| simSample {simFrame} | R Documentation |
A convenience wrapper for setting up multiple samples using setup
with control class SampleControl.
simSample(x, design = character(), group = character(),
method = srs, size = NULL, prob = NULL, ..., k = 1)
x |
the data.frame to sample from. |
design |
a character, logical or numeric vector specifying the variables (columns) to be used for stratified sampling. |
group |
a character string, single integer or logical vector specifying a variable (column) to be used for sampling whole groups rather than individual observations. |
method |
a function to be used for sampling (defaults to
srs). It should return a vector containing the indices
of the sampled items (observations or groups). |
size |
an optional non-negative integer giving the number of items (observations or groups) to sample. For stratified sampling, a vector of non-negative integers, each giving the number of items to sample from the corresponding stratum. |
prob |
an optional numeric vector giving the probability weights |
... |
additional arguments to be passed to method. |
k |
a single positive integer giving the number of samples to be set up. |
There are some restrictions on the argument names of the function
supplied to method. If it needs population data as input,
the corresponding argument should be called x and should expect
a data.frame. If the sampling method only needs the population size
as input, the argument should be called N. Note that method is
not expected to have both x and N as arguments, and that the
latter is much faster for stratified sampling or group sampling.
Furthermore, if the function has arguments for sample size and probability
weights, they should be called size and prob, respectively.
Note that a function with prob as its only argument is perfectly valid
(for probability proportional to size sampling). Further arguments of
method may be passed directly via the ... argument.
An object of class "SampleSetup".
Andreas Alfons, alfons@statistik.tuwien.ac.at
setup, SampleControl,
SampleSetup
data(eusilc)
## simple random sampling
srss <- simSample(eusilc, size = 20, k = 3)
draw(eusilc[, c("id", "eqIncome")], srss, i = 1)
## group sampling
gss <- simSample(eusilc, group = "hid", size = 10, k = 3)
draw(eusilc[, c("hid", "id", "eqIncome")], gss, i = 2)
## stratified sampling
stss <- simSample(eusilc, design = "region",
size = c(2, 5, 5, 3, 4, 5, 3, 5, 2), k = 3)
draw(eusilc[, c("id", "region", "eqIncome")], stss, i = 3)