Simulate Gaussian clusters

Usage

simulate_clusters(
  n,
  centers,
  sds = 1,
  proportions = NULL,
  labels = NULL,
  seed = NULL,
  batch = NULL
)

Arguments

n: Sample size.
centers: Matrix with one row per cluster and one column per variable, or a tidy data frame with columns cluster, variable and center.
sds: Scalar, per-variable vector, cluster-by-variable matrix, or a tidy data frame with columns cluster, variable and sd.
proportions: Optional cluster proportions.
labels: Optional cluster labels.
seed: Optional random seed.
batch: Optional single positive whole number. When given, the simulator runs batch times and returns a plain list of batch results, each exactly what the same call without batch returns. With a seed, every dataset gets its own seed drawn from seed, so the whole batch is reproducible; without one the datasets are consecutive draws from the session's random-number stream. The default NULL returns a single result.

Value

A simulab_sim base data.frame with one row per observation and columns id, cluster (the true membership label) and one column per variable. One tidy table comes from as.data.frame(): what = "parameters" has one row per cluster-variable combination with columns cluster, variable, center, sd and proportion.

Examples

# `centers` is one row per cluster and one column per variable.
result <- simulate_clusters(
  n = 150,
  centers = matrix(c(0, 0, 4, 4, 0, 4), nrow = 3, byrow = TRUE),
  sds = 1,
  seed = 1
)
head(result)
#> <simulab_sim:clusters> 6 rows x 4 columns
#>   id   cluster           V1         V2
#> 1  1 Cluster 2  4.291446236  5.8031419
#> 2  2 Cluster 3 -0.443291873  3.6688680
#> 3  3 Cluster 3  0.001105352  2.3944866
#> 4  4 Cluster 1  0.074341324  0.1971934
#> 5  5 Cluster 2  3.410479054  4.2631756
#> 6  6 Cluster 1 -0.568668733 -0.9858267
#> 
#> Truth (parameters):
#>     cluster variable center sd proportion
#> 1 Cluster 1       V1      0  1  0.3333333
#> 2 Cluster 2       V1      4  1  0.3333333
#> 3 Cluster 3       V1      0  1  0.3333333
#> 4 Cluster 1       V2      0  1  0.3333333
#> 5 Cluster 2       V2      4  1  0.3333333
#> 6 Cluster 3       V2      4  1  0.3333333
as.data.frame(result, what = "parameters")
#>     cluster variable center sd proportion
#> 1 Cluster 1       V1      0  1  0.3333333
#> 2 Cluster 2       V1      4  1  0.3333333
#> 3 Cluster 3       V1      0  1  0.3333333
#> 4 Cluster 1       V2      0  1  0.3333333
#> 5 Cluster 2       V2      4  1  0.3333333
#> 6 Cluster 3       V2      4  1  0.3333333