cluster_sampling_designer: Create a design for cluster random sampling

Description

Builds a cluster sampling design for an ordinal outcome variable for a population with N_blocks strata, each with N_clusters_in_block clusters, each of which contains N_i_in_cluster units. The sampling strategy involves sampling n_clusters_in_block clusters in each stratum, and then sampling n_i_in_cluster units in each cluster. Outcomes within clusters have intra-cluster correlation approximately equal to ICC.

Usage

cluster_sampling_designer(N_blocks = 1, N_clusters_in_block = 1000,
  N_i_in_cluster = 50, n_clusters_in_block = 100,
  n_i_in_cluster = 10, icc = 0.2, args_to_fix = NULL)

Arguments

N_blocks

An integer. Number of blocks (strata). Defaults to 1 for no blocks.

N_clusters_in_block

An integer or vector of integers of length N_blocks. Number of clusters in each block in the population.

N_i_in_cluster

An integer or vector of integers of length sum(N_clusters_in_block). Number of units per cluster sampled.

n_clusters_in_block

An integer. Number of clusters to sample in each block (stratum).

n_i_in_cluster

An integer. Number of units to sample in each cluster.

icc

A number in [0,1]. Intra-cluster Correlation Coefficient (ICC).

args_to_fix

A character vector. Names of arguments to be args_to_fix in design.

Value

A stratified cluster sampling design.

Details

Key limitations: The design assumes a args_to_fix number of clusters drawn in each stratum and a args_to_fix number of individuals drawn from each cluster.

See vignette online.

Examples

Run this code

# NOT RUN {
# To make a design using default arguments:
cluster_sampling_design <- cluster_sampling_designer()
# A design with varying block size and varying cluster size
cluster_sampling_design <- cluster_sampling_designer(
  N_blocks = 2, N_clusters_in_block = 6:7, N_i_in_cluster = 3:15, 
  n_clusters_in_block = 5,  n_i_in_cluster = 2)
# }

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