msc: Multiplicative Scatter Correction

In pls: Partial Least Squares and Principal Component Regression

Multiplicative Scatter Correction

Description

Performs multiplicative scatter/signal correction on a data matrix.

Usage

msc(X, reference = NULL)

## S3 method for class 'msc'
predict(object, newdata, ...)

## S3 method for class 'msc'
makepredictcall(var, call)

Arguments

X, newdata	numeric matrices. The data to scatter correct.
reference	numeric vector. Spectre to use as reference. If NULL, the column means of X are used.
object	an object inheriting from class "msc", normally the result of a call to msc with a single matrix argument.
...	other arguments. Currently ignored.
var	A variable.
call	The term in the formula, as a call.

Details

makepredictcall.msc is an internal utility function; it is not meant for interactive use. See makepredictcall for details.

Value

Both msc and predict.msc return a multiplicative scatter corrected matrix, with attribute "reference" the vector used as reference spectre. The matrix is given class c("msc", "matrix"). For predict.msc, the "reference" attribute of object is used as reference spectre.

Author(s)

Bjørn-Helge Mevik and Ron Wehrens

References

Martens, H., Næs, T. (1989) Multivariate calibration. Chichester: Wiley.

See Also

mvr, pcr, plsr, stdize

Examples

data(yarn)
## Direct correction:
Ztrain <- msc(yarn$NIR[yarn$train,])
Ztest <- predict(Ztrain, yarn$NIR[!yarn$train,])

## Used in formula:
mod <- plsr(density ~ msc(NIR), ncomp = 6, data = yarn[yarn$train,])
pred <- predict(mod, newdata = yarn[!yarn$train,]) # Automatically scatter corrected

pls documentation built on Nov. 18, 2023, 1:11 a.m.