Performs k-means clustering on subject-level features to obtain initial
cluster assignments for mixture model fitting.
Usage
assign_by_kmeans(features, K)
Arguments
- features
An (N x d) matrix of subject-level features, where
N is the number of subjects and d is the feature dimension.
- K
Number of clusters to create.
Value
An integer vector of length N containing cluster labels (1..K).
Each element indicates which cluster the corresponding subject is assigned to.
Details
This function uses the standard k-means algorithm with 10 random starts
(nstart=10) to find a stable clustering solution. The resulting
cluster assignments can be used as initial values for EM algorithms in
mixture models.
Examples
if (FALSE) { # \dontrun{
# Create example features
features <- matrix(rnorm(100), nrow = 10, ncol = 10)
# Assign to 3 clusters
z_init <- assign_by_kmeans(features, K = 3)
table(z_init) # Check cluster sizes
} # }