Sum of squares block weighting: allows to scale blocks of variables,
but keeping the relative weights of the variables inside a block.
Usage
blockNorm(X, targetnorm = 1)
Arguments
- X
a numeric matrix to transform (optionally a data frame that can
be coerced to a numerical matrix).
- targetnorm
desired sum of squares for a block of variables
(default = 1)
Value
a list with components Xscaled, the scaled matrix and f, the
scaling factor
Details
The function computes a scaling factor, which, multiplied by the
input matrix,
produces a matrix with a pre–determined sum of squares.
Note
This is a R port of the MBnorm.m function of the MB matlab toolbox
by Fran van den Berg.
References
Eriksson, L., Johansson, E., Kettaneh, N., Trygg, J.,
Wikstrom, C., and Wold, S., 2006. Multi- and Megavariate Data Analysis.
MKS Umetrics AB.
Examples
X <- matrix(rnorm(100), ncol = 10)
# Block normalize to sum of square equals to 1
res <- blockNorm(X, targetnorm = 1)
sum(res$Xscaled^2) # check
#> [1] 1
