inclusion_prob {sps} | R Documentation |
Calculate inclusion probabilities
Description
Calculate stratified (first-order) inclusion probabilities.
Usage
inclusion_prob(x, n, strata = gl(1, length(x)), alpha = 0.001, cutoff = Inf)
becomes_ta(x, alpha = 0.001, cutoff = Inf)
Arguments
x |
A positive and finite numeric vector of sizes for units in the population (e.g., revenue for drawing a sample of businesses). |
n |
A positive integer vector giving the sample size for each stratum,
ordered according to the levels of |
strata |
A factor, or something that can be coerced into one, giving the strata associated with units in the population. The default is to place all units into a single stratum. |
alpha |
A numeric vector with values between 0 and 1 for each stratum,
ordered according to the levels of |
cutoff |
A positive numeric vector of cutoffs for each stratum, ordered
according to the levels of |
Details
Within a stratum, the inclusion probability for a unit is given by
. These values can be greater
than 1 in practice, and so they are constructed iteratively by taking units
with
(from largest to smallest)
and assigning these units an inclusion probability of 1, with the remaining
inclusion probabilities recalculated at each step. If
, then
any ties among units with the same size are broken by their position.
The becomes_ta()
function reverses this operations and finds the critical
sample size at which a unit enters the take-all stratum. This value is
undefined for units that are always included in the sample (because their
size exceeds cutoff
) or never included.
Value
inclusion_prob()
returns a numeric vector of inclusion probabilities for
each unit in the population.
becomes_ta()
returns an integer vector giving the sample size at which a
unit enters the take-all stratum.
See Also
sps()
for drawing a sequential Poisson sample.
Examples
# Make inclusion probabilities for a population with units
# of different size
x <- c(1:10, 100)
(pi <- inclusion_prob(x, 5))
# The last unit is sufficiently large to be included in all
# samples with two or more units
becomes_ta(x)
# Use the inclusion probabilities to calculate the variance of the
# sample size for Poisson sampling
sum(pi * (1 - pi))