# Create a design for cluster random sampling

Source:`R/cluster_sampling_designer.R`

`cluster_sampling_designer.Rd`

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.

## 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

```
# 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)
```