prop_allocation: Construct a proportional allocation

In sps: Sequential Poisson Sampling

Construct a proportional allocation

Description

Generate a proportional-to-size allocation for stratified sampling.

Usage

prop_allocation(
  x,
  n,
  strata,
  initial = 0L,
  divisor = divisor_method("Jefferson/D'Hondt"),
  ties = c("largest", "first")
)

divisor_method(
  name = c(
    "Jefferson/D'Hondt",
    "Webster/Sainte-Lague",
    "Imperiali",
    "Huntington-Hill",
    "Danish",
    "Adams",
    "Dean"
    )
  )

Arguments

x	⁠[numeric >= 0]⁠ A positive and finite numeric vector of sizes for units in the population (e.g., revenue for drawing a sample of businesses).
n	⁠[integer(1) >= 0]⁠ A positive integer giving the sample size.
strata	⁠[factor]⁠ 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.
initial	⁠[integer >= 0]⁠ A positive integer vector giving the initial (or minimal) allocation for each stratum, ordered according to the levels of strata. A single integer is recycled for each stratum using a special algorithm to ensure a feasible allocation; see details. Non-integers are truncated towards 0. The default allows for no units to be allocated to a stratum.
divisor	⁠[function]⁠ A function for the divisor (highest-averages) apportionment method. The default uses the Jefferson/D'Hondt method. See details for other possible functions.
ties	⁠[character(1)]⁠ Either "largest" to break ties in favor of the stratum with the largest size (the default), or "first" to break ties in favor of the ordering of strata.
name	⁠[character(1)]⁠ Name of the divisor function. See details.

Details

The prop_allocation() function gives a sample size for each level in strata that is proportional to the sum of x across strata and adds up to n. This is done using the divisor (highest-averages) apportionment method \bibcitep|balinski+young1982|appendix A, for which there are a number of different divisor functions:

Jefferson/D'Hondt

⁠\(a) a + 1⁠

Webster/Sainte-Laguë

⁠\(a) a + 0.5⁠

Imperiali

⁠\(a) a + 2⁠

Huntington-Hill

⁠\(a) sqrt(a * (a + 1))⁠

Danish

⁠\(a) a + 1 / 3⁠

Adams

⁠\(a) a⁠

Dean

⁠\(a) a * (a + 1) / (a + 0.5)⁠

Note that a divisor function with d(0) = 0 (i.e., Huntington-Hill, Adams, Dean) should have an initial allocation of at least 1 for all strata. In all cases, ties are broken according to the sum of x if ties = "largest"; otherwise, if ties = "first", then ties are broken according to the levels of strata.

In cases where the number of units with non-zero size in a stratum is smaller than its allocation, the allocation for that stratum is set to the number of available units, with the remaining sample size reallocated to other strata proportional to x. This is similar to PROC SURVEYSELECT in SAS with ALLOC = PROPORTIONAL.

Passing a single integer for the initial allocation first checks that recycling this value for each stratum does not result in an allocation larger than the sample size. If it does, then the value is reduced so that recycling does not exceed the sample size. This recycled vector can be further reduced in cases where it exceeds the number of units in a stratum, the result of which is the initial allocation. This special recycling ensures that the initial allocation is feasible.

Value

prop_allocation() returns a named integer vector of sample sizes for each stratum in strata.

divisor_method() returns a function giving the desired divisor function.

References

\bibshow

*

See Also

sps() for stratified sequential Poisson sampling.

expected_coverage() to calculate the expected number of strata in a sample without stratification.

strAlloc() in the PracTools package and the optimall package for other allocation methods.

Examples

# Make a population with units of different size
x <- c(rep(1:9, each = 3), 100, 100, 100)

# ... and 10 strata
s <- rep(letters[1:10], each = 3)

# Generate an allocation
prop_allocation(x, 15, s, initial = 1)

sps documentation built on Aug. 31, 2026, 5:10 p.m.