Package 'MOSAlloc'

Title: Constraint Multiobjective Sample Allocation
Description: Provides a framework for multipurpose optimal resource allocation in survey sampling, extending the classical optimal allocation principles introduced by Tschuprow (1923) and Neyman (1934) to multidomain and multivariate allocation problems. The primary method mosalloc() allows for the consideration of precision and cost constraints at the subpopulation level while minimizing either a vector of sampling errors or survey costs across a broad range of optimal sample allocation problems. The approach supports both single- and multistage designs. For single-stage stratified random sampling, the mosallocSTRS() function offers a user- friendly interface. Sensitivity analysis is supported through the problem's dual variables, which are naturally obtained via the internal use of the Embedded Conic Solver from the 'ECOSolveR' package. See Willems (2025, <doi:10.25353/ubtr-9200-484c-5c89>) for a detailed description of the theory behind 'MOSAlloc'.
Authors: Felix Willems [aut, cre] (ORCID: <https://orcid.org/0009-0005-2987-5825>), Ralf Münnich [ths] (ORCID: <https://orcid.org/0000-0001-8285-5667>)
Maintainer: Felix Willems <[email protected]>
License: GPL (>= 3)
Version: 1.2.5
Built: 2026-05-15 08:22:34 UTC
Source: https://github.com/cran/MOSAlloc

Help Index

Constructor for precision constraints

Description

A helper function for generating precision matrix A and corresponding right-hand side a under stratified random sampling (STRS) as input to the multiobjective allocation function mosalloc().

Usage

constructArestrSTRS(X_var, X_tot, N, list, fpc = TRUE)

Arguments

X_var

(type: matrix) A matrix containing stratum- (rows) and variable- (columns) specific precision units.
X_tot

(type: matrix) A matrix containing stratum- (rows) and variable- (columns) specific totals.
N

(type: vector) A vector of stratum sizes.
list

(type: list) A list of lists taking subpopulation- (domain/area) specific arguments. Elements are lists containing the following components which in turn correspond to one specific precision restriction:
..$stratum_id (type: numeric) A vector containing the indices of the strata considered for the current restriction. The indices must coincide with the corresponding row numbers of X_var and X_tot.
..$variate (type: character or numeric) The column name or column index of X_var to be addressed.
..$measure (type: character) "RSE" (relative standard error) or "VAR" (variance).
..$bound (type: numeric) An upper bound to "RSE" (or "VAR").
..$name (type: character) The name of the subpopulation (domain/area).
fpc

(type: logical) A TRUE or FALSE statement indicating whether the finite population correction should be considered.

Value

The function constructArestrSTRS() returns a list containing

A (type: matrix) a precision matrix for the quality restrictions and

a (type: vector) a precision vector for the corresponding right-hand side
usable as input to the multiobjective allocation function mosalloc().

Examples

# Artificial population of 50 568 business establishments and 5 business
# sectors (data from Valliant, R., Dever, J. A., & Kreuter, F. (2013).
# Practical tools for designing and weighting survey samples. Springer.
# https://doi.org/10.1007/978-1-4614-6449-5, Example 5.2 pages 133-9)

# See also https://umd.app.box.com/s/9yvvibu4nz4q6rlw98ac/file/297813512360
# file: Code 5.3 constrOptim.example.R

Nh <- c(6221, 11738, 4333, 22809, 5467) # stratum sizes

# Revenues
mh.rev <- c(85, 11, 23, 17, 126) # mean revenue
Sh.rev <- c(170.0, 8.8, 23.0, 25.5, 315.0) # standard deviation revenue

# Employees
mh.emp <- c(511, 21, 70, 32, 157) # mean number of employees
Sh.emp <- c(255.50, 5.25, 35.00, 32.00, 471.00) # std. dev. employees

# Proportion of establishments claiming research credit
ph.rsch <- c(0.8, 0.2, 0.5, 0.3, 0.9)

# Proportion of establishments with offshore affiliates
ph.offsh <- c(0.06, 0.03, 0.03, 0.21, 0.77)

# Matrix containing stratum-specific variance components
X_var <- cbind(Sh.rev**2,
               Sh.emp**2,
               ph.rsch * (1 - ph.rsch) * Nh/(Nh - 1),
               ph.offsh * (1 - ph.offsh) * Nh/(Nh - 1))
colnames(X_var) <- c("rev", "emp", "rsch", "offsh")

# Matrix containing stratum-specific totals
X_tot <- cbind(mh.rev, mh.emp, ph.rsch, ph.offsh) * Nh
colnames(X_tot) <- c("rev", "emp", "rsch", "offsh")

# Examples
#----------------------------------------------------------------------------
# Example 1: Assume we require at maximum 5 % relative standard error (RSE)
# for estimates of the proportion of businesses with offshore affiliates.
#
# The input \code{A} and \code{a} to \code{mosalloc} can be calculated as
# follows:
A <- matrix(ph.offsh * (1 - ph.offsh) * Nh**3/(Nh - 1)/sum(Nh * ph.offsh)**2,
nrow = 1)
a <- sum(ph.offsh * (1 - ph.offsh) * Nh**2/(Nh - 1)
)/sum(Nh * ph.offsh)**2 + 0.05**2

# Using \code{constructArestrSTRS()} this can also be done via
list <- list(list(stratum_id = 1:5, variate = "offsh", measure = "RSE",
                  bound = 0.05, name = "pop"))
Ac <- constructArestrSTRS(X_var, X_tot, Nh, list, fpc = TRUE)

# or equivalently by
list <- list(list(stratum_id = 1:5, variate = 4, measure = "RSE",
                  bound = 0.05, name = "pop"))
Ac <- constructArestrSTRS(X_var, X_tot, Nh, list, fpc = TRUE)

# Evaluation of the output
Ac$A - A
Ac$a - a

# Example 2: Assume we require at maximum 5 % relative standard error for
# estimates of the proportion of businesses with offshore affiliates and
# for estimates of the proportion of businesses claiming research credit
# separately for strata 1:2 and 3:5 each.

list <- list(list(stratum_id = 1:2, variate = "offsh", measure = "RSE",
                  bound = 0.05, name = "D1"),
             list(stratum_id = 3:5, variate = "offsh", measure = "RSE",
                  bound = 0.05, name = "D2"),
             list(stratum_id = 1:2, variate = "rsch", measure = "RSE",
                  bound = 0.05, name = "D1"),
             list(stratum_id = 3:5, variate = "rsch", measure = "RSE",
                  bound = 0.05, name = "D2"))
Ac <- constructArestrSTRS(X_var, X_tot, Nh, list, fpc = TRUE)

Constructor for cost constraints

Description

A helper function for generating cost coefficient matrix C and corresponding right-hand side c under stratified random sampling (STRS) as input to the the multiobjective allocation function mosalloc().

Usage

constructCrestrSTRS(H, list)

Arguments

H

(type: numeric) The number of strata.
list

(type: list) A list of lists taking subpopulation- (domain/area) specific arguments. Elements are lists containing the following components which correspond to one specific cost restriction:
..$stratum_id (type: numeric) A vector containing the indices of the strata considered for the current restriction. The indices must coincide with the row numbers of X_var and X_tot.
..$c_coef (type: numeric) A vector of length length(stratum_id) containing the stratum-specific cost components for the set of strata that is going to be bounded by above and/or below.
..$c_upper (type: numeric) The cost upper bound value. NULL if not present.
..$c_lower (type: numeric) The cost lower bound value. NULL if not present.
..$name (type: character) The name of the subpopulation (domain/area).

Value

The function constructCrestrSTRS() returns a list

C (type: matrix): a cost matrix for the cost restrictions and

c (type: vector): a cost vector for the corresponding right-hand side
usable as input to the multiobjective allocation function mosalloc().

Examples

# Artificial population of 50 568 business establishments and 5 business
# sectors (data from Valliant, R., Dever, J. A., & Kreuter, F. (2013).
# Practical tools for designing and weighting survey samples. Springer.
# https://doi.org/10.1007/978-1-4614-6449-5, Example 5.2 pages 133-9)

# See also https://umd.app.box.com/s/9yvvibu4nz4q6rlw98ac/file/297813512360
# file: Code 5.3 constrOptim.example.R

Nh <- c(6221, 11738, 4333, 22809, 5467) # stratum sizes
H <- length(Nh)
ch <- c(120, 80, 80, 90, 150) # stratum-specific cost of surveying
budget <- 300000

# Examples
#----------------------------------------------------------------------------
# Example 1: Assume we want so specify one overall cost constraint for the
# five strata. The cost of surveying must not exceed 300000 $.

# The input \code{C} and \code{C} to \code{mosalloc} can be specified as
# follows:

C <- matrix(ch, nrow = 1)
c <- as.vector(budget)

# Using \code{constructCrestrSTRS} this can also be done via
list <- list(list(stratum_id = 1:5, c_coef = ch, c_lower = NULL,
                  c_upper = budget, name = "Overall"))
Cc <- constructCrestrSTRS(H, list)

# Evaluation of the output
Cc$C - C
Cc$c - c

# Example 2: In addition to the overall cost constraint from Example 1,
# we want to specify a minimum sample size for strata 1 to 3.

# The input \code{C} and \code{C} to \code{mosalloc} can be specified as
# follows:

C <- rbind(ch,
           ch * c(-1, -1, -1, 0, 0))
c <- c(budget,                        # Maximum overall survey budget
       - 0.5 * budget)                # Minimum overall budget for strata 1-3

# Using \code{constructCrestrSTRS} this can also be done via
list <- list(list(stratum_id = 1:5, c_coef = ch, c_lower = NULL,
                  c_upper = budget, name = "Overall"),
             list(stratum_id = 1:3, c_coef = ch[1:3], c_lower = 0.5 * budget,
                  c_upper = NULL, name = "1to3"))
Cc <- constructCrestrSTRS(H, list)

# Evaluation of the output
Cc$C - C
Cc$c - c

Constructor for cost objective components

Description

A helper function for generating cost matrix D and fixed cost vector d under stratified random sampling (STRS) as input to the multiobjective allocation function mosalloc().

