Run K-clustering on principal components

This function implements the clustering procedure used in Discriminant Analysis of Principal Components (DAPC, Jombart et al. 2010). This procedure consists in running successive K-means with an increasing number of clusters (k), after transforming data using a principal component analysis (PCA). For each model, several statistical measures of goodness of fit are computed, which allows to choose the optimal k using the function gt_cluster_pca_best_k(). See details for a description of how to select the optimal k and vignette("adegenet-dapc") for a tutorial.

Usage

gt_cluster_pca(
  x = NULL,
  n_pca = NULL,
  k_clusters = c(1, round(nrow(x$u)/10)),
  method = c("kmeans", "ward"),
  n_iter = 1e+05,
  n_start = 10,
  quiet = FALSE
)

Arguments

x

a gt_pca object returned by one of the gt_pca_* functions.

n_pca

number of principal components to be fed to the LDA.

k_clusters

number of clusters to explore, either a single value, or a vector of length 2 giving the minimum and maximum (e.g. 1:5). If left NULL, it will use 1 to the number of pca components divided by 10 (a reasonable guess).

method

either 'kmeans' or 'ward'

n_iter

number of iterations for kmeans (only used if method="kmeans")

n_start

number of starting points for kmeans (only used if method="kmeans")

quiet

boolean on whether to silence outputting information to the screen (defaults to FALSE)

Value

a gt_cluster_pca object, which is a subclass of gt_pca with an additional element 'cluster', a list with elements:

• 'method' the clustering method (either kmeans or ward)

• 'n_pca' number of principal components used for clustering

• 'k' the k values explored by the function

• 'WSS' within sum of squares for each k

• 'AIC' the AIC for each k

• 'BIC' the BIC for each k

• 'groups' a list, with each element giving the group assignments for a given k