Separates a single stratum of the population file into n clusters and finds the centroid of each cluster, where n is the sample size. Not intended to be called directly.
Centroids(popfile, nrefs, desvars, ctype, imax, nst)population file - dataframe containing information relating to all plots in the stratum.
scalar defining the number of reference plots - required sample size for the stratum.
character vector containing the names of the design variables.
clustering type - either k-means ('km') or Ward's D2 ('WD').
maximum number of iterations when calling the k-means clustering procedure.
number of random initial centroid sets when calling the k-means clustering procedure.
dataframe containing centroids.
dataframe containing centroid means.
The virtual plots are partitioned so as to minimise the sums of squares of distances from plots to cluster centroids. This is done by using a multivariate clustering procedure such as k-means clustering (Hartigan & Wong, 1979) or Ward's D2 clustering (Murtagh & Legendre, 2013), using standardized design variables and a Euclidean distance metric.
Hartigan & Wong (1979) Algorithm AS 136: a K-means clustering algorithm. Applied Statistics 28, 100-108, DOI:10.2307/2346830.
Murtagh, M & Legendre, P. (2014) Ward's hierarchical agglomerative clustering method: which algorithms implement Ward's criterion? Journal of Classification, 31, 274-295, DOI: 10.1007/s00357-014-9161-z.
## Centroids(popfile, nrefs, desvars, ctype='km', imax=200, nst=20)
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