cluster_rs implements a random sampling procedure in which groups of units are sampled together (as a cluster). This function conducts complete random sampling at the cluster level, unless simple = TRUE, in which case simple_rs analogues are used.

cluster_rs(clusters = NULL, n = NULL, n_unit = NULL, prob = NULL,
prob_unit = NULL, simple = FALSE, check_inputs = TRUE)

## Arguments

clusters A vector of length N that indicates which cluster each unit belongs to. Use for a design in which n clusters are sampled. (optional) unique(n_unit) will be passed to n. Must be the same for all units (optional) Use for a design in which either floor(N_clusters*prob) or ceiling(N_clusters*prob) clusters are sampled. The probability of being sampled is exactly prob because with probability 1-prob, floor(N_clusters*prob) clusters will be sampled and with probability prob, ceiling(N_clusters*prob) clusters will be sampled. prob must be a real number between 0 and 1 inclusive. (optional) unique(prob_unit) will be passed to the prob argument and must be the same for all units. logical, defaults to FALSE. If TRUE, simple random sampling of clusters. When simple = TRUE, please do not specify n. logical. Defaults to TRUE.

## Value

A numeric vector of length N that indicates if a unit is sampled (1) or not (0).

## Examples

clusters <- rep(letters, times=1:26)

S <- cluster_rs(clusters = clusters)
table(S, clusters)
#>    clusters
#> S    a  b  c  d  e  f  g  h  i  j  k  l  m  n  o  p  q  r  s  t  u  v  w  x  y
#>   0  0  0  3  4  0  6  7  0  9  0 11 12  0  0 15  0  0 18 19 20  0  0  0  0 25
#>   1  1  2  0  0  5  0  0  8  0 10  0  0 13 14  0 16 17  0  0  0 21 22 23 24  0
#>    clusters
#> S    z
#>   0 26
#>   1  0
S <- cluster_rs(clusters = clusters, n = 13)
table(S, clusters)
#>    clusters
#> S    a  b  c  d  e  f  g  h  i  j  k  l  m  n  o  p  q  r  s  t  u  v  w  x  y
#>   0  1  0  3  4  5  0  7  8  9  0  0 12  0 14  0 16  0 18  0  0 21  0 23  0  0
#>   1  0  2  0  0  0  6  0  0  0 10 11  0 13  0 15  0 17  0 19 20  0 22  0 24 25
#>    clusters
#> S    z
#>   0  0
#>   1 26