osc_wold: Orthogonal Signal Correction (OSC) Approach by Wold et al.

In mt: Metabolomics Data Analysis Toolbox

Orthogonal Signal Correction (OSC) Approach by Wold et al.

Description

Orthogonal signal correction (OSC) approach by Wold et al.

Usage

  osc_wold(x, y, center=TRUE,osc.ncomp=4,pls.ncomp=10,
           tol=1e-3,iter=20,...)

Arguments

x	A numeric data frame or matrix to be pre-processed.
y	A vector or factor specifying the class for each observation.
center	A logical value indicating whether the data set should be centred by column-wise.
osc.ncomp	The number of components to be used in the OSC calculation.
pls.ncomp	The number of components to be used in the PLS calculation.
tol	A scalar value of tolerance for OSC computation.
iter	The number of iteration used in OSC calculation.
...	Arguments passed to or from other methods.

Value

A list containing the following components:

x	A matrix of OSC corrected data set.
R2	R2 statistics. It is calculated as the fraction of variation in X after OSC correction.
angle	An angle used for checking if scores t is orthogonal to y. An angle close to 90 degree means that orthogonality is achieved in the correction process.
w	A matrix of OSC weights.
p	A matrix of OSC loadings.
t	A matrix of OSC scores.
center	A logical value indicating whether the data set has been centred by column-wise.

Author(s)

Wanchang Lin

References

Wold, S., Antti, H., Lindgren, F., Ohman, J.(1998). Orthogonal signal correction of nearinfrared spectra. Chemometrics Intell. Lab. Syst., 44: 175-185.

Svensson, O., Kourti, T. and MacGregor, J.F. (2002). An investigation of orthogonal correction algorithms and their characteristics. Journal of Chemometrics, 16:176-188.

Westerhuis, J. A., de Jong, S., Smilde, A, K. (2001). Direct orthogonal signal correction. Chemometrics Intell. Lab. Syst., 56: 13-25.

See Also

osc, predict.osc, osc_sjoblom, osc_wise

Examples

data(abr1)
cl   <- factor(abr1$fact$class)
dat  <- abr1$pos

## divide data as training and test data
idx <- sample(1:nrow(dat), round((2/3)*nrow(dat)), replace=FALSE) 

## construct train and test data 
train.dat  <- dat[idx,]
train.t    <- cl[idx]
test.dat   <- dat[-idx,]        
test.t     <- cl[-idx] 

## build OSC model based on the training data
res <- osc_wold(train.dat, train.t)
names(res)

## pre-process test data by OSC
test.dat.1 <- predict.osc(res,test.dat)$x

mt documentation built on June 22, 2024, 12:24 p.m.