Usage

constructDobjCostSTRS(X_cost, X_fixed, list)

Arguments

X_cost

(type: matrix) A matrix containing stratum- (rows) and type- (columns) specific cost coefficients associated with fixed cost. Types of cost might be, e.g. '$ US', 'minutes', 'sample size', etc.
X_fixed

(type: matrix) A matrix containing stratum- (rows) and type- (columns) specific cost coefficients associated with fixed cost.
list

(type: list) A list of lists taking subpopulation- (domain/area) specific arguments. Elements are lists containing the following components corresponding to one specific cost type:
..$stratum_id (type: numeric) A vector containing the indices of the strata considered for the current objective. The indices must coincide with the row numbers of X_cost.
..$c_type (type: character or numeric) The column name or column index of X_cost to be addressed.
..$name (type: character) The name of the subpopulation (domain/area).

Value

The function constructDobjCostSTRS() returns a list containing

$D (type: matrix): the cost coefficient matrix for cost objectives and

$d (type: vector): the vector of fixed costs
usable as input to the multiobjective allocation function mosalloc().

Examples

# Assume we are given two regions stratified into three strata each. We now
# might balance the cost of surveying between both regions.

# Stratum-specific variable cost
ch <- c(25, 40, 33, 18, 53, 21)
names(ch) <- c("R1_S1", "R1_S2", "R1_S3",
               "R2_S1", "R2_S2", "R2_S3")

# Stratum-specific fixed cost
cf <- c(55, 50, 55, 50, 55, 50)
names(cf) <- c("R1_S1", "R1_S2", "R1_S3",
               "R2_S1", "R2_S2", "R2_S3")

# The input \code{D} and \code{d} to \code{mosalloc()} can be specified as
# follows:

D <- matrix(c(ch[1:3], rep(0, 6), ch[4:6]), 2, 6, byrow = TRUE)
d <- as.vector(c(sum(cf[1:3]), sum(cf[4:6])))

# Using \code{constructDobjCostSTRS()} this can also be done via

X_cost <- matrix(ch, ncol = 1)
colnames(X_cost) <- "$ US"

X_fixed <- matrix(cf, ncol = 1)
colnames(X_fixed) <- "$ US"

list <- list(list(stratum_id = 1:3, c_type = "$ US", name = "R1"),
             list(stratum_id = 4:6, c_type = "$ US", name = "R2"))
Dc <- constructDobjCostSTRS(X_cost, X_fixed, list)

# Evaluation of the output
Dc$D - D
Dc$d - d

Constructor for precision objective components

Description

A helper function for generating precision matrix D and finite population correction d under stratified random sampling (STRS) as input to the multiobjective allocation function mosalloc().

Usage

constructDobjPrecisionSTRS(X_var, X_tot, N, list, fpc = TRUE)

Arguments

X_var

(type: matrix) A matrix containing stratum- (rows) and variable- (columns) specific precision units.
X_tot

(type: matrix) A matrix containing stratum- (rows) and variable- (columns) specific totals.
N

(type: vector) A vector of stratum sizes.
list

(type: list) A list of lists taking subpopulation- (domain/area) specific arguments. Elements are lists containing the following components which in turn correspond to one specific precision restriction:
..$stratum_id (type: numeric) A vector containing the indices of the strata considered for the current objective component. The indices must coincide with the corresponding row numbers of X_var and X_tot.
..$variate (type: character or numeric) The column name or column index of X_var to be addressed.
..$measure (type: character or numeric) As character "relVAR" (relative variance) or "VAR" (variance). A numerical between 0 and 1 indicates a gradation coefficient that balances between "relVar" (..$measure = 0) and "VAR" (..$measure = 1).
..$name (type: character) The name of the subpopulation (domain/area).
fpc

(type: logical) A TRUE or FALSE statement indicating whether the finite population correction should be considered.

Value

The function constructDobjPrecisionSTRS() returns a list containing

$D (type: matrix): the precision matrix for quality objectives and

$d (type: vector): the vector of finite population corrections
usable as input to the multiobjective allocation function mosalloc().

Examples

# Artificial population of 50 568 business establishments and 5 business
# sectors (data from Valliant, R., Dever, J. A., & Kreuter, F. (2013).
# Practical tools for designing and weighting survey samples. Springer.
# https://doi.org/10.1007/978-1-4614-6449-5, Example 5.2 pages 133-9)

# See also https://umd.app.box.com/s/9yvvibu4nz4q6rlw98ac/file/297813512360
# file: Code 5.3 constrOptim.example.R

Nh <- c(6221, 11738, 4333, 22809, 5467) # stratum sizes

# Revenues
mh.rev <- c(85, 11, 23, 17, 126) # mean revenue
Sh.rev <- c(170.0, 8.8, 23.0, 25.5, 315.0) # standard deviation revenue

# Employees
mh.emp <- c(511, 21, 70, 32, 157) # mean number of employees
Sh.emp <- c(255.50, 5.25, 35.00, 32.00, 471.00) # std. dev. employees

# Proportion of estabs claiming research credit
ph.rsch <- c(0.8, 0.2, 0.5, 0.3, 0.9)

# Proportion of estabs with offshore affiliates
ph.offsh <- c(0.06, 0.03, 0.03, 0.21, 0.77)

budget <- 300000 # overall available budget
n.min  <- 100 # minimum stratum-specific sample size

# Matrix containing stratum-specific variance components
X_var <- cbind(Sh.rev**2,
               Sh.emp**2,
               ph.rsch * (1 - ph.rsch) * Nh/(Nh - 1),
               ph.offsh * (1 - ph.offsh) * Nh/(Nh - 1))
colnames(X_var) <- c("rev", "emp", "rsch", "offsh")

# Matrix containing stratum-specific totals
X_tot <- cbind(mh.rev, mh.emp, ph.rsch, ph.offsh) * Nh
colnames(X_tot) <- c("rev", "emp", "rsch", "offsh")

# Examples
#----------------------------------------------------------------------------
# Example 1: Assume we want to minimize the variation of estimates for
# revenue.
#
# The input \code{D} and \code{d} to \code{mosalloc()} can be calculated as
# follows:

D <- matrix(Sh.rev**2 * Nh**2, nrow = 1) # objective variance components
d <- sum(Sh.rev**2 * Nh) # finite population correction

# Using \code{constructDobjPrecisionSTRS()} this can also be done via
list <- list(list(stratum_id = 1:5, variate = "rev", measure = "VAR",
                  name = "pop"))
Dc <- constructDobjPrecisionSTRS(X_var, X_tot, Nh, list, fpc = TRUE)

# or equivalently by
list <- list(list(stratum_id = 1:5, variate = 1, measure = "VAR",
                  name = "pop"))
Dc <- constructDobjPrecisionSTRS(X_var, X_tot, Nh, list, fpc = TRUE)

# Evaluate output
Dc$D - D
Dc$d - d

# Example 2: Minimization of the maximum coefficient of variation of
# estimates for the total revenue, the number of employee, the number of
# businesses claimed research credit, and the number of businesses with
# offshore affiliates

# The input \code{D} and \code{d} to \code{mosalloc()} can be calculated as
# follows:

D <- rbind(Sh.rev**2 * Nh**2 / sum(Nh * mh.rev)**2,
           Sh.emp**2 * Nh**2 / sum(Nh * mh.emp)**2,
           ph.rsch * (1 - ph.rsch) * Nh**3/(Nh - 1)/sum(Nh * ph.rsch)**2,
           ph.offsh * (1 - ph.offsh) * Nh**3/(Nh - 1)/sum(Nh * ph.offsh)**2)
d <- as.vector(D %*% (1 / Nh)) # finite population correction

# Using \code{constructDobjPrecisionSTRS()} this can also be done via
list <- list(list(stratum_id = 1:5, variate = "rev", measure = "relVAR",
                  name = "pop"),
             list(stratum_id = 1:5, variate = "emp", measure = "relVAR",
                  name = "pop"),
             list(stratum_id = 1:5, variate = "rsch", measure = "relVAR",
                  name = "pop"),
             list(stratum_id = 1:5, variate = "offsh", measure = "relVAR",
                  name = "pop"))
Dc <- constructDobjPrecisionSTRS(X_var, X_tot, Nh, list, fpc = TRUE)

# Evaluation of the output
Dc$D - D
Dc$d - d

Multiobjective sample allocation for constraint multivariate and multidomain optimal allocation in survey sampling

Description

Computes solutions to standard sample allocation problems under various precision and cost restrictions. The input data is transformed and parsed to the Embedded COnic Solver (ECOS) from the 'ECOSolveR' package. Multiple survey purposes can be optimized simultaneously through a weighted Chebyshev minimization. Note that in the case of multiple objectives, mosalloc() does not necessarily lead to Pareto optimality. This highly depends on the problem structure. A strong indicator for Pareto optimality is when the weighted objective values given by Dbounds are constant over all objective components or when all components of Qbounds equal 1. In addition, mosalloc() can handle twice-differential convex decision functionals (in which case Pareto optimality is ensured). mosalloc() returns dual variables, enabling a detailed sensitivity analysis.

