# Within-class sampling The function divides a datset in two sets, "train" vs "test", using a stratified sampling on defined classes. If argument `y = NULL` (default), the sampling is random within each class. If not, the sampling is systematic (regular grid) within each class over the quantitative variable $y$. latin1 ```r sampcla(x, y = NULL, m) ``` ## Arguments - `x`: A vector (length $m$) defining the class membership of the observations. - `y`: A vector (length $m$) defining the quantitative variable for the systematic sampling. If `NULL` (default), the sampling is random within each class. - `m`: Either an integer defining the equal number of test observation(s) to select per class, or a vector of integers defining the numbers to select for each class. In the last case, vector `m` must have a length equal to the number of classes present in `x`, and be ordered in the same way as the ordered class membership. ## Returns - **train**: Indexes (i.e. position in $x$) of the selected observations, for the training set. - **test**: Indexes (i.e. position in $x$) of the selected observations, for the test set. - **lev**: classes - **ni**: number of observations in each class ## References Naes, T., 1987. The design of calibration in near infra-red reflectance analysis by clustering. Journal of Chemometrics 1, 121-134. ## Note The second example is a representative stratified sampling from an unsupervised clustering. ## Examples ```r ## EXAMPLE 1 x <- sample(c(1, 3, 4), size = 20, replace = TRUE) table(x) sampcla(x, m = 2) s <- sampcla(x, m = 2)$test x[s] sampcla(x, m = c(1, 2, 1)) s <- sampcla(x, m = c(1, 2, 1))$test x[s] y <- rnorm(length(x)) sampcla(x, y, m = 2) s <- sampcla(x, y, m = 2)$test x[s] ## EXAMPLE 2 data(cassav) X <- cassav$Xtrain y <- cassav$ytrain N <- nrow(X) fm <- pcaeigenk(X, nlv = 10) z <- stats::kmeans(x = fm$T, centers = 3, nstart = 25, iter.max = 50) x <- z$cluster z <- table(x) z p <- c(z) / N p psamp <- .20 m <- round(psamp * N * p) m random_sampling <- sampcla(x, m = m) s <- random_sampling$test table(x[s]) Systematic_sampling_for_y <- sampcla(x, y, m = m) s <- Systematic_sampling_for_y$test table(x[s]) ```

sampcla function