data: Datasets with Example Populations
In stratallo: Optimum Sample Allocation in Stratified Sampling

data	R Documentation

Datasets with Example Populations

Description

Example datasets containing artificial populations for testing and demonstrating optimum sample allocation algorithms.

Usage

pop10s_bounds_ucost

pop507s_ucost

pop969s_ucost

pop2d4s

pop9d278s

Format

pop10s_bounds_ucost: Population with 10 strata, lower and upper bounds on sample sizes, and associated surveying costs. A matrix with 10 rows and 5 variables:

N: stratum size
S: standard deviation of study variable in the stratum.
m: lower bound for sample size in the stratum.
M: upper bound for sample size in the stratum.
unit_cost: cost of surveying one element in the stratum.

pop507s_ucost: Population with 507 strata and associated surveying costs. A matrix with 507 rows and 3 columns:

N: stratum size.
S: standard deviation of study variable in the stratum.
unit_cost: cost of surveying one element in the stratum.

pop969s_ucost: Population with 969 strata and associated surveying costs. A matrix with 969 rows and 3 columns:

N: stratum size.
S: standard deviation of study variable in the stratum.
unit_cost: cost of surveying one element in the stratum.

pop2d4s: Population with 2 domains and 4 strata. A list with the following elements:

H_counts: strata counts in each domain.
N: stratum sizes.
S: standard deviations of study variable in strata.
total: totals in domains, i.e., the sum of the study variable values for population elements in each domain.
kappa: priority weights for domains.
rho: total * sqrt(kappa).
rho2: total^2 * kappa.
n_max: See dca_nmax() or dca().

pop9d278s: Population with 9 domains and 278 strata. A list with the following elements:

H_counts: strata counts in each domain.
N: stratum sizes.
S: standard deviations of study variable in strata.
total: totals in domains, i.e., the sum of the study variable values for population elements in each domain.
kappa: priority weights for domains.
rho: total * sqrt(kappa).
rho2: total^2 * kappa.
n_max: See dca_nmax() or dca().