Usage

mosalloc(
  D,
  d,
  A = NULL,
  a = NULL,
  C = NULL,
  c = NULL,
  l = 2,
  u = NULL,
  opts = list(sense = "max_precision", f = NULL, df = NULL, Hf = NULL, init_w = 1,
    mc_cores = 1L, pm_tol = 1e-05, max_iters = 100L, print_pm = FALSE)
)

Arguments

D

(type: matrix) The objective matrix. A matrix of either precision or cost units.
d

(type: vector) The objective vector. A vector of either fixed precision components (e.g. finite population corrections) or fixed costs.
A

(type: matrix) A matrix of precision units for precision constraints.
a

(type: vector) The right-hand side vector of the precision constraints.
C

(type: matrix) A matrix of cost coefficients for cost constraints
c

(type: vector) The right-hand side vector of the cost constraints.
l

(type: vector) A vector of lower box constraints.
u

(type: vector) A vector of upper box constraints.
opts

(type: list) The options used by the algorithms:
$sense (type: character) Sense of optimization (default = "max_precision", alternative "min_cost").
$f (type: function) Decision functional over the objective vector (default = NULL).
$df (type: function) The gradient of f (default = NULL).
$Hf (type: function) The Hesse matrix of f (default = NULL).
$init_w (type: numeric or matrix) Preference weightings (default = 1; The weight for first objective component must be 1).
$mc_cores (type: integer) The number of cores for parallelizing multiple input weightings stacked rowwise (default = 1L).
$pm_tol (type: numeric) The tolerance for the projection method (default = 1e-5).
max_iters (type: integer) The maximum number of iterations (default = 100L).
$print_pm (type: logical) A TRUE or FALSE statement whether iterations of the projection method should be printed (default = FALSE).

Value

mosalloc() returns a list containing the following components:

$w The initial preference weighting opts$init_w.

$n The vector of optimal sample sizes.

$J The optimal objective vector.

$Objective The objective value with respect to decision functional f. NULL if opts$f = NULL.

$Utopian The component-wise univariate optimal objective vector. NULL if opts$f = NULL.

$Normal The vector normal to the Pareto frontier at $J.

$dfJ The gradient of opts$f evaluated at $J.

$Sensitivity The dual variables of the objectives and constraints.

$Qbounds The Quality bounds of the Lorentz cones.

$Dbounds The weighted objective constraints ($w * $J).

$Scalepar An internal scaling parameter.

$Ecosolver A list of ECOSolveR returns including:
...$Ecoinfostring The info string of ECOSolveR::ECOS_csolve().
...$Ecoredcodes The redcodes of ECOSolveR::ECOS_csolve().
...$Ecosummary Problem summary of ECOSolveR::ECOS_csolve().

$Timing Run time info.

$Iteration A list of internal iterates. NULL if opts$f = NULL.

Note

Precision optimization (opts$sense == "max_precision", opts$f == NULL)

The mathematical problem solved is

min⁡n,z,t{t:Dz−d≤w−1t,Az≤a,Cn≤c,1≤nizi ∀i,l≤n≤u}\min_{n, z, t}\{t: Dz-d\leq w^{-1}t, Az\leq a, Cn\leq c, 1\leq n_iz_i\, \forall i, l\leq n \leq u\}

with 1≤l≤u≤N\textbf{1}\leq l\leq u \leq N, where

  • n,z∈Rmn,z\in\mathbb{R}^m and t∈Rt\in\mathbb{R} are the optimization variables,

  • D∈{M∈RkD×m:∑iMij>0 ∀j,∑jMij>0 ∀i}D\in\{M\in\mathbb{R}^{k_D\times m}: \sum_i M_{ij}>0\, \forall j, \sum_j M_{ij}>0\, \forall i\} a matrix of nonnegative objective precision components with kDk_D the number of precision objectives (the number of variables of interest),

  • d∈RkDd\in\mathbb{R}^{k_D} the objective right hand-side (RHS), e.g. the finite population correction (fpc),

  • A∈RkA×mA\in\mathbb{R}^{k_A\times m} a nonnegative precision matrix with kAk_A the number of precision constraints, a∈RkAa\in \mathbb{R}^{k_A} the corresponding RHS, e.g. fpc + (upper bound to the coefficient of variation)^2,

  • C∈RkC×mC\in\mathbb{R}^{k_C\times m} a cost matrix with kCk_C the number of cost constraint,

  • c∈RkCc\in\mathbb{R}^{k_C} the corresponding RHS,

  • l,u∈Rml,u\in\mathbb{R}^m the bounds to the sample size vector nn,

  • N∈NmN\in\mathbb{N}^m the vector of population sizes, and

  • ww a given strictly positive preference weighting.

Special cases of this formulation are

  • Neyman-Tschuprow allocation (Neyman, 1934 and Tschuprow, 1923):

    min⁡n{∑h=1H(Nh2Sh2nh−NhSh2):∑h=1Hnh≤c}⇔min⁡n,z,t{t:Dz−d≤t,Cn≤c,1≤nizi ∀i}\min_n \Big\{\sum_{h=1}^H \Big(\frac{N_h^2S_h^2}{n_h}-N_hS_h^2\Big):\sum_{h=1}^H n_h \leq c\Big\} \quad \Leftrightarrow \quad \min_{n, z, t}\{t: Dz-d\leq t, Cn\leq c, 1\leq n_iz_i\, \forall i\}

    with D=(N12S12,…,NH2SH2)D = (N_1^2S_1^2,\dots,N_H^2S_H^2), d=∑h=1HNhSh2d = \sum_{h=1}^H N_hS_h^2, C=(1,…,1)C = (1,\dots,1) and cc a maximum sample size. Here, HH is the number of strata, NhN_h the size of stratum hh and Sh2S_h^2 the variance of the variable of interest in stratum hh.

  • box-constrained optimal allocation:

    min⁡n,z,t{t:Dz−d≤t,Cn≤c,1≤nizi ∀i,l≤n≤u}\min_{n, z, t}\{t: Dz-d\leq t, Cn\leq c, 1\leq n_iz_i\, \forall i, l\leq n \leq u\}

    with D=(N12S12,…,NH2SH2)D = (N_1^2S_1^2,\dots,N_H^2S_H^2), d=∑h=1HNhSh2d = \sum_{h=1}^H N_hS_h^2, C=(1,…,1)C = (1,\dots,1) and cc a maximum sample size (cf. Srikantan, 1963 and Münnich et al., 2012). Here, ll and uu are bounds to the optimal sample size vector.

  • cost and precision constrained univariate optimal allocation:

    min⁡n,z,t{t:Dz−d≤t,Az≤a,Cn≤c,1≤nizi ∀i,l≤n≤u}\min_{n, z, t}\{t: Dz-d\leq t, Az\leq a, Cn\leq c, 1\leq n_iz_i\, \forall i, l\leq n \leq u\}

    with kD=1k_D = 1 (cf. Willems, 2025, Chapter 3).

  • multivariate optimal allocation with weighted sum scalarization:

    min⁡n,z,t{t:w⊤Dz−w⊤d≤t,Az≤a,Cn≤c,1≤nizi ∀i,l≤n≤u}\min_{n, z, t}\{t: w^\top Dz-w^\top d\leq t, Az\leq a, Cn\leq c, 1\leq n_iz_i\, \forall i, l\leq n \leq u\}

    where w∈RkDw\in\mathbb{R}^{k_D} is a strictly positive preference weighting (cf. Folks and Antle, 1965, and Rupp, 2018). Note that for this case the problem reduces to cost and precision constrained univariate optimal allocation. Solutions are ensured to be optimal in the Pareto sense.

  • box-constraint two-stage cluster sampling:

    min⁡nI,nII{(NI2SI2nI−NISI2)+NInI∑j=1NI(NIIj2SIIj2nIIj−NIIjSIIj2):cInI+nINI∑j=1NIcIIjnIIj≤cmax,\quad \quad \quad\min_{n_\textbf{I},n_\textbf{II}} \Big\{\Big(\frac{N_\textbf{I}^2 S_\textbf{I}^2}{n_\textbf{I}}-N_\textbf{I}S_\textbf{I}^2\Big) + \frac{N_\textbf{I}}{n_\textbf{I}}\sum_{j=1}^{N_\textbf{I}} \Big(\frac{N_{\textbf{II}j}^2S_{\textbf{II}j}^2}{n_{\textbf{II}j}}- N_{\textbf{II}j}S_{\textbf{II}j}^2\Big): c_{\textbf{I}}n_\textbf{I} + \frac{n_\textbf{I}}{N_\textbf{I}} \sum_{j=1}^{N_\textbf{I}}c_{\textbf{II}j}n_{\textbf{II}j} \leq c_\textrm{max},\hspace{3.5cm}

    lI≤nI≤uI,lII≤nII≤uII}\hspace{9.1cm} l_\textbf{I}\leq n_\textbf{I}\leq u_\textbf{I}, l_\textbf{II}\leq n_\textbf{II}\leq u_\textbf{II} \Big\}

