Select sampling units using simple or stratified systematic samplin. In the context of twostage cluster sampling, select Secondary Sampling Units (SSU) in one or more Primary Sampling Units (PSU), using systematic sampling.
1 2 
psu.ssu 

su 

N 

write 
logical. If 
... 
further arguments passed to 
When N
is defined, psu.ssu
is ignored (not need to be defined). If N
has one element, su
must too and the result is a simple systematic selection. If N
has more than one element, su
must have the same number of elements and each oredered pair represent an strata. Thus, when N has more than one element, the result is a stratified sampling with systematic selection within each strata (see examples).
A matrix
. For the second stage in a twostage cluster sampling, the names of columns are the identifiers of selected psu, coerced by as.character
to avoid scientific notation in case the identifiers be of class
numeric
. The rows correspond to the selected SSU within each psu. For simple systematic sampling, the rows correspond to the selected sampling units. For stratified sampling, each column represent an strata and the rows correspond to the selected sampling units in each strata.
Levy P and Lemeshow S (2008). Sampling of populations: methods and applications, Fourth edition. John Wiley and Sons, Inc.
http://oswaldosantos.github.io/capm
SamplePPS
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17  # Load data with PSU identifiers and sizes.
data(psu.ssu)
# Take a sample of 10 PSU, with probability
# proportional to size and with replacement.
selected.psu < SamplePPS(psu.ssu, 10, write = FALSE)
# Take a systematic sampling of 5 SSU within each
# PSU of selected.psu.
SampleSystematic(selected.psu, 5, write = FALSE)
# Simple systematic sampling
SampleSystematic(su = 5, N = 100)
# Stratified systematic sampling
SampleSystematic(su = c('Urban' = 50, 'Rural' = 10),
N = c('Urban' = 4000, 'Rural' = 150))

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