blockNorm: Sum of squares block weighting

In l-ramirez-lopez/prospectr: Miscellaneous Functions for Processing and Sample Selection of Spectroscopic Data

Sum of squares block weighting

Description

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)

Details

The function computes a scaling factor, which, multiplied by the input matrix, produces a matrix with a pre–determined sum of squares.

Value

a list with components Xscaled, the scaled matrix and f, the scaling factor

Note

This is a R port of the ‘MBnorm.m’ function of the MB matlab toolbox by Fran van den Berg.

Author(s)

Antoine Stevens

References

Eriksson, L., Johansson, E., Kettaneh, N., Trygg, J., Wikstrom, C., and Wold, S., 2006. Multi- and Megavariate Data Analysis. MKS Umetrics AB.

See Also

blockScale, standardNormalVariate, detrend

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

l-ramirez-lopez/prospectr documentation built on Feb. 18, 2024, 7:52 a.m.