Hard or soft block scaling
Hard or soft block scaling of a spectral matrix to constant group variance. In multivariate calibration, block scaling is used to down-weight variables, when one block of variables dominates other blocks. With hard block scaling, the variables in a block are scaled so that the sum of their variances equals 1. Wen soft block scaling is used, the variables are scaled such that the sum of variable variances is equal to the square root of the number of variables in a particular block.
blockScale(X, type = 'hard', sigma2 = 1)
X |
a numeric matrix or vector to process (optionally a data frame that can be coerced to a numerical matrix). |
type |
the type of block scaling: 'hard' or 'soft'. |
sigma2 |
the desired total variance of a block (ie sum of the variances
of all variables, default = 1), applicable when |
a list with Xscaled, the scaled matrix and f, the scaling
factor.
Antoine Stevens
Eriksson, L., Johansson, E., Kettaneh, N., Trygg, J., Wikstrom, C., and Wold, S., 2006. Multi- and Megavariate Data Analysis. MKS Umetrics AB.
X <- matrix(rnorm(100), ncol = 10) # Hard block scaling res <- blockScale(X) # sum of column variances == 1 apply(res$Xscaled, 2, var)
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