Description Usage Arguments Value Author(s) References Examples
Sample size to estimate a continuous outcome using a stratified random sampling design.
1 2  epi.ssstrataestc(strata.n, strata.xbar, strata.sigma, epsilon,
error = "relative", nfractional = FALSE, conf.level = 0.95)

strata.n 
vector of integers, defining the number of individual listing units in each strata. 
strata.xbar 
vector of numbers, defining the expected means of the continuous variable to be estimated for each strata. 
strata.sigma 
vector of numbers, defining the expected standard deviation of the continous variable to be estimated 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 
nfractional 
logical, return fractional sample size. 
conf.level 
scalar number, the level of confidence in the computed result. 
A list containing the following:
strata.sample 
the estimated sample size for each strata. 
strata.total 
the estimated total size. 
strata.stats 

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).
Levy PS, Lemeshow S (1999). Sampling of Populations Methods and Applications. Wiley Series in Probability and Statistics, London, pp. 175  179.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23  ## EXAMPLE 1 (from Levy and Lemeshow 1999, page 176  178):
## We plan to take a sample of the members of a health maintenance
## organisation (HMO) for purposes of estimating the average number
## of hospital episodes per person per year. The sample will be selected
## from membership lists according to age (under 45 years, 45  64 years,
## 65 years and over). The number of members in each strata are 600, 500,
## and 400 (respectively). Previous data estimates the mean number of
## hospital episodes per year for each strata as 0.164, 0.166, and 0.236
## (respectively). The variance of these estimates are 0.245, 0.296, and
## 0.436 (respectively). How many from each strata should be sampled to be
## 95% that the sample estimate of hospital episodes is within 20% of the
## true value?
strata.n < c(600,500,400)
strata.xbar < c(0.164,0.166,0.236)
strata.sigma < sqrt(c(0.245,0.296,0.436))
epi.ssstrataestc(strata.n, strata.xbar, strata.sigma, epsilon = 0.20,
error = "relative", nfractional = FALSE, conf.level = 0.95)
## The number allocated to the under 45 years, 45  64 years, and 65 years
## and over stratums should be 224, 187, and 150 (a total of 561). These
## results differ from the worked example provided in Levy and Lemeshow where
## certainty is set to approximately 99%.

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