On Thu, 8 Apr 2010, ONKELINX, Thierry wrote:
Dear Thomas,
Thank you for your informative answer. We used epi.stratasize() to
estimate the required sample size per stratum. Notice in the example
below that it can select a sample size smaller than 2 in the very small
strata. Would you recommend to sample at least two items per stratum or
rather to merge some strata a priori until the sample size is at least
2?
Merging the strata would be best
Or is there a better way to estimate the sample size per stratum?
Note that the stratification only aims to get a good geographical
coverage (the strata a geographical regions). We are not interested in
estimates per stratum.
library(epiR)
N <- c(39, 270, 1060, 1336, 118, 26, 154, 10, 3)
epi.stratasize(strata.n = N, strata.mean = 0.9, epsilon = 0.05, method =
"proportion")
$strata.sample
[1] 2 15 57 72 6 1 8 1 0
$total.sample
[1] 162
The probability of sampling was proportional with the area (larger
polygons are more likely to be selected than smaller ones). So we will
use weights = I(1/Area), as you suggested.
If you are using probability proportional to size and you want to use
finite-population correctsions, you also need to specify the fpc= argument
differently. The simplest version is an approximation that uses only the
marginal sampling probabilities
svydesign(id=~1, fpc=~p, pps="brewer", strata=~strat
where p is a variable with the actual sampling probability (not just
proportional to sampling probability).
Also, how did you do the sampling? It's quite hard to do unequal probability
sampling without replacement (the R sample() function doesn't actually do it,
though the sampling package does).
-thomas
Thomas Lumley Assoc. Professor, Biostatistics
tlum...@u.washington.edu University of Washington, Seattle
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