    ⇔min⁡n,z,t{t:Dz−d≤t,Cn≤c,1≤nizi ∀i,l≤n≤u}\Leftrightarrow \quad \min_{n, z, t}\{t: Dz-d\leq t, Cn\leq c, 1\leq n_iz_i\, \forall i, l\leq n \leq u\}\hspace{4cm}

    with D=(NI2SI2−NI∑j=1NINIIjSIIj2,NINII12SII12,…,NINIINI2SIINI2)D = (N_\textbf{I}^2S_\textbf{I}^2 - N_\textbf{I}\sum_{j=1}^{N_\textbf{I}} N_{\textbf{II}j}S_{\textbf{II}j}^2, N_\textbf{I}N_{\textbf{II}1}^2 S_{\textbf{II}1}^2,\dots, N_\textbf{I}N_{\textbf{II}N_\textbf{I}}^2S_{\textbf{II}N_\textbf{I}}^2), d=NISI2d = N_\textbf{I}S_\textbf{I}^2, C=[C1,C2]C = [C_1, C_2], where C1=(cI,lII⊤,−uII⊤)⊤C_1=(c_\textbf{I},l_\textbf{II}^\top,-u_\textbf{II}^\top)^\top and C2=[NI−1cII⊤;−I;I]C_2 = [N_\textbf{I}^{-1}c_\textbf{II}^\top;-\textbf{I};\textbf{I}] (I\textbf{I} is the identity matrix), c=(cmax,0,…,0)⊤c = (c_\textrm{max},0,\dots,0)^\top, where l=(lI,lIlII⊤)⊤l = (l_\textbf{I},l_\textbf{I}l_\textbf{II}^\top)^\top and u=(uI,uIuII⊤)⊤u = (u_\textbf{I},u_\textbf{I}u_\textbf{II}^\top)^\top (cf. Willems, 2025, Chapter 3). Here, NIN_\textbf{I} is the number of clusters, NIIjN_{\textbf{II}j} the size of cluster jj, SI2S_\textbf{I}^2 the between cluster variance and SIIj2S_{\textbf{II}j}^2 the within cluster variances of the variable of interest. Furthermore, cmaxc_\textrm{max} is a maximum expected cost, cIc_\textbf{I} a variable cost for sampling one cluster, and cIIjc_{\textbf{II}j} a variable cost for sampling one unit in cluster jj. The optimal number of clusters to be drawn and the optimal sample sizes are given through n=(nI,nInII1,…,nInIINI)⊤n = (n_\textbf{I}, n_\textbf{I}n_{\textbf{II}1},\dots, n_\textbf{I}n_{\textbf{II} N_\textbf{I}})^\top.

For the special cases above, solutions are unique and, thus, Pareto optimal. For the general multiobjective problem formulation this is not the case. However, a strong indicator for uniqueness of solutions is nizi=1 ∀in_iz_i = 1\, \forall i (Qbounds) or Dz−d=w−1tDz-d = w^{-1}t (Dbounds). Uniqueness can be ensured via a stepwise procedure implemented in mosallocStepwiseFirst().

Precision optimization (opts$sense == "max_precision", opts$f ==ff, opts$f == ∇f\nabla f, opts$f == HfHf)

The mathematical problem solved is

min⁡n,z{f(Dz−d):Az≤a,Cn≤c,1≤nizi ∀i,l≤n≤u}\min_{n, z}\{f(Dz-d): Az\leq a, Cn\leq c, 1\leq n_iz_i\, \forall i, l\leq n \leq u\}

with components as specified above and where f:RkD→R,x↦f(x)f:\mathbb{R}^{k_D}\rightarrow \mathbb{R}, x \mapsto f(x) is a twice-differentiable convex decision functional. E.g. a pp-norm f(x)=∥x∥pf(x) = \lVert x \rVert_p with p∈Np\in\mathbb{N}.

Cost optimization (opts$sense == "min_cost")

The mathematical problem solved is

min⁡n,z,t{t:Dn−d≤1t,Az≤a,Cn≤c,1≤nizi ∀i,l≤n≤u}\min_{n, z, t}\{t: Dn-d\leq\textbf{1}t, Az\leq a, Cn\leq c, 1\leq n_iz_i\, \forall i, l\leq n \leq u\}

with 1≤l≤u≤N1\leq l\leq u \leq N. Hence, the only difference to precision optimization is the type of objective constraint Dn−d≤1tDn-d\leq\textbf{1}t.

Special cases of this formulation are

  • the cost optimal allocation (possibly multivariate, i.e. kA≥2k_A\geq 2):

    min⁡n,z,t{t:Dn−d≤1t,Az≤a,1≤nizi ∀i}\min_{n, z, t}\{t: Dn-d\leq\textbf{1}t, Az\leq a, 1\leq n_iz_i\, \forall i\}

    where D⊤D^\top is a vector of stratum-specific sampling cost and dd some fixed cost.

References

See:

Folks, J.L., Antle, C.E. (1965). Optimum Allocation of Sampling Units to Strata when there are R Responses of Interest. Journal of the American Statistical Association, 60(309), 225-233. doi:10.1080/01621459.1965.10480786.

Münnich, R., Sachs, E., Wagner, M. (2012). Numerical solution of optimal allocation problems in stratified sampling under box constraints. AStA Advances in Statistical Analysis, 96, 435-450. doi:10.1007/s10182-011-0176-z.

Neyman, J. (1934). On the Two Different Aspects of the Representative Method: The Method of Stratified Sampling and the Method of Purposive Selection. Journal of the Royal Statistical Society, 97(4), 558–625.

Tschuprow, A.A. (1923). On the Mathematical Expectation of the Moments of Frequency Distribution in the Case of Correlated Observations. Metron, 2(3,4), 461-493, 646-683.

Rupp, M. (2018). Optimization for Multivariate and Multi-domain Methods in Survey Statistics (Doctoral dissertation). Trier University. doi:10.25353/UBTR-8351-5432-14XX.

Srikantan, K.S. (1963). A Problem in Optimum Allocation. Operations Research, 11(2), 265-274.

Willems, F. (2025). A Framework for Multiobjective and Uncertain Resource Allocation Problems in Survey Sampling based on Conic Optimization (Doctoral dissertation). Trier University. doi:10.25353/ubtr-9200-484c-5c89.

Examples

# Artificial population of 50 568 business establishments and 5 business
# sectors (data from Valliant, R., Dever, J. A., & Kreuter, F. (2013).
# Practical tools for designing and weighting survey samples. Springer.
# https://doi.org/10.1007/978-1-4614-6449-5, Example 5.2 pages 133-9)

# See also <https://umd.app.box.com/s/9yvvibu4nz4q6rlw98ac/file/297813512360>
# file: Code 5.3 constrOptim.example.R

Nh <- c(6221, 11738, 4333, 22809, 5467) # stratum sizes
ch <- c(120, 80, 80, 90, 150) # stratum-specific cost of surveying

# Revenues
mh.rev <- c(85, 11, 23, 17, 126) # mean revenue
Sh.rev <- c(170.0, 8.8, 23.0, 25.5, 315.0) # standard deviation revenue

# Employees
mh.emp <- c(511, 21, 70, 32, 157) # mean number of employees
Sh.emp <- c(255.50, 5.25, 35.00, 32.00, 471.00) # std. dev. employees

# Proportion of estabs claiming research credit
ph.rsch <- c(0.8, 0.2, 0.5, 0.3, 0.9)

# Proportion of estabs with offshore affiliates
ph.offsh <- c(0.06, 0.03, 0.03, 0.21, 0.77)

budget <- 300000 # overall available budget
n.min  <- 100 # minimum stratum-specific sample size

# Examples
#----------------------------------------------------------------------------
# Example 1: Minimization of the variation of estimates for revenue subject
# to cost restrictions and precision restrictions to the coefficient of
# variation of estimates for the proportion of businesses with offshore
# affiliates.

l <- rep(n.min, 5) # minimum sample size per stratum
u <- Nh            # maximum sample size per stratum
C <- rbind(ch,
           ch * c(-1, -1, -1, 0, 0))
c <- c(budget,                        # Maximum overall survey budget
       - 0.5 * budget)                # Minimum overall budget for strata 1-3

# We require at maximum 5 % relative standard error for estimates of
# proportion of businesses with offshore affiliates
A <- matrix(ph.offsh * (1 - ph.offsh) * Nh**3/(Nh - 1)/sum(Nh * ph.offsh)**2,
 nrow = 1)
a <- sum(ph.offsh * (1 - ph.offsh) * Nh**2/(Nh - 1)
)/sum(Nh * ph.offsh)**2 + 0.05**2

D <- matrix(Sh.rev**2 * Nh**2, nrow = 1) # objective variance components
d <- sum(Sh.rev**2 * Nh) # finite population correction

opts <- list(sense = "max_precision",
             f = NULL, df = NULL, Hf = NULL,
             init_w = 1,
             mc_cores = 1L, pm_tol = 1e-05,
             max_iters = 100L, print_pm = FALSE)

sol <- mosalloc(D = D, d = d, A = A, a = a, C = C, c = c, l = l, u = u,
                opts = opts)

# Check solution statement of the internal solver to verify feasibility
sol$Ecosolver$Ecoinfostring # [1] "Optimal solution found"

# Check constraints
c(C[1, ] %*% sol$n) # [1] 3e+05
c(C[2, ] %*% sol$n) # [1] -150000
c(sqrt(A %*% (1 / sol$n) - A %*% (1 / Nh))) # 5 % rel. std. err.

