sampks: Kennard-Stone sampling

In mlesnoff/rchemo: Dimension reduction, Regression and Discrimination for Chemometrics

Kennard-Stone sampling

Description

The function divides the data X in two sets, "train" vs "test", using the Kennard-Stone (KS) algorithm (Kennard & Stone, 1969). The two sets correspond to two different underlying probability distributions: set "train" has higher dispersion than set "test".

Usage

sampks(X, k, diss = c("eucl", "mahal"))

Arguments

X	X-data (n, p) to be sampled.
k	An integer defining the number of training observations to select.
diss	The type of dissimilarity used for selecting the observations in the algorithm. Possible values are "eucl" (default; Euclidean distance) or "mahal" (Mahalanobis distance).

Value

Indexes (i.e. row numbers in X) for sets "train" and "test".

References

Kennard, R.W., Stone, L.A., 1969. Computer aided design of experiments. Technometrics, 11(1), 137-148.

Examples

n <- 10 ; p <- 3
X <- matrix(rnorm(n * p), ncol = p)

k <- 7
sampks(X, k = k)  

n <- 10 ; k <- 25
X <- expand.grid(1:n, 1:n)
X <- X + rnorm(nrow(X) * ncol(X), 0, .1)
s <- sampks(X, k)$train 
plot(X)
points(X[s, ], pch = 19, col = 2, cex = 1.5)

mlesnoff/rchemo documentation built on April 15, 2023, 1:25 p.m.