osc_wise: Orthogonal Signal Correction (OSC) Approach by Wise and...
In mt: Metabolomics Data Analysis Toolbox

Description Usage Arguments Value Author(s) References See Also Examples

Orthogonal signal correction (OSC) approach by Wise and Gallagher.

1 2	osc_wise(x, y, center=TRUE,osc.ncomp=4,pls.ncomp=10, tol=1e-3,iter=20,...)

`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.

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.

Wanchang Lin

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

Wise, B. M. and Gallagher, N.B. http://www.eigenvector.com/MATLAB/OSC.html.

osc, predict.osc, osc_sjoblom, osc_wold

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_wise(train.dat, train.t)
names(res)

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