NC.sample {NCSampling}

Nearest Centroid (NC) Sample

Description

Selects NC sample in multiple strata.

Usage

NC.sample(popfile, nrefs, desvars, ctype, imax, nst)

Arguments

popfile

dataframe containing information on all plots in the population.

nrefs

vector containing the sample size of each stratum.

desvars

vector containing the names of the design variables.

ctype

clustering type - either k-means ('km') or Wards D ('WD').

imax

maximum number of iterations for the k-means procedure.

nst

number of initial random sets of cluster means for the k-means procedure.

Details

In each stratum the population of virtual plots is segregated into n clusters where n is the stratum sample size (number of reference plots). The virtual plots are partitioned so as to minimise the sums of squares of distances from plots to cluster centroids. This is achieved by using a multivariate clustering procedure such as k-means clustering (Hartigan & Wong, 1979) or Ward's D clustering (Murtagh & Legendre, 2013), using standardized design variables and a Euclidean distance metric. Following determination of the cluster centroids, the virtual plot, in the candidate set, closest to each centroid is selected as a reference plot.

Value

A list with components:-

popfile

population file - dataframe, as above, with reference plots designated as 'R'

cmns

centroid means

Author(s)

G. Melville

References

G. Melville & C. Stone. (2016) Optimising nearest neighbour information - a simple, efficient sampling strategy for forestry plot imputation using remotely sensed data. Australian Forestry, 79:3, 217:228, DOI: 10.1080/00049158.2016.1218265.

Hartigan & Wong (1979) Algorithm AS 136: a K-means clustering algorithm. Applied Statistics 28, 100-108, DOI:10.2307/2346830.

Murtagh, M & Legendre, P. (2013) Ward's hierarchical agglomerative clustering method: Which algorithms implement Ward's criterion? Journal of Classification.

See Also

See also NC.sample.

Examples

## NC.sample(popfile, nrefs, desvars, ctype='km', imax=200, nst=20)