sampdp: Duplex sampling

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

Duplex sampling

Description

The function divides the data X in two sets, "train" vs "test", using the Duplex algorithm (Snee, 1977).

The training and test sets returned by the algorithm are of equal size. The evential remaining observations can be added a posteriori to the training set.

Usage

sampdp(X, k, diss = c("euclidean", "mahalanobis", "correlation"))

Arguments

X	A matrix or data frame in which row observations are selected.
k	An integer defining the number of training observations to select. Must be <= n / 2.
diss	The type of dissimilarity used for selecting the observations in the algorithm. Possible values are "euclidean" (default; Euclidean distance), "mahalanobis" (Mahalanobis distance), or "correlation". Correlation dissimilarities are calculated by sqrt(.5 * (1 - rho)).

Value

A list of vectors of the indexes (i.e. row numbers in X) of the selected observations.

References

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

Snee, R.D., 1977. Validation of Regression Models: Methods and Examples. Technometrics 19, 415-428. https://doi.org/10.1080/00401706.1977.10489581

Examples

set.seed(seed = 1)
n <- 10 ; p <- 3
X <- matrix(rnorm(n * p, mean = 10), ncol = p, byrow = TRUE)
set.seed(seed = NULL)
k <- 3

sampdp(X, k = 4)
sampdp(X, k = 4, diss = "mahalanobis")

###################################

data(datcass)

X <- datcass$Xr

fm <- pca_eigenk(X, ncomp = 10)
z <- sampdp(fm$T, k = 20, diss = "mahalanobis")
z

plotxy(fm$T, zeroes = TRUE, pch = 16) 
points(fm$T[z$test, 1:2], col = "red", pch = 16, cex = 1.3)

mlesnoff/rnirs documentation built on April 24, 2023, 4:17 a.m.