View source: R/direct_orthogonal.R
direct_orthogonal | R Documentation |
This function implements the Direct Orthogonalization (DO) algorithm, as proposed
by Andersson (1999), to filter out variation from predictor variables,
\textbf{X}
, that is orthogonal to the response variable(s), \textbf{Y}
.
direct_orthogonal(
x,
y,
ncomp = 2,
center = TRUE,
scale = FALSE,
max_iter = 10,
tol = 0.001
)
x |
A matrix or data frame of the predictor variables |
y |
A vector, matrix or data frame of the response variable(s) |
ncomp |
An integer specifying the number of principal components to retain for orthogonal processing. Default is 2. |
center |
A logical value specifying whether to center the data. Default is |
scale |
A logical value specifying whether to scale the data. Default is |
max_iter |
An integer representing the maximum number of iterations. The default value is 10. |
tol |
A numeric value representing the tolerance for convergence. The default value is 1e-3. |
Contrary to the Orthogonal Signal Correction (OSC) algorithm, Wold et al. (1998), which uses inverse
Partial Least Squares (PLS) regression to filter out the orthogonal signal, DO
filters out the orthogonal signal directly by orthogonalization of the
\textbf{X}
matrix. Principal Components Analysis (PCA) is performed on the
orthogonalized \textbf{X}
to obtain the scores \textbf{T}
and loadings
\textbf{P}
matrices. Direct Orthogonalization is typically simpler and
faster than OSC.
A list with the following components:
correction
: The corrected matrix.
loading
: The loadings matrix.
score
: The scores matrix.
Christian L. Goueguel
Andersson, C.A., (1999). Direct orthogonalization. Chemometrics Intell. Lab. Syst., 47(1):51-63
Pierna, J.A.F., Massart, D.L., de Noord, O.E., Ricoux, P., (2001). Direct orthogonalization: some case studies. Chemometrics Intell. Lab. Syst., 55(1-2):101-108
Wold, S., Antti, H., Lindgren, F., Ohman, J. (1998). Orthogonal signal correction of near-infrared spectra. Chemometrics Intell. Lab. Syst., 44(1):175-185.
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