#----------------------------------------------------------------------------
# Example 2: Minimization of the maximum relative variation of estimates for
# the total revenue, the number of employee, the number of businesses claimed
# research credit, and the number of businesses with offshore affiliates
# subject to cost restrictions

l <- rep(n.min, 5) # minimum sample size ber stratum
u <- Nh            # maximum sample size per stratum
C <- rbind(ch, ch * c(-1, -1, -1, 0, 0))
c <- c(budget, - 0.5 * budget)
A <- NULL # no precision constraint
a <- NULL # no precision constraint

# Precision components (Variance / Totals^2) for multidimensional objective
D <- rbind(Sh.rev**2 * Nh**2/sum(Nh * mh.rev)**2,
           Sh.emp**2 * Nh**2/sum(Nh * mh.emp)**2,
           ph.rsch * (1 - ph.rsch) * Nh**3/(Nh - 1)/sum(Nh * ph.rsch)**2,
           ph.offsh * (1 - ph.offsh) * Nh**3/(Nh - 1)/sum(Nh * ph.offsh)**2)

d <- as.vector(D %*% (1 / Nh)) # finite population correction

opts <- list(sense = "max_precision",
             f = NULL, df = NULL, Hf = NULL,
             init_w = 1,
             mc_cores = 1L, pm_tol = 1e-05,
             max_iters = 100L, print_pm = FALSE)

sol <- mosalloc(D = D, d = d, C = C, c = c, l = l, u = u, opts = opts)

# Obtain optimal objective value
sol$J # [1] 0.0017058896 0.0004396972 0.0006428475 0.0017058896

# Obtain corresponding normal vector
sol$Normal # [1] 6.983113e-01 1.337310e-11 1.596167e-11 3.016887e-01

# => Revenue and offshore affiliates are dominating the solution with a
#    ratio of approximately 2:1 (sol$Normal[1] / sol$Normal[4])

#----------------------------------------------------------------------------
# Example 3: Example 2 with preference weighting

w <- c(1, 3.85, 3.8, 1.3) # preference weighting
l <- rep(n.min, 5) # minimum sample size ber stratum
u <- Nh            # maximum sample size per stratum
C <- rbind(ch, ch * c(-1, -1, -1, 0, 0))
c <- c(budget, - 0.5 * budget)
A <- NULL # no precision constraint
a <- NULL # no precision constraint

D <- rbind(Sh.rev**2 * Nh**2/sum(Nh * mh.rev)**2,
           Sh.emp**2 * Nh**2/sum(Nh * mh.emp)**2,
           ph.rsch * (1 - ph.rsch) * Nh**3/(Nh - 1)/sum(Nh * ph.rsch)**2,
           ph.offsh * (1 - ph.offsh) * Nh**3/(Nh - 1)/sum(Nh * ph.offsh)**2)

d <- as.vector(D %*% (1 / Nh))

opts <- list(sense = "max_precision",
             f = NULL, df = NULL, Hf = NULL,
             init_w = w,
             mc_cores = 1L, pm_tol = 1e-05,
             max_iters = 100L, print_pm = FALSE)

mosalloc(D = D, d = d, C = C, c = c, l = l, u = u, opts = opts)

#----------------------------------------------------------------------------
# Example 4: Example 2 with multiple preference weightings for simultaneous
# evaluation

w <- matrix(c(1.0, 1.0, 1.0, 1.0,       # matrix of preference weightings
              1.0, 3.9, 3.9, 1.3,
              0.8, 4.2, 4.8, 1.5,
              1.2, 3.5, 4.8, 2.0,
              2.0, 1.0, 1.0, 2.0), 5, 4, byrow = TRUE)
w <- w / w[,1]     # rescale w (ensure the first weighting to be one)
l <- rep(n.min, 5) # minimum sample size ber stratum
u <- Nh            # maximum sample size per stratum
C <- rbind(ch, ch * c(-1, -1, -1, 0, 0))
c <- c(budget, - 0.5 * budget)
A <- NULL # no precision constraint
a <- NULL # no precision constraint

D <- rbind(Sh.rev**2 * Nh**2/sum(Nh * mh.rev)**2,
           Sh.emp**2 * Nh**2/sum(Nh * mh.emp)**2,
           ph.rsch * (1 - ph.rsch) * Nh**3/(Nh - 1)/sum(Nh * ph.rsch)**2,
           ph.offsh * (1 - ph.offsh) * Nh**3/(Nh - 1)/sum(Nh * ph.offsh)**2)

d <- as.vector(D %*% (1 / Nh))

opts <- list(sense = "max_precision",
             f = NULL, df = NULL, Hf = NULL,
             init_w = w,
             mc_cores = 1L, pm_tol = 1e-05,
             max_iters = 100L, print_pm = FALSE)

sols <- mosalloc(D = D, d = d, C = C, c = c, l = l, u = u, opts = opts)
lapply(sols, function(sol){sol$Qbounds})

#----------------------------------------------------------------------------
# Example 5: Example 2 where a weighted sum scalarization of the objective
# components is minimized

l <- rep(n.min, 5) # minimum sample size ber stratum
u <- Nh            # maximum sample size per stratum
C <- matrix(ch, nrow = 1)
c <- budget
A <- NULL # no precision constraint
a <- NULL # no precision constraint

# Objective variance components
D <- rbind(Sh.rev**2 * Nh**2/sum(Nh * mh.rev)**2,
           Sh.emp**2 * Nh**2/sum(Nh * mh.emp)**2,
           ph.rsch * (1 - ph.rsch) * Nh**3/(Nh - 1)/sum(Nh * ph.rsch)**2,
           ph.offsh * (1 - ph.offsh) * Nh**3/(Nh - 1)/sum(Nh * ph.offsh)**2)

d <- as.vector(D %*% (1 / Nh)) # finite population correction

# Simple weighted sum as decision functional
wss <- c(1, 1, 0.5, 0.5) # preference weighting (weighted sum scalarization)

Dw <- wss %*% D
dw <- as.vector(Dw %*% (1 / Nh))

opts <- list(sense = "max_precision",
             f = NULL, df = NULL, Hf = NULL,
             init_w = 1,
             mc_cores = 1L, pm_tol = 1e-05,
             max_iters = 1000L, print_pm = FALSE)

# Solve weighted sum scalarization (WSS) via mosalloc
sol_wss <- mosalloc(D = Dw, d = dw, C = C, c = c, l = l, u = u, opts = opts)

# Obtain optimal objective values
J <- D %*% (1 / sol_wss$n) - d

# Reconstruct solution via a weighted Chebyshev minimization
wcm <- J[1] / J
opts = list(sense = "max_precision",
            f = NULL, df = NULL, Hf = NULL,
            init_w = matrix(wcm, 1),
            mc_cores = 1L, pm_tol = 1e-05,
            max_iters = 1000L, print_pm = FALSE)

sol_wcm <- mosalloc(D = D, d = d, C = C, c = c, l = l, u = u, opts = opts)

# Compare solutions
rbind(t(J), sol_wcm$J)
#            [,1]         [,2]         [,3]        [,4]
# [1,] 0.00155645 0.0004037429 0.0005934474 0.001327165
# [2,] 0.00155645 0.0004037429 0.0005934474 0.001327165

rbind(sol_wss$n, sol_wcm$n)
#          [,1]     [,2]     [,3]     [,4]     [,5]
# [1,] 582.8247 236.6479 116.7866 839.5988 841.4825
# [2,] 582.8226 236.6475 116.7871 839.5989 841.4841

rbind(wss, sol_wcm$Normal / sol_wcm$Normal[1])
#    [,1]      [,2]      [,3]      [,4]
#wss    1 1.0000000 0.5000000 0.5000000
#       1 0.9976722 0.4997552 0.4997462

#----------------------------------------------------------------------------
# Example 6: Example 1 with two subpopulations and a p-norm as decision
# functional

l <- rep(n.min, 5) # minimum sample size per stratum
u <- Nh            # maximum sample size per stratum
C <- rbind(ch, ch * c(-1, -1, -1, 0, 0))
c <- c(budget, - 0.5 * budget)

# At maximum 5 % relative standard error for estimates of proportion of
# businesses with offshore affiliates
A <- matrix(ph.offsh * (1 - ph.offsh) * Nh**3/(Nh - 1)/sum(Nh * ph.offsh)**2,
 nrow = 1)
a <- sum(ph.offsh * (1 - ph.offsh) * Nh**2/(Nh - 1)
)/sum(Nh * ph.offsh)**2 + 0.05**2

D <- rbind((Sh.rev**2 * Nh**2)*c(0,0,1,1,0),
           (Sh.rev**2 * Nh**2)*c(1,1,0,0,1))# objective variance components
d <- as.vector(D %*% (1 / Nh)) # finite population correction

# p-norm solution
p <- 5 # p-norm
opts <- list(sense = "max_precision",
             f = function(x) sum(x**p),
             df = function(x) p * x**(p - 1),
             Hf = function(x) diag(p * (p - 1) * x**(p - 2)),
             init_w = 1,
             mc_cores = 1L, pm_tol = 1e-05,
             max_iters = 1000L, print_pm = TRUE)

sol <- mosalloc(D = D, d = d, C = C, c = c, l = l, u = u, opts = opts)

c(sol$Normal/sol$dfJ)/mean(c(sol$Normal/sol$dfJ))
# [1] 0.9999972 1.0000028

#----------------------------------------------------------------------------
# Example 7: Example 2 with p-norm as decision functional and only one
# overall cost constraint

l <- rep(n.min, 5) # minimum sample size ber stratum
u <- Nh            # maximum sample size per stratum
C <- matrix(ch, nrow = 1)
c <- budget
A <- NULL # no precision constraint
a <- NULL # no precision constraint

# Objective precision components
D <- rbind(Sh.rev**2 * Nh**2/sum(Nh * mh.rev)**2,
           Sh.emp**2 * Nh**2/sum(Nh * mh.emp)**2,
           ph.rsch * (1 - ph.rsch) * Nh**3/(Nh - 1)/sum(Nh * ph.rsch)**2,
           ph.offsh * (1 - ph.offsh) * Nh**3/(Nh - 1)/sum(Nh * ph.offsh)**2)

d <- as.vector(D %*% (1 / Nh)) # finite population correction

# p-norm solution
p <- 5 # p-norm
opts <- list(sense = "max_precision",
             f = function(x) sum(x**p),
             df = function(x) p * x**(p - 1),
             Hf = function(x) diag(p * (p - 1) * x**(p - 2)),
             init_w = 1,
             mc_cores = 1L, pm_tol = 1e-05,
             max_iters = 1000L, print_pm = TRUE)

sol <- mosalloc(D = D, d = d, C = C, c = c, l = l, u = u, opts = opts)

c(sol$Normal/sol$dfJ)/mean(c(sol$Normal/sol$dfJ))
# [1] 1.0014362 0.9780042 1.0197807 1.0007789

