randomizr (version 2.0.1)

strata_rs: Stratified Random Sampling

Description

strata_rs draws a sample separately within each of several groups (strata) defined by covariates, using complete random sampling inside every stratum. For example, 50 of 100 men and 75 of 200 women might be sampled. Stratifying guarantees how much of the sample comes from each group, which keeps small groups from being underrepresented by chance.

Usage

strata_rs(
  strata = NULL,
  prob = NULL,
  prob_unit = NULL,
  n = NULL,
  n_unit = NULL,
  strata_n = NULL,
  strata_prob = NULL,
  check_inputs = TRUE
)

Value

A numeric vector of length N indicating whether each unit is sampled (1) or not (0).

Arguments

strata

A vector of length N indicating which stratum each unit belongs to. Can be a character, factor, or numeric vector. (required)

prob

Use for a design in which either floor(N_stratum*prob) or ceiling(N_stratum*prob) units are sampled within each stratum. Which of the two is used is itself random: the ceiling is drawn with probability equal to the fractional part of N_stratum*prob and the floor otherwise, which makes each unit's probability of inclusion exactly prob. Must be a real number between 0 and 1 inclusive. (optional)

prob_unit

Must be of length N. tapply(prob_unit, strata, unique) will be passed to strata_prob, so it must be constant within each stratum. (optional)

n

Use for a design in which the scalar n gives the fixed number of units to sample in every stratum. This count does not vary across strata. (optional)

n_unit

Must be of length N. tapply(n_unit, strata, unique) will be passed to strata_n, so it must be constant within each stratum. (optional)

strata_n

Use for a design in which the numeric vector strata_n gives the number of units to sample within each stratum. Must be as long as the number of strata, in the same order as sort(unique(strata)). (optional)

strata_prob

Use for a design in which strata_prob gives the probability of being sampled within each stratum. Must be in the same order as sort(unique(strata)). Differs from prob in that the probability of being sampled can vary across strata. (optional)

check_inputs

Logical. Whether to verify before sampling that the arguments are internally consistent: that counts do not exceed the stratum sizes, that probabilities lie between 0 and 1, that stratum-level arguments have one entry per stratum, 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

The number sampled per stratum can be left to the function, set as a common count or probability across strata (n, prob), or set stratum by stratum (strata_n, strata_prob). When the probability varies across strata the sample is not self-weighting, and strata_rs_probabilities() gives the inclusion probabilities needed to weight it.

See Also

complete_rs(), strata_and_cluster_rs(), block_ra(), strata_rs_probabilities()

Examples

strata <- rep(c("A", "B", "C"), times = c(50, 100, 200))

S <- strata_rs(strata = strata)
table(strata, S)

# The same probability in every stratum
S <- strata_rs(strata = strata, prob = 0.3)
table(strata, S)

# The same count in every stratum
S <- strata_rs(strata = strata, n = 20)
table(strata, S)

# A different probability in each stratum, in the order of sort(unique(strata))
S <- strata_rs(strata = strata, strata_prob = c(0.1, 0.2, 0.3))
table(strata, S)

# The same, specified unit by unit
S <- strata_rs(strata = strata,
               prob_unit = rep(c(0.1, 0.2, 0.3), times = c(50, 100, 200)))
table(strata, S)

# A different count in each stratum
S <- strata_rs(strata = strata, strata_n = c(20, 30, 40))
table(strata, S)

S <- strata_rs(strata = strata,
               n_unit = rep(c(20, 30, 40), times = c(50, 100, 200)))
table(strata, S)