Simulate Cluster Data with Known Centers

Generate multivariate data from a mixture of Gaussians with fully specified cluster centers and standard deviations. Designed for testing clustering algorithms where the ground truth is known.

Usage

simulate_clusters(n, centers, sds = 1, props = NULL, seed = NULL)

Arguments

n

Integer or integer vector. If a single value, the total sample size (allocated by props). If a vector of length k, the per-cluster sizes.

centers

Numeric matrix (k x d). Each row is a cluster centroid. k = number of clusters, d = number of dimensions.

sds

Numeric scalar, vector of length k, or matrix (k x d). Standard deviations per cluster (and optionally per dimension). Scalar is recycled. Default: 1.

props

Numeric vector of length k or NULL. Mixing proportions when n is a single value. Normalised internally. Default: equal mixing.

seed

Integer or NULL. Random seed.

Value

A saqr_sim object with:

$data

data.frame with columns x1...xd and integer column true_cluster (1...k).

$params

list with centers (matrix), sds (k x d matrix), props (normalised), n (per-cluster sizes).

Examples

centers <- matrix(c(0, 0,
                    5, 5,
                    10, 0), nrow = 3, byrow = TRUE)
r <- simulate_clusters(n = 300, centers = centers, seed = 1)
plot(x2 ~ x1, data = r$data, col = r$data$true_cluster)

r$params$centers
#>      [,1] [,2]
#> [1,]    0    0
#> [2,]    5    5
#> [3,]   10    0

# Per-cluster sizes and SDs
r2 <- simulate_clusters(n = c(100, 50, 150), centers = centers,
                         sds = c(0.5, 1.0, 2.0), seed = 42)