#----------------------------------------------------------------------------
# Example 8: Minimization of sample sizes subject to precision constraints

l <- rep(n.min, 5) # minimum sample size ber stratum
u <- Nh            # maximum sample size per stratum

# We require at maximum 4.66 % relative standard error for the estimate of 
# total revenuee, 5 % for the number of employees, 3 % for the proportion of
# businesses claiming research credit, and 3 % for the proportion of
# businesses with offshore affiliates
A <- rbind(Sh.rev**2 * Nh**2/sum(Nh * mh.rev)**2,
           Sh.emp**2 * Nh**2/sum(Nh * mh.emp)**2,
           ph.rsch * (1 - ph.rsch) * Nh**3/(Nh - 1)/sum(Nh * ph.rsch)**2,
           ph.offsh * (1 - ph.offsh) * Nh**3/(Nh - 1)/sum(Nh * ph.offsh)**2)
a <- as.vector(A%*%(1 / Nh) + c(0.0466, 0.05, 0.03, 0.03)**2)

# We do not consider any additional sample size or cost constraints
C <- NULL # no cost constraint
c <- NULL # no cost constraint

# Since we minimize the sample size, we define D and d as follows:
D <- matrix(1, nrow = 1, ncol = length(Nh)) # objective cost components
d <- as.vector(0)                           # vector of possible fixed cost

opts <- list(sense = "min_cost", # Sense of optimization is survey cost
             f = NULL,
             df = NULL,
             Hf = NULL,
             init_w = 1,
             mc_cores = 1L, pm_tol = 1e-05,
             max_iters = 100L, print_pm = TRUE)

sol <- mosalloc(D = D, d = d, A = A, a = a, l = l, u = u, opts = opts)

sum(sol$n) # [1] 2843.219
sol$J # [1] 2843.219

#----------------------------------------------------------------------------
#----------------------------------------------------------------------------
# Note: Sample size optimization for two-stage cluster sampling can be
#       reduced to the structure of optimal stratified random samplin when
#       considering expected costs. Therefore, mosalloc() can handle such
#       designs. A benefit is that mosalloc() allows relatively complex
#       sample size restrictions such as box constraints for subsampling.
#       Optimal sample sizes at secondary stages have to be reconstructed
#       from sol$n.
#
# Example 9: Optimal number of primary sampling units (PSU) and secondary
# sampling units (SSU) in 2-stage cluster sampling.

set.seed(1234)
pop <- data.frame(value = rnorm(100, 100, 35),
                  cluster = sample(1:4, 100, replace = TRUE))

CI <- 36  # Sampling cost per PSU
CII <- 10 # Average sampling cost per SSU

NI <- 4                   # Number of PSUs
NII <- table(pop$cluster) # PSU/cluster sizes

S2I <- var(by(pop$value, pop$cluster, sum)) # between PSU variance
S2II <- by(pop$value, pop$cluster, var)     # within PSU variances

D <- matrix(c(NI**2 * S2I - NI * sum(NII * S2II), NI * NII**2 * S2II), 1)
d <- as.vector(NI * S2I) # = D %*% (1 / c(NI, NI * NII))

C <- cbind(c(CI, rep(2, NI), -NII),
           rbind(rep(CII / NI, 4), -diag(4), diag(4)))
c <- as.vector(c(500, rep(0, 8)))

l <- c(2, rep(4, 4))
u <- c(NI, NI * NII)

opts <- list(sense = "max_precision",
             f = NULL,
             df = NULL,
             Hf = NULL,
             init_w = 1,
             mc_cores = 1L, pm_tol = 1e-05,
             max_iters = 100L, print_pm = TRUE)

sol <- mosalloc(D = D, d = d, C = C, c = c, l = l, u = u, opts = opts)

# Optimum number of clusters to be drawn
sol$n[1] # [1] 2.991551

# Optimum number of elements to be drawn within clusters
sol$n[-1] / sol$n[1] # [1] 12.16454 11.60828 15.87949 12.80266

Multiobjective sample allocation for constraint multivariate and multidomain optimal allocation in survey sampling (a stepwise optimality procedure is processed first to force Pareto optimality of the solution)

Description

Computes solutions to standard sample allocation problems under various precision and cost restrictions. The input data is transformed and parsed to the Embedded COnic Solver (ECOS) from the 'ECOSolveR' package. Multiple survey purposes are optimized simultaneously through a stepwise weighted Chebyshev minimization which forces Pareto optimality of solutions (cf. mosalloc()).

Usage

mosallocStepwiseFirst(
  D,
  d,
  A = NULL,
  a = NULL,
  C = NULL,
  c = NULL,
  l = 2,
  u = NULL,
  opts = list(sense = "max_precision", init_w = 1, mc_cores = 1L, max_iters = 100L)
)

Arguments

D

(type: matrix) The objective matrix. A matrix of either precision or cost units.
d

(type: vector) The objective vector. A vector of either fixed precision components (e.g. finite population corrections) or fixed costs.
A

(type: matrix) A matrix of precision units for precision constraints.
a

(type: vector) The right-hand side vector of the precision constraints.
C

(type: matrix) A matrix of cost coefficients for cost constraints
c

(type: vector) The right-hand side vector of the cost constraints.
l

(type: vector) A vector of lower box constraints.
u

(type: vector) A vector of upper box constraints.
opts

(type: list) The options used by the algorithms:
$sense (type: character) Sense of optimization (default = "max_precision", alternative "min_cost").
$init_w (type numeric or matrix) Preference weightings (default = 1; The weight for first objective component must be 1).
$mc_cores (type: integer) The number of cores for parallelizing multiple input weightings stacked rowwise (default = 1L).
max_iters (type: integer) The maximum number of iterations (default = 100L).

Value

The function mosallocStepwiseFirst() returns a list containing the following components:

$w The initial preference weighting opts$init_w.

$n The vector of optimal sample sizes.

$J The optimal objective vector.

$Objective The objective value with respect to decision functional f. NULL if opts$f = NULL.

$Utopian Always NULL (consistency to mosalloc() output). NULL if opts$f = NULL.

$Normal The vector normal to the Pareto frontier at $J.

$dfJ Always NULL (consistency to mosalloc() output).

$Sensitivity The dual variables of the objectives and constraints.

$Qbounds The Quality bounds of the Lorentz cones.

$Dbounds The weighted objective constraints ($w * $J).

$Scalepar An internal scaling parameter.

$Ecosolver A list of ECOSolveR returns including:
...$Ecoinfostring The info string of ECOSolveR::ECOS_csolve().
...$Ecoredcodes The redcodes of ECOSolveR::ECOS_csolve().
...$Ecosummary Problem summary of ECOSolveR::ECOS_csolve().

$Timing Run time info.

$Iteration Always NULL (consistency to mosalloc() output).

References

See:

Willems, F. (2025). A Framework for Multiobjective and Uncertain Resource Allocation Problems in Survey Sampling based on Conic Optimization (Doctoral dissertation). Trier University. doi:10.25353/ubtr-9200-484c-5c89.

Examples

# Artificial population of 50 568 business establishments and 5 business
# sectors (data from Valliant, R., Dever, J. A., & Kreuter, F. (2013).
# Practical tools for designing and weighting survey samples. Springer.
# https://doi.org/10.1007/978-1-4614-6449-5, Example 5.2 pages 133-9)

# See also https://umd.app.box.com/s/9yvvibu4nz4q6rlw98ac/file/297813512360
# file: Code 5.3 constrOptim.example.R

Nh <- c(6221, 11738, 4333, 22809, 5467) # stratum sizes
ch <- c(120, 80, 80, 90, 150) # stratum-specific cost of surveying

# Revenues
mh.rev <- c(85, 11, 23, 17, 126) # mean revenue
Sh.rev <- c(170.0, 8.8, 23.0, 25.5, 315.0) # standard deviation revenue

# Employees
mh.emp <- c(511, 21, 70, 32, 157) # mean number of employees
Sh.emp <- c(255.50, 5.25, 35.00, 32.00, 471.00) # std. dev. employees

# Proportion of estabs claiming research credit
ph.rsch <- c(0.8, 0.2, 0.5, 0.3, 0.9)

# Proportion of estabs with offshore affiliates
ph.offsh <- c(0.06, 0.03, 0.03, 0.21, 0.77)

budget <- 300000 # overall available budget
n.min  <- 100 # minimum stratum-specific sample size

#----------------------------------------------------------------------------
# Problem: Minimization of the maximum relative variation of estimates for
# the total revenue, the number of employee, the number of businesses claimed
# research credit and the number of businesses with offshore affiliates
# subject to cost restrictions

l <- rep(n.min, 5) # minimum sample size ber stratum
u <- Nh            # maximum sample size per stratum
C <- rbind(ch, ch * c(-1, -1, -1, 0, 0))
c <- c(budget, - 0.5 * budget)
A <- NULL # no precision constraint
a <- NULL # no precision constraint

