# Block autoscaling Functions managing blocks of data. - `blockscal`: Autoscales a list of blocks (i.e. sets of columns) of a training X-data, and eventually the blocks of new X-data. The scaling factor (computed on the training) is the "norm" of the block, i.e. the square root of the sum of the variances of each column of the block. - `mblocks`: Makes a list of blocks from X-data. - `hconcat`: Concatenates horizontally the blocks of a list. latin1 ```r blockscal(Xtrain, X = NULL, weights = NULL) mblocks(X, blocks) hconcat(X) ``` ## Arguments - `Xtrain`: A list of blocks of training X-data - `X`: For `blockscal`: A list of blocks of new X-data. For `mblocks`: X-data. For `hconcat`: a list of blocks of X-data. - `blocks`: A list (of same length as the number of blocks) giving the column numbers in `X`. - `weights`: Weights ($n, 1$) to apply to the training observations. Internally, weights are "normalized" to sum to 1. Default to `NULL` (weights are set to $1 / n$). ## Returns For `mblocks`: a list of blocks of X-data. For `hconcat`: a matrix concatenating a list of data blocks. For `blockscal`: - **Xtrain**: A list of blocks of training X-data, after block autoscaling. - **X**: A list of blocks of new X-data, after block autoscaling. - **disp**: The scaling factor (computed on the training). ## Note The second example is equivalent to MB-PLSR ## Examples ```r n <- 10 ; p <- 10 Xtrain <- matrix(rnorm(n * p), ncol = p) ytrain <- rnorm(n) m <- 2 Xtest <- matrix(rnorm(m * p), ncol = p) colnames(Xtest) <- paste("v", 1:p, sep = "") Xtrain Xtest blocks <- list(1:2, 4, 6:8) zXtrain <- mblocks(Xtrain, blocks = blocks) zXtest <- mblocks(Xtest, blocks = blocks) zXtrain blockscal(zXtrain, zXtest) res <- blockscal(zXtrain, zXtest) hconcat(res$Xtrain) hconcat(res$X) ## example of equivalence with MB-PLSR n <- 10 ; p <- 10 Xtrain <- matrix(rnorm(n * p), ncol = p) ytrain <- rnorm(n) m <- 2 Xtest <- matrix(rnorm(m * p), ncol = p) colnames(Xtest) <- paste("v", 1:p, sep = "") Xtrain Xtest blocks <- list(1:2, 4, 6:8) X1 <- mblocks(Xtrain, blocks = blocks) X1 <- lapply(1:length(X1), function(x) scale(X1[[x]])) res <- blockscal(X1) zXtrain <- hconcat(res$Xtrain) nlv <- 3 fm <- plskern(zXtrain, ytrain, nlv = nlv) ```

blocks function