In wwojciech/stratallo: Optimum Sample Allocation in Stratified Sampling

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Optimum Sample Allocation in Stratified Sampling with `stratallo`

Functions in this package provide solution to classical problem in survey methodology - an optimum sample allocation in stratified sampling. In this context, the optimum allocation is in the classical Tschuprow-Neyman's sense and it satisfies additional lower or upper bounds restrictions imposed on sample sizes in strata. In particular, it is assumed that the variance of the stratified $\pi$ estimator is of the following generic form:

$$ V_{st} = \sum_{h=1}^{H} \frac{A_h^2}{n_h} - A_0, $$

where $H$ denotes total number of strata, $(n_1,\ldots,n_H)$ is the allocation vector with strata sample sizes, and population parameters $A_0$, $A_h > 0$, $h = 1,\ldots,H$, do not depend on the $x_h$, $h = 1,\ldots,H$.

A minor modification of the classical optimum sample allocation problem leads to the minimum cost allocation. This problem lies in the determination of a vector of strata sample sizes that minimizes total cost of the survey, under assumed fixed level of the stratified $\pi$ estimator's variance. As in the case of the classical optimum allocation, the problem of minimum cost allocation can be complemented by imposing upper bounds on sample sizes in strata.

There are few different algorithms available to use, and one them is based on a popular sample allocation method that applies Neyman allocation to recursively reduced set of strata.

Package stratallo provides two user functions:

opt()
optcost()

that solve sample allocation problems briefly characterized above as well as the following helpers functions:

var_st()
var_st_tsi()
asummary()
ran_round()
round_oric().

Functions var_st() and var_st_tsi() compute a value of the variance $V_{st}$. The var_st_tsi() is a simple wrapper of var_st() that is dedicated for the case of stratified $\pi$ estimator of the population total with stratified simple random sampling without replacement design in use. Helper asummary() creates a data.frame object with summary of the allocation. Functions ran_round() and round_oric() are the rounding functions that can be used to round non-integers allocations (see section Rounding, below). The package comes with three predefined, artificial populations with 10, 507 and 969 strata. These are stored under pop10_mM, pop507 and pop969 objects, respectively.

See package's vignette for more details.

Installation

You can install the released version of stratallo package from CRAN with:

install.packages("stratallo")

Examples

These are basic examples that show how to use opt() and optcost() functions to solve different versions of optimum sample allocation problem for an example population with 4 strata.

library(stratallo)

Define example population.

N <- c(3000, 4000, 5000, 2000) # Strata sizes.
S <- c(48, 79, 76, 16) # Standard deviations of a study variable in strata.
A <- N * S
n <- 190 # Total sample size.

Tschuprow-Neyman allocation (no inequality constraints).

xopt <- opt(n = n, A = A)
xopt
sum(xopt) == n
# Variance of the st. estimator that corresponds to the optimum allocation.
var_st_tsi(xopt, N, S)

One-sided upper bounds.

M <- c(100, 90, 70, 80) # Upper bounds imposed on the sample sizes in strata.
all(M <= N)
n <= sum(M)

xopt <- opt(n = n, A = A, M = M)
xopt
sum(xopt) == n
all(xopt <= M) # Does not violate upper-bounds constraints.
# Variance of the st. estimator that corresponds to the optimum allocation.
var_st_tsi(xopt, N, S)

One-sided lower bounds.

m <- c(50, 120, 1, 2) # Lower bounds imposed on the sample sizes in strata.
n >= sum(m)

xopt <- opt(n = n, A = A, m = m)
xopt
sum(xopt) == n
all(xopt >= m) # Does not violate lower-bounds constraints.
# Variance of the st. estimator that corresponds to the optimum allocation.
var_st_tsi(xopt, N, S)

Box constraints.

m <- c(100, 90, 500, 50) # Lower bounds imposed on sample sizes in strata.
M <- c(300, 400, 800, 90) # Upper bounds imposed on sample sizes in strata.
n <- 1284
n >= sum(m) && n <= sum(M)

xopt <- opt(n = n, A = A, m = m, M = M)
xopt
sum(xopt) == n
all(xopt >= m & xopt <= M) # Does not violate any lower or upper bounds constraints.
# Variance of the st. estimator that corresponds to the optimum allocation.
var_st_tsi(xopt, N, S)

Minimization of the total cost with optcost() function

A <- c(3000, 4000, 5000, 2000)
A0 <- 70000
unit_costs <- c(0.5, 0.6, 0.6, 0.3) # c_h, h = 1,...4.
M <- c(100, 90, 70, 80)
V <- 1e6 # Variance constraint.
V >= sum(A^2 / M) - A0

xopt <- optcost(V = V, A = A, A0 = A0, M = M, unit_costs = unit_costs)
xopt
sum(A^2 / xopt) - A0 == V
all(xopt <= M)

Rounding.

m <- c(100, 90, 500, 50)
M <- c(300, 400, 800, 90)
n <- 1284

# Optimum, non-integer allocation under box constraints.
xopt <- opt(n = n, A = A, m = m, M = M)
xopt

xopt_int <- round_oric(xopt)
xopt_int