# Variance components for multidimensional objective
D <- rbind(Sh.rev**2 * Nh**2/sum(Nh * mh.rev)**2,
           Sh.emp**2 * Nh**2/sum(Nh * mh.emp)**2,
           ph.rsch * (1 - ph.rsch) * Nh**3/(Nh - 1)/sum(Nh * ph.rsch)**2,
           ph.offsh * (1 - ph.offsh) * Nh**3/(Nh - 1)/sum(Nh * ph.offsh)**2)

d <- as.vector(D %*% (1 / Nh)) # finite population correction

opts = list(sense = "max_precision",
            init_w = 1,
            mc_cores = 1L,
            max_iters = 100L)

res1 <- mosallocStepwiseFirst(D = D, d = d, C = C, c = c, l = l, u = u,
                              opts = opts)
w <- res1$J[1] / res1$J
w # [1] 1.000000 3.879692 2.653655 1.000000

opts = list(sense = "max_precision",
            init_w = w,
            mc_cores = 1L,
            max_iters = 100L)

res2 <- mosallocStepwiseFirst(D = D, d = d, C = C, c = c, l = l, u = u,
                              opts = opts)
res2$w # [1] 1.000000 3.879692 2.653655 1.000000

# Compare to function mosalloc (without stepwise procedure)
opts = list(sense = "max_precision",
            f = NULL, df = NULL, Hf = NULL,
            init_w = w,
            mc_cores = 1L, pm_tol = 1e-05,
            max_iters = 100L, print_pm = FALSE)
res3 <- mosalloc(D = D, d = d, C = C, c = c, l = l, u = u, opts = opts)

# Compare objectives
rbind(res1$J, res2$J, res3$J)
#            [,1]         [,2]         [,3]       [,4]
#[1,] 0.00170589 0.0004396972 0.0006428453 0.00170589
#[2,] 0.00170589 0.0004396971 0.0006428420 0.00170589
#[3,] 0.00170589 0.0004396971 0.0006428440 0.00170589

# Compare optimal sample sizes
rbind(res1$n, res2$n, res3$n)
#          [,1]     [,2]     [,3]     [,4]     [,5]
# [1,] 958.0510 290.7446 147.1789 602.8856 638.2686
# [2,] 958.0455 290.7447 147.1871 602.8847 638.2692
# [3,] 958.0488 290.7446 147.1822 602.8853 638.2688

(Single-stage) stratified random sampling interface for functions mosalloc() and mosallocStepwiseFirst()

Description

Interface for the functions mosalloc() and mosallocStepwiseFirst() of the same package which allows for a user-friendly data handling in the case of single-stage stratified random sampling.

Usage

mosallocSTRS(
  X_var,
  X_tot,
  N,
  listD,
  listA = NULL,
  listC = NULL,
  fpc = TRUE,
  l = 2,
  u = NULL,
  opts,
  ForceOptimality = FALSE,
  X_cost = NULL,
  X_fixed = NULL
)

Arguments

X_var

(type: matrix) A matrix containing stratum- (row) and variable- (column) specific precision units (e.g. variances).
X_tot

(type: matrix) A matrix containing stratum- (row) and variable- (column) specific totals.
N

(type: vector) A vector of stratum sizes.
listD

(type: list) A list of lists taking subpopulation- (domain/area) specific arguments.
If opts$sense == "max_precision", elements are lists containing the following components which correspond to one specific precision target each:
..$stratum_id (type: numeric) A vector containing the indices of the strata considered for the current restriction. The indices must coincide with the row numbers of X_var and X_tot.
..$variate (type: character or numeric) The column name or column index of X_var to be addressed.
..$measure (type: character or numeric) A character ("relVAR" (relative variance) or "VAR" (variance)) or a numerical value between 0 and 1 indicates a gradation coefficient that balances between "relVar" (..$measure = 0) and "VAR" (..$measure = 1).
..$name (type: character) The name of the subpopulation (domain/area).
If opts$sense == "min_cost", elements are lists containing the following components which correspond to one specific cost target each:
..$stratum_id (type: numeric) A vector containing the indices of the strata considered for the current objective. The indices must coincide with the row numbers of X_cost.
..$c_type (type: character or numeric) The column name or column index of X_cost to be addressed.
..$name (type: character) The name of the subpopulation (domain/area).
listA

(type: list) A list of lists taking subpopulation- (domain/area) specific arguments. Elements are lists containing the following components which in turn correspond to one specific precision restriction:
..$stratum_id (type: numeric) A vector containing the indices of the strata considered for the current restriction. The indices must coincide with the corresponding row numbers of X_var and X_tot.
..$variate (type: character or numeric) The column name or column index of X_var to be addressed.
..$measure (type: character) "RSE" (relative standard error) or "VAR" (variance).
..$bound (type: numeric) An upper bound to "RSE" (or "VAR").
..$name (type: character) The name of the subpopulation (domain/area).
listC

(type: list) A list of lists taking subpopulation- (domain/area) specific arguments. Elements are lists containing the following components which correspond to one specific cost restriction:
..$stratum_id (type: numeric) A vector containing the indices of the strata considered for the current restriction. The indices must coincide with the row numbers of X_var and X_tot.
..$c_coef (type: numeric) A vector of length length(stratum_id) containing the stratum-specific cost components for the set of strata that is going to be bounded by above and/or below.
..$c_upper (type: numeric) The cost upper bound value. NULL if not present.
..$c_lower (type: numeric) The cost lower bound value. NULL if not present.
..$name (type: character) The name of the subpopulation (domain/area).
fpc

(type: logical) A TRUE or FALSE statement indicating whether the finite population correction should be considered.
l

(type: vector) A vector of lower box constraints.
u

(type: vector) A vector of upper box constraints.
opts

(type: list) The options used by the algorithms:
$sense (type: character) Sense of optimization (default = "max_precision", alternative "min_cost").
$df (type: function) The gradient of f (default = NULL).
$Hf (type: function) The Hesse matrix of f (default = NULL).
$method (type: character) A character indicating scalarization method (default = "WCM", alternative "WSS"), $f must be NULL.
$f (type: function) Decision functional over the objective vector (default = NULL). The weighted Chebyshev minimization (WCM) minimizes for weighted maximum objective component. The weighted sum scalarization (WSS) minimizes for the weighted sum. For either case the weights are given by $init_w.
$init_w (type: numeric or matrix) Preference weightings (default = 1; The weight for first objective component must be 1).
$mc_cores (type: integer) The number of cores for parallelizing multiple input weightings stacked rowwise (default = 1L).
$pm_tol (type: numeric) The tolerance for the projection method (default = 1e-5).
max_iters (type: integer) The maximum number of iterations (default = 100L).
$print_pm (type: logical) A TRUE or FALSE statement whether iterations of the projection method should be printed (default = FALSE).
ForceOptimality

(type: logical) A TRUE or FALSE statement indicating whether Pareto optimality should be ensured. Default is FALSE (for most practical problem formulations Pareto optimality is achieved by construction, e.g. in the case of one overall cost constraint). If TRUE, additional computation time is required. In this case, mosallocStepwiseFirst() is used internally.
X_cost

(type: matrix) A matrix containing stratum- (row) and type- (column) specific cost coefficients associated with fixed cost. Types of cost might be, e.g. '$ US', 'minutes', 'sample size', etc. Default is NULL. The argument is required in the case of cost minimization (opt$sense == "min_cost").
X_fixed

(type: matrix) A matrix containing stratum- (rows) and type- (columns) specific cost coefficients associated with fixed cost. Default is NULL. Used solely in the case of cost minimization (opt$sense == "min_cost").

Value

A mosaSTRS object or a list of mosaSTRS objects. A mosaSTRS object is a list containing the following components:

$sense Sense of optimization; max precision or min_cost.

$method The method used, either weighted sum scalarization (WSS) or weighted Chebyshev minimization (WCM).

$init_w The initial preference weightings (opts$init_w).

$opt_w The optimal weightings w.r.t. opts$init_w as opts$init_w might not lead to Pareto optimality. NULL if ForceOptimality = FALSE.

$n_opt The vector of optimal sample sizes.

$objective The objective values, including the sensitivity and the RSE.

$precision A data frame corresponding to the precision constraints.

$cost A data frame corresponding to the cost constraints.

$problem_components A list containing the input data to the optimization problem.

$output_mosalloc A list of function returns of mosalloc().

If opts$init_w has multiple rows, the function returns a list of mosaSTRS objects whose length equals the number of rows.

Examples

# Artificial population of 50 568 business establishments and 5 business
# sectors (data from Valliant, R., Dever, J. A., & Kreuter, F. (2013).
# Practical tools for designing and weighting survey samples. Springer.
# https://doi.org/10.1007/978-1-4614-6449-5, Example 5.2 pages 133-9)

# See also https://umd.app.box.com/s/9yvvibu4nz4q6rlw98ac/file/297813512360
# file: Code 5.3 constrOptim.example.R

Nh <- c(6221, 11738, 4333, 22809, 5467) # stratum sizes
ch <- c(120, 80, 80, 90, 150) # stratum-specific cost of surveying

# Revenues
mh.rev <- c(85, 11, 23, 17, 126) # mean revenue
Sh.rev <- c(170.0, 8.8, 23.0, 25.5, 315.0) # standard deviation revenue

# Employees
mh.emp <- c(511, 21, 70, 32, 157) # mean number of employees
Sh.emp <- c(255.50, 5.25, 35.00, 32.00, 471.00) # std. dev. employees

# Proportion of estabs claiming research credit
ph.rsch <- c(0.8, 0.2, 0.5, 0.3, 0.9)

# Proportion of estabs with offshore affiliates
ph.offsh <- c(0.06, 0.03, 0.03, 0.21, 0.77)

budget <- 300000 # overall available budget
n.min  <- 100 # minimum stratum-specific sample size

# Matrix containing stratum-specific variance components
X_var <- cbind(Sh.rev**2,
               Sh.emp**2,
               ph.rsch * (1 - ph.rsch) * Nh/(Nh - 1),
               ph.offsh * (1 - ph.offsh) * Nh/(Nh - 1))
colnames(X_var) <- c("rev", "emp", "rsch", "offsh")

# Matrix containing stratum-specific totals
X_tot <- cbind(mh.rev, mh.emp, ph.rsch, ph.offsh) * Nh
colnames(X_tot) <- c("rev", "emp", "rsch", "offsh")

# Examples
#----------------------------------------------------------------------------
# Example 1: Univariate minimization of the variation of estimates for
# revenue subject to cost restrictions and precision restrictions to the
# relative standard error of estimates for the proportion of businesses with
# offshore affiliates. Additionally, there is one overall cost constraint and
# at least half of the provided budget must be spend to strata 1 to 3.

