Description Usage Arguments Value Note Author(s) References See Also Examples
Data preprocessing by orthogonal signal correction (OSC).
1 2 3 4 5 6 7 8 
formula 
A formula of the form 
data 
Data frame from which variables specified in 
x 
A matrix or data frame containing the explanatory variables if no formula is given as the principal argument. 
y 
A factor specifying the class for each observation if no formula principal argument is given. 
method 
A method for calculating OSC weights, loadings and scores. The following methods are supported:

center 
A logical value indicating whether the data set should be centred by columnwise. 
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. 
subset 
An index vector specifying the cases to be used in the training sample. 
na.action 
A function to specify the action to be taken if 
An object of class osc
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 for the calibration (training) data. 
angle 
An angle used for checking if scores 
w 
A matrix of OSC weights. 
p 
A matrix of OSC loadings. 
t 
A matrix of OSC scores. 
call 
The (matched) function call. 
center 
A logical value indicating whether the data set has been centred by columnwise. 
osc.ncomp 
The number of component used in OSC computation. 
pls.ncomp 
The number of component used in PLS computation. 
method 
The OSC algorithm used. 
This function may be called giving either a formula and optional data frame, or a matrix and grouping factor as the first two arguments.
Wanchang Lin
Wold, S., Antti, H., Lindgren, F., Ohman, J.(1998). Orthogonal signal correction of near infrared spectra. Chemometrics Intell. Lab. Syst., 44: 175185.
Westerhuis, J. A., de Jong, S., Smilde, A, K. (2001). Direct orthogonal signal correction. Chemometrics Intell. Lab. Syst., 56: 1325.
Sjoblom. J., Svensson, O., Josefson, M., Kullberg, H., Wold, S. (1998). An evaluation of orthogonal signal correction applied to calibration transfer of near infrared spectra. Chemometrics Intell. Lab. Syst.,44: 229244.
Svensson, O., Kourti, T. and MacGregor, J.F. (2002). An investigation of orthogonal correction algorithms and their characteristics. Journal of Chemometrics, 16:176188.
Wise, B. M. and Gallagher, N.B. http://www.eigenvector.com/MATLAB/OSC.html.
predict.osc
, osc_wold
, osc_sjoblom
,
osc_wise
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21  data(abr1)
cl < factor(abr1$fact$class)
dat < abr1$pos
## divide data as training and test data
idx < sample(1:nrow(dat), round((2/3)*nrow(dat)), replace=FALSE)
## construct train and test data
train.dat < dat[idx,]
train.t < cl[idx]
test.dat < dat[idx,]
test.t < cl[idx]
## build OSC model based on the training data
res < osc(train.dat, train.t, method="wise", osc.ncomp=2, pls.ncomp=4)
names(res)
res
summary(res)
## preprocess test data by OSC
test.dat.1 < predict(res,test.dat)$x

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