sampclas: Within-class sampling

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

Within-class sampling

Description

The function divides the data x in two sets, "train" vs "test", using a atratified sampling on pre-defined classes.

If argument y = NULL (default), the sampling is random within each class. If not, the sampling is systematic (regular grid) over the quantitative variable y.

Usage

sampclas(x, y = NULL, k, seed = NULL)

Arguments

x	A vector of integers of length n defining the class membership of the observations.
y	A vector of length n defining the quantitative variable for the systematic sampling. If NULL (default), the sampling is random within each class.
k	Either an integer defining the (equal) number of training observation(s) to select per class, or a vector of integers defining the numbers to select for each class. In the last case, vector k must have a length equal to the number of classes present in vector x, and be ordered in the same way as the ordered class membership.
seed	An integer defining the seed for the random simulations, or NULL (default). See set.seed.

Value

A list of vectors of the indexes (i.e. position in x) of the selected observations.

References

Naes, T., 1987. The design of calibration in near infra-red reflectance analysis by clustering. Journal of Chemometrics 1, 121-134.

Examples

x <- sample(c(1, 3, 4), size = 20, replace = TRUE)
x
table(x)

z <- sampclas(x, k = 2, seed = 1)
z
x[z$train]

z <- sampclas(x, k = c(1, 2, 1), seed = 1)
z
x[z$train]

y <- rnorm(length(x))
z <- sampclas(x, y, k = 2)
z
x[z$train]

########## Representative stratified sampling from an unsupervised clustering

data(datcass)
X <- datcass$Xr
y <- datcass$yr
n <- nrow(X)

fm <- pca_eigenk(X, ncomp = 10)
z <- kmeans(x = fm$T, centers = 3, nstart = 25, iter.max = 50)
x <- z$cluster
z <- table(x)
p <- z / n
p

psamp <- .70
k <- round(psamp * n * p)
k

## Random

z <- sampclas(x, k = k, seed = 1)
z

## Systematic for y

z <- sampclas(x, y, k = k)
z

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