# Specify objectives via listD
listD <- list(list(stratum_id = 1:5, variate = "rev", measure = "relVAR",
                   name = "pop"))

# Specify precision constraints via listA
listA <- list(list(stratum_id = 1:5, variate = "offsh", measure = "RSE",
                   bound = 0.05, name = "pop"))

# Specify cost constraints via listC
listC <- list(list(stratum_id = 1:5, c_coef = ch, c_lower = NULL,
                   c_upper = budget, name = "Overall"),
              list(stratum_id = 1:3, c_coef = ch[1:3],
                   c_lower = 0.5 * budget, c_upper = NULL, name = "1to3"))

# Specify stratum-specific box constraints
l <- rep(n.min, 5) # minimum sample size per stratum
u <- Nh            # maximum sample size per stratum

# Specify parameter for mosalloc (method = "WSS")
opts <- list(sense = "max_precision",
             f = NULL, df = NULL, Hf = NULL,
             method = "WSS", init_w = 1,
             mc_cores = 1L, pm_tol = 1e-05,
             max_iters = 100L, print_pm = FALSE)

# Run mosallocSTRS with weighted sum scalarization (WSS)
resWSS  <- mosallocSTRS(X_var, X_tot, Nh, listD, listA, listC,
                             fpc = TRUE, l, u, opts)

summary(resWSS)

# Specify parameter for mosalloc (method = "WCM")
opts = list(sense = "max_precision",
            f = NULL, df = NULL, Hf = NULL,
            method = "WCM", init_w = 1,
            mc_cores = 1L, pm_tol = 1e-05,
            max_iters = 100L, print_pm = FALSE)

# Run mosallocSTRS with weighted Chebyshec minimization (WCM)
resWCM  <- mosallocSTRS(X_var, X_tot, Nh, listD, listA, listC,
                             fpc = TRUE, l, u, opts)

summary(resWCM)

# The optimal sample sizes vector can also be obtained by
summary(resWCM)$n_opt

# Hint: For univariate allocation problems 'WSS' and 'WCM' are equivalent!

#----------------------------------------------------------------------------
# Example 2: Minimization of the maximum relative variation of estimates for
# the total revenue, the number of employee, the number of businesses claimed
# research credit and the number of businesses with offshore affiliates
# subject to one overall cost constraint and at least half of the provided
# budget must be spend to strata 1 to 3.

# Specify objectives via listD
listD <- list(list(stratum_id = 1:5, variate = "rev", measure = "relVAR",
                  name = "pop"),
             list(stratum_id = 1:5, variate = "emp", measure = "relVAR",
                  name = "pop"),
             list(stratum_id = 1:5, variate = "rsch", measure = "relVAR",
                  name = "pop"),
             list(stratum_id = 1:5, variate = "offsh", measure = "relVAR",
                  name = "pop"))

# Specify cost constraints via listC
listC <- list(list(stratum_id = 1:5, c_coef = ch, c_lower = NULL,
                   c_upper = budget, name = "Overall"),
              list(stratum_id = 1:3, c_coef = ch[1:3],
                   c_lower = 0.5 * budget, c_upper = NULL, name = "1to3"))

# Specify stratum-specific box constraints
l <- rep(n.min, 5) # minimum sample size per stratum
u <- Nh            # maximum sample size per stratum

# Specify parameter for mosalloc (method = "WSS")
opts = list(sense = "max_precision",
            f = NULL, df = NULL, Hf = NULL,
            method = "WSS", init_w = 1,
            mc_cores = 1L, pm_tol = 1e-05,
            max_iters = 100L, print_pm = FALSE)

# Run mosallocSTRS with weighted sum scalarization (WSS)
resWSS  <- mosallocSTRS(X_var, X_tot, Nh, listD, NULL, listC,
                             fpc = TRUE, l, u, opts)

summary(resWSS)

# Specify parameter for mosalloc (method = "WCM")
opts = list(sense = "max_precision",
            f = NULL, df = NULL, Hf = NULL,
            method = "WCM", init_w = 1,
            mc_cores = 1L, pm_tol = 1e-05,
            max_iters = 100L, print_pm = FALSE)

# Run mosallocSTRS with weighted Chebyshec minimization (WCM)
resWCM  <- mosallocSTRS(X_var, X_tot, Nh, listD, NULL, listC,
                             fpc = TRUE, l, u, opts)

summary(resWCM)

# Since the WCM does not necessarily lead to Pareto optimal allocations,
# we might force this via a internal stepwise procedure by setting 
# ForceOptimality = TRUE.

resWCM_FO  <- mosallocSTRS(X_var, X_tot, Nh, listD, NULL, listC,
                             fpc = TRUE, l, u, opts, ForceOptimality = TRUE)

summary(resWCM_FO)

#----------------------------------------------------------------------------
# Example 3: Example 2 with multiple sets of preference weightings.

# Define a set of preference weightings, e.g.
w_1 <- c(1, 1, 1, 1)
w_2 <- c(1, 2, 2, 1)
w_3 <- c(1, 5, 5, 1)

# Combine the weightings to a matrix stacked rowwise
w <- rbind(w_1, w_2, w_3)

# Specify parameter for mosalloc() (method = "WCM"; not yet possible with WSS)
opts = list(sense = "max_precision",
            f = NULL, df = NULL, Hf = NULL,
            method = "WCM", init_w = w,
            mc_cores = 1L, pm_tol = 1e-05,
            max_iters = 100L, print_pm = FALSE)

# Run mosallocSTRS with weighted Chebyshec minimization (WCM)
res  <- mosallocSTRS(X_var, X_tot, Nh, listD, NULL, listC, fpc = TRUE,
                       l, u, opts)

summary(res)

#----------------------------------------------------------------------------
# Example 4: Minimization of survey cost subject to quality restrictions on 
# subpopulation level.

X_cost <- matrix(ch, 5, 1, dimnames = list(1:5,"cost"))

# Specify cost objectives via listD
listD <- list(list(stratum_id = 1:5, c_type = "cost", name = "pop"))

# Specify quailty restrictions via listD. Here: 5 % relative standard error
listA <- list(list(stratum_id = 1:2, variate = "rev", measure = "RSE",
                  bound = 0.05, name = "S1-2"),
              list(stratum_id = 3:5, variate = "rev", measure = "RSE",
                  bound = 0.05, name = "S3-5"),
             list(stratum_id = 1:2, variate = "emp", measure = "RSE",
                  bound = 0.05, name = "S1-2"),
             list(stratum_id = 3:5, variate = "emp", measure = "RSE",
                  bound = 0.05, name = "S3-5"),
             list(stratum_id = 1:2, variate = "rsch", measure = "RSE",
                  bound = 0.05, name = "S1-2"),
             list(stratum_id = 3:5, variate = "rsch", measure = "RSE",
                  bound = 0.05, name = "S3-5"),
             list(stratum_id = 1:2, variate = "offsh", measure = "RSE",
                  bound = 0.05, name = "S1-2"),
             list(stratum_id = 3:5, variate = "offsh", measure = "RSE",
                  bound = 0.05, name = "S3-5"))

# Specify cost constraints
listC <- NULL

# Specify stratum-specific box constraints
l <- rep(n.min, 5) # minimum sample size per stratum
u <- Nh            # maximum sample size per stratum

# Specify parameters for mosalloc()
opts = list(sense = "min_cost",
            f = NULL, df = NULL, Hf = NULL,
            method = "WCM", init_w = 1,
            mc_cores = 1L, pm_tol = 1e-05,
            max_iters = 100L, print_pm = FALSE)

# Run mosallocSTRS()
res  <- mosallocSTRS(X_var, X_tot, Nh, listD, listA, NULL, fpc = TRUE,
                     l, u, opts, X_cost = X_cost)

summary(res)

# Optimal sample sizes
summary(res)$n_opt

Print a summary.mosaSTRS object

Description

Print-function for class summary.mosaSTRS.

Usage

## S3 method for class 'summary.mosaSTRS'
print(x, ...)

Arguments

x

an object inheriting from class summary.mosaSTRS, representing the results of the function mosallocSTRS()
...

some methods for this generic require additional arguments. None are used in this method.

Value

Invisibly returns x.

Summary a mosaSTRS object

Description

Summary-function for class mosaSTRS

Usage

## S3 method for class 'mosaSTRS'
summary(object, ...)

Arguments

object

an object inheriting from class mosaSTRS, representing the results of the function mosallocSTRS(). This can also be a list of mosaSTRS objects.
...

some methods for this generic require additional arguments. None are used in this method.

Value

Either a summary.mosaSTRS object for a mosaSTRS object or a list of summary.mosaSTRS objects for a list of mosaSTRS objects. A summary.mosaSTRS object is a list containing the following components:

$vname Name of object.

$sense Sense of optimization; max precision or min_cost.

$method The method used weighted sum scalarization (WSS) or weighted Chebyshev minimization (WCM).

$objout A data frame corresponding to the objectives, including the values, the sensitivity, the weights and the RSE.

$precision A data frame corresponding to the precision constraints.

$cost A data frame corresponding to the cost constraints.

$n_opt A vector of optimal sample sizes w.r.t the weights.