stratallo

The goal of stratallo is to provide methods computing solution to classical problem in survey methodology - an optimum sample allocation problem in stratified sampling scheme with simple random sampling without replacement design in each stratum. In this context, the optimal allocation is in the classical Tschuprow-Neyman’s sense (Tschuprow 1923; Neyman 1934), and it satisfies additional upper bounds restrictions imposed on sample sizes in strata. There are four different algorithms available to use, and one of them is Neyman optimal allocation applied in a recursive way (see Sarndal, Swensson, and Wretman 1992, Remark 12.7.1, p. 466). All the algorithms are described in detail, including proofs in Wojciak (2019).

Installation

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

install.packages("stratallo")

Example

This is a basic example which shows you how to solve a common problem of optimal sample allocation for an example population with 4 strata.

library(stratallo)

N <- c(3000, 4000, 5000, 2000) # strata sizes
S <- c(48, 79, 76, 17) # standard deviations of a study variable in strata
M <- c(100, 90, 70, 80) # upper bounds constraints imposed on the sample sizes in strata
all(M <= N) 
#> [1] TRUE
n <- 190 # total sample size
n <= sum(M)
#> [1] TRUE

nopt_ <- nopt(n = n, N = N, S = S, M = M)
nopt_
#> [1] 34.979757 76.761134 70.000000  8.259109
sum(nopt_)
#> [1] 190

# verbose output + variance for a given allocation
nopt(n = n, N = N, S = S, M = M, verbose = TRUE, variance = TRUE)
#> $nopt
#> [1] 34.979757 76.761134 70.000000  8.259109
#> 
#> $ksi
#> [1] 0.000242915
#> 
#> $J
#> [1] 3 2 1 4
#> 
#> $J_
#> [1] 2 1 4
#> 
#> $var
#> [1] 4035156476

References