.95)

## Four clinics need to be sampled to meet the requirements of the survey.

## EXAMPLE 2:
## We want to estimate the mean bodyweight of deer on a farm. There are 278
## animals present. We anticipate the mean body weight to be around 200 kg
## and the standard deviation of body weight to be 30 kg. We would like to
## be 95% certain that our estimate is within 10 kg of the true mean. How
## many deer should be sampled?

epi.sssimpleestc(N = 278, xbar = 200, sigma = 30, epsilon = 10,
error = "absolute", nfractional = FALSE, conf.level = 0.95)

## A total of 31 deer need to be sampled to meet the requirements of the survey.
```

[Package epiR version 2.0.38 Index]