either the number of calibration samples to
select or a set of cluster centres to initiate the
k-means clustering.
pc
optional. If not specified, k-means is run
directly on the variable (Euclidean) space.
Alternatively, a PCA is performed before k-means and
pc is the number of principal components kept. If
pc < 1, the number of principal components kept
corresponds to the number of components explaining at
least (pc * 100) percent of the total variance.
iter.max
maximum number of iterations allowed for
the k-means clustering. Default is iter.max = 10
(see ?kmeans)
method
the method used for selecting calibration
samples within each cluster: either samples closest to
the cluster centers (method = 0, default), samples
farthest away from the centre of the data (method =
1) or random selection (method = 2)
.center
logical value indicating whether the input
matrix should be centered before Principal Component
Analysis. Default set to TRUE.
.scale
logical value indicating whether the input
matrix should be scaled before Principal Component
Analysis. Default set to FALSE.
Details
K-means sampling is a simple procedure based on cluster
analysis to select calibration samples from large
multivariate datasets. The method can be described in three
points (Naes et al.,2001):
Perform a PCA and decide how many
principal component to keep,
Carry out a k-means
clustering on the principal component scores and choose the
number of resulting clusters to be equal to the number of
desired calibration samples,
Select one sample from
each cluster.
Value
a list with components:
'model' numeric vector giving the row
indices of the input data selected for calibration
'test' numeric vector giving the row
indices of the remaining observations
'pc'
if the pc argument is specified, a numeric
matrix of the scaled pc scores
'cluster' integer vector indicating the
cluster to which each point was assigned
'centers' a matrix of cluster
centres
Author(s)
Antoine Stevens and Leonardo Ramirez-Lopez
References
Naes, T., 1987. The design of calibration in near infra-red
reflectance analysis by clustering. Journal of Chemometrics
1, 121-134.
Naes, T., Isaksson, T., Fearn, T., and Davies, T., 2002. A
user friendly guide to multivariate calibration and
classification. NIR Publications, Chichester, United
Kingdom.
See Also
kenStone, honigs,
duplex, shenkWest
Examples
data(NIRsoil)
sel <- naes(NIRsoil$spc,k=5,p=.99,method=0)
plot(sel$pc[,1:2],col=sel$cluster+2) # clusters
# points selected for calibration with method = 0
points(sel$pc[sel$model,1:2],col=2,pch=19,cex=1)
sel2 <- naes(NIRsoil$spc,k=sel$centers,p=.99,method=1) # pre-defined centers can also be provided
# points selected for calibration with method = 1
points(sel$pc[sel2$model,1:2],col=1,pch=15,cex=1)
providing 
.