complete_rs_probabilities: Inclusion probabilities: Complete Random Sampling

Description

Returns each unit's probability of being sampled under complete random sampling, where the sample size is fixed on every draw.

Usage

complete_rs_probabilities(
  N,
  n = NULL,
  n_unit = NULL,
  prob = NULL,
  prob_unit = NULL,
  check_inputs = TRUE
)

Value

A numeric vector of length N giving each unit's probability of being included in the sample.

Arguments

N: The number of units in the sampling frame. Must be a positive integer. (required)
n: Use for a design in which exactly n units are sampled. (optional)
n_unit: unique(n_unit) will be passed to n; must be the same for all units and of length N. (optional)
prob: Use for a design in which either floor(N*prob) or ceiling(N*prob) units are sampled, chosen so that each unit's probability of inclusion is exactly prob. Must be a real number between 0 and 1 inclusive. (optional)
prob_unit: unique(prob_unit) will be passed to prob; must be the same for all units and of length N. Under complete random sampling the probability cannot vary by unit; use simple_rs() if it must. (optional)
check_inputs: Logical. Whether to verify before sampling that the arguments are internally consistent: that n does not exceed N, that probabilities lie between 0 and 1, that vectors are of length N, and so on. Defaults to TRUE. Set to FALSE to skip the checks when drawing many samples from arguments that have already been verified; declaring the design once with declare_rs() and drawing from it with draw_rs() does this for you. (optional)

Details

These are the quantities inverse-probability weights are built from: weight each sampled unit by the reciprocal of its inclusion probability, which obtain_inclusion_probabilities() extracts for you.

Examples

Run this code

probs <- complete_rs_probabilities(N = 100)
table(probs)

probs <- complete_rs_probabilities(N = 100, n = 50)
table(probs)

probs <- complete_rs_probabilities(N = 100, prob = 0.3)
table(probs)