| inclusion_prob | R Documentation |
Calculate stratified (first-order) inclusion probabilities.
inclusion_prob(x, n, strata = gl(1, length(x)), alpha = 0.001, cutoff = Inf)
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 |
Within a stratum, the inclusion probability for a unit is given by π = n * x / ∑ x. These values can be greater than 1 in practice, and so they are constructed iteratively by taking units with π >= 1 - α (from largest to smallest) and assigning these units an inclusion probability of 1, with the remaining inclusion probabilities recalculated at each step. If α > 0, then any ties among units with the same size are broken by their position.
A numeric vector of inclusion probabilities for each unit in the population.
sps() for drawing a sequential Poisson sample.
# Make a population with units of different size x <- c(1:10, 100) # Use the inclusion probabilities to calculate the variance of the # sample size for Poisson sampling pi <- inclusion_prob(x, 5) sum(pi * (